Handbook of youth prevention science

Handbook of Youth Prevention Science

The Handbook of Youth Prevention Science describes current research and practice in mental health preventive interventions for youth. Traditional prevention research focused on preventing specific disorders, e.g., substance abuse, conduct disorders, or criminality. This produced “silos” of isolated knowledge about the prevention of individual disorders without acknowledging the overlapping goals, strategies, and impacts of prevention programs. This handbook reflects current research and practice by organizing prevention science around comprehensive systems that reach across all disorders and all institutions within a community. Throughout the book preventive interventions are seen as complementary components of effective mental health programs, not as replacements for therapeutic interventions. Key Features: Comprehensive—This handbook is the first to bring together and integrate the important work that is currently being conducted on comprehensive youth prevention programs that cut across all mental health disorders and practice disciplines. Evidence-Based—The focus throughout is on the science of prevention and how that science informs the theory, practice, and methodology related to preventive interventions with youth. Issue-Oriented—Contemporary debates about the research-to-practice gap in prevention science are carefully examined, including issues of practice fidelity, program dissemination, taking programs to scale, and infrastructures needed for effective prevention practices. Definition of Mental Health—Outcomes of youth prevention programs are broadly described as the promotion of psychological wellness in addition to the prevention of youth maladjustment. Expertise—The lead editor (Beth Doll) is well known for her research on school-based prevention, particularly for interventions that enhance the school learning environment. She chaired the National Association of School Psychologists’ (NASP) Presidential Taskforce on Prevention. This book is suitable for researchers, instructors, and graduate students in the child and adolescent mental health professions: school psychology, school counseling, special education, school social work, child clinical psychology, and the libraries serving them. It is also suitable for graduate course work in these fields. Dr. Beth Doll is a Professor and Director of the School Psychology program at the University of Nebraska–Lincoln. Dr. William (Bill) Pfohl is a Professor of Psychology at Western Kentucky University in Bowling Green, Kentucky. Dr. Jina Yoon is Associate Professor and Co-Director of the School and Community Psychology program at Wayne State University in Detroit, Michigan.

Handbook of Youth Prevention Science

Edited by Beth Doll William Pfohl Jina Yoon

First published 2010 by Routledge 270 Madison Ave, New York, NY 10016 Simultaneously published in the UK by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business This edition published in the Taylor & Francis e-Library, 2010. To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk. © 2010 Taylor and Francis All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Handbook of youth prevention science/Beth Doll, William Pfohl, Jina Yoon, editors. p. cm. Includes bibliographical references and index. 1. School mental health services. 2. School children— Mental health services. 3. Problem children—Education. 4. Child psychology. 5. Educational counseling. I. Doll, Beth, 1952– II. Pfohl, William. III. Yoon, Jina. LB3430.H37 2010 371.7'13—dc22 2009035360 ISBN 0-203-86641-X Master e-book ISBN

ISBN 10: 0–8058–6331–1 (hbk) ISBN 10: 0–8058–6332–X (pbk) ISBN 10: 0–203–86641–X (ebk) ISBN 13: 978–0–8058–6331–4 (hbk) ISBN 13: 978–0–8058–6332–1 (pbk) ISBN 13: 978–0–203–86641–2 (ebk)

This handbook is dedicated to Jean A. Baker, who would have been one of our co-editors if she had not died in January 2008. She was a wise and caring colleague, and was so very young to die.

Contents

Figures and Tables 1 The Current Status of Youth Prevention Science

x 1

BETH DOLL, UNIVERSITY OF NEBRASKA–LINCOLN JINA YOON, WAYNE STATE UNIVERSITY

2 Placing Prevention into the Context of School Improvement

19

HOWARD S. ADELMAN AND LINDA TAYLOR, UNIVERSITY OF CALIFORNIA, LOS ANGELES

3 School Mental Health: Prevention at All Levels

45

KEVIN DWYER, AMERICAN INSTITUTES FOR RESEARCH ERIKA VAN BUREN, DISTRICT OF COLUMBIA DEPARTMENT OF MENTAL HEALTH

4 Screening for Mental Health and Wellness: Current School-Based Practices and Emerging Possibilities

70

ERIN DOWDY, MICHAEL FURLONG, KATIE EKLUND, ELINA SAEKI, AND KRISTIN RITCHEY, UNIVERSITY OF CALIFORNIA, SANTA BARBARA

5 Implementing Universal Screening Systems Within an RtI-PBS Context

96

HILL M. WALKER AND HERBERT H. SEVERSON, OREGON RESEARCH INSTITUTE AND THE UNIVERSITY OF OREGON GALE NAQUIN AND CYNTHIA D’ATRIO, UNIVERSITY OF NEW ORLEANS EDWARD G. FEIL, OREGON RESEARCH INSTITUTE LEANNE HAWKEN AND CHRISTIAN SABEY, UNIVERSITY OF UTAH

6 Building Conditions for Learning and Healthy Adolescent Development: A Strategic Approach

121

DAVID OSHER AND KIMBERLY KENDZIORA, AMERICAN INSTITUTES FOR RESEARCH

7 Assessment for Integrated Screening and Prevention Using the Resiliency Scales for Children and Adolescents

141

SANDRA PRINCE-EMBURY, THE RESILIENCY INSTITUTE OF ALLENHURST, LLC

8 Social Support: How to Assess and Include It in Research on Prevention and Youth Outcomes MICHELLE K. DEMARAY, CHRISTINE K. MALECKI, LYNDSAY N. JENKINS, AND CHRISTY M. CUNNINGHAM, NORTHERN ILLINOIS UNIVERSITY

163

viii Contents 9 Peer Support as a Means of Improving School Safety and Reducing Bullying and Violence

177

HELEN COWIE, UNIVERSITY OF SURREY PETER K. SMITH, GOLDSMITHS, UNIVERSITY OF LONDON

10 The Developmental Implications of Classroom Social Relationships and Strategies for Improving Them

194

JAN N. HUGHES AND LISA K. BARROIS, TEXAS A&M UNIVERSITY

11 Factors Influencing Teacher Interventions in Bullying Situations: Implications for Research and Practice

218

JODI BURRUS NEWMAN, KARIN S. FREY, AND DIANE CARLSON JONES, UNIVERSITY OF WASHINGTON

12 Development, Evaluation, and Diffusion of a National Anti-Bullying Program, KiVa

238

CHRISTINA SALMIVALLI AND ANTTI KÄRNÄ, DEPARTMENT OF PSYCHOLOGY, UNIVERSITY OF TURKU ELISA POSKIPARTA, CENTRE FOR LEARNING RESEARCH, UNIVERSITY OF TURKU

13 Promoting the Well-Being of School Communities: A Systemic Approach

253

CHRYSE HATZICHRISTOU, KONSTANTINA LYKITSAKOU, AIKATERINI LAMPROPOULOU, AND PANAYIOTA DIMITROPOULOU, UNIVERSITY OF ATHENS, GREECE

14 Promoting Student Resilience: Strong Kids Social and Emotional Learning Curricula

273

OANH K. TRAN, CALIFORNIA STATE UNIVERSITY, EAST BAY KENNETH W. MERRELL, UNIVERSITY OF OREGON

15 Stimulating Positive Social Interaction: What Can We Learn from TIGER (Kanjertraining)?

286

LILIAN VLIEK, INSTITUTE OF KANJERTRAINING, ALMERE, THE NETHERLANDS BRAM OROBIO DE CASTRO, UTRECHT UNIVERSITY, UTRECHT, THE NETHERLANDS

16 A Hybrid Framework for Intervention Development: Social Justice for Bullying in Low-Resource Schools

307

SAMUEL Y. SONG, SEATTLE UNIVERSITY WAKAKO SOGO, UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL

17 Check & Connect: Enhancing School Completion Through Student Engagement

327

SANDRA L. CHRISTENSON, UNIVERSITY OF MINNESOTA AMY L. RESCHLY, UNIVERSITY OF GEORGIA

18 Prevention and Early Intervention for Preschool Children at Risk for Learning and Behavior Problems MARIBETH GETTINGER, CARRIE BALL, LAURA MULFORD, AND ALICIA HOFFMAN, UNIVERSITY OF WISCONSIN–MADISON

349

Contents ix 19 Partnering to Achieve School Success: A Collaborative Care Model of Early Intervention for Attention and Behavior Problems in Urban Contexts

375

THOMAS J. POWER, HEATHER JONES LAVIN, JENNIFER A. MAUTONE, AND NATHAN J. BLUM, THE CHILDREN’S HOSPITAL OF PHILADELPHIA, UNIVERSITY OF PENNSYLVANIA SCHOOL OF MEDICINE

20 Dissemination of Evidence-Based Programs in the Schools: The Coping Power Program

393

JOHN E. LOCHMAN, NICOLE R. POWELL, CAROLINE L. BOXMEYER, AND RACHEL BADEN, UNIVERSITY OF ALABAMA

21 Prevention, Early Childhood Intervention, and Implementation Science

413

SAMUEL L. ODOM, UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL MARCI HANSON, SAN FRANCISCO STATE UNIVERSITY JOAN LIEBER, UNIVERSITY OF MARYLAND KAREN DIAMOND, PURDUE UNIVERSITY SUSAN PALMER, UNIVERSITY OF KANSAS GRETCHEN BUTERA, INDIANA UNIVERSITY EVA HORN, UNIVERSITY OF KANSAS

22 Taking Effective Prevention to Scale

433

BRIAN K. BUMBARGER, DANIEL F. PERKINS, AND MARK T. GREENBERG, PENN STATE UNIVERSITY

23 Youth Policy and Politics in the United States: Toward an Increased Focus on Prevention

445

SIOBHAN M. COONEY, THOMAS R. KRATOCHWILL, AND STEPHEN A. SMALL, UNIVERSITY OF WISCONSIN–MADISON

Editors Contributors Index

461 462 469

Figures and Tables

List of Figures 2.1

2.2 2.3 2.4 2.5 4.1 5.1 5.2 5.3 5.4 6.1 6.2 6.3 6.4 6.5 7.1 7.2 7.3 7.4 7.5 11.1 12.1 13.1 14.1 15.1

From Primary Prevention to Treatment of Serious Problems: A Continuum of Community-School Programs to Address Barriers to Learning and Enhance Healthy Development A Continuum of Interconnected Systems for Meeting the Needs of All Students Toward a Comprehensive System for Addressing Barriers to Learning: Moving from a Two- to a Three-Component Framework for School An Enabling Component to Address Barriers, Re-engage Students in Classroom Instruction, and Enhance Healthy Development at a School Site Intervention Content Arenas Overview of Subjective Well-Being Sample Student Risk Screening Scale (SRSS) Screening Form for an Entire Class SWIS Office Discipline Referral Form SSPD/ESP Multiple Gating Procedure Preventing Violent and Destructive Behavior in Schools “Student Connection Survey” (aka Conditions for Learning Survey) Score Report to Schools Mean Conditions for Learning Scores for Middle Grades and High School Correlation Between PSAE Subscales and Conditions for Learning Scales (Grade 11) Correlation Between Conditions for Learning and GPA Scores (High School) Conditions for Learning Scores by On Track Status (Grade 9 Students) Resiliency Profiles for Bobby, Linda and Joe Resiliency Profiles for Clinical and Non-Clinical Adolescent Groups Emotional Reactivity Subscale Profiles Sense of Mastery Subscale Profiles Sense of Relatedness Subscale Profiles Relationships Affecting Teacher Interventions in Bullying Situations A Scene from the KiVa Computer Game (“I Can” Component), Version for Grade Levels 4–6 A Synthetic Approach to School Community Well-Being Prevention of Mental Illness Through Continuum of Supports Positive Social Interaction

22 23 26 27 28 84 100 103 105 108 125 130 130 131 132 150 151 155 156 158 220 241 256 275 300

Figures and Tables xi 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 15.10 15.11 15.12 15.13 15.14 16.1 16.2 18.1 18.2 18.3 19.1 21.1

Self-Esteem Aggressive Behavior Depressive Thoughts Perceived Social Acceptance Relationship with the Teacher Emotional Well-Being Positive Social Interaction Self-Esteem Aggressive Behavior Depressive Thoughts Perceived Social Acceptance by Classmates Relationship with the Teacher Emotional Well-Being Hybrid Intervention Manual Development Protective Peer Ecology Framework in Schools EMERGE Multi-Tier Intervention Hierarchy EMERGE and Control Children’s Performance on Emergent Literacy Measures FACET Steps and Outcomes Effective Intervention for a Child with ADHD Typically Involves a Partnership Involving the Family, School, and Primary Care Practice Elements of the Implementation Process

300 300 300 301 301 301 301 302 302 302 302 303 303 312 317 361 363 368 378 418

List of Tables 2.1 3.1 4.1

4.2 4.3 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8

Outline Aid for Analyzing Key Facets of School-Oriented Prevention Efforts Percentage of Schools Providing Various Mental Health Services by School Level, 2002–2003 Percentage of Students by Grade Level and Survey Cohort Year Who Felt So Sad or Hopeless Almost Every Day for Two or More Weeks in a Row That They Stopped Doing Some Usual Activities During the 12 Months Before the Survey from Youth Risk Behavior Surveillance Survey 1999–2007 Content and Psychometric Properties of Omnibus Youth Mental Health Screening Instruments Mental Health Screening Implementation Considerations Number and Percentage of Students in the Chicago School District and in the Surveyed Sample by Student Demographics Middle Grades: Correlations Among Conditions for Learning Constructs High School: Correlations Among Conditions for Learning Constructs School Level Correlations Between Conditions for Learning Constructs and the Logarithm of Enrollment Correlations Between School Safety, School Suspensions, and the Number of Suspended Students Correlations Between School Safety and Neighborhood Crime Correlations Between Challenge (and other Conditions for Learning Constructs) and Dropout School Level Correlation Between Conditions for Learning Constructs and the Average Class Size in High School

20 51

73 77 88 128 128 129 132 133 134 134 135

xii Figures and Tables 6.9 7.1 7.2 7.3 8.1 8.2 10.1 10.2 12.1 12.2 12.3 13.1 13.2

13.3 13.4 13.5 13.6 15.1 17.1 17.2 17.3 17.4 19.1 19.2 23.1

Correlations Between Graduation Rates and Conditions for Learning Constructs T-Score Ranges for the Vulnerability Index and Cumulative Percentages by Age Band RSCA Screening Model 1: Individual Level Screening Model II for Aggregate Classroom Level Six Commonly Used Measures of Social Support in Youth Reliability and Validity Evidence for the Six Most Commonly Used Social Support Scales in Youth Study Characteristics Evaluation of Studies The Core Components (Universal and Indicated Actions) of the KiVa Anti-Bullying Program The Study Sample in the Two Phases of Program Evaluation Diffusion of KiVa: Strategies to Promote the Dissemination, Adoption, Implementation, and Maintenance of the Program Frequencies of Children’s Pre–Post Responses to Indicative Open Questions of the Thematic Units “Communication Skills” and “Self Concept” Pre–Post Comparisons of Students’ Responses to an Open Question of the Unit “Identification, Expression and Management of Emotions” for the Experimental and Control Group, for Migrant and Native Students (Athens Sample), and for Students with Low and High Peer Acceptance (Cypriot Sample) Pre–Post Comparisons for Items of the Thematic Unit “Diversity and Culture” Comparison of Social Skills of Students After One and Two Years of Participation in the Program Pre–Post Comparisons of Teachers’ Evaluation of Sense of Community Pre–Post Comparisons of Teachers’ Evaluation of Competence Descriptive Statistics for Outcome Measures of Training and Control Group Core Elements of Check & Connect Model of Student Engagement Alterable Variables Associated with School Dropout Guidelines for Establishing Criteria for Intensive Interventions Illustrative Examples of Student Connect Intensive Interventions Components of PASS Family Involvement in Each Component of PASS Strategies for Bringing a Preventative Focus to Youth Policy

135 151 152 154 171 172 200 202 242 244 246 264

265 265 266 266 267 299 330 336 338 339 382 385 451

1

The Current Status of Youth Prevention Science Beth Doll, University of Nebraska–Lincoln Jina Yoon, Wayne State University

Youth prevention science is the empirically rigorous examination of practices that prevent psychosocial disturbances or promote psychological wellness in children and adolescents. These include careful examinations of the outcomes of policies and preventive interventions; and developmental investigations of the correlates, moderators, and mediators of youth disturbance or competence that provide the conceptual and theoretical underpinnings for preventive interventions. In its earliest incarnation, the prevention of psychosocial maladjustment in youth was termed the “mental hygiene” movement, drawing parallels between practices to strengthen psychological wellness in youth and community practices that promote physical health by cleaning the water and containing community waste and sewage (Hosman, Jane-Liopis, & Saxena, 2005). However, many prominent preventionists maintain that the science of youth prevention did not truly emerge until 1986 when the National Mental Health Association (NMHA) published its Commission Report recommending that youth prevention efforts be strengthened (Commission on the Prevention of Mental/Emotional Disabilities, 1987). Albee (1996) believes that the origins began at least 10 years earlier. His own commitment to prevention science began when he worked with the 1960 Joint Commission on Mental Illness and Health, tasked with evaluating the United States’ mental health needs and resources and making recommendations for a national mental health program. He became convinced that, “No amount of research on treatment, even if resulting in success, can reduce incidence (rate of new cases)” (Albee, 2005, p. 313). He dates the birth of modern prevention science to the 1960s and 1970s when ambitious social programs were implemented to counteract the effects of poverty, prejudice, and other socially toxic stressors in the United States. A classic example of such social programs is the early intervention with preschool children that began in the United States during the 1960s (Gettinger, Ball, Mulford, & Hoffman, this volume; Odom, Butera, Horn, Palmer, Diamond, & Lieber, this volume). By 1977, during Jimmy Carter’s presidency, education and social engineering were identified in a report of the President’s Commission on Mental Health as important strategies to prevent social disturbances and promote psychological wellness. This socioecological perspective on youth prevention science was shared by the NMHA’s commission when it specifically recommended comprehensive, multi-agency community programs to ensure that all babies were healthy and wanted, prevent adolescent pregnancy, integrate promotion of psychosocial competence into school programs, and help children and adults cope effectively with adversity (Commission on the Prevention of Mental/Emotional Disabilities, 1987).

2 Beth Doll and Jina Yoon

Definitions of Prevention Science Discussions of youth prevention research are inevitably complicated by inconsistent use of terms caused, in part, by the imperfect fit of borrowed medical terms to the sociopsychological phenomenon of youth prevention (U.S. Department of Health and Human Services, 1999). For example, primary prevention, secondary prevention, and tertiary prevention are classic terms attributed to the Commission on Chronic Illness (1957). Primary prevention refers to the prevention of diseases before they develop, secondary prevention refers to prevention of diseases’ worsening or recurrence, and tertiary prevention describes reductions in the functional impact of diseases subsequent to their development. These terms were widely adopted by mental health professionals to describe youth prevention programs, such as the Primary Mental Health Project (Cowan, 1994). Still, prevention researchers and practitioners were troubled by the terms’ implicit assumption that a disease was being prevented. Critics of the terms detailed multiple respects in which medical models were a poor fit for social, emotional, and educational prevention efforts. Four principal arguments were emphasized in these debates: First, a focus on preventing diseases implies that health is the absence of disease. Critics of medical models argued, instead, that authentic definitions of social and psychological health must describe the characteristics of wellness and competence in addition to diagnostic criteria of pathology. Some researchers used the term “primary prevention” as an umbrella term to describe health promotion as well as disease prevention, but others did not follow suit, arguing that the terms had been contaminated by their emphasis on disease. Second, a focus on preventing disease suggests that the purpose of services is to cure or eliminate disorders so that the youth can proceed through life “disease-free.” Critics of medical models endorse an alternative purpose of strengthening youths’ functional competence to meet the demands of their daily lives regardless of whether pathological symptoms of disorders are reduced or eliminated (Albee 1996; Biglan, 2004). Others broaden the purpose of youth prevention to include influencing “problems” that impair youths’ capacities to succeed in their daily lives and not just preventing “mental disorders” as defined within the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision, American Psychiatric Association, 2000). Third, a focus on preventing disease requires that the presence or absence of a disorder be reliably identified. Diagnostic reliability, difficult enough within physical medicine, is often elusive within psychological disorders (Kaplan, 2000). Diagnostic criteria included in the DSM frequently require subjective judgments about the strength of a symptom from imprecise descriptions of the person or members of their family. Moreover, diagnoses do not always lead to effective treatment; they are not always correct; and they are not essential when problems are the result of risky behaviors or environmental risk rather than mental disorders. Fourth, researchers concur that investigations describing the correlates, occurrence, or non-occurrence of a disorder are not sufficient to support a mature prevention science. Instead, effective prevention strategies require understanding the causes and mechanisms underlying negative life outcomes so that it is possible to alter or shift these mechanisms and increase the likelihood of positive outcomes (U.S. Department of Health and Human Services, 1999). In the face of these and other criticisms, the Institute of Medicine convened a Committee on the Prevention of Mental Disorders and charged them with the task of reviewing the status of mental health prevention research (Mrazek & Haggerty, 1994). That committee’s report recommended that the term “prevention” be reserved for services provided before a mental

The Current Status of Youth Prevention Science 3 disorder is present, and proposed the alternative terms of universal, selective, and indicated prevention which they had adopted with some modification from the earlier work of Gordon (1983). Within their spectrum of mental health services, universal prevention described preventive interventions provided to everyone in a community or group; selective prevention described services provided to those members of a group who were at higher-than-average risk for a mental disorder; and indicated prevention described services provided to group members at high risk for a mental disorder and who already evidenced some symptoms of a disorder, although without meeting the disorder’s full diagnostic criteria. (In current terminology, some researchers also use the word “targeted” instead of “indicated.”) Although the committee intended to provide a common language across prevention researchers and practitioners, its recommendations were widely criticized because the definitions continued to presuppose medical models of prevention and failed to acknowledge the importance of wellness promotion (Albee, 2004; Kaplan, 2000; Weissberg, Kumpfer, & Seligman, 2003). The American Psychological Association’s Task Force on Prevention endorsed a broader definition that incorporated both. Alternatively, Weisz, Sandler, Durlak, and Anton (2005) attempted to reconcile the wellness promotion and disease prevention models of mental health prevention by proposing a mental health intervention framework that included wellness promotion, universal prevention, selective prevention, indicated prevention, and therapeutic intervention. At the same time as these definitional debates have raged within youth prevention science, schools have been rapidly moving to adopt a population-based model of prevention actualized into a three-tier approach in which the first tier is universal services, the second tier includes selective services to youth who evidence early signs of emotional or behavioral problems, and the third tier includes indicated services for students with significant emotional and behavioral problems (Dwyer & Van Buren, this volume; Osher et al., this volume; Walker et al., this volume). These tiers of intervention are frequently framed within a Response-toIntervention model in which youth are identified for progressively more intense services based on data showing that their response to less intensive interventions was inadequate (Cooney, Kratochwill, & Small, this volume). Because these data can be used to describe either negative markers of maladjustment or positive indicators of student competence, educational threetier approaches can be framed as preventing disabilities or promoting developmental competence. The three-tier approaches are represented within the U.S. Individuals with Disabilities Education Act as strategies to prevent educational disturbances and interrupt premature identification of students for special education services. These definitional debates are not esoteric but are grounded in the very real dilemma that funding mechanisms outside of education are often wedded to the medical models of youth prevention, at least in the United States, despite the consensus among youth prevention scientists that prevention practices and research must emphasize socioenvironmental perspectives (Albee, 2004). The emphasis on promoting competence and diminishing the impact of environmental adversity is strikingly different from National Institute of Mental Health’s commitment to a program of well-controlled research designed to systematically examine the impact of preventive interventions on the emergence of psychopathology (Mrazek & Haggerty, 1994). NIMH funding explicitly excludes programs to promote psychosocial wellness and focuses instead on biological models of causation and particularly on neurobiological causes of specific mental disorders. Moreover, priorities in mental health funding in the United States continue to emphasize programs to treat psychopathology over prevention programs (Cooney et al., this volume). At the same time, social-environmental frameworks are highly compatible with public education’s mission to promote the success of all youth. One likely outcome is the emergence of at least two competing frameworks for youth prevention

4 Beth Doll and Jina Yoon science—one operating within population-based agencies such as public schools and another dominant within health service agencies. Dwyer and Van Buren (this volume) make a more optimistic prediction—that with time, researchers will come to understand that the two perspectives (environmental supports for wellness and biological contributions to psychopathology) are not truly competing.

Conceptual Foundations for Youth Prevention Since 1980, developmental research has shed light on the complex phenomenon of child and adolescent mental health and their developmental competence (Bumbarger, Perkins, & Greenberg, this volume). In particular, cumulative knowledge from developmental psychopathology and epidemiology research has contributed to the conceptual foundations for youth prevention science. In the United States, epidemiological research in developmental psychopathology has significantly altered estimates of the prevalence of mental disorders in youth. Whereas pre-1980 estimates of childhood mental illness suggested that between 5% and 7% of school-aged children met the criteria for one or more mental disorders described by the Diagnostic and Statistical Manual of Mental Disorders (4th Edition; American Psychiatric Association, 1996), epidemiological studies funded by the National Institute of Mental Health consistently identified between 18% and 22% of their community samples as meeting the criteria for psychopathology (Doll, 1996; U.S. Department of Health and Human Services, 1999). The functional impact of these mental disorders on children’s developmental success has not been fully described by researchers, but the relevance of psychological health and wellness to subsequent adult success is amply documented. The striking prevalence of psychopathology among youth is particularly alarming, given that early signs of maladjustment often proceed to later difficulties in educational, social, and occupational roles during adolescence and adulthood. Results of these epidemiological studies emphasized the importance of very early intervention to promote psychological wellness and prevent long-term disturbances. When prevention research identifies the early warning signs that place children at a higher risk of failure, early preventive interventions can be more effective and less costly than therapeutic interventions provided once children meet diagnostic criteria for a disorder. Moreover, epidemiological evidence for incidence, prevalence, and severity of youth problem behaviors provides important direction so that communities can “systematically prioritize child and adolescent problems in terms of their cost and the likely benefits of preventing each of them” (Biglan, Mrazek, Carnine, & Flay, 2003, p. 433). As such, epidemiological research strategies are critical in evaluating the effectiveness of prevention efforts and ensuring that prevention approaches are guided by evidence. As a second contribution, the field of developmental psychopathology has documented a number of individual characteristics and contextual variables that are responsible for the different forms and severity of developmental maladjustment (Mash & Dozois, 2003). One important finding from this research is that socioecological features of children’s lives are as important as individual child characteristics in predicting children’s healthy development or the incidence and severity of psychopathology (Coie et al., 1993; Doll & Lyon, 1998; Werner, 2006). For example, children’s exposure to poverty, family violence, parental psychopathology, or community violence significantly increases their chances of developing a debilitating mental illness, while their access to caring adults, high-quality parenting, and effective community support services can protect some children from psychopathology. These findings further suggest that evidence-based social and mental health services should emphasize the creation of strong and effective caretaking systems (i.e., family, school, and community) as

The Current Status of Youth Prevention Science 5 much as the remediation of individual characteristics. Moreover, coordinated prevention efforts across families, schools, and communities are likely to be more effective than isolated, stand-alone programs (Greenberg et al., 2003; Kumpfer, Alvarado, Tait, & Turner, 2002; Wandersman & Florin, 2003). A third important perspective of developmental psychopathology is the recognition of ongoing reciprocal influence of multiple factors in explaining youth outcomes. Such concepts as heterotypic continuity, multi-finality, and equi-finality highlight the multi-factorial, longitudinal nature of developmental maladjustment that is further complicated by mediating and moderating influences. As we learn more about the risk and protective factors associated with certain forms of psychopathology, community prevention strategies and approaches are likely to become more specific and tailored. Although it is challenging to capture all the possible pathways in each disorder and it is not all that clear at this point what factors determine the developmental courses, the delineation of a coherent developmental pathway describes possible trajectories of early signs or symptoms and suggests strategies for prevention as well as for intervention. For example, it is well documented that a small number of children with Oppositional Defiant Disorder (ODD) in middle childhood will develop Conduct Disorder (CD) in later childhood or adolescence (Lahey, Loeber, Quay, Frick, & Grimm, 1992; Loeber & Farrington, 2000). Given that a majority of CD youth show ODD characteristics prior to onset of CD, prevention efforts could include specific goals of targeting variables that stop the progression from ODD to CD. Preventive interventions have the potential to interrupt youths’ trajectories into social or emotional disturbance, ameliorating risks and redirecting them along pathways into social health and psychological wellness.

Youth Prevention Programs and Practices As research accumulates documenting the importance of addressing maladjustment among youth, and describing the characteristics of community practices that are likely to promote psychological health and wellness, demands have grown for effective prevention strategies and programs for youth that incorporate: (1) ongoing assessment of the prevalence and incidence of youth difficulties; (2) development of strong and supportive relationships within family, school, and community contexts and with coordinated efforts across these different contexts; (3) multi-year efforts with special attention to developmental issues and changes; and (4) emphasis on enhancing social and emotional competence as well as fighting to reduce risk factors. This framework for youth prevention is well reflected in numerous successful prevention programs (e.g., Conduct Problems Prevention Research Group, 1999; Cook, Murphy, & Hunt, 2000; Greenberg & Kusche, 1998; Nation et al., 2003; Solomon, Battistich, Watson, Schaps, & Lewis, 2000). Other characteristics of effective prevention programs were identified in Nation et al.’s (2003) synthesis of the prevention research to date: effective programs are comprehensive, use a variety of teaching methods, are grounded in strong theoretical frameworks, are implemented at developmentally appropriate times in children’s lives, are compatible with the community norms and culture, and are delivered by appropriately trained practitioners. In this volume, 10 different prevention programs are directly described by researchers, spanning universal, selective and indicated strategies and exemplifying key features of supportive relationships, competence promotion, and ongoing assessment and evaluation Supportive Relationships in Youth Ecologies. Interpersonal relationships lie at the heart of youth prevention efforts. These relational networks are complex and reciprocal, including teachers, families, peers, and other critical community members (Demaray et al., this

6 Beth Doll and Jina Yoon volume; Hughes & Barrois, this volume). Youth prevention interventions invariably incorporate strategies to strengthen these relationships and increase emotional social support in order to improve the psychological well-being of youth and limit the impact that stress and adversity have on their competence. One notable finding of social relationships research is that very early relationships of young children are especially important for personal adjustment and well-being in adolescence and adulthood (Bumbarger et al., this volume). The research review by Demaray et al. (this volume) aligns the goals of social support with anticipated outcomes of youth prevention interventions while that provided by Hughes and Barrois (this volume) relates the social contexts of classrooms to the multiple risk and protective factors underlying students’ school success. Interventions described in this volume include social relationship components that strengthen teachers’ supportive actions to interrupt bullying incidents (Newman et al., this volume); foster strong and authentic relationships between mentors and students at risk of not completing school (Christenson & Reschly, this volume); and strengthen peer social relationships by stimulating children’s values about and responsibility for peer interactions (Vliek & Orobio de Castro, this volume). Cowie and Smith (this volume) describe strategies that prompt peers to offer emotional and social support to classmates in distress with the potential to create cooperative communities that benefit students offering support as well as those receiving support. Other chapters use similar terms to describe cooperative communities including “peer ecology” (Song & Sogo, this volume), “classroom climate” (Hughes & Barrois, this volume), “classroom social context” (Salmivalli et al., this volume), or “community well-being” (Hatzichristou, Lykitsakou, Lampropoulou, & Dimitropoulou, this volume). In educational settings, Demaray et al. (this volume) suggest that this ecology makes unique contributions to students’ school success over and above that made by students’ individual engagement in school. As illustration, the classwide component of the Coping Power program, in which all children in a class were taught social cognitive and emotional regulation skills, enhanced the impact of the individual Coping Power program provided to highly aggressive students in those classrooms (Lochman, Powell, Boxmeyer, & Badr, this volume). Bullying Prevention Programs. Several preventive interventions described in this volume address bullying, a particularly destructive form of peer aggression in which a stronger child repeatedly harasses a weaker child. Bullying has the potential to disrupt peer relations and youth social competence. Because schools are sites with many opportunities for minimally supervised interactions among peers, they are frequently sites where peer bullying occurs. A common goal of these interventions is to boost the results of school bullying prevention programs which Salmivalli, Kärnä, and Poskiparta (this volume) describe “as more modest than was hoped for.” Newman et al. (this volume) describe peer bullying within the context of teachers’ relationships with students, explaining factors that influence teacher decisions to intervene to stop bullying. Their explanation serves as a rationale for Phase 2 of the Steps to Respect bullying prevention program. Three chapters in this volume (Salmivalli et al., Cowie & Smith, and Song & Sogo) call for onlooker interventions to shift the ways that children respond when peers are being bullied. Salmivalli et al. describe the innovative use of game technology to reinforce students’ knowledge, skills, and motivation to confront and challenge bullying when it occurs in their presence. Cowie and Smith predicate their intervention on strategies to prompt classmates to provide social support, while Song and Sogo comment on the pragmatic benefits of peer ecology interventions in that large numbers of students are a ready resource of any school-based program. Family Connections. Many comprehensive preventive interventions include components that train parents or other primary caretakers in particular skills or maintain heightened

The Current Status of Youth Prevention Science 7 communication between families and the prevention program. Families are the primary caretakers in natural youth environments and their participation in prevention efforts is the most obvious way to integrate wellness-promoting strategies into the everyday lives of youth. For example, a parent component of the Coping Power program (Lochman et al., this volume) prepares parents to reinforce skills that their aggressive children are learning in the child component. The Netherlands’ TIGER program includes parallel parent training groups when offered within counseling centers, and parent evenings when provided through local schools (Vliek & Orobio de Castro, this volume). The Partnering to Achieve School Success (PASS) program (Power, Lavin, Mautone, & Blum, this volume) forges strong partnerships between pediatric care settings and parents of children with attention problems, and incorporates additional optional family interventions or family–school consultation depending on the family’s need and interest. Mentors in the Check and Connect program (Christenson & Reschly, this volume) are in regular contact with families of students at risk of not completing school, and work to strengthen the compatibility of schools and families. The KiVa program includes a parents’ guide (Salmivalli et al., this volume) while the early literacy intervention described by Gettinger et al. (this volume) includes a family literacy center in which parents are provided with materials and coaching in literacy-promoting practices. Competence Promotion. The importance of building skills and promoting competence has been well supported by prevention scientists (Bumbarger et al., this volume; Catalano, Berglund, Ryan, Lonczak, & Hawkins 2004; Collaborative for Academic, Social and Emotional Learning, 2003; Weissberg et al., 2003). Inevitably, proposed prevention programs are grounded in comprehensive conceptual frameworks that translate developmental theory and research into practices to promote competence. One of the most prominent frameworks for competence promotion is Social Emotional Learning and Tran and Merrell (this volume) describe a comprehensive school-based program in which teachers provide social emotional instruction to their students. As other examples: Gettinger et al. (this volume) provide a comprehensive description of the theory underlying competence promotion in early childhood; Prince-Embury’s (this volume) conceptual framework for youth resilience describes research identifying individual characteristics that were present in youth who were successful despite adversity; Song and Sogo (this volume) explain the powerful potential outcome of preparing school-aged children to protect each other from peer aggression; Hatzichristou et al. (this volume) describe a school-wide intervention to promote psychological wellness and the wellbeing of the school community; and Vliek and Orobio de Castro (this volume) explain the theory underlying the use of personal responsibility and values to promote prosocial behavior in the Netherlands. Assessment and Program Evaluation. Assessment and early identification of youth who could benefit from prevention programs is essential to the success of universal and selective programs and to planning goals and priorities for prevention programs (Demaray et al., this volume). The availability of increasing numbers of technically sound and highly innovative universal screening assessment strategies is an exciting development within youth prevention science, and makes it possible to collect risk and success data from large numbers of youth without sacrificing accuracy and reliability. Both Walker et al. (this volume) and Dowdy et al. (this volume) provide comprehensive descriptions of the characteristics necessary for universal screening measures. In addition, Dowdy et al. (this volume) describe three universal screening measures that are technically adequate to identify children and adolescents who qualify or nearly qualify as having one or more mental disorder, along with strategies for planning and implementing universal screening in a community’s schools. One of these three measures, Systematic Screening for Behavior Disorders, is described in more detail by Walker

8 Beth Doll and Jina Yoon et al. (this volume) along with the key role that it played in the Jefferson Parish Public School System’s use of universal screening and a three-tiered prevention model to reduce behavioral disorders among its students. Alternatively, Prince-Embury (this volume) explains how definitions of technical adequacy might be operationalized in statistical analyses, and how the strength-based Resiliency Scales for Children and Adolescents meet these criteria for universal screening strategies. Osher and Kendziora (this volume) blur the boundaries between assessment and intervention when they use the AIR Survey of the Social and Emotional Conditions for Learning to prompt ongoing assessment and monitoring, which in turn engages school staff in continuous improvement and early intervention. Youth Prevention and Schools. Schools are inevitably the sites where much youth prevention occurs (Adelman & Taylor, this volume; Dwyer & Van Buren, this volume; Lochman et al., this volume; Power et al., this volume; Tran & Merrell, this volume). Of all community agencies, schools have the daily contact with children and adolescents that makes it cost-effective to provide broad prevention programs across the youth population of a community. Multiple schools in contingent communities often share common concerns and risks for youth, and can pool their resources to plan and provide prevention programs. Moreover, school sites have the potential to improve access to preventive interventions by vulnerable youth, such as those who are impoverished or from under-represented groups, by eliminating barriers such as the stigma associated with services in unfamiliar places, transportation to and from intervention sites, and scheduling around family work responsibilities. Given a shared mission to promote youth wellness, resources of schools and nearby community agencies could potentially be braided together so that schools’ and communities’ goals can be accomplished efficiently. It is not surprising, then, that many of the prevention programs and practices in this volume describe preventive interventions that occur in schools. Sadly, the infrastructure for school-based prevention programs remains inadequate. The number of school-based prevention programs is very small relative to the need, and those that do exist often focus narrowly on a single problem (Adelman & Taylor, this volume). In particular, the vast majority of universal prevention programs implemented in schools focus narrowly on the prevention of drug, tobacco, or alcohol use (Foster et al., 2005). Perhaps more critical, a clear focus on youth psychological wellness and prevention of psychopathology is not an acknowledged part of many schools’ mission (Dwyer & Van Buren, this volume; Walker et al., this volume). For example, despite the increasing availability of population-based assessments that are conceptually sophistically and technically sound, schools and other youth-serving agencies have limited familiarity with universal assessment procedures and are not yet making good use of the innovations (Walker et al., this volume). Instead, they are more accustomed to traditional referral-based identification of students for services that frequently delay identification of troubled students until it is too late to use the most costeffective preventive interventions (Walker et al., this volume). In most schools, youth prevention programs are marginalized within the larger educational efforts to improve schools (Adelman & Taylor, this volume) and school-community partnerships are hampered by historic divisions that separate education from other community agencies (Dwyer & Van Buren, this volume). While schools are providing more population-based youth prevention programs than other community agencies, these efforts have been characterized by many researchers as too little and too late (Dwyer & Van Buren, this volume; Walker et al., this volume). Youth with a significant need for preventive intervention are often not identified or they are identified at an age when interventions are less likely to be effective and more likely to be costly.

The Current Status of Youth Prevention Science 9

Methodological Issues in Youth Prevention Science Researchers and practitioners in youth prevention science have spent the past two decades discussing, refining, and strengthening standards for high-quality research to show which prevention practices improve outcomes for youth, with which populations, under which conditions, and at what cost. These research standards build upon those specified in the late 1990s for all psychosocial interventions (Chambless et al., 1996; Hayes, Barlow, & NelsonGray, 1999; Lonigan, Elbert, & Bennett-Johnson, 1998; U.S. Department of Health and Human Services, 1999). Evidence-based psychotherapeutic interventions are those that have been examined in at least two well-designed group studies or a series of well-conducted singlesubject studies; conducted by unrelated research teams; in which participants were randomly assigned to treatment and control groups; with results demonstrating that improvements in symptoms occur, are not due to chance, are due to the intervention of interest, represent an improvement over no treatment at all, and are at least as strong as those produced by existing interventions. Still, the fit between these generic standards and the unique circumstances posed by youth prevention research is not altogether satisfactory. In evaluations of preventive interventions, the data of interest will frequently be the absence rather than presence of symptoms of disorders or disturbances. This is because interventions to prevent psychosocial disturbances are applied before symptoms are evident or when signs of a disorder first emerge but do not yet meet diagnostic criteria. The measure of the intervention’s success may be the continued absence of problems rather than the remission of problems that were already in evidence. Documenting the continued absence of problems often requires data describing populationbased prevalence or incidence rates. These data require population-based assessments that are epidemiological in design and the designs, tools, and procedures for these assessments are unfamiliar to many agencies or professionals. In many cases, the outcomes of interest for a preventive intervention are displaced by decades or generations in time (Cooney et al., this volume). For example, Kellam, Rebok, Ialongo, & Mayer (1994) showed that enhanced behavior management by first-grade teachers could result in significantly fewer conduct problems six years later. Newman et al. (this volume) report that many outcomes of the Steps to Respect program were not apparent until the program had been in place for 18 months. Longitudinal designs struggle mightily with participant attrition (Hughes & Barrois, this volume). Distal outcomes are particularly difficult to track in communities characterized by high mobility because shifts in residence and dispersal of a population can complicate the task of identifying or tracking youth who were recipients of a school or community intervention many years earlier (Wandersman & Florin, 2003). On the face of it, it ought to be possible to describe the success of prevention programs using existing community indices of health, employment, social, or educational success that are routinely collected across decades. For example, Walker and colleagues (this volume) note that archival school records accrue naturally over time and are unobtrusive ways to track student success. They describe existing strategies for mining these records systematically and objectively. In practice, however, existing agency data is often insufficient for the task. The limitation of existing community records is exemplified by the United States’ 50-year attempt to track high school graduation and dropout rates (Barclay & Doll, 2001). Even with strengthened requirements for state reporting of graduation rates following the No Child Left Behind Act of 2001, there are at least three prominent ways that states compute these rates: longitudinal descriptions of the number of students from a ninth-grade cohort who graduate four years later, the percentage of school leavers who leave with a diploma, or the number of

10 Beth Doll and Jina Yoon graduates from a cohort of students who initially entered school (Swanson, 2003). All three strategies are careful to not penalize schools for students who simply transfer from one district to another. However, the mobility of residence combined with imprecise district records can make it impossible to determine which students were high school dropouts and which students transferred to and completed high school in another community. Some students transfer their files but never show up in the new school, while other students never request a file transfer but still enroll in a new school. Some students transfer to a new high school but subsequently leave without graduating. In some instances, students who left school without graduating returned later to complete their degree. In an attempt to provide a more accurate index, some policymakers have suggested that communities simply track the number of residents with high school diplomas but this, too, has proved to be an imperfect indicator of a local school district’s success in graduating its students. The frequency of earned diplomas is easily distorted by the in-migration of large numbers of residents with limited education, as is the case in many destinations of immigrants into the US. Moreover, functional indicators such as high school graduation are often related to community risk factors such as poverty, unemployment, or access to community support services, and population trends in these risk behaviors can be shifting even while prevention programs are being implemented—such that the impact of a prevention program could be masked by increased adversity within the community or could be inflated by decreased adversity (Pentz, 2004). Prevention researchers also chafe at the emphasis on disorders and dysfunction that dominates traditional psychotherapeutic intervention research (Adelman & Taylor, this volume). The mere absence of a deficit is not necessarily the best early indicator of youths’ future competence; instead the most useful assessment data may be indices of the social, emotional, and behavioral health of youth. For example, “lowering the high school dropout rate” is an inadequate definition of a prevention program’s goal since simply leaving school with a diploma does not necessarily place graduates on a trajectory towards life success and adult competence. A more appropriate goal is “raising rates of successful school completion” in which school completers leave school with a diploma and adequate preparation for productive home, work, and community lives (Christenson & Reschly, this volume). Unfortunately, protocols for tracking positive indicators of psychosocial wellness are less fully developed than those assessing pathology and disturbance. Still, Dowdy et al. (this volume) review very recent research to demonstrate that including positive measures of life satisfaction or school connectedness in addition to negative measures of problems or pathological symptoms heightens the sensitivity and predictive power of universal screening strategies. Ultimately, the purpose of youth prevention research is to alter community programs and practices so that these predispose youth to be successful and competent adults (Bumbarger et al., this volume). This is a much broader purpose than remediating a disorder, and it requires that programs not only have meaningful and significant impact on youth in well-controlled research studies (program efficacy); but also have a significant and important youth impact when implemented within actual communities (program effectiveness); be sustained over time (program sustainability); be broadly adopted in diverse communities beyond those in which the program was first developed (program scalability); and be integrated within the permanent policies, practices, and resources of the community (systemic program supports; Kellam & Langevin, 2003). In deference to these unique demands of high-quality prevention research, the Society for Prevention Research (Flay et al., 2005) has developed five standards for evidence of prevention programs’ efficacy, four additional standards for demonstrating programs’ effectiveness, and three additional standards to demonstrate that the program is ready for broad dissemination:

The Current Status of Youth Prevention Science 11 Under these Standards, an efficacious intervention will have been tested in at least two rigorous trials that (1) involved defined samples from defined populations; (2) used psychometrically sound measures and data collection procedures; (3) analyzed their data with rigorous statistical approaches; (4) showed consistent positive effects (without serious iatrogenic effects); and (5) reported at least one significant long term followup. An effective intervention under these Standards will not only meet all standards for efficacious interventions, but also will have (1) manuals, appropriate training, and technical support available to allow third parties to adopt and implement the intervention; (2) been evaluated under real-world conditions in studies that included sound measurement of the level of implementation and engagement of the target audience (in both the intervention and control conditions); (3) indicated the practical importance of the intervention outcome effects; and (4) clearly demonstrated to whom intervention findings can be generalized. An intervention recognized as ready for broad dissemination under these Standards will not only meet all standards for efficacious and effective interventions, but will also provide (1) evidence of the ability to “go to scale”; (2) clear cost information; and (3) monitoring and evaluation tools so that adopting agencies can monitor or evaluate how well the intervention works in their settings. (p. 151). Several researchers append additional phases to this program of youth prevention research: examinations of programs’ sustainability once they have been shown to be effective in actual practice settings, and empirical investigations of the infrastructure necessary for prevention programs that have been taken to scale (Adelman & Taylor, this volume; Bumbarger et al., this volume; Kellam & Langevin, 2003). Hughes and Barrois (this volume) apply the five efficacy standards to eight universal preventive interventions that had the common goal of increasing positive social interactions in a school classroom, and report that only two programs met all of the standards for efficacy. Three of the eight had at least one well-controlled study of the program efficacy, a mighty accomplishment given that the studies’ participants were typically randomized at the school or classroom level and the studies thus required a large number of participants. Four of the eight had demonstrated that program effects persisted at least six months after the intervention had been completed. Many of the chapters in this volume illustrate the kinds of logistical barriers that prevent researchers from satisfying the Flay et al. standards in program efficacy studies. For example, Vliek and Orobio de Castro (this volume) used a delayed treatment design with random assignment to evaluate the TIGER program but their sample of 28 classrooms left their study underpowered to detect some program effects and there were practical and ethical limits on how long treatment could be delayed in their control classrooms. Hughes and Barrois’ review illustrates that youth prevention science has just begun to provide the kinds of evidence that communities need to identify promising practices that could be applied with good results in real-world settings. Another discouraging reality is that the translation of efficacious programs into communityadopted practices has been elusive. Too many prevention programs that have been demonstrated to work in carefully controlled research studies (evidence of intervention efficacy) are not adopted for actual use by communities, schools, or mental health centers (no evidence of intervention effectiveness). Just as important, in those instances where efficacious programs are adopted by communities, they are rarely implemented with integrity and their results are less dramatic than was suggested by the earlier research studies. For example, programs delivered in community settings may differ in the amount of intervention, the quality of program delivery, the degree to which key components were actually implemented, or the level of participation by

12 Beth Doll and Jina Yoon the children and adolescents or their families (Lochman et al., this volume; Odom et al., this volume). As Song and Sogo (this volume) point out, this is particularly pronounced for underresourced communities such as urban centers or remote rural communities. Actual providers of preventive interventions are not always trained mental health providers, but instead may be classroom teachers, daycare workers, or others. Even though these practitioners are trained to provide the program, they may not implement the program as planned. When manualized preventive interventions are implemented as part of large scale up investigations, it can be extremely difficult to ensure that the interventions are uniformly implemented in sites that are widely dispersed (Gettinger et al., this volume). Moreover, the fidelity of program implementation can shift over time (poorer implementation later in the program rather than earlier). There are many reasons why this might be the case. It may be difficult to provide sufficient dosage of community interventions to show effects (Wandersman & Florin, 2003). Interventions provided through communities require maintaining a community collaboration—which is difficult to do. Frequently, resources diminish once soft money disappears and the program is not sustained. As a result, youth preventive interventions are frequently marginalized and deference is given to school improvement goals that emphasize academic achievement (Adelman & Taylor, this volume). Weisz et al. (2005) conclude that most children and adolescents have very limited access to effective prevention programs because of the continuing divide between research and practice, and the gaps in the evidence base. Furious debates continue about whether this gap between efficacy and effectiveness describes a failure of communities to devote the resources necessary for high-quality youth prevention programs, or a failure of researchers to develop programs that are sufficiently pragmatic and adequate for the complexity of community conditions. Some researchers have identified limitations in community agencies that contribute to their inability to replicate prevention programs with fidelity (Elliott & Mihalic, 2004). These include inadequacies in the site’s capacity or preparation for the program (Bumbarger et al., this volume); difficulties training agency staff to deliver the program, particularly when training is provided under conditions of high staff absenteeism or turnover; inadequate technical assistance for the agency including instances in which technical assistance was not included in the agency budget for the program or was not able to respond quickly enough to problems that arose; and misjudgments of the program dosage necessary to produce the hoped-for effects among the community’s youth. Similar limitations have been described to explain why school-based prevention programs lack integrity and are unsuccessful in some communities (Botvin, 2004): Schools may lack sufficient in-school training and support to carry out the program; or they may be struggling with multiple instances of limited resources such as overcrowded classrooms or budgets that are inadequate for basic program materials. Teachers in the school may have multiple competing pressures including poor student discipline, low teacher morale and burnout, or an overpowering emphasis on basic academic instruction. Nation et al. (2003) point out that practitioners and sites often do not have the resources to implement research-based programs. While some community prevention efforts can begin on a smaller scale and then be broadened over time, it is difficult to implement many prevention programs “piecemeal” because preventive interventions depend on universal screening strategies to select the right youth for intervention, while screening strategies require that there be an appropriate intervention program to serve youth once they have been identified. Consequently, Biglan and his colleagues (2003) call for research on the influence of financial consequences on the practices of schools and other organizations as a necessary first step towards the widespread adoption of prevention practices.

The Current Status of Youth Prevention Science 13 An alternative perspective is that manualized interventions may not be suitable for use in all communities and populations. Castro, Barrera, and Martinez (2004) suggest that problems with intervention fidelity may actually be necessary adaptations that make manualized interventions culturally appropriate for diverse communities. Youth prevention programs developed within a research agenda may not be immediately applicable in communities that differ in their members’ language, ethnicity, socioeconomic status, or cultural values and beliefs. Other researchers agree, and clarify that youth prevention programs can only be strictly replicated in communities where the program is culturally and linguistically compatible with the population, where needs assessment data demonstrates that the program is needed, and where program participants were involved in the decisions about how and why the program is being implemented (Ringwalt, Vincus, Ennett, Johnson, & Rohrback, 2004). A key focus of current prevention science is how to adapt manualized interventions so that these appropriately accommodate diverse community cultures and needs. For example, Power et al. (this volume) describe implementation of an indicated preventive intervention for low-income children with attention problems that has been modularized: decisions about which program modules to provide are made within effective family-practitioner teams, so that these accommodate the unique needs of each family. The description by Lochman et al. (this volume) of the Coping Power program provides a cogent example of “fidelity with flexibility” in which emphasis is placed on the key factors that contribute to the quality of the Coping Power program’s reliable delivery while other aspects of the program may be supplemented or altered to match characteristics of the children or their parents. Christenson and Reschly (this volume) explain how the Check and Connect program is individualized to particular schools and students. Several researchers have suggested that manualized preventive interventions need to include procedures for communities to adapt the program to their unique demands: identifying the “tailoring variables” that moderate the effect of the intervention, describing strategies for assessing the tailoring variables, and providing decision rules for systematically modifying the intervention based upon the results of the assessments (Collins, Murphy, & Bierman, 2004). Contemporary prevention researchers also stress the importance of building practitioners’ capacities for implementing prevention programs in actual settings (Bumbarger et al., this volume; Gettinger et al., this volume; Kaftarian et al., 2004; Lochman et al, this volume; Odom et al., this volume). A common finding is that intervention training provided at the beginning of a program is ineffective unless it is supplemented with ongoing consultation between practitioners and intervention experts. As one example of this kind of research, Salmivalli et al. (this volume) describe an “exceptionally large” array of supports provided to schools implementing the KiVa program for the prevention of school bullying including user-friendly and attractive materials, training programs incorporating key persons chosen by the schools, virtual training provided over a program website, tools to monitor the program impact, and a biannual KiVa conference days to disseminate research and provide for sharing experiences. Newman et al. (this volume) report the key importance of a “coaching” component for teachers who implement the Steps to Respect program. In the EMERGE preschool literacy program, teacher training is augmented by literacy coaches who regularly review program monitoring data with students and help them plan literacy instruction that is responsive to the data. Tran and Merrell (this volume) pose an alternative strategy by designing a low-cost, time-limited schoolwide program to promote student resilience that is carefully matched to the instructional competencies of classroom teachers. In their chapter, they describe procedural strategies that strengthen program fidelity and note that a longer, high-intensity program might produce stronger gains but is less likely to be adopted.

14 Beth Doll and Jina Yoon Methods for prevention science have evolved over time and researchers now use more sophisticated designs and measures. Large numbers of preventive interventions have demonstrated early promise of efficacy. Still, these results continue to be preliminary as very few programs have conducted long-term follow-ups of program impact, described independent replications of program impacts, reported studies with sample sizes large enough for adequate statistical power, or provided sufficient information describing size of program effect.

Implementation Science The dynamic tension between fidelity and adaptation is one of the important themes in current prevention research and may only be resolved through innovative research methodologies that rigorously examine the effectiveness of prevention programs in practice, as well as the conditions under which they are most likely to be effective, and the factors that affect community agencies’ willingness to use evidence-based practices (Biglan et al., 2003; Kaftarian et al., 2004). Odom et al. (this volume) and Bumbarger et al. (this volume) refer to this as “implementation science” and mark it as a priority for the next wave of research on youth prevention. Specifically, emerging studies of youth prevention science need to examine the relation that exists between the degree of program implementation and youth outcomes, describe key features that must be present for a program to be effective and identify the factors that affect program fidelity. A necessary and particularly challenging component of implementation research is technically sound assessment of the degree to which a program has been implemented. Commonly used methods to describe degree of implementation include observation, self-report, interview, and archival records (Odom et al., this volume). Direct measures such as observations may be more intrusive but less vulnerable to bias than practitioners’ self-report or interviews, but the observations’ intrusiveness can alter the implementation fidelity and limit the generalizability of results to programs implemented outside of research settings. Thus, multiple measures are often collected to describe program implementation, with different methods balanced against their impact on practice, and relations examined between the proportion of the program that was implemented and its impact on participants. It is increasingly apparent that implementation studies must also provide a thorough examination of all participant perspectives including those of the children themselves if they are to adequately represent the acceptability and potential impact of a prevention program (Cowie & Smith, this volume). At least initially, this is likely to require mixed-method designs to incorporate qualitative as well as quantitative data to detect program implementation factors and to fully describe program outcomes. Pentz (2004) has suggested that studies of communities’ implementation of preventive interventions will routinely have three comparison groups: participants in programs delivered by trained interventionists and implemented with fidelity, participants in programs delivered by interventionists who were trained but did not implement the program accurately, and participants in programs delivered by interventionists who were not trained. To some extent, characteristics of the interventions themselves may be critical determinants of their implementation. The Diffusion of Intervention Theory (Dusenbury & Hansen, 2004) suggests that interventions are more amenable to diffusion when they fit with existing community values and needs, are relatively simple to use, have clear advantages over other programs, have been tried and evaluated locally, and have a positive reputation. An underemphasized aspect of implementation is youth policy or the degree to which the empirical findings of youth prevention science are reflected in local, state, and national policies for practice and funding. As Cooney et al. (this volume) explain, high-quality research

The Current Status of Youth Prevention Science 15 is only one factor responsible for communities’ course of action. In particular, the timeline of prevention research, its reliance on peer-refereed science, its preference for scholarly discourse, and its focus on long-term outcomes place it at cross-purposes with public decisionmakers’ preference for immediacy, short-term and localized benefits, and simply-stated recommendations. They describe a set of practical steps for researchers to prompt policymakers to attend to and implement prevention science so as to garner greater influence over youth prevention practices. Barring systemic implementation research, Adelman and Taylor (this volume) argue that the demands for communities to use only evidence-based prevention programs are premature. Instead, they suggest that such demands prematurely push practices developed in highly controlled laboratory situations into widespread practice based on unwarranted assumptions about their effectiveness. Moreover, they point out that the expectation that communities should select programs from approved “lists” perpetuates fragmented services that address one problem at a time. For schools, in particular, the degree to which teachers apply a program’s prevention principles in their daily interactions with students may be just as important as the degree to which a manualized curriculum is delivered to students (Hughes & Barrois, this volume). While implementation research is ongoing, a viable alternative to evidencebased programs in the interim will be the incorporation of local program evaluation into community program implementations (Adelman & Taylor, this volume).

Summary As the chapters in this volume illustrate, this is a moment of uncommon importance in youth prevention science. Innovative youth prevention practices are being drawn out of the previous decades’ robust empirical descriptions of developmental pathways into human competence and pathology. Promise is evident in the proliferation of efficacious prevention programs that emphasize high-quality social relationships, family connections, competence promotion, universal screening, and convenient (often school-located) sites. These prevention programs are being carefully examined in concert with rigorous standards for intervention research, and subsequently are being transported into community policy and practice. Tension continues to be reflected in debates to reconcile wellness promotion and disease prevention models; over the central role played by socioecological relative to psychobiological determinants of mental health; to define the standards that should be used to conduct high-quality prevention research; over the ways to balance rigor with community impact; and over the necessary balance of intervention fidelity with community demands for relevant and culturally appropriate practice. Still, consensus is evident in recognition that the phenomena of risk, resilience, and prevention are complex and multi-faceted; that an important gap exists between rigorous prevention research and community prevention practices; and that it is the impact of prevention programs as implemented in communities that matters most. New emphases are being placed on the strategies to translate prevention science into thoughtful and effective prevention policies. At present, the translation of prevention science into evidence-based prevention practice is both frustrating and compelling and holds tempting opportunities to make long-lasting impressions on the mental health and well-being of youth.

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16 Beth Doll and Jina Yoon Albee, G. W. (2004). Prevention of mental disorders. In W. E. Pickren, Jr., & S. F. Schneider (Eds.), Psychology and the National Institute of Mental Health: A historical analysis of science, practice, and policy (pp. 295–315). Washington, DC: American Psychological Association. Albee, G. W. (2005). Prevention of mental disorders. In W. E. Pickren & S. F. Schneider (Eds.), Psychology and the National Institute of Mental Health: A historical analysis of science, practice, and policy. Washington, DC: American Psychological Association. American Psychiatric Association. (2000). The diagnostic and statistical manual of mental disorders— fourth edition, text revised. Washington DC: Author. Barclay, J. R., & Doll, B. (2001). Early prospective studies of the high school drop out. School Psychology Quarterly, 16, 357–369. Biglan, A. (2004). Contextualism and the development of effective prevention practices. Prevention Science, 5, 15–21. Biglan, A., Mrazek, P. J., Carnine, D., & Flay, B. R. (2003). The integration of research and practice in the prevention of youth problem behaviors. American Psychologist, 58, 433–440. Botvin, G. J. (2004). Advancing prevention science and practice: Challenges, critical issues, and future directions. Prevention Science, 5, 69–72. Castro, F. G., Barrera, M., & Martinez, C. R. (2004). The cultural adaptation of prevention interventions: Resolving tensions between fidelity and fit. Prevention Science, 5, 41–45. Catalano, R. F., Berglund, M. L., Ryan, J. A. M., Lonczak, H. S., & Hawkins, J. D. (2004). Positive youth development in the United States: Research findings on evaluations of positive youth development programs. Annals of the American Academy of Political and Social Science. Special Issue: Positive Development: Realizing the Potential of Youth, 591, 98–124. Chambless, D. L., Sanderson, W. C., Shoham, V., Johnson, S. B., Pope, K. S., Critis-Cristoph, P., Baker, M., Johnson, B., Woody, S. R., Sue, S., Beutler, L., Williams, D. A., & McCurry, S. (1996). An update on empirically validated therapies. Clinical Psychologist, 49, 5–14. Coie, J. D., Watt, N. F., West, S. G., Hawkins, J. D., Asarnow, J. R., Markan, H. J., Ramey, S. L., Shure, M., & Long, B. (1993). The science of prevention: A conceptual framework and some directions for a national research program. American Psychologist, 48, 1013–1022. Collaborative for Academic, Social and Emotional Learning. (2003). Safe and sound: An educational leader’s guide to evidence-based social and emotional learning programs. Retrieved March 13, 2008, from http://www.casel.org/pub/safeandsound.php Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185–196. Commission on Chronic Illness. (1957). Chronic illness in the United States (Vol. 1). Cambridge, MA: Harvard University Press. Commission on the Prevention of Mental/Emotional Disabilities. (1987). The Commission on the Prevention of Mental/Emotional Disabilities. Journal of Primary Prevention, 7, 175–241. Conduct Problems Prevention Research Group. (1999). Initial impact of the Fast Track prevention trial for conduct problems: II. Classroom effects. Journal of Consulting and Clinical Psychology, 67, 648–657. Cook, T. D., Murphy, R. F., & Hunt, H. D. (2000). Comer’s School Development Program in Chicago: A theory-based evaluation. American Educational Research Journal, 37, 535–597. Cowen, E. (1994). The enhancement of competence: Challenges and opportunities. American Journal of Community Psychology, 22, 149–179. Doll, B. (1996). Prevalence of psychiatric disorders in children and youth: An agenda for advocacy by school psychology. School Psychology Quarterly, 11, 20–46. Doll, B., & Lyon, M. (1998.) Risk and resilience: Implications for the practice of school psychology. School Psychology Review, 27, 348–363. Dusenbury, L., & Hansen, W. B. (2004). Pursuing the course from research to practice. Prevention Science, 5, 55–59. Elliott, D. S., & Mihalic, S. (2004). Issues in disseminating and replicating effective prevention programs. Prevention Science, 5, 47–53.

The Current Status of Youth Prevention Science 17 Flay, B. R., Biglan, A., Boruch, R. F., Castro, F. G., Gottfredson, D., Kellam, S., Moscicki, E. K., Schinke, S., Valentine, J. C., & Ji, P. (2005). Standards of evidence: Criteria for efficacy, effectiveness, and dissemination. Prevention Science, 6, 151–175. Foster, S., Rollefson, M., Doksum, T., Noonan, D., Robinson, G., & Teich, J. (2005). School mental health services in the United States 2002–2003. DHHS Pub. No. (SMA) 05-4068. Rockville, MD: Center for Mental Health Services, Substance Abuse and Mental Health Services Administration. Gordon, R. S. (1983). An operational classification of disease prevention. Public Health Reports, 98, 107–109. Greenberg, M. T., & Kusché, C. A. (1998). Blueprints for violence prevention: The PATHS Project (Vol. 10). Boulder, CO: Institute of Behavioral Science, Regents of The University of Colorado. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474. Hayes, S. C., Barlow, D. H., & Nelson-Gray, R. O. (1999). The scientist practitioners: Research and accountability in the age of managed care (2nd ed.). Boston: Allyn & Bacon. Hosman, C., Jane-Liopis, E., & Saxena, S. (Eds). (2005) Prevention of mental disorders: Effective interventions and policy options. Oxford: Oxford University Press. Kaftarian S., Robinson, E., Compton, W., Davis Watts, B., & Volkow, N. (2004). Blending prevention research and practice in schools: Critical issues and suggestions. Prevention Science, 5, 1–3. Kaplan, R. M. (2000). Two pathways to prevention. American Psychologist, 55, 382–396. Kellam, S. G., & Langevin, D. J. (2003). A framework for understanding “evidence” in prevention research and programs. Prevention Science, 4, 137–153. Kellam, S. G., Rebok, G. W., Ialongo, N., & Mayer, L. S. (1994). The course and malleability of aggressive behavior from early first grade into middle school: Results of a developmental epidemiologicallybased prevention trial. Journal of Child Psychology & Psychiatry & Allied Disciplines, 35, 259–281. Kumpfer, K. L., Alvarado, R., Tait, C., & Turner, C. (2002). Effectiveness of school-based family and children’s skills training for substance abuse prevention among 6–8 year old rural children. Psychology of Addictive Behaviors, 16, 565–571. Lahey, B. B., Loeber, R., Quay, H. C., Frick, P. J., & Grimm, S. (1992). Oppositional defiant and conduct disorders: Issues to be resolved for DSM-IV. Journal of the American Academy of Child and Adolescent Psychiatry, 31, 539–546. Loeber, R., & Farrington, D. P. (2000). Young children who commit crime: Epidemiology, developmental origins, risk factors, early interventions, and policy implications. Development and Psychopathology, 12, 737–762. Lonigan, C. J., Elbert, J. C., & Bennett-Johnson, S. (1998) Empirically supported psychosocial interventions for children: An overview. Journal of Clinical Child Psychology, 27, 138–145. Mash, E. J., & Dozois, D. J. A. (2003). Child psychopathology: A developmental-systems perspective. In E. J. Mash, & R. A. Barkley (Eds.), Child psychopathology (2nd ed.). (pp. 3–71). New York, NY: Guilford Press. Mrazek, P. J., & Haggerty, R. J. (Eds). (1994). Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press. Nation, M., Crusto, C., Wandersman, A., Kumpfer, K. L., Seybolt, D., Morrissey-Kane, E., & Davino, K. (2003). What works in prevention: Principles of effective prevention programs. American Psychologist, 58, 449–456. Pentz, M. A. (2004). Form follows function: Designs for prevention effectiveness and diffusion research. Prevention Science, 5, 23–29. Ringwalt, C. L., Vincus, A., Ennett, S., Johnson, R., & Rohrbach, L. A. (2004). Reasons for teachers’ adaptation of substance use prevention curricula in schools with non-white student populations. Prevention Science, 5, 61–67. Solomon, D., Battistich, V, Watson, M., Schaps, E., & Lewis, C. (2000). A six-district study of educational change: Direct and mediated effects of the Child Development Project. Social Psychology of Education, 4, 3–51.

18 Beth Doll and Jina Yoon Swanson, C. B. (2003). NCLB implementation report: State approaches for calculating high school graduation rates. Washington, DC: The Urban Institute. U.S. Department of Health and Human Services. (1999). Mental health: A report of the Surgeon General. Rockville, MD: U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services, National Institutes of Health, National Institute of Mental Health, 1999. Wandersman, A., & Florin, P. (2003). Community interventions and effective prevention. American Psychologist, 58, 441–448. Weissberg, R. P., Kumpfer, K. L., & Seligman, M. E. P. (2003). Prevention that works for children and youth: An introduction. American Psychologist, 58, 425–432. Weisz, J. R., Sandler, I. N., Durlak, J. A., & Anton, B. S. (2005). Promoting and protecting youth mental health through evidence-based prevention and treatment. American Psychologist, 60, 628–648. Werner, E. E. (2006). What can we learn about resilience from large-scale longitudinal studies? In S. Goldstein & R. B. Brooks (Eds.), Handbook of resilience in children (pp. 91–105). New York, NY: Springer.

2

Placing Prevention into the Context of School Improvement Howard S. Adelman and Linda Taylor University of California, Los Angeles

Few argue against efforts to prevent educational, psychosocial, physical, and mental health problems, or against the desirability of doing so on a large scale. Arguments arise, however, about costs, effectiveness, and the role of schools (among other major systems). Beyond specific and narrowly defined public health concerns, prevention is not a high priority in public policy and practice. Beyond immunizations, prevention initiatives for children and adolescents have focused mainly on reducing specific risk-taking behaviors (Center for Mental Health in Schools, 2007a). This has led to an overemphasis on observed problems and on approaching them as separate entities and to de-emphasizing analyses and pursuit of common underlying causes. It also has contributed to the tendency to downplay the promotion of wellness as an invaluable end in and of itself (e.g., Cowen, 1997). Prevention programs in schools are relatively few in number and usually are funded as discrete projects, often with “soft” money (e.g., see the lists described in CASEL, 2003; Center for Mental Health in Schools, 2005a; Cochrane Library, 2007; Cowen, Hightower, Pedro-Carroll, Work, Wyman, & Haffey, 1996; Durlak, 1995; Durlak & Wells, 1997; Greenberg, Weissberg, O’Brien, Zins, Fredericks, Resnik et al., 2003; SAMHSA, 2007; Scattergood, Dash, Epstein, & Adler, 1998; Weisz, Sandler, Durlak, & Anton, 2005). Moreover, existing programs are so fragmented that they often produce inappropriate redundancy, and counterproductive competition, and work against the type of systemic collaboration that is essential for establishing interprogram connections on a daily basis and over time. All this increases costs, reduces effectiveness, and perpetuates widespread marginalization of prevention initiatives. What prevails is a vicious cycle of unsatisfactory policy, research, practice, and training. The cycle is likely to continue as long as prevention is viewed narrowly. To move the field forward, the concept of prevention must be framed in a comprehensive context. Moreover, schools and communities must work together in new ways, and the work must be fully integrated into school improvement policy, planning, implementation, and accountability. This chapter places primary prevention along with the promotion of healthy development at one end of a full continuum of interventions, with each level of the continuum conceived as an integrated system. Then, the continuum is placed into the context of school improvement and explored in terms of a comprehensive, multi-faceted component designed to ensure all youngsters have an equal opportunity to succeed at school. Throughout the chapter, implications are discussed with respect to policy, research, practice, and training, including some general ramifications for systemic change.

Prevention in Broad Context Prevention initiatives have many facets. At a school, for example, approaches may be schoolwide with the intent of having an impact on all students; they may be limited to a classroom;

20 Howard S. Adelman and Linda Taylor Table 2.1 Outline Aid for Analyzing Key Facets of School-Oriented Prevention Efforts I. Form of initiative A. policy (federal, state, local) B. practice C. capacity building D. systemic change II. Context for practice A. community-wide B. school-wide C. in classroom as part of regular program D. an “add-on” program in or outside the regular class E. part of “clinical” services III. Stage of prevention A. primary B. secondary (early-after-onset) C. tertiary (ameliorating severe/chronic problems in ways that prevent exacerbating the conditions and that minimize their influence as secondary instigating factors) IV. Focus A. focal point of intervention 1. environment(s) 2. person(s) 3. both B. intended range of impact 1. a broad-band intervention 2. for one or more specific targets C. breadth of approach 1. directed at a categorical problem 2. multi-faceted D. general area of concern 1. addressing barriers to development, learning, and positive functioning 2. promoting healthy development E. domain 1. knowledge 2. skills 3. attitudes F. strategy 1. instruction* 2. behavior modification 3. enhancing expectations and opportunities for positive behavior 4. counseling/therapy

V.

VI.

VII.

VIII.

IX.

5. physical health programs and services 6. social support 7. social services 8. student to student support and socialization 9. school-home-community partnerships 10. enhancing security and policing measures 11. multiple strategies 12. comprehensive, school-wide approaches Level of schooling/student development A. elementary/middle/high school B. specific grade, age, or stage of development Degree of integration with other interventions A. isolated B. coordinated with others C. systematically integrated Stage of intervention development A. formative B. fully developed, but unevaluated C. empirically supported Scope of implementation A. limited project 1. at one site 2. at several sites B. systemic change initiative 1. still at pilot demonstration stage 2. being phased in—at a few sites 3. being phased in—at many sites 4. all sites involved Approach to evaluation A. focused only on accountability demands B. formative program evaluation C. summative program evaluation 1. of efficacy 2. of effectiveness when replicated under natural conditions 3. cost-effectiveness analyses D. designed as evaluation research

* Content Focus of Curricular Approaches When the emphasis is on curriculum to prevent psychosocial problems (violence, substance abuse, delinquency, pregnancy, eating disorders, learning problems, etc.) and/or promote healthy socioemotional development and effective functioning, the content focus may be on: X assets-building (including strengthening academics, developing protective factors, expanding areas of competence and self-discipline)

X socio-emotional development (e.g., understanding self and others, enhancing positive feelings toward self and others, cognitive and interpersonal

Placing Prevention into the Context of School Improvement 21 problem-solving, social skills, emotional “intelligence”) X building character (e.g., values) X physical development (e.g., diet/nutrition, sports/recreation) X fostering abilities (e.g., one or more of the multiple “intelligences,” enrichment)

X fostering hope (e.g., positive expectations for the future; perceptions of selfdetermination) X resistance education X stress reduction X symptom reduction

they may target a specific group and a specific problem. Various strategies may be used to promote healthy development or address factors that interfere with positive functioning. Table 2.1 outlines some key categories that can aid in differentiating among school-oriented prevention efforts. The outline reflects the fact that prevention encompasses not only discrete strategies but also broad, multi-faceted approaches. Policy-oriented discussions increasingly are recognizing the importance of multi-faceted approaches in accounting for social, economic, political, and cultural factors that can interfere with or promote development, learning, and teaching (Adelman & Taylor, 1997, 2006a; Center for Mental Health in Schools, 1997, 2005b; Dryfoos, 1998; Schorr, 1997). Such policies also reflect a basic assumption that many problems are not discrete, and therefore, interventions that address root causes can minimize the trend to develop separate programs for every observed problem. In turn, this is viewed as enabling increased coordination and integration of resources which can increase cost-effectiveness and impact. Major policies and practices for addressing such factors can be grouped into five areas for purposes of analyzing the state of the art and making recommendations. The areas are: (1) measures to abate inequities/restricted opportunities, (2) primary prevention and early age interventions, (3) identification and amelioration of learning, behavior, emotional, and health problems as early as feasible, (4) ongoing amelioration of mild-moderate learning, behavior, emotional, and health problems, and (5) ongoing treatment of and support for chronic/severe/pervasive problems. The range of interventions can be appreciated better along a continuum. As illustrated in Figure 2.1, the continuum ranges from primary prevention (including a focus on wellness or competence enhancement), through approaches for treating problems early-after-onset, and extending on to narrowly focused treatments for severe/chronic problems. In keeping with public education and public health perspectives, the continuum encompasses and expands efforts to enable academic, social, emotional, and physical development and address behavior, learning, and emotional problems at every school.1 Such a continuum provides one template for assessing the degree to which the set of community and school programs serving local geographic or catchment areas is comprehensive, multi-faceted, and integrated. The programs cited in Figure 2.1 are seen as integrally related. Therefore, it seems likely that the impact of each can be exponentially increased through organizing them into an integrated set of systems. These can be conceived as three interconnected levels of intervention: (1) systems to promote healthy development and prevent problems, (2) systems to intervene as early after the onset of a problem as is feasible, and (3) systems of care. As illustrated in Figure 2.2, the assumption is that effectiveness at the upper levels will result in fewer persons requiring intervention at lower levels. Note that the continuum encompasses the concepts of primary, secondary, and tertiary prevention, as well as the Institute of Medicine’s classification of a continuum of care which groups prevention approaches according to target population into a three-tiered categorical schema: universal, selective, and indicated (Mrazek & Haggerty, 1994). By stressing the importance of integrating interventions across a continuum of systems, the

22 Howard S. Adelman and Linda Taylor Examples of focus and types of intervention

Intervention Continuum Primary prevention

(Programs and services aimed at system changes and individual needs) 1.

2.

Public health protection, promotion, and maintenance to foster opportunities, positive development, and wellness • economic enhancement of those living in poverty (e.g., work/welfare programs) • safety (e.g., instruction, regulations, lead abatement programs) • physical and mental health (incl. healthy start initiatives, immunizations, dental care, substance abuse prevention, violence prevention, health/mental health education, sex education and family planning, recreation, social services to access basic living resources, and so forth) Preschool-age support and assistance to enhance health and psychosocial development

• systems’ enhancement through multidisciplinary team work, consultation, and staff

3.

Early-schooling targeted interventions

• orientations, welcoming and transition support into school and community life for

4.

students and their families (especially immigrants) support and guidance to ameliorate school adjustment problems personalized instruction in the primary grades additional support to address specific learning problems parent involvement in problem solving comprehensive and accessible psychosocial and physical and mental health programs (incl. a focus on community and home violence and other problems identified through community needs assessment)

rD is

• • • • •

tri

Early-after-onset intervention

development education and social support for parents of preschoolers quality day care quality early education appropriate screening and amelioration of physical and mental health and psychosocial problems

bu tio n

• • • •

Improvement and augmentation of ongoing regular support

• enhance systems through multidisciplinary team work, consultation, and staff development

• preparation and support for school and life transitions • teaching "basics" of support and remediation to regular teachers (incl. use of available resource personnel, peer and volunteer support)

fo

• parent involvement in problem solving • resource support for parents-in-need (incl. assistance in finding work, legal aid, ESL and citizenship classes, and so forth)

N ot

• comprehensive and accessible psychosocial and physical and mental health • •

5.

interventions (incl. health and physical education, recreation, violence reduction programs, and so forth) Academic guidance and assistance (incl. use of response to intervention) Emergency and crisis prevention and response mechanisms

Other interventions prior to referral for intensive, ongoing targeted treatments

• enhance systems through multidisciplinary team work, consultation, and staff development

• short-term specialized interventions (including resource teacher instruction and

Treatment for severe/chronic problems

6.

family mobilization; programs for suicide prevention, pregnant minors, substance abusers, gang members, and other potential dropouts) Intensive treatments • referral, triage, placement guidance and assistance, case management, and resource coordination • family preservation programs and services • special education and rehabilitation • dropout recovery and follow-up support • services for severe-chronic psychosocial/mental/physical health problems

Adapted from Adelman & Taylor (1993)

Figure 2.1 From Primary Prevention to Treatment of Serious Problems: A Continuum of CommunitySchool Programs to Address Barriers to Learning and Enhance Healthy Development.

Placing Prevention into the Context of School Improvement 23 School resources

Community resources

(facilities, stakeholders, programs, services)

(facilities, stakeholders, programs, services)

Examples: • General health education • Social and emotional learning programs • Recreation programs • Enrichment programs • Support for transitions • Conflict resolution • Home involvement • Drug and alcohol education

• • • • • • •

Drug counseling Pregnancy prevention Violence prevention Gang intervention Dropout prevention Suicide prevention Learning/behavior accommodations and response to intervention • work programs

Examples: • Recreation & Enrichment • Public health & safety programs • Prenatal care • Home visiting programs • Immunizations • Child abuse education • Internships & community service programs • Economic development

Systems for promoting healthy development & preventing problems primary prevention – includes universal interventions (low end need/low cost per individual programs)

Systems of early intervention • Early identification to treat health problems • Monitoring health problems • Short-term counseling • Foster placement/group homes • Family support • Shelter, food, clothing • Job programs

early-after-onser – includes selective & indicated interventions (moderate need, moderate cost per individual)

Systems of care treatment/indicated interventions for servere and chronic problems (High end need/high cost per individual programs)

• Special education for learning disabilities. emotional disturbance, and other health impairments

• • • • • • •

Emergency/crisis treatment Family preservation Long-term therapy Probation/mcarceration Disabilities programs Hospitalization Drug treatment

Systemic collaboration is essential to establish interprogram connections on a daily basis and over time to ensure seamless intervention within each system and among systems of prevention, systems of early intervention, and systems of care. Such collaboration involves horizontal and vertical restructuring of programs and services (a) within jurisdictions, school districts, and community agencies (e.g., among departments, divisions, units, schools, clusters of schools) (b) between jurisdictions, school and community agencies, public and private sectors; among schools; among community agencies ∗ Various venues, concepts, and initiatives permeate this continuum of intervention systems. For example, venues such as day care and preschools, concepts such as social and emotional learning and development, and initiatives such as positive behavior support, response to intervention, and coordinated school health. Also, a considerable variety of staff are involved.

Figure 2.2 A Continuum of Interconnected Systems for Meeting the Needs of All Students.

framework illustrated in Figure 2.2 moves discussion of prevention beyond a focus on discrete interventions. Specifically, it underscores the importance of horizontal and vertical restructuring of programs and services (a) within jurisdictions, school districts, and community agencies (e.g., among departments, divisions, units) and (b) between jurisdictions, school and community agencies, public and private sectors, among clusters of schools, and among community agencies. Finally, note that the continuum includes a system for promoting healthy development and has the intention of incorporating a holistic and developmental emphasis that envelops

24 Howard S. Adelman and Linda Taylor individuals, families, and the contexts in which they live, work, and play. Also implicit is the principle that the least restrictive and nonintrusive forms of intervention required to appropriately address problems and accommodate diversity are to be used. Most schools have some programs and services that fit along the entire continuum. However, the emphasis is mostly on discrete services, and interventions are not coalesced into integrated systems. Moreover, the tendency to focus mostly on the most severe problems has skewed the process so that too little is done to prevent and intervene early after the onset of a problem. As a result, public education has been characterized as a system that “waits for failure.”

Enhancing School Improvement Policy It is one thing to argue for prevention; it is quite another to argue that schools should pursue prevention comprehensively as part of their school improvement agenda. Such an argument must be framed in the context of the mission of schools. That mission, of course, is to educate the young. In pursuing this mission, schools constantly are engaged in planning improvements. The need to do so has been accentuated by current accountability demands stemming from the No Child Left Behind Act. Unfortunately, the narrow focus of prevailing school accountability measures has resulted in school improvement guidance documents that give short shrift to everything but academic instruction and that simplistically address factors that interfere with learning and teaching. For example, under the federal education act, the U.S. Department of Education (2006) has framed non-regulatory guidance for pursuing school improvement at schools that are “underperforming.” They stress that school improvement processes and timetables should be designed to create a sense of urgency about reform and to focus identified schools on quickly and efficiently improving student outcomes. This statement underscores the reality that federal, and most organizational, guidance for school improvement emphasizes seeking the quickest and most direct ways to address specific factors identified as interfering with learning and teaching (Annenberg Institute, 2006; NCREL, no date). As a result, the trend is for school improvement planning to marginalize attention to many interfering factors (Center for Mental Health in Schools, 2005b). This is the case for both internal and external barriers to learning. Fortunately, relatively few youngsters start out with internal dysfunctions or disabilities that lead to learning, behavior, and emotional problems. For many children and adolescents, however, a range of external factors is interfering with schools’ accomplishing their mission. And, as research indicates, the primary causes for most youngsters’ learning, behavior, and emotional problems are extrernal factors (related to neighborhood, family, school, and/or peers). Anyone who works with young people is all too familiar with the litany of barriers to learning, development, and teaching, such as a host of factors confronting recent immigrants and families living in poverty and, for any student, violence, drugs, and frequent school changes (Adelman & Taylor, 2006a; Catalano & Hawkins, 1995). Such barriers are strongly related to the achievement gap and to student (and teacher) dropouts. It is the impact of so many barriers that argues for schools and communities offering much more in the way of prevention programs. For this to happen, however, the agenda for school improvement must be rethought.

School Improvement Policy Marginalizes Prevention and All Other Efforts to Address Barriers Policymakers have indicated concern about the limited efficacy of student supports, such as those created to prevent and ameliorate learning and behavior problems, substance abuse,

Placing Prevention into the Context of School Improvement 25 violence, school dropouts, delinquency, teen pregnancy, and so forth (Adler & Gardner, 1994; Center for Mental Health in Schools, 2007b). In response, some reformers have attributed the unsatisfactory outcomes to the fragmented and isolated way such programs and services operate. Limited efficacy does seem inevitable as long as so many interventions are carried out in a piecemeal fashion and with little follow-through. Therefore, attention has been directed toward reducing the widespread fragmentation through increased coordination and integration of school-based and linked interventions. In particular, “integrated services” policies have been enacted to reduce redundancy, waste, and ineffectiveness resulting from the many piecemeal, categorically funded approaches. Some of the initiatives for integrated interventions have meshed with the emerging movement to expand school and community connections and enhance the infrastructure for youth development (Adelman & Taylor, 2007a; Adler & Gardner, 1994; Blank, Berg, & Melaville, 2006; Burt, 1998; Cahill, 1994, 1998; Catalano, Berglund, Ryan, Lonczak, & Hawkins, 2002; Catalano & Hawkins, 1995; Dryfoos, 1998; Dryfoos & Maguire, 2002; Pittman, 2002; Rothman, 2007; Schorr, 1997). However, by focusing primarily on coordination and integration, policymakers have failed to deal with the overriding issue, namely that the whole enterprise is marginalized in school improvement policy and practice. This reality not only seriously hampers efforts to reduce fragmentation, it keeps schools from effectively addressing factors interfering with learning, development, and teaching and re-engaging students in classroom instruction.

Toward Countering Marginalization: Expanding the Framework for School Improvement For prevention to play a significant role in the lives of children and their families, school improvement policy and practice for addressing interfering factors must undergo a transformation. Because policy for addressing barriers is so marginalized, schools and communities continue to operate with virtually no commitment and no major frameworks to guide them toward comprehensive and multi-faceted approaches for large-scale prevention and amelioration of problems. This is clearly seen in the lack of attention given these matters in school improvement plans and program quality reviews (Center for Mental Health in Schools, 2005b). This is also evident in the token way addressing barriers is dealt with in the preservice and continuing education of administrators, teachers, and others in state departments of education, district offices, and at schools. We suggest that a major breakthrough in the battle against learning, behavior, and emotional problems can be achieved only when school improvement policy, planning, implementation, and accountability fully address factors interfering with learning. This requires more than specific prevention and early intervention programs, more than outreach to link with community resources, more than coordinating school-owned services, more than coordinating school services with community services, and more than creating family resource centers, full service schools, and community schools. None of these constitute a comprehensive, multi-faceted, and cohesive approach. And, the growing consensus is that a comprehensive approach is essential to cope with the complex concerns confronting students, their families, schools, and neighborhoods (e.g., Adelman, 1993; Adelman & Taylor, 1997, 2006a, 2008; Catalano & Hawkins, 1995; Comer, 1997; Dryfoos & Maguire, 2002; Greenwald, Hedges, & Laine, 1996; Schorr, 1997). The frameworks illustrated in Figures 2.1 and 2.2 not only help ensure that prevention is perceived in a broad intervention context, they highlight the type of comprehensive approach

26 Howard S. Adelman and Linda Taylor that schools should include in school improvement planning. However, unless the current policy framework for school improvement is expanded, this is unlikely to happen. From our perspective, a high-level policy emphasis on developing a comprehensive, multifaceted continuum and doing so as an interconnected set of systems is the key not only to unifying fragmented activity, but to using all available resources in the most productive manner. As a fundamental step in this direction, it has been proposed that policymakers move from a two- to a three-component policy framework (see Adelman & Taylor, 1994, 1997, 1998, 2006b; Center for Mental Health in Schools, 1997, 2005b). As highlighted in Figure 2.3, the proposed third component encompasses a policy commitment to comprehensively enable learning by addressing barriers. When policy and practice are viewed through the lens of this third component, it becomes evident how much is missing in current efforts to enable all students to learn and develop. Establishment of this “enabling” component is intended to elevate efforts to prevent and ameliorate problems at a high policy level and integrate the work as a fundamental and essential facet of school improvement. It is important to stress that addressing barriers is not a separate agenda from a school’s instructional mission. A three-component framework is intended to fully integrate the enabling, instructional, and management components with each other (see Figure 2.3). The third component provides both a basis for combating marginalization and a focal point for developing a comprehensive framework for policy and practice. It can also help address fragmentation by providing a focus for weaving together disparate approaches to preventing and ameliorating psychosocial problems and promoting wellness. The usefulness of the concept of an enabling component as a broad unifying force is evidenced by the growing attention it is receiving at state and local education agencies (where it often is called a “Learning Supports Component” or a “Comprehensive System of Student Support”). Some of the venues are highlighted in a report from the Center for Mental Health in Schools (2005c). Current state of affairs

Direct facilitation of learning & development

Instructional/ developmental component Management component

Governance and resource management

Moving toward a comprehensive system

Student & family assistance Besides offering a small amount of school-owned student “support” services, schools outreach to the community to add a few school-based/linked services.

Governance and resource management

Instructional/ developmental component

Addressing barriers to learning

Enabling component∗

Management component

Governance and resource management

∗ The enabling component is designed to enable learning by addressing factors that interfere with learning, development, and teaching and with re-engaging students in classroom instruction. It is established in policy and practice as primary and essential and is developed into a comprehensive approach by weaving together school and community resources. Some venues where this comprehensive approach is adopted refer to the third component as a learning supports component.

Figure 2.3 Toward a Comprehensive System for Addressing Barriers to Learning: Moving from a Twoto a Three-Component Framework for School.

Placing Prevention into the Context of School Improvement 27 Figure 2.4 depicts a comprehensive enabling component as first addressing barriers to learning, development, and teaching and then re-engaging students in classroom instruction. For schools, such a component covers the three-level continuum of intervention systems outlined in Figure 2.2 and organizes all support programs, services, and activities into a well-circumscribed set of content arenas. Figure 2.5 provides an example that organizes interventions into content arenas designed to: •

• •

enhance regular classroom strategies to enable learning (e.g., improving instruction for students who have become disengaged from learning at school and for those with mild-moderate learning and behavior problems; includes a focus on prevention, early intervening, and use of strategies such as Response to Intervention) support transitions (i.e., assisting students and families as they negotiate school and grade changes and many other transitions) increase home and school connections An enabling component to address barriers and re-engage students in classroom instruction∗ Range of learners (categorized in terms of their response to academic instruction at any given point in time)

I=

Motivational ready & able

Not very motivated/ lacking prerequisite knowledge II = & skills/ different learning rates & styles/ minor vulnerabilities

No barriers

Enabling component Barriers to learning, develop., teaching

(1) Addressing interfering factors

Instructional component (a) Classroom teaching + (b) Enrichment activity

Desired outcomes

(2) Re-engaging students in classroom instruction

Avoidant/ very deficient in current III = capabilities/ has a disability/ major health problems ∗In some places, an enabling component is called a learning supports component. Whatever it is called, the component is to be developed as a comprehensive system of learning supports at a school site.

Figure 2.4 An Enabling Component to Address Barriers, Re-engage Students in Classroom Instruction, and Enhance Healthy Development at a School Site.

28 Howard S. Adelman and Linda Taylor

Classroom-based approaches to enable learning Crisis/emergency assistance & prevention

Student & family assistance Infrastructure (e.g., leadership, resource-oriented mechanisms) Community outreach

Support for transitions Home involvement in schooling

Note: An enhanced school climate (culture/sense of community) is an emergent quality resulting from a well-designed and implemented learning supports component. Adapted from adelman, H.S. & Taylor, L. (1994). On understanding intervention in psychology and education. Westport, CT: Praeger.

Figure 2.5 Intervention Content Arenas.

• • •

respond to and, where feasible, prevent crises increase community involvement and support (outreach to develop greater community involvement and support, including enhanced use of volunteers) facilitate student and family access to effective services and special assistance as needed.

Each of these is briefly described in the appendix. Note that the three levels of Figure 2.2 and six arenas of Figure 2.5 can be formed into a matrix that can be used as a tool for mapping how well a school improvement plan encompasses an enabling component. The map provides data for analyzing what is in place and what is missing related to preventing and ameliorating problems (Adelman & Taylor, 2006c). Such analyses provide a basis for planning and setting priorities. Developing a cohesive enabling component in schools with a strong emphasis on preventing problems requires significant systemic changes (Adelman & Taylor, 2007b). The initial emphasis is primarily on weaving together what schools already have (e.g., pupil services, special and compensatory education and other categorical programs). Then, the focus expands to enhance an integrated set of systems and to link school with home and community resources (e.g., formally connecting school programs with assets at home, in the business and faith communities, and neighborhood enrichment, recreation, and service resources). Accomplishing all this not only involves reframing intervention, it requires redesigning organizational and operational infrastructure, and rethinking the roles and functions of personnel at schools and central offices. Various states and localities moving to pursue school improvement in terms of three primary and essential components have adopted other designations for their enabling component. For example, the state education agencies in California and Iowa and various

Placing Prevention into the Context of School Improvement 29 districts across the country have adopted the term Learning Supports. The Hawai’i Department of Education uses the term Comprehensive Student Support System (CSSS). Building on this, proposed legislation in California refers to a Comprehensive Pupil Learning Supports System. The Berkeley (CA) Unified School District calls it a Universal Student Support System. See Center for Mental Health in Schools (2005c) and the Center’s toolkit for rebuilding student and learning supports for examples of these pioneering initiatives: http://smhp.psych.ucla.edu/ pdfdocs/studentsupport/toolkit/aida.pdf.

Rethinking Organizational and Operational Infrastructure The next concern in enhancing how prevention is pursued throughout a school district involves redesigning the organizational and operational infrastructure. Given that prevention is fully integrated into a comprehensive component for addressing barriers to learning and teaching, the focus of the redesign is on the whole component, not on prevention per se. And, given that the component to address barriers must be fully integrated into school improvement, infrastructure changes at all levels of a district are needed that can make this a reality. In designing and rethinking infrastructure, the fundamental principle remains: structure follows function. So the key to a well-designed infrastructure is first to delineate functions (and related tasks and processes) in ways that are consistent with a school’s “big picture” visionary goals. Then, the focus is on establishing an integrated set of operational mechanisms (e.g., personnel and resources) that enable accomplishment of such major functions in a cost-effective and efficient manner. For school districts, the vision of leaving no child behind requires ensuring that all students have an equal opportunity to succeed at school. Consistent with the three components highlighted in Figure 2.3, achieving such a vision requires effectively pursuing three fundamental functions: (1) facilitating learning and development; (2) addressing barriers to learning and teaching in ways that enable learning and development; and (3) governing/managing the district. To accomplish these fundamental functions and related tasks and processes, an interconnected set of organizational and operational mechanisms must be established to guide and carry out the work on a regular basis. Such infrastructure mechanisms enable leaders to steer together and to empower and work productively with staff. The type of work tasks involved include: designing and directing activity, planning and implementing specific organizational and program objectives, allocating and monitoring resources with a clear content and outcome focus, facilitating coordination and integration to ensure cohesive implementation, managing communication and information, providing support for capacity-building and quality improvement, ensuring accountability, and promoting self-renewal.

Current Infrastructure In our research, we have reviewed existing district line-authority hierarchy charts, descriptions of unit organization, and, where available, detailed descriptions of infrastructure mechanisms. A particular focus has been on how districts organize to provide interventions for addressing barriers to learning and teaching. In general, districts tend to organize around: a b

levels of schooling (e.g., elementary, secondary, early education), traditional arenas of activity, discipline affiliations, funding streams, and categorical programs (e.g., curriculum and instruction; assessment; student supports including

30 Howard S. Adelman and Linda Taylor

c

counseling and guidance, attendance, psychological and social services, health; specific types of support personnel such as counselors, psychologists, social workers, nurses; professional development; special education; specific types of compensatory education such as Title I and English language learners; gifted and talented; safe and drug-free schools; athletics, youth development, and after-school programs; homeless education; alternative schools; dropout prevention; adult education), operational concerns (e.g., finances and budget, payroll and business services, facilities, human resources, labor relations, enrollment services, information technology, security, transportation, food, emergency preparedness and response, grants and special programs, legal considerations).

All the school districts we sampled have administrators, managers, and staff who have roles related to the districts’ various efforts to prevent and ameliorate problems. However, the programs, services, and initiatives often are divided among several associate or assistant superintendents, their middle managers (e.g., directors or coordinators for specific programs), and a variety of line staff. The result is that activities related to the function of addressing barriers to learning and teaching are dispersed, often in counterproductive ways, over several divisions or departments. These include units designated “Student Services,” “Teaching and Learning,” “Title I,” “Parent/Community Partnerships,” “Grant and Special Projects,” “Youth Development,” and so forth. Special education may be embedded in a “Student Support” unit, in a “Teaching and Learning” unit, or organized as a separate unit. Regardless of the unit involved, we find that the work being carried out tends to center primarily around allocating and monitoring resources, assuring compliance and accountability, providing some support for school improvement, generating some ongoing staff development, offering a few district-wide programs and services for students, and outreaching to a minimal degree to community agencies. In general, districts tend not to be organized in ways that emphasize moving toward a comprehensive system for addressing barriers. Indeed, the matter is so marginalized that little attention is given to 1

2 3 4

enhancing the policy framework for school improvement in ways that incorporate all efforts to address barriers to learning and teaching under a broad and unifying umbrella concept established as a primary and essential component of a school’s mission, reframing interventions in ways that are consistent with such a broad, unifying concept, rethinking organizational and operational infrastructure at a school, for the feeder pattern of schools, and at the district level, facilitating major systemic change in organizations such as schools and school districts that have well-established institutional cultures.

Redesigning Infrastructure Most districts could benefit from rethinking their organizational and operational infrastructure. And, from the perspective of preventing and ameliorating problems related to learning and teaching, well-designed, compatible, and interconnected infrastructures are essential at schools, for school complexes, and at the district level. Both school and district levels play key roles in weaving together existing school and community resources and developing a full continuum of interventions over time. Moreover, establishing content and resource-oriented infrastructure mechanisms enables programs and

Placing Prevention into the Context of School Improvement 31 services to function in an increasingly cohesive, cost-efficient, and equitable way. Elsewhere we have explored infrastructure redesign at some length (Adelman & Taylor, 2006b, 2007a; Center for Mental Health in Schools, 2005d, 2005e, 2008). Here, we can only highlight a few major points. From the School Outward. Because daily contact with students happens at the school level, a district’s infrastructure should be designed from the school outward. That is, conceptually, the first concern is with delineating an effective infrastructure for a school. The new infrastructure must have leadership and staff mechanisms that support development of a comprehensive and cohesive system for addressing barriers to learning and teaching, and these mechanisms must be fully integrated into school improvement efforts. Schools in the same geographic or catchment area have a number of shared concerns. Thus, it is important to link a family of schools together to enhance equity and maximize use of limited resources by minimizing redundancy and achieving economies of scale. A properly reworked school infrastructure enables development of well-designed mechanisms for connecting a family or complex (e.g., feeder pattern) of schools, as well as establishing collaborations with surrounding community resources. For prototype illustrations of operational and organization infrastructure redesign for schools, feeder patterns, and district offices, see the references cited just above in this section. Finally, central district units need to be rethought in ways that best support the work at the school and complex levels. Specifically, the key role for these central units should be to provide leadership and build capacity for establishing and maintaining an effective infrastructure (a) at every school and (b) for connecting a family of schools in a neighborhood. All this involves reframing the work of personnel and redistributing authority (power). With this in mind, it is essential at all levels to have appropriate incentives, safeguards, and adequate resources and support for all involved in making the required systemic changes. A few specifics will help clarify the nature and scope of such changes. At the School Level. Obviously administrative leadership is key to ending marginalization of efforts to address behavior, learning, and emotional problems. Usually, the principal and whoever else is part of a school leadership team are enmeshed mainly in improving instruction and management/governance. That is, no one on such a team may be focusing on developing a comprehensive and systemic component for preventing and ameliorating problems. One way to change this is to assign the role to someone already on the leadership team and provide the individual with training to carry it out effectively. Alternatively, someone in the school who is involved with student supports (e.g., a pupil services professional, a Title I coordinator, or a special education resource specialist) can be invited to join the leadership team, assigned responsibility and accountability for ensuring the vision for preventing and ameliorating problems is not lost, and provided additional training for the tasks involved. Besides administrative leadership, another key to ending marginalization of efforts to address behavior, learning, and emotional problems is establishment of a mechanism (e.g., a team) that focuses specifically on how resources are used for problem prevention and amelioration. Few schools have resource-oriented mechanisms to ensure appropriate resource use. Such mechanisms contribute to cost-efficacy by ensuring activity is planned, implemented, and evaluated in a coordinated and increasingly integrated manner. Creation of such mechanisms is essential for braiding together existing school and community resources, and encouraging cohesive interventions. Teams established for this purpose have been designated by a variety of names including “Resource Coordinating Team,” “Resource Management Team,” and “Learning Supports Resource Team.” Note that resource-oriented mechanisms do not focus on specific students, but on how a system’s resources are used most effectively

32 Howard S. Adelman and Linda Taylor (Adelman, 1993; Adelman & Taylor, 1998, 2008; Center for Mental Health in Schools, 2005d; Lim & Adelman, 1997; Rosenblum, DiCecco, Taylor, & Adelman, 1995). One of the primary and essential tasks that a school-based, resource-oriented mechanism undertakes is that of mapping and analyzing available school and community resources (e.g., programs, services, personnel, facilities). A comprehensive “gap” assessment is generated as mapped resources are analyzed in the context of unmet needs and desired outcomes. Analyses of what is available, effective, and needed provide a sound basis for formulating priorities, redeploying resources, and developing strategies to link with additional resources at other schools, district sites, and in the community. When a resource-oriented team is established, efforts are made to bring together representatives of all relevant programs and services. This might include, for example, school counselors, psychologists, nurses, social workers, attendance and dropout counselors, health educators, special education staff, after-school program staff, bilingual and Title I program coordinators, safe and drug-free school staff, and union reps. Such a team also should include representatives of any community agency that is significantly involved with a school. Beyond these stakeholders, it is advisable to add the energies and expertise of classroom teachers, noncertificated staff, parents, and older students. Where creation of “another team” is seen as a burden, existing teams, such as student or teacher assistance teams and school crisis teams, have demonstrated the ability to perform resource-oriented tasks. However, in adding the resource-oriented tasks to another team’s work, great care must be taken to structure the agenda so sufficient time is devoted to the additional tasks. For small schools, a large team often is not feasible, but a two-person team can still do a reasonable and responsible job. Properly constituted at the school level, a resource-oriented team provides what often is a missing link for managing and enhancing programs and systems in ways that integrate, strengthen, and stimulate new and improved interventions. It also can provide leadership in guiding school personnel and stakeholders in evolving the school’s vision, priorities, and practices for addressing barriers and re-engaging students. Connecting a Family of Schools. As noted above, schools in the same geographic or catchment area have a number of shared concerns. A multi-site mechanism can connect schools in a feeder pattern with each other and with the district and the community. Such a mechanism helps ensure cohesive and equitable deployment of resources and can reduce costs by minimizing redundancy, enhancing the pooling of resources, and pursuing economies of scale. By assuming leadership and communication roles, it can (a) coordinate and integrate programs serving multiple schools; (b) identify and meet common needs with respect to guidelines and staff development; (c) ensure quality improvement across sites; and (d) create links and collaboration among schools and with community agencies. In this last respect, it can play a potent role in community outreach both to create formal working relationships and to ensure that all participating schools have access to such resources. Formed as a resource council, the mechanism convenes a monthly meeting that includes one or two representatives from resource teams in a family of schools (e.g., a high school and its feeder middle and elementary schools). Natural starting points for councils are sharing assessments of need, resource maps, analyses, and recommendations for reform and restructuring. Specific areas of initial focus usually are local, high-priority concerns, such as addressing violence and developing prevention programs and safe school and neighborhood plans. In efforts to link schools with community resources, multi-school councils are especially attractive to community agencies who often do not have the time or personnel to make independent arrangements with every school. In this respect, representatives from resource

Placing Prevention into the Context of School Improvement 33 councils can be invaluable members of neighborhood planning groups (e.g., “Service Planning Area Councils,” “Local Management Boards”). They bring information about specific schools, clusters of schools, and local neighborhoods and do so in ways that reflect the importance of school-community collaboration. At the District Level. If districts are to effectively support development of a comprehensive system for preventing and ameliorating problems at every school, they need to ensure potent administrative leadership and capacity-building support. And it is crucial that such leadership be established at a high enough level to ensure the administrator is always an active participant at key planning and decision-making (e.g., is a cabinet-level administrative leader such as an associate superintendent). In reworking district infrastructure, this administrator is assigned responsibility and accountability for coalescing all resources related to addressing barriers in ways that enhance the prevention and amelioration of problems. The resources of concern come from the general fund, compensatory education, special education, and other categorical funding streams, and special projects. This encompasses special initiatives, grants, and programs for afterschool, wellness, dropout prevention, attendance, drug abuse prevention, violence prevention, pregnancy prevention, parent/family/health centers, volunteer assistance, and community resource linkages to schools. Relevant personnel encompass student support staff, such as school psychologists, counselors, social workers, and nurses, and the full range of compensatory and special education staff. The appointed administrator will need to establish mechanisms for accomplishing the unit’s work. These should be comparable to content and process mechanisms established for the instructional component. We suggest establishing a cabinet-like structure consisting of leaders for the major content described in Figure 2.5 and the appendix to this chapter. Organizing in this way moves the enterprise away from the marginalization, fragmentation, unnecessary redundancy, and counterproductive competition that has resulted from organizing around traditional programs and/or in terms of specific disciplines. The intent is for personnel to have accountability for advancing a designated arena and working in ways that ensure all arenas are integrated. A formal infrastructure link also is needed to ensure full integration with district school improvement planning and decision-making. This means the leader (and key staff) must be included at relevant school improvement planning and decision-making tables.

Personnel Retraining at All Levels The type of systemic changes described call for new roles and functions (Adelman & Taylor, 1997, 2006b, 2007b). Such changes provide both a challenge and an opportunity for many school professionals to move beyond just coping with the problems manifested by specific students. In doing so, they can ensure that schools play a much greater role in preventing and ameliorating factors that interfere with learning, development, and teaching. With respect to personnel retraining for new roles and functions, there is growing interest in identifying common skills among education support professionals so they can cover an overlapping range of intervention activity and fully integrate education supports into the fabric of daily school improvement efforts. This is consistent with the view that specialist-oriented activity and training should be balanced with a generalist perspective (e.g., Henggeler, 1995). Proposals and pilot programs have focused on cross-disciplinary training and interprofessional education to better equip school professionals to assume expanding roles and functions (Brandon & Meuter, 1995; Lawson, 1998; Lawson & Hooper-Briar, 1994; Research

34 Howard S. Adelman and Linda Taylor and Training Center on Family Support and Children’s Mental Health, 1996; Ysseldyke, Burns, Dawson, Kelley, Morrison, Ortiz, Rosenfield, & Telzrow, 2008). In general, there is growing recognition of underlying commonalities among a variety of student problems and of the role generalist strategies can play in preventing and ameliorating them (Carnegie Council on Adolescent Development, 1995). All this is consistent with fostering less emphasis on intervention ownership and more attention on accomplishing desired outcomes through flexible roles and functions for staff (see Adelman & Taylor, 1994; Lawson & Hooper-Briar, 1994; Lipsky & Gartner, 1992). Recent work also demonstrates the value of redeploying and training a cadre of pupil services personnel as change agents in moving schools toward better approaches for addressing barriers to learning (Adelman, 1993; Adelman & Taylor, 1994; Center for Mental Health in Schools, 2005e 2005f; Lim & Adelman, 1997). Designated as organization facilitators, such professionals are retooled to come to the work with a relevant base of knowledge and skills. The additional training provides them with an understanding of the specific activities and mechanisms required for establishing and maintaining comprehensive, integrated approaches and increases their capacity for dealing with the processes and problems of organizational change.

Prevention and the Evidence-Based Practice and Accountability Dilemmas Do You Have Data to Support Adopting That Approach? Can You Prove It’s Worth Continuing to Do That? In the context of school improvement, these questions dominate efforts to enhance and sustain school-based prevention programs. Although understandable in light of the unfulfilled promise of so many programs and the insatiable demands on limited resources, premature demands for data are producing dilemmas. Prevention researchers and practitioners appreciate the importance of drawing on the science-base and they understand they must be accountable for the outcomes of their practices. At the same time, most have experienced the dilemmas raised by data demands that ignore the complexities associated with developing and evaluating interventions to prevent and ameliorate major problems. We find that few leaders for school improvement argue, in principle, against using the best data available to inform decisions. Many are concerned, however, about the current reliance on an underdeveloped science-base for making program decisions and on narrow-band measures for demonstrating accountability. Analyses have long stressed that school improvement, first and foremost, needs the kind of data that can help advance practice and policy (e.g., General Accounting Office, 1989). The danger is that a limited body of research and an overemphasis on achievement testing to ensure accountability will reify an unsatisfactory status quo.

Concerns and Controversies about the Existing Evidence-Base The movement for evidence-based practices is reshaping public policy in ways that have generated a host of cautions (e.g., Education Week, 2006; Flay, Biglan, Boruch, Castro, Gottfredson, Kellam, Moscicki, Schinke, Valentine, & Ji, 2005; Gorman, 2002, 2003; Government Accountability Office, 2007; Weiss, Murphy-Graham, Petrosino, & Gandhi, 2008). A central concern is that practices developed under highly controlled laboratory conditions are being pushed prematurely into widespread application based on unwarranted assumptions. This concern is especially salient when the evidence-base comes from shortterm studies and has not included samples representing major subgroups with whom the practice will be used.

Placing Prevention into the Context of School Improvement 35 In general, the rush to provide empirical support for interventions that can be implemented in schools has increased the tendency to by-pass discussion of significant methodological problems that limit claims about the science-base for many interventions. This leads to an overstatement of expertise, which in turn contributes to the mystification of the general public and many practitioners. None of this helps improve school-based practices. Indeed, overstating the evidence-base usually leads to a backlash. Such a backlash already has emerged around claims about the science underlying the prevention practices that schools are being asked to adopt (e.g., Gorman, 2003). With all the factors that continue to hamper the progress of prevention science, it is a mistake to do anything that feeds into public concern about the overselling of outcome evidence. Until researchers demonstrate that a prototype is effective under “real-world” conditions, it can only be considered a promising and not a proven practice. Even then it must be determined whether it is a best practice. And, with respect to the designation of best, it is well to remember that best simply denotes that a practice is better than whatever else is currently available. How good it is depends on complex analyses related to costs and benefits. Despite clear limitations, specific interventions increasingly are prescribed officially, and others are proscribed by policymakers and funders. This especially has been the situation surrounding school-based prevention programs (Gorman, 2002). As official lists have been generated, the growing concern is that only those practitioners who choose from these lists will be rewarded. And a trend to select only from what is on the list surely will exacerbate the tendency for schools to adopt discrete programs, rather than develop and evaluate a comprehensive, multi-faceted, and cohesive system for addressing barriers and re-engaging students. This can only perpetuate current fragmentation, inappropriate redundancy, counterproductive competition for sparse resources, and marginalization.

Current Accountability Mandates Also Create a Dilemma Accountability is a tool that can be used to encourage people and organizations to meet appropriate standards, but it also can generate issues and problems. Current demands related to school improvement illustrate the matter. First, we should note that two unfounded presumptions at the core of current school accountability policies are that: (1) any approach in widespread use must be at a relatively evolved stage of development and thus warrants the cost of summative evaluation; and (2) major conceptual and methodological problems associated with evaluating program efficacy and effectiveness are resolved. The reality, of course, is that some school programs must be introduced prior to adequate development with a view to evolving them based on what is learned each day. As evaluation methodologists clearly acknowledge, the most fundamental problems related to summative evaluation have not been solved. This is particularly the case when it comes to large-scale program replication (Adelman & Taylor, 1997, 2007b; Durlak & Wells, 1997; Replication and Program Services, 1993; Sarason, 1990; Weisz, Donenberg, Han & Weiss, 1995). Second, it should be stressed that the prevailing focus in school accountability is on specific evidence of results—usually in terms of readily measured immediate benefits—and on cost containment. This has led to policies pressuring schools and districts to produce quick improvements in achievement test score averages. As we have suggested in this chapter, one major factor that makes that demand unrealistic in many schools is the absence of a comprehensive and multi-faceted component to prevent and ameliorate problems. The irony is that schools cannot devote the time, talent, and other resources necessary for developing such

36 Howard S. Adelman and Linda Taylor a component because their resources are tied up in being accountable for a school improvement policy that is too narrowly conceived. As a result, schools are caught in a major dilemma. Raising academic standards and expectations includes eliminating social promotion, closing the achievement gap, and reducing dropouts. However, to do all this effectively, schools need to develop a comprehensive system of learning supports. Unfortunately, their current approach to school improvement precludes more than a marginal focus on establishing a comprehensive system for preventing and ameliorating problems and re-engaging students in classroom instruction. The dilemma is compounded by the pressure to choose mainly from a list of discrete programs judged to have an adequate evidence-base. There are undeniable benefits from demonstrating that intended outcomes are achieved. However, if one is not careful, accountability biases and pressures can reshape research and practice (Adelman, 1986; Adelman & Taylor, 1994; Burchard & Schaefer, 1992; Cuban, 1990; Tyack & Cuban, 1995). In most organizations, what is measured receives direct attention, and what is not measured is marginalized. Achievement testing for school accountability is a case in point. Policymakers have decided to collect data only on a relatively small set of academic goals (e.g., reading, math). Because school accountability stresses only academic achievement test gains, matters for which accountability data are not gathered, such as learning supports and social and emotional learning, are given short shrift. Indeed, as more and more resources are used to meet data demands, fewer resources are available for improving the way long-standing and complex problems are addressed and healthy development is promoted. Over the past few decades, social, political, and economic forces pressing for the use of evidence-based practices and immediate accountability increasingly have reshaped what transpires in schools. As is evidenced by the ongoing struggle to advance school-based prevention and early intervention programs, the impact on prevention science has been a negative one (e.g., see Albee & Gullotta, 1997; Bond & Compas, 1989; Dryfoos, 1990; Durlak, 1995; Elias, 1997; Schorr, 1988; Slavin, Karweit, & Wasik, 1994; Weissberg, Gullotta, Hamptom, Ryan, & Adams, 1997; Weiss, Murphy-Graham, Petrosino, & Gandhi, 2008).

Addressing the Data Dilemmas As Sararson (2003) warns: “Intervention confronts a host of problems for which current knowledge and research are inadequate, incomplete, and even misleading” (p. 209). Because the current science-base fails to account for the full scope of a school’s obligations to meet the needs of the society and its citizens, he stresses that schools need to adopt a combined moralscientific stance in making decisions about practices. With specific reference to prevention science, a direct way to deal with the data dilemma is to ensure that data collection is pursued within the context of an evaluative research agenda. Although there are many unresolved concerns related to evaluative research, scholarly work has advanced the way such activity is conceived in education and psychology, and thus there are ample methodological guidelines (Adelman, 1986; Adelman & Taylor, 1994; Chen & Rossi, 1992; Hollister & Hill, 1995; Knapp, 1995; Pogrow, 1998; Scriven, 1993; Sechrest & Figueredo, 1993; Weiss, 1995).2 First and foremost the methodology calls for formative evaluation; that is, data gathering and analyses that can help with the developmental facets of a research and development agenda. At the same time, such formative evaluations should and can be designed with a view

Placing Prevention into the Context of School Improvement 37 to summative evaluation of efficacy and effectiveness and with deference to immediate accountability demands and cost-benefit analyses.

Concluding Comments The next decade must mark a turning point in how schools and communities address the problems of children and youth. In particular, if the mandates of the No Child Left Behind Act and the Individuals with Disabilities Education Act 2004 are to be achieved, schools can and need to focus much more on prevention. Currently, however, prevention in schools is not a high priority. For this to change, schoolbased prevention cannot be pursued as a separate agenda. It must be fully integrated into efforts to counter learning, behavior, and emotional problems and promote personal and social growth. And, in turn, these efforts must be fully integrated into school improvement processes. Clearly, there is much work to be done as schools across the country strive to prevent and ameliorate factors causing so many students to be left behind.

Notes Author note: This article was prepared in conjunction with work done by the Center for Mental Health in Schools at UCLA which is partially supported by funds from the U.S. Department of Health and Human Services, Public Health Services, Health Resources and Services Administration, Bureau of Maternal and Child Health, Office of Adolescent Health. 1 There are too many references to cite related to the continuum, but a perspective on the range of work directly relevant to schools can be garnered from the following resources: Adelman & Taylor (2006a, 2006b), Albee & Gullotta (1997), Borders & Drury (1992), Carnegie Council on Adolescent Development (1988), CASEL (2003), Center for Mental Health in Schools (2004, 2005a), Cochrane Library (2007), Dryfoos (1990, 1994, 1998), Durlak (1995), Duttweiler (1995), Gottfredson & Gottfredson (2001), Gottfredson & Wilson (2003); Henggeler (1995), Hoagwood & Erwin (1997), Hoagwood, Olin, Kerker, Kratochwill, Crowe, & Saka, (2007), Jimerson & Furlong (2006), Karoly, Greenwood, Everingham, et al. (1998), Kazdin (1993), Larson (1994), Scattergood, Dash, Epstein, & Adler (1998), Schorr (1988, 1997), Slavin, Karweit, & Wasik (1994), Smink & Schargel (2004), Thomas & Grimes (2008). Also, see the other cited references for relevant resources. 2 For a discussion of the similarities and differences between research and evaluation, see Adelman (1986).

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38 Howard S. Adelman and Linda Taylor Adelman, H. S., & Taylor, L. (2006c). Mapping a school’s resources to improve their use in preventing and ameliorating problems. In C. Franklin, M. B. Harris, & P. Allen-Mears (Eds.), School social work and mental health workers training and resource manual. New York: Oxford University Press. Adelman, H. S., & Taylor, L. (2007a). Fostering school, family, and community involvement. Guidebook in series, Safe and secure: Guides to creating safer schools (Guide 7, Rev.). Portland, OR: Northwest Regional Educational Laboratory. Adelman, H. S., & Taylor, L. (2007b). Systemic change for school improvement. Journal of Educational and Psychological Consultation, 17, 55–77. Adelman, H. S., & Taylor, L. (2008). Best practices in the use of resource teams to enhance learning supports. In A. Thomas & J. Grimes (Eds). Best practices in school psychology B V. Bethesda, MD: National Association of School Psychologists. Adler, L., & Gardner, S. (Eds.). (1994). The politics of linking schools and social services. Washington, DC: Falmer Press. Albee, G. W., & Gullotta, T. P. (Eds.). (1997). Primary prevention works. Thousand Oaks, CA: Sage. Annenburg Institute (2006). Tools for school-improvement planning. Retrieved on July 1, 2007 from http://www.annenberginstitute.org/tools/index.php Blank, M., Berg, A., & Melaville, A. (2006). Community-based learning. Washington, DC: Coalition for Community Schools. Bond, L., & Compas, B. (Eds.). (1989). Primary prevention in the schools. Newbury Park: Sage. Borders, L. D., & Drury, S. M. (1992). Comprehensive school counseling programs: A review for policymakers and practitioners. Journal of Counseling & Development, 70, 487–498. Brandon, R. N., & Meuter, L. (1995). Proceedings: National Conference on Interprofessional Education and Training. Seattle: Human Services Policy Center, University of Washington. Burchard, J. D. & Schaefer, M. (1992). Improving accountability in a service delivery system in children’s mental health. Clinical Psychology Review, 12, 867–882. Burt, M. R. (1998) Reasons to invest in adolescents. Paper prepared for the “Health Futures of Youth II: Pathways to Adolescent Health.” Washington, DC: Maternal and Child Health Bureau, DHHS. Cahill, M. (1994). Schools and communities: A continuum of relationships. New York: The Youth Development Institute, The Fund for the City of New York. Cahill, M. (1998) Development of a core set of principles for community strategies to enhance youth health and development. Paper prepared for “Health Futures of Youth II; Pathways to Adolescent Health.” Washington, DC: Maternal and Child Health Bureau, DHHS. Carnegie Council on Adolescent Development. (1988). Review of school-based health services. New York: Carnegie Foundation. Carnegie Council on Adolescent Development. (1995). Great transitions: Preparing adolescents for a new century. New York: Carnegie Corporation. CASEL (2003). Safe and Sound: An Educational Leader’s Guide to Evidence-Based Social and Emotional Learning (SEL) Programs. Chicago: Author. Catalano, R. F., M, Berglund, M. L., Ryan, J., Lonczak, H. & Hawkins, J. D. (2002). Positive youth development in the United States: Research findings on evaluations of positive youth development programs. Prevention and Treatment 5 (15). Retrieved on July 1, 2007 from http://journals.apa.org/ prevention/volume5/pre0050015a.html Catalano, R. F., & Hawkins, J. D. (1995) Risk-focused prevention: Using the social development strategy. Seattle, WA: Developmental Research and Programs, Inc. Center for Mental Health in Schools. (1997). Addressing barriers to learning: Closing gaps in schoolcommunity policy and practice. Los Angeles: Author. Retrieved on July 1, 2007 from http://smhp.psych.ucla.edu/pdfdocs/barriers/closinggaps.pdf Center for Mental Health in Schools. (2004). Addressing Barriers to Student Learning & Promoting Healthy Development: A Usable Research-Base. Los Angeles: Author. Retrieved on July 1, 2007 from http://smhp.psych.ucla.edu/pdfdocs/briefs/BarriersBrief.pdf Center for Mental Health in Schools. (2005a). Annotated “lists” of empirically supported/evidence based

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40 Howard S. Adelman and Linda Taylor Education Week (2006, Feb 15). White House suggest model used in reading to elevate math skills. Retrieved July 1, 2007 from www.edweek.org Elias, M. J. (1997). Reinterpreting dissemination of prevention programs as widespread implementation with effectiveness and fidelity. In R. P. Weissberg, T. P. Gullotta, R. L. Hamptom, B. A. Ryan, & G. R. Adams (Eds.), Establishing preventive services (pp. 253–289). Thousand Oaks, CA: Sage. Flay, B. R., Biglan, A., Boruch, R. F., Castro, F. G., Gottfredson, D., Kellam, S., Moscicki, E. R., Schinke, S., Valentine, J. C., & Ji, P. (2005). Standards of evidence: Criteria for efficacy, effectiveness and dissemination. Prevention Science, 6, 151–175. General Accounting Office. (1989). Prospective evaluation methods: The prospective evaluation synthesis. GAO/PEMD-10.1.10. Washington, DC: Author. Retrieved July 1, 2007 from http://www.gao.gov/ special.pubs/10_1_10.PDF Gorman, D. M. (2002). Defining and operationalizing a research-based prevention: A critique (with case examples) of the US Department of Education’s Safe, Disciplined and Drug-Free Exemplary Programs. Evaluation and Program Planning, 25, 295–302. Gorman, D. M. (2003). Alcohol & drug abuse: The best of practices, the worst of practices: The making of science-based primary prevention programs. Psychiatric Services, 54, 1087–1089. Gottfredson , G. D., & Gottfredson, D. C. (2001). What schools do to prevent problem behavior and promote safe environments. Journal of Educational & Psychological Consultation, 12, 313–344. Gottfredson, D. C. & Wilson, D. B. (2003). Characteristics of effective school-based substance abuse prevention. Prevention Science, 4, 27–38. Government Accountability Office (2007). Reading First: States report improvements in reading instruction, but additional procedures would clarify education’s role in ensuring proper implementation by states. Washington, DC: Author. Retrieved July 1, 2007 from http://www.gao.gov/new.items/ d07161.pdf. Greenberg, M. T., Weissberg, R. P., O’Brien, M. E., Zins, J. E., Fredericks, L., Resnik, H., et al. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474. Greenwald, R., Hedges, L. V., & Laine, R. D. (1996). The effect of school resources on student achievement. Review of Educational Research, 66, 361–396. Henggeler, S. W. (1995). A consensus: Conclusions of the APA Task Force report on innovative models or mental health services for children, adolescents, and their families. Journal of Clinical Child Psychology, 23, 3–6. Hoagwood, K., & Erwin, H. (1997). Effectiveness of school-based mental health services for children: A 10-year research review. Journal of Child and Family Studies, 6, 435–451. Hoagwood, K. E., Olin, S. S., Kerker, B. D, Kratochwill, T. R., Crowe, M., & Saka, N. (2007). Empirically based school interventions targeted at academic and mental health functioning. Journal of Emotional and Behavioral Disorders, 15, 66–92. Hollister, G., & Hill, J. (1995). Problems in the evaluation of community-wide initiatives. A paper prepared for the Roundtable on Comprehensive Community Initiatives. Russell Sage Foundation. Jimerson, S. R., & Furlong, M. J. (Eds.). (2006). Handbook of school violence and school safety: From research to practice. Englewood Cliffs, NJ: Lawrence Erlbaum. Karoly, L. A., Greenwood, P. W., Everingham, S. S., Hoube, J., Kilburn, M. R., Rydell, C. P., Sanders, M., & Chiesa, J. (1998). Investing in our children: What we know and don’t know about the costs and benefits of early childhood interventions. Santa Monica, CA: RAND. Available online at http://www.rand.org/pubs/monograph_reports/MR898/ Kazdin, A. E. (1993). Adolescent mental health: Prevention and treatment programs. American Psychologist, 48, 127–141. Knapp, M. (1995). How shall we study comprehensive, collaborative services for children and families? Educational Researcher, 24, 5–16. Larson, J. (1994). Violence prevention in the schools: A review of selected programs and procedures. School Psychology Review, 23, 151–164. Lawson, H. A. (1998). Academically based community scholarship, consultation as collaborative

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Appendix Content Areas to Address Barriers to Learning (1) Classroom-Based Approaches encompass: •

•

•

• •

Opening the classroom door to bring available supports in (e.g., peer tutors, volunteers, aides trained to work with students-in-need; resource teachers and student support staff work in the classroom as part of the teaching team) Redesigning classroom approaches to enhance teacher capability to prevent and handle problems and reduce need for out-of-class referrals (e.g., personalized instruction; special assistance as necessary; developing small group and independent learning options; reducing negative interactions and over-reliance on social control; expanding the range of curricular and instructional options and choices; systematic use of prereferral interventions) Enhancing and personalizing professional development (e.g., creating a Learning Community for teachers; ensuring opportunities to learn through co-teaching, team teaching, and mentoring; teaching intrinsic motivation concepts and their application to schooling) Curricular enrichment and adjunct programs (e.g., varied enrichment activities that are not tied to reinforcement schedules; visiting scholars from the community) Classroom and school-wide approaches used to create and maintain a caring and supportive climate.

Emphasis at all times is on enhancing feelings of competence, self-determination, and relatedness to others at school and reducing threats to such feelings. (2) Crisis Assistance and Prevention encompasses: • •

Ensuring immediate assistance in emergencies so students can resume learning Providing follow-up care as necessary (e.g., brief and longer-term monitoring)

Placing Prevention into the Context of School Improvement 43 • • •

• •

Forming a school-focused Crisis Team to formulate a response plan and take leadership for developing prevention programs Mobilizing staff, students, and families to anticipate response plans and recovery efforts Creating a caring and safe learning environment (e.g., developing systems to promote healthy development and prevent problems; bullying and harassment abatement programs) Working with neighborhood schools and community to integrate planning for response and prevention Capacity-building to enhance crisis response and prevention (e.g., staff and stakeholder development, enhancing a caring and safe learning environment).

(3) Support for Transitions encompasses: •

• • • •

• •

Welcoming and social support programs for newcomers (e.g., welcoming signs, materials, and initial receptions; peer buddy programs for students, families, staff, volunteers) Daily transition programs (e.g., for before school, breaks, lunch, after school) Articulation programs (e.g., grade to grade—new classrooms, new teachers; elementary to middle school; middle to high school; in and out of special education programs) Summer or intersession programs (e.g., catch-up, recreation, and enrichment programs) School-to-career/higher education (e.g., counseling, pathway, and mentor programs; broad involvement of stakeholders in planning for transitions; students, staff, home, police, faith groups, recreation, business, higher education) Broad involvement of stakeholders in planning for transitions (e.g., students, staff, home, police, faith groups, recreation, business, higher education) Capacity-building to enhance transition programs and activities.

(4) Home Involvement in Schooling encompasses: •

•

• • •

•

Addressing specific support and learning needs of family (e.g., support services for those in the home to assist in addressing basic survival needs and obligations to the children; adult education classes to enhance literacy, job skills, English-as-a-second language, citizenship preparation) Improving mechanisms for communication and connecting school and home (e.g., opportunities at school for family networking and mutual support, learning, recreation, enrichment, and for family members to receive special assistance and to volunteer to help; phone calls and/or emails from teacher and other staff with good news; frequent and balanced conferences—student-led when feasible; outreach to attract hard-to-reach families—including student dropouts) Involving homes in student decision-making (e.g., families prepared for involvement in program planning and problem-solving) Enhancing home support for learning and development (e.g., family literacy; family homework projects; family field trips) Recruiting families to strengthen school and community (e.g., volunteers to welcome and support new families and help in various capacities; families prepared for involvement in school governance) Capacity-building to enhance home involvement.

44 Howard S. Adelman and Linda Taylor (5) Community Outreach for Involvement and Support encompasses: •

•

• • •

Planning and implementing outreach to recruit a wide range of community resources (e.g., public and private agencies; colleges and universities; local residents; artists and cultural institutions, businesses and professional organizations; service, volunteer, and faith-based organizations; community policy and decision-makers) Systems to recruit, screen, prepare, and maintain community resource involvement (e.g., mechanisms to orient and welcome, enhance the volunteer pool, maintain current involvements, enhance a sense of community) Reaching out to students and families who don’t come to school regularly—including truants and dropouts Connecting school and community efforts to promote child and youth development and a sense of community Capacity-building to enhance community involvement and support (e.g., policies and mechanisms to enhance and sustain school-community involvement, staff/stakeholder development on the value of community involvement, “social marketing”).

(6) Student and Family Assistance encompasses: •

•

•

•

•

• •

Providing extra support as soon as a need is recognized and doing so in the least disruptive ways (e.g., prereferral interventions in classrooms; problem-solving conferences with parents; open access to school, district, and community support programs) Timely referral interventions for students and families with problems based on response to extra support (e.g., identification/screening processes, assessment, referrals, and follow-up—school-based, school-linked) Enhancing access to direct interventions for health, mental health, and economic assistance (e.g., school-based, school-linked, and community-based programs and services) Care monitoring, management, information sharing, and follow-up assessment to coordinate individual interventions and check whether referrals and services are adequate and effective Mechanisms for resource coordination and integration to avoid duplication, fill gaps, garner economies of scale, and enhance effectiveness (e.g., braiding resources from school-based and linked interveners, feeder pattern/family of schools, community-based programs; linking with community providers to fill gaps) Enhancing stakeholder awareness of programs and services Capacity-building to enhance student and family assistance systems, programs, and services.

3

School Mental Health Prevention at All Levels Kevin Dwyer, Senior Associate, American Institutes for Research Erika Van Buren, District of Columbia Department of Mental Health Jeremy, an 11-year-old, sixth-grade child at a large urban school district, is falling increasingly behind in reading and math. Jeremy’s behavior is troubling; sometimes he curses at the teacher, or threatens and hits his classmates. His attendance is less than the desired 80%, and he is reported to have no friends among his classmates. Jeremy is regularly suspended but serves “inschool suspension” to keep him in the building. The counselor and school social worker have had frequent contacts with Jeremy’s caregiver and grandmother, and while she is responsive to their recommendations, she is unable to monitor his out-of-home activities. She indicates that Jeremy’s mother has been in and out of drug rehabilitation, and that his father was murdered. Jeremy’s grandmother says that he cries himself to sleep more often than not, and says he “hates school.” Jeremy’s school has provided many interventions. He was referred and found eligible for special education services, and resources were provided for reading and behavior, including a referral to the city mental health clinic where Jeremy was placed on a “priority” waiting list. The school’s team has also provided incidence-driven consultation to the classroom teacher and “as needed” counseling when Jeremy disrupts his class. After several months and a series of interventions, the measured results have been minimal in changing Jeremy’s disruptive behaviors or improving his attendance or academics.

This is not an unusual scenario. Many schools which serve 400 or more predominantly poor children have at least 60 children with equally complex and intensive needs. Researchers also report that many of these schools have more than 20% of children entering each year with multiple risk factors for mental health and behavioral problems. These problems in early grades escalate year-to-year, having a dramatic negative impact on academic learning for these children and their peers (Walker & Sprague, 1999). In an effort to address the growing numbers of students with complex behavioral and academic challenges, schools and their caring staff can become overwhelmed. Moreover, school interventions frequently come too-little, too-late. As a result, most schools and communities fail to adequately address the very complex social-emotional needs of these children.

The “Domino Effect” of Academic and Behavioral Problems Academic and behavioral problems like those experienced by Jeremy often place students on a road that is paved with school adjustment difficulties, gradual disengagement from school life, and inevitable school failure and dropout. Other research has shown that sixth-grade children who are academically deficient in reading or math, or who received poor marks for behavior, had only a 10% chance of graduating with their peers, and a 20% chance of graduating a year late (Balfanz & Herzog, 2006). Research has also revealed that as many as 80% of

46 Kevin Dwyer and Erika Van Buren youth found delinquent and incarcerated have a diagnosable mental health disorder (Cocozza & Skowyra, 2000). Other research suggests that more than half of students with a diagnosable mental illness drop out of high school (U.S. Department of Education, 2006). Jeremy is very likely to become one of these casualties. In many schools, these youth consume a great deal of the school’s resources and attention; they constantly disrupt classroom instruction; consume the time and resources of disciplinarians, specialists and others; and lead frustrated school staff to reluctantly recommend a deadend “alternative” placement. These are the youth who are at risk of delinquency (Shader, 2001). Sadly, belief in the effectiveness of school mental health services suffers collateral damage from this scenario, as well. Since the school “provided mental health services” (i.e., social work involvement, family contact and/or clinic referral) and the child did not improve, school mental health services therefore must not work. Caring staff and well-intended special education and related services interventions are not enough to address the needs of students exhibiting a multitude of problems, who are attempting to cope with overwhelming trauma and loss, possible depression, or post-traumatic stress disorder. Fragmented services, delivered as a patchwork of disconnected interventions, will fail. Programs that provide little care management and lack intensive, individualized academic and behavioral intervention alignment (the combining and simultaneous delivery of academic and behavioral interventions) are all too common. Jeremy is fragile, acting out, and coping with loss, social isolation, and the frustrations associated with academic delays. His multiple needs require an intensive, well-planned and monitored wraparound system of services: services that address behavioral, emotional, academic and social needs with multi-systemic elements and strong care-giver supports to give him— and his equally troubled peers—a fighting chance to succeed. Schools must provide the home for such programs. Moreover, intensive interventions are critical but by no means sufficient. The school climate must be mentally healthy. The climate must sustain and support mental health by building and maintaining peer and adult connections with all students, providing an atmosphere that promotes the prevention of problems, identifying students early who are placed at risk of problems, and supporting the positive behaviors and academic progress of recovering students.

Overview This chapter will build the case in support of school-based prevention, discuss the state of mental health service delivery for children and youth in the United States, describe common childhood mental health challenges, and describe effective school mental health services, with an additional focus on successful prevention and mental health promotion. It will also explore solutions to the dilemma of how our resources are currently utilized, relative to what is needed to maximize student success. The reader will be provided with ideas and remedies to enable school and community mental health providers to partner with others, be progressive leaders in fostering mentally healthy schools, and ensure that children needing services receive effective, coordinated, proven interventions that produce positive results. Moreover, these challenges and solutions will be discussed within the broader policy and cultural context of school communities to shed light on the importance of the family, school and macro system ecologies in promoting positive student well-being at all levels.

School Mental Health: Prevention at All Levels 47

Embracing the True Potential of Schools School mental health services are critical to the academic mission of schools. Both the research literature and practice-based evidence clearly indicate that social-emotional skills and wellbeing are critical to academic learning; that safe and caring schools and classrooms enhance academic success; that reinforcing positive behaviors increases instructional time; and that supported, challenging instruction increases academic learning (see Osher, Dwyer & Jimerson, 2006). It is also no secret that the interaction between positive student development and safe and caring learning environments produces a synergistic effect, in which one enhances the other. Academically successful students are more likely to see their teachers as caring and less likely to have discipline problems and vice versa (Durlak, 1998). Conversely, students whose academic performance and behaviors are identified as problematic are more likely to experience more negative encounters with school staff and punishment within the classroom, producing an aversion to school and increasing their potential for dropout (National Center for Education, Disability and Juvenile Justice, n.d.). Integrating and aligning effective mental health and education services for children is vitally important to ensure academic learning and the development of life-long social-emotional skills. This integration is becoming increasingly self-evident to many stakeholders and has been acknowledged by leading federal, state and local policymakers, professional organizations and agencies (Weist & Paternite, 2006). Schools provide the perfect milieu for improving the availability of quality services through the implementation of broad-based mental health promotion and prevention programming (Mills et al., 2006). The construct of mental health promotion refers broadly to the processes which serve to optimize the positive mental health and well-being of all individuals, regardless of history or risk for mental health problems (Canadian Mental Health Association, 1999; World Health Organization, 2002). Mental health promotion can include efforts to develop policies, procedures and environments that support positive mental health functioning, and initiatives that help individuals and communities to develop the skills and assets necessary to advocate on behalf of their own mental health needs, as well as to foster resilience against adversity that can increase risk for mental health problems. Within the context of schools, this can include curricula which target the development of social and emotional skills, school-wide initiatives to cultivate positive school climate, and the development of school-community partnerships that promote a sense of belonging, civic engagement and responsibility, to name but a few. It is important to mention that mental health promotion is distinct from traditional “mental health treatment” in that the purpose is to maintain positive mental health outcomes for all (i.e., increase sense of efficacy, personal control, determination, and other protective factors), rather than treating symptoms of mental illness. Moreover, central to the mission of such initiatives is a commitment to utilize the natural support networks already existing within families and communities; to use a diverse range of culturally and linguistically responsive strategies that reflect the needs of the populations served; and to incorporate “. . . active citizen involvement in identifying mental health needs, setting priorities, controlling and implementing solutions, and evaluating progress towards goals” (Canadian Mental Health Association, 1999). Mental health prevention refers to the myriad of interventions and strategies designed to decrease the known risk factors for mental health and academic issues, while simultaneously bolstering the protective factors that protect against the impact of said risk. School-based prevention science has consistently documented the utility and effectiveness of the public health

48 Kevin Dwyer and Erika Van Buren model of prevention—a model which includes a “three-tiered” approach for understanding and addressing the diverse levels of need among students and families within the school community. While numerous terms have been used to name these three levels of prevention, this approach typically includes the following dimensions: •

•

•

Universal Prevention. School-based universal prevention embodies mental health promotion in action. These systems, programs and strategies can be implemented with the entire school community to develop the skills and assets that promote healthy social, emotional and academic functioning in students, to protect against psychosocial difficulties, and create learning environments that will promote and sustain positive youth development and functioning (National Center for Education, Disability and Juvenile Justice, n.d.; JJ/SE Shared Agenda, 2007). Early Intervention. School-based early intervention strategies with students who possess specific individual and environmental risk factors for mental health and academic problems, and/or those students for whom universal prevention strategies have not worked and who are exhibiting initial signs of problems. Examples may include providing supports to students who have recently experienced trauma or personal loss, children who have experienced an unusual increase in school absences, or who have received one disciplinary referral rather than five (National Center for Education, Disability and Juvenile Justice, n.d., JJ/SE Shared Agenda, 2007). Early intervention is viewed as a means to intervene at the onset of psychosocial and school adjustment problems as a means of preventing unnecessary special education placement (Foster et al., 2005). Intensive Intervention. This form of intervention is necessary for about 1–3% of the student population (National Center for Education, Disability and Juvenile Justice, n.d., JJ/SE Shared Agenda, 2007). Students experiencing significant emotional and behavioral problems receive a full continuum of mental health and academic services and supports. The wraparound process has been lauded as an effective approach for structuring and delivering these services. This includes the coordination and planning of services across multiple system to address the diverse ecologies of student and family life as well as home, community and school-based case management and service provision and monitoring in the least restrictive placement. Extensive family involvement is central to the implementation of the process (National Center for Education, Disability and Juvenile Justice, n.d.; JJ/SE Shared Agenda, 2007).

In school communities serving students placed at risk by poverty’s adverse effects these percentage distributions are remarkably different, requiring far greater intervention resources. Studies have shown that in communities burdened by poverty, at least 18% of the school student population have severe mental and behavioral problems, needing intensivelevel interventions, and 40% are at risk with moderate behavioral and academic problems, needing planned early interventions, leaving only 42% having the foundation to respond to universal school-wide interventions and instruction (Baker, Kamphaus, Horne & Winsor, 2006). There is a growing body of evidence supporting the use of the public health model for the design and implementation of comprehensive school-based mental health services. Research suggests that adopting the use of universal, early intervention, and intensive interventions can make a difference in the potential for dropout or graduation for a significant percentage of youth (See School Psychology Review, Volume 32 Number 3, 2003: “Emerging models for promoting children’s mental health: Linking systems for prevention and intervention”; Eggert,

School Mental Health: Prevention at All Levels 49 Thompson, Herting, Nicholas, & Dickers, 1994; Felner, Brand, Adan, Mulhall, Flowers, Satrain, & DuBois, 1993; Hahn, Leavitt, & Aaron, 1994; Reyes & Jason, 1991). Implementation of mental health promotion interventions in the form of universal prevention curricula such as Lifeskills Training (Botvin, Eng, & Williams, 1980), Project ACHIEVE (Knoff & Batsche, 1995), and Responding in Peaceful and Positive Ways (RiPP) have resulted in a host of positive student outcomes, including significant reductions in substance use, violent behavior, discipline referrals, suspensions, special education referrals and placement, and grade retention (Botvin, Griffin, & Nichols, 2006; Spoth, Clair, Shin, & Redmond, 2006). Another commonly cited advantage of school mental health, prevention and related services includes their potential to improve access to services for vulnerable and underserved populations by eliminating traditional barriers to mental health services and supports. Such services can reduce the stigma associated with mental health problems and treatment which often preclude help-seeking behaviors, by offering services in students’ natural environments (Mills et al., 2006). School-based services can also improve access by providing unique opportunities to engage parents and families in the process of fostering their children’s positive development and well-being (Mills et al., 2006). One simple example includes increased opportunities for parents and mental health service providers to interact more frequently and informally, and to provide ongoing feedback and input regarding student needs and progress at common interface points within the school (i.e., report card pick-up days, parent-teacher conferences), as opposed to the once-per-week, “50-minute hour” which has typically characterized the structure of treatment within psychology and other mental health disciplines (Weist & Paternite, 2006; Weist, Evans, & Lever, 2003). Both empirical and practice-based evidence tell us what works and what doesn’t when allocating and structuring our schools’ resources to better address the holistic needs of all students. Therefore, it would seem that such potential would be actualized in many if not most school communities. Unfortunately, reality is quite different; school mental health services are frequently marginalized or operate on a distinct and parallel plane to academic instruction (Adelman & Taylor, 1997). Schools have yet to see themselves as agents of social-emotional, behavioral and mental health skill development and well-being. Community, state and federal leaders demand that schools focus on academic standards, and these standards provide the criterion by which schools are compared and ranked for quality. Schools are rarely ranked on “caring” or their ability to help students “learn interpersonal problem-solving” and conflict resolution. Respect for teachers and students’ sense of responsibility for self are expected, required and often rewarded but rarely overtly taught. In order for schools to move from talking about the critical link between academic and social-emotional skills to implementing effective, integrated practices, dramatic changes are required in the behavior of most stakeholders, particularly school staff and leadership. New methods will need to be learned to replace previously learned methods. Rules, regulations, procedures and resource allocations will require review and change. Learning a new behavior (i.e., reinforcing engaged time-on-task) to replace an old behavior (correcting a student for not working) requires a dramatic refocusing by teachers both in how they respond, and in which behaviors they respond to or reinforce (Doll, 2006).

A Status Report on Child and School Mental Health While historically ignored, the importance of children’s mental health and recognition of childhood mental illness have grown considerably over the past half-century. Mental Health:

50 Kevin Dwyer and Erika Van Buren A Report of the Surgeon General (1999) dedicated a full, 97-page chapter to children. It reported study findings that, “21% of U.S. children ages 9 to 17 had a diagnosable mental or addictive disorder associated with at least minimal impairment . . .” (p. 123) and that 11% were found to be significantly functionally impaired and 5% extremely impaired, showing that mental illness is a significant public health problem. Of equal importance to these high percentages is the report’s finding that among all children the most common diagnosable disorders are anxiety and mood disorders (such as depression and bi-polar disorders) and not the behavior disorders that schools most often identify and associate with mental health problems. That same Surgeon General’s report defined mental illness as: . . . the term that refers collectively to all diagnosable mental disorders. Mental disorders are health conditions that are characterized by alterations in thinking, mood or behavior (or some combination thereof) associated with distress and/or impaired functioning . . . Alterations in thinking, mood, or behavior contribute to a host of problems—patient distress, impaired functioning, or heightened risk of death, pain, disability, or loss of freedom. (p. 5) Intensity and duration delineate mental health disorders from what the report calls “mental health conditions,” the latter being either less intense or occurring for a short duration. Conversely, this same report defined mental health as: . . . a state of successful performance of mental function, resulting in productive activities, fulfilling relationships with other people and the ability to adapt to change and cope with adversity. Mental health is indispensable to personal well-being, family and interpersonal relationships, and contribution to community or society . . . mental health is the springboard of thinking and communication skill, learning, emotional growth, resilience, and self-esteem. (p. 4) School mental health has been defined broadly by national collaborative groups as encompassing mental wellness and the prevention, identification and treatment of mental disorders in children, families and staff. In 2001, the Policy Leadership Cadre for Mental Health in Schools defined school mental health to include “. . . considerations of the school’s role related to both positive mental health (e.g., promotion of social and emotional development) and mental health problems (psychosocial concerns and mental disorders) of students, their families and school staff” (Policy Leadership Cadre for Mental Health in Schools, 2001, pp. 5–6). So the question remains: is the field of school mental health living up to its definition and its potential to improve access to and availability of an array of mental health services and supports to students and families in need? The empirical literature suggests that access to mental health services through the schools is far more universal than many would believe. Across all public schools, the access to school mental health services approaches 90%, such that students and families can seek assistance from school providers. However, it is important to note that access to services is by no means synonymous with direct, individualized or appropriately intensive, and sustained interventions. The most extensive study of school mental health services, sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA), included a sample of 83,000 public elementary, middle and high schools. Results of the study showed that 87% of schools reported that all of the students attending their schools were “eligible to receive mental health

School Mental Health: Prevention at All Levels 51 services” (Foster et al., 2005). Schools within the sample reported the use of a significant array of intensive mental health services (see Table 3.1), including: assessment; consultation; individual counseling; therapy, and family supports. Mental health services, categorized as counseling, psychological, social work and family supports services, were also listed as related services for children who were eligible for special education. While some form of universal prevention appeared to be implemented in most schools, the vast majority (78%) of programs noted in the study focused narrowly on the prevention of drug, tobacco and alcohol use. Universal prevention curricula designed to foster social and emotional skill development were implemented in 59% of schools, while early intervention strategies were provided in 63% of schools within in the study. These results have been echoed by other national research on the implementation of comprehensive prevention initiatives (Carlson & Knesting, 2007). In sum, the authors concluded: “it is now well documented that, insofar as children receive any mental health services, schools are the major provider” (Chapter 1, p. 1). The aforementioned figures suggest that schools appear to be making strides in bridging the gap between the need for school mental health services and access to these services. Nevertheless, there remains a significant level of unmet need for many groups of students and their families. Although most mental health services are provided in the schools, fewer than one in 17 children needing such services receive them and those that do, receive far less than needed. Even children receiving “psychological services” as a related service on an Individualized Education Program plan rarely have those services defined in a manner that can ensure they receive an appropriate evaluation for services. Students from diverse ethnic and cultural backgrounds, including those who have recently immigrated to this country, continue to experience disparities in access to school-based services. It is well documented that students of color are placed at disproportionate risk of academic and eventual school failure. The non-graduation rates are particularly abysmal for males from ethnic minority backgrounds where, in some school systems, their non-graduation rates surpass 75%. Ethnic disparities in access and availability are due to a host of risk factors, including the dearth of sufficient school-based resources to provide quality education and mental health services (EDDJP). This disturbing reality is supported by findings from the SAMHSA study which revealed that eligibility for mental health services was supported less in

Table 3.1 Percentage of Schools Providing Various Mental Health Services by School Level, 2002–2003 Mental Health Service

Elementary (%)

Middle (%)

High School (%)

Assessment Behavior Management Consultation Crisis Intervention Referral to Special Programs Individual Counseling/Therapy Case Management Group Counseling/Therapy Family Support Services Substance Abuse Counseling Medication/Medication Management

90 89 87 85 75 74 70 59 34 33

87 86 86 83 79 70 67 56 53 35

86 82 82 81 72 68 61 58 56 33

Source: Foster, S., Rollefson, M., Doksum, T., Noonan, D., Robinson, G., & Teich, J. (2005). School mental health services in the United States 2002–2003. DHHS Pub. No. (SMA) 05–4068. Rockville, MD: Center for Mental Health Services, Substance Abuse and Mental Health Services Administration. Retrieved December 9, 2007, from http://download.ncadi.samhsa.gov/ken/pdf/SMA05-4068/SMA05-4068.pdf

52 Kevin Dwyer and Erika Van Buren schools across the country with high percentages of students of color, such that eligibility for services was more often restricted to students placed in special education (Foster et al., 2005). Access and availability for ethnic and cultural populations can also be undermined by cultural biases and stereotyping that can occur throughout the referral, assessment, diagnosis and intervention processes, and increase the likelihood of familial mistrust, resistance and dropout from treatment (Coutinho & Oswald, 2004; Hosp & Reschly, 2003; 2004; Skiba et al., 2008). Despite mounting support for a continuum of school-based prevention services at all levels, schools also tend to demonstrate a bias in providing services to students with serious and pervasive problems alone, while prevention and early intervention strategies are less commonly utilized (Foster et al., 2005; Weist & Christodulu, 2000). Schools that provide intensive interventions for students with serious emotional problems may yield some positive results in helping those students; nevertheless, it is commonly seen that providing intensive services only for the most serious problems alone will not change a school. It is valid on the face that “solving” the problems of disruptive students will effect change. However, the trap of addressing only the complex problems of youth with the highest levels of need, without addressing the multiple levels of intervention required for mentally healthy safe and supportive schools, is that the school itself may remain a toxic environment, continuing to produce more problems like the “dropout factories” noted previously. Specifically, if classrooms remain unorganized or behaviorally corrosive, new problems will surface for intensive service providers; children can learn to exhibit problem behaviors and schools will remain mentally unhealthy environments. The leadership and staff’s preoccupation with serious problems may reduce readiness for active participation in systemic, school-wide change and restructuring of how resources are allocated. Teachers may see the clinicians as the responsible agents to address mental and behavioral pathologies and staff may see their responsibility as primarily to help identify and refer other problem children and families to those clinicians. Consequently, this can overwhelm already overtaxed school mental health providers, further “siloing” their work and transforming their role into a “one-stop shop” for managing crises and putting out fires within their respective schools (Weist, 1997). Public mental health prevention and early intervention are just beginning to emerge, starting with a greater awareness of the interaction between environment and social-emotional well-being. (See: From Neurons to Neighborhood: The Science of Early Childhood Developments, 2000, Institute of Medicine, Washington DC: National Academy Press.) In summary, both a public awareness of child mental health and an acknowledgement of the prevalence of child mental illness have recently gained national attention as a public health concern. School-based mental health services and supports are beginning to make headway in improving access and availability of services to students and families with varying levels of mental health support needs; upwards of 70–80% of children who receive mental health services appear to be receiving them in the school (Foster et al., 2005). Nevertheless, school-based services still struggle to catch up with the overwhelming proportion of unmet need within their school communities. Particular groups of students and families, including students of color, students without formal diagnoses, and students who are just beginning to exhibit problems, have more limited access, and may ultimately fall through systemic cracks. Some researchers have suggested that the lack of a unified child mental health policy agenda has brought the field to the point where we have accumulated a mound of evidence supporting the need for such services and so we possess the tools and technologies to intervene effectively, yet we are unable to reach the populations most in need of them (see Lourie & Hernandez, 2003). Within the realm of school mental health, our ability to align and integrate educational and

School Mental Health: Prevention at All Levels 53 mental health policies, procedures and goals may be one of the single most critical determinants of our ability to engender the public will that is necessary to facilitate implementation of mental health and full-scale prevention services in schools, and ultimately improve service access to children, youth and families.

The Great Systemic Divide between Education and Mental Health The practitioner reader may ask: “Why doesn’t my school system get it? Why don’t the systems use the child-centered integrated developmental approach endorsed by most educational and mental health experts?” Although schools are the nation’s primary children’s mental health service provider, school leadership, local policymakers and stakeholders continue to perceive this as a role of public health, rather than public education. In fact, it has been asserted that schools are often actively resistant to taking on this critical role in fostering child development and mental health (Adelman & Taylor 2000). The primary mission of public education has traditionally been to ensure literacy in preparing the nation’s citizens and workforce. The fundamental cause of these barriers is rooted in these firmly entrenched traditional beliefs, and the resultant resource constraints, conflicting demands, inadequate knowledge about what works and availability of skilled professionals to implement interventions that target wholestudent development and wellness. As previously stated, traditions and governmental structures have polarized the governing and delivery of education and mental health. Schools are almost always separately managed and funded from general local or state government, which is responsible for health, safety, transportation and other services. Secondly, many community leaders see mental health as a treatment provided by a myriad of private and public clinicians and agencies, clearly separate from education. The education and public mental health governing processes and policies, as well as the necessary resource streams, are remarkably different. Access to free public education from kindergarten through high school is viewed as a right for all regardless of income, whereas access to publicly funded mental health services is limited to complex eligibility requirements. Public education is designed to prepare youth for employment and citizenship whereas public mental health has been designed to treat the most serious ill, economically disadvantaged children and adults. These differing traditional roles and beliefs, as well as the divide between the organizational supports that currently drive these systems, are significant barriers that must be addressed to ensure a full array of school mental health prevention and intervention services. Student services continue to make their mark in improving education and the mental wellness of children and youth. Nevertheless, at the same time, student services professions are marginalized in addressing both children’s mental health and, some would say, the academic achievement of children (Adelman & Taylor, 1997). Some education leaders continue to see student services as a branch of special education, made up of eligibility assessors or IEP managers. National data suggests that this perception is reflected in the allocation of resources (including use of time) and such progressive ideals and roles are rare. Public mental health prevention and early intervention are just beginning to emerge, starting with a greater awareness of the interaction between environment and social-emotional well-being, or “neurons to neighborhoods” (National Research Council & Institute of Medicine, 2000). In addition, elected officials from governors to local mayors and councils have begun to see the need to align, expand and integrate services in policy and others have initiated efforts to successfully address these barriers. Public policy also supports the inclusion of mental health and education. The nation’s principals’ and superintendents’ associations

54 Kevin Dwyer and Erika Van Buren acknowledge the need for mentally healthy development and recognize its connection to academic achievement. Yet the funders, families and practitioners in the local districts fail to see schools as responsible for teaching those required social and mental health skills or for providing mental health interventions. The skills are often perceived as the responsibility of the home, and agents of morality and treatment the responsibility of clinical agencies. The percentage of schools that teach social skills, which the Collaborative for Academic, Social, and Emotional Learning (CASEL) has defined (Greenberg et al., 2003) to include a focus on building a caring school climate, positive discipline and the provision of an array of mental health supports, has slowly grown, but has not yet been recognized or adopted as a nationally funded priority. Weist and Paternite (2006) suggest that local and state-level autonomy in decision-making around mental health policy and practice have created significant variability in the types and quality of services offered, which has consequently contributed to inactivity in mobilizing reform within child-serving systems, including schools. The rate of progress in moving this agenda forward may likely depend on national and state leadership coordination and recognition of this priority. Likewise, recognition may well be dependent upon the ability of prevention researchers to bridge the divide between research and practice by proving the effectiveness of such preventive instruction and mental health service integration in schools, and disseminating this knowledge more effectively and efficiently to major stakeholder groups. Historical and Current Developments in School Mental Health Services: Translating Policies into Practice Although it is not possible to definitively conclude that the dramatic increase in numbers of school social workers and school psychologists since 1975 has been the result of federal special education and related services laws and regulations, it is hard to ignore that Public Law 94-142 and its regulations had a causal influence on this positive outcome. This law mandated that every school system create access to individuals who deliver eligibility assessment services and other related services to children to determine eligibility for special education. Numerous university programs were established with the law’s federal training grants (Ysseldyke, 1984). In addition, within the realm of school psychology, the first Blueprint for Training and Practice (Ysseldyke, 1984) was funded by special education funds. Progressive research has demonstrated that such practices funded under special education and related laws have led to significant advances in school mental health practice, including grants to train curriculum-based measurement and assessment, the precursor of “Response to Intervention,” instructional methods, classroom behavioral management and preventive school-wide discipline practices, cultural competence, functional behavioral assessment, alternative schools, problem-solving and coordinated services (Knoff & Batsche, 1995). School mental health services for all students followed much later, enhanced by legislative initiatives outside the U.S. Department of Education through the “demonstration” grant processes of SAMHSA and the Child and Maternal Health Administration of the Department of Health and Human Services. Nevertheless, special education law remained the most significant driving force for granting children, families and educators access to schoolprovided direct and indirect mental health services. This was confirmed by the aforementioned document: Mental Health: A Report of the Surgeon General (2000) that indicated that children’s access to mental health services, although severely limited, was through the schools, provided by school’s psychologists, counselors, school nurses and other student service providers.

School Mental Health: Prevention at All Levels 55 Funding of counseling, school social work and psychological services under Medicaid has been another boost to the provision of school mental health services to children and their families. This amendment to Title 19 of the Social Security Act in 1996 made Medicaid the payor of several related services including psychological services for eligible children. Most states allow Medicaid billing for school-provided related services to children who are eligible for that health insurance program. The establishment of the Safe Schools, Healthy Students federal grants and the school mental health technical assistance centers (Adelman et al., 1999) have continued to support the critical roles that schools play in the continuum of mental health services. More recently, the President’s New Freedom Commission on Mental Health explicitly cited the improvement and expansion of comprehensive school mental health programs as a recommendation for effective mental health system reform (Mills et al., 2006). The National Association of School Psychologists and other professional and mental health advocates consistently support legislation and regulations that enable access to effective school mental health services. State certification standards have improved and been adopted by many states that meet the agreed-upon standards for education and mental health. The national certification requirements are well respected and have become the standard for university training programs. Numerous benefits have emerged from building the capacity of schools to support the social and emotional development and needs of children and youth. Many school-wide positive changes, including use of effective behavioral management strategies and school-wide efforts to transition from the use of ineffective suspension and expulsion to positive discipline, are linked to the efforts of student support personnel, including school counselors, psychologists and social workers. And, yes, evidence has begun to accumulate that these services have been critical in supporting best practices in academic instruction (Collaborative for Academic, Social, and Emotional Learning, 2008; Eber, Sugai, Smith, & Scott, 2002; Elias, 2006; Kutash, Duchnowski, & Lynn, 2006; Lewis, Hudson, Richter, & Johnson, 2004). However, it is still unfortunately rare that schools record the provision of recognized “best practice” services and match those to student mental health and academic achievement outcomes. Helping educators to see the interaction between behavior and academic instruction through the use of data-driven problem-solving and decision-making processes has positively changed some schools. While measurement based on curriculum and fidelity to proven instructional practices has been a persistent mantra of student service providers, it has also become clear that measurement of both social-emotional learning and academic achievement go hand-in-hand and inform schools as to what is working to improve learning (Collaborative for Academic, Social, and Emotional Learning, 2008). Measurement has become institutionalized through No Child Left Behind and “Response to Intervention” language in federal special education eligibility procedures. Most likely driven by quality review and accountability mandates at the local, state and/or federal levels, school improvement and problem-solving teams are beginning to identify measurable indicators of school climate and mental health as targets for change (i.e., student attendance rates), to collect and monitor this data to gauge the progress of their initiatives and modify them accordingly. Mental health professionals, particularly school psychologists, have served as practitioner cheerleaders for “best practices,” and as facilitators for collecting data and translating learning, behavioral and mental health research into practice. More than at any time in the past, multiple examples of “best practice” interventions have been identified that can be functionally used in most public schools. We also know what is

56 Kevin Dwyer and Erika Van Buren unproven and what is unlikely to work. For example, school-wide teacher-taught character education can improve behavior and attendance (see the What Works Clearinghouse: http://ies.ed.gov/ncee/wwc/) while imported instruction such as Drug Abuse Resistance Education (DARE) has no measurable effect on reducing student drug use (Ennett, Tobler, Ringwalt, & Flewelling, 1994). Ineffective interventions are less than useless and may actually do harm by increasing cynicism towards school mental health practices and wasting valuable fiscal, human and material resources. Moreover, poorly applied, proven interventions can be equally as harmful. Large and small school districts have been observed using unproven practices (volunteers providing pull-out reading support) or ineffectively mastered proven practices (unmonitored Positive Behavioral Supports). These districts are often both disappointed and puzzled by the lack of desired behavioral and academic outcomes, and consequently teachers and administrators become reluctant to try innovative ideas and lose hope. Yet, carefully planned, resourced and monitored programs and interventions do work. Monitoring both the outputs (what you are doing) and the outcomes (results) of programs and interventions not only provides “proof,” but also informs you of your fidelity to the program (Walcott & Riley-Tillman, 2007). A leader’s commitment to research-based instructional and mental health interventions has proven critical. Time and time again it has been cited in the chronicles of educators, practitioners, and researchers alike that a principal’s visible and proactive commitment to program implementation and evaluation is critical to its success. In one school system where ethnic disparities in reading achievement became evident over several years, a comprehensive research-based reading initiative was started under the direction of the superintendent. Each elementary school was resourced with a master reading specialist/coach and in-service training was provided to all primary grade teachers. Principals were evaluated as instructional leaders in literacy. Technology was provided (DIBELS) so that all children could be frequently monitored in their progress and individualized remedies were prescribed for those falling behind. Progress of ethnic and gender groups, by classroom and school, were all monitored and remedies provided when classroom or school-wide progress lagged. The success of the program was dramatic, in that third-grade reading scores for all students increased, including those of children identified as Hispanic and African American. Measuring Outcomes of School Mental Health Prevention and Intervention Proving that the three-tiered prevention-intervention construct works in schools requires a great deal of planning and management. Too frequently “programs” are initiated without a commitment to measurement of base rate data or the actual measurement of the inputs—the elements of the program itself. It is a difficult process, but there are some tips that many have applied with success, even when the interventions have been started before the pre-intervention measures have been gathered. For example, many have used general school-wide public data from the past three to five years, accounting for any significant changes in staffing, program and population. When looking at individual classrooms or students, some have used trend data over time and the interventions provided. The student is monitored twice a year for several years using individualized self-report, teacher and parent reports and discipline and report card data. The American Institutes for Research is using a model like this as one part of the New York City, United Way’s Safe Schools, Successful Students Initiative in six middle schools in the South Bronx (Osher, Dwyer, & Jackson, 2004). This program uses multiple quantitative and qualitative measures of program implementation and outcomes, including observations and interviews to assess school climate, student and school-reported

School Mental Health: Prevention at All Levels 57 psychosocial functioning, family involvement, and staff satisfaction. To capture baseline school-level functioning, the evaluation conducts comparative analyses with archival indicators of school-level climate and culture that are reported annually through the district as part of their accountability reporting procedures. Where difficulties come into play in the implementation of many three-tiered initiatives is in the lack of measures of the inputs, be they school-wide, targeted or intensive. For example, many schools report they are “using” Second Step (Frey, Hirschstein, & Guzzo, 2000) or Positive Behavioral Interventions and Supports (PBIS) but the quality may not be monitored and quality can vary from a “paper-only” program to one with significant fidelity. The first author of this chapter has had teachers report that they held one faculty meeting on Second Step, and no one was checking as to what they were doing in the classroom with that minimal exposure. He has walked through schools that report they use PBIS without seeing any posted signs reminding students of the positive behavioral expectations. So when the effect size is measured and the quality of the intervention is unknown, what do we know at the end of the day? The inputs have to be measured and ranked on a quality/fidelity scale much as we measure fidelity to the instructional methods for reading. The remedy for this challenge is to designate a well-trained, competent site-based professional to validate training needs, train and evaluate the fidelity of implementation and ensure measures of effects. The school’s principal should also be involved in this instructional leadership responsibility in supervising staff. Both the site-based coach and the principal’s monitoring are recordable inputs. Likert scales can be used to evaluate team functions and processes. In addition to school-wide initiatives, intensive interventions must also be evaluated to monitor progress in reaching treatment goals and to make midcourse corrections where necessary. Again, what are the actual inputs? Are the interventions “deep” enough to address the complexity of student problems? Individual student self-report measures should go beyond symptom scales and look at other risk and protective factors, including the development of social skills, connectedness to school, and academic self-efficacy.

Components of Effective School-Based Mental Health Practices An extensive monograph on school mental health programs involving school psychologists sought to analyze and classify the types of services being provided (Nastasi, Pluymert, Varjas, & Moore, 2002). The programs cited in the report were self-nominated, but were criterionreviewed by a panel for inclusion. Programs were reviewed for their use of a four-level prevention-intervention service model, including (1) prevention, (2) risk-reduction, (3) early intervention, and (4) treatment services. Results of the review suggested that most programs delivered more than one level of service but few delivered all four levels (prevention, riskreduction, early intervention, and treatment). The report identified many common factors across programs. Nearly all programs used a team problem-solving approach in the development and implementation of interventions. In addition, the inclusion of stakeholders beyond the school staff was noted as critical to the design and implementation of these teams. Almost all of the programs staffed more than one school mental health professional, and most included interagency staffing models, including community mental health centers and other child-serving agencies. Most programs documented best practices based on sound theory. Required outcome measures were presented and almost all used multiple measures, including qualitative observations. Fidelity in program implementation was less regularly reported as were formal measures of longitudinal results.

58 Kevin Dwyer and Erika Van Buren The processes and strategies mentioned above are foundational pieces of any schoolwide mental health initiative, but do not magically unfold overnight, or over weeks or months for that matter. Establishing school-wide readiness, fostering organizational and leadership support for the initiative, building mutually beneficial partnerships and changing attitudes about mental health problems and interventions are part of an ongoing developmental process that must be accounted for on the front end of school reform and intervention planning, and that requires multiple strategies to address properly. Viewing effective comprehensive schoolbased prevention through a closer lens elucidates specific “active ingredients” or successful strategies that have consistently emerged from the pool of evidence-based and best practice models of successful and sustainable school-based prevention and intervention. Successful Initiatives Are Facilitated by Site-Based “Coaches/Facilitators/Implementers” Although there is scant research available, school/agency-based coaches or facilitators have been observed to be most effective in making sure that initiatives are implemented with fidelity and that data requirements are followed. We do know that staff training is most effective when trained staff have the opportunity to see skills modeled and have the support of a trusted coach providing immediate feedback regarding their implementation (Garet et al., 2001). The authors of this chapter have observed the coaching model used in several grants, and throughout the process, survey data has shown staff feeling more confident in using new skills when supported by frequent coaching. Some systems have used in-house coaches and others have used an itinerate support model (two to three schools for each coach). Although initially expensive, the in-house model seems the most effective method of ensuring fidelity. Other models such as having the monitoring and support come from existing staff or an administrator may also be effective. Coaches must be highly skilled in the interventions they are training and supporting. They must also have effective interpersonal skills and be proficient in adult learning styles. Teachers and staff must have trust in the coach and be able to access coaching support in a timely manner. Adults, much like older students, are frequently reluctant to ask for help when they need it, particularly if they believe that they are “expected to know” the skill. Therefore, it is critical that coaches regularly monitor and support staff. Many coaches have used grade-level monitoring, focusing on common proficiencies and then focusing on “skills that are more difficult to maintain,” seeking suggestions and support from peers. Coaches also have the potential to serve in the role of cultural liaisons or brokers between the lead and community partners, and the host schools for prevention initiatives. Knowledge of the student and family communities, the complex cultures and competing demands of schools, as well as mastery of intervention model are invaluable in facilitating collaboration, translating diverse interdisciplinary argot, values and norms, and managing the interorganizational dynamics of difference that can emerge when multiple stakeholders come to the table. Successful Initiatives Are Supported by Stakeholders Who Believe They Can Implement the Desired Interventions When asking systems to change, and requiring members to learn new skills and refocus their approaches to tasks, there is a general principle that 80% of the implementers need to “buy into” the desired skills, responsibilities and vision of the program. It is not enough to have coaches and the support of the principal or agency head if the classroom teachers, counselor

School Mental Health: Prevention at All Levels 59 and clinical social workers are not on board in believing that the required changes will enable them to be more successful (and lower their stress). Frequently the first step to “buy-in” is demonstrating that learning the new skill will reduce frustration, including from classroom disruptions that consume energy and precious instructional time. For example, the research staff for Project ACHIEVE (Knoff & Batsche, 1995) observed classes and recorded amounts of lost time caused by disruptions to instruction. When staff saw that engaged instructional time could be increased by giving students training in social skills and reflective thinking, “buy-in” was sustained. Moreover, providing opportunities for systematic decision-making input and needs assessment from staff can be automatically engaging and promote support for the initiative. For example, as part of its preplanning processes, the PBIS model has a prerequisite of written support from at least 80% of staff before intervention planning can move forward. Teacher buy-in can be enhanced by focusing on “learning behaviors” (McDermott, 1999) that have been shown to support academic success. Schaefer (2004) and others have demonstrated that teachers are excellent in identifying keystone behaviors that they can then teach and reinforce. Likewise, teachers recognize that faulty learning behaviors are relatively common in classrooms and therefore conducive to classroom and school-wide interventions. The focus on academic behaviors rather than on “social skills” and discipline may help gain teacher and administrative support for prevention programs. Successful Initiatives Include Measurement of Vital Stakeholder-Identified Outcome One way to increase staff support is to establish agreed-upon measurable outcomes that are meaningful to those front-line workers who interact directly with students. Measuring schoollevel reductions in office referrals may mean little to a teacher who is temporarily relieved of the disruptions by the referred student. Temporary stress reduction is a strong reinforcer. Seeing the cumulative loss to the student, the class and the teacher over time may assist the team in defining a more meaningful outcome for such teachers. Having staff identify outcome measures, using existing school measures (report cards, attendance, formative and summative assessments) as the base, has been demonstrated to increase compliance in data collection. New cumbersome measures are less likely to be supported or used. Measures such as instructional time and individual students’ time-on-task will require both observations and more school resources. Individualized behavioral and academic measures are time-intensive and thus less consistently applied. Overcoming this resistance through direct paraprofessional support or incentives can be critical to securing the necessary data to determine the student’s response to interventions. Questionnaires on school climate and staff (student and parent) satisfaction surveys are usually acceptable measures. Partners, school mental health and clinical providers are most commonly expected to connect their services to measurable outcomes and for students whose IEP contains such services a connection to school progress is required. Successful Initiatives Generate Clearly Identified and Required Interventions and Outputs That Can Be Replicated One of the more serious problems found among school-collected measures are the actual staff and leadership behaviors that are being maintained that produce positive outcomes. Outputs are the things we do that result in the desired outcomes we achieve. Running teacher support teams (TST) that provide little timely and effective ideas, versus teams that respond with immediacy, follow-up, evaluate if needed, and modify supports, are examples of different

60 Kevin Dwyer and Erika Van Buren outputs, although on the surface they may look generically the same (both have regular properly staffed TST meetings). Frequency, intensity and actual descriptions of the interventions are critical. This is more common in academic instruction than in behavioral instruction and management. For instance, manualized cognitive behavioral therapies are effective in documenting evidence of the connection between outputs and outcomes. Individualized Education Programs (IEPs) have been designed to connect interventions to academic and behavioral goals. However, they are too commonly less informative than they could be in helping a new teacher or other staff know clearly what outputs are required to sustain progress. Furthermore, using outputs to inform us as to what elements of prevention programming should be sustained is also very important. Measuring what is required to learn and maintain a school-wide social skills program is critical to its replication in going to scale within a school system. The Collaborative for Academic, Social and Emotional Learning (CASEL) is one organization that has identified criteria for implementing such school-wide programs (Elias, Zins, Graczyk, & Weisberg, 2003). Successful Initiatives Use Structured Team Problem-Solving Processes Structured team problem-solving has been shown to be an effective tool for schools and agencies to ensure meaningful intervention planning and resource allocation that will appropriately address agreed-upon goals and visions (Osher, Dwyer & Jackson, 2004; Dwyer & Osher, 2000). In fact, the three-tiered prevention-intervention construct requires team problemsolving and can be the most critical element in any program’s implementation. However, there are a number of essential factors and cautions when setting out to establish problemsolving teams. Lessons from the field tell us there are a number of clear right and wrong methods for establishing and implementing these processes, which can have clear implications first for staff buy-in and support of the initiative, and ultimately for program success. It is important to note that team problem-solving has been seen by teachers as time consuming and ineffective because the necessary staff support to make the recommendations effective is not available, and suggestions are frequently not backed up by training. When staff members are untrained in team problem-solving, the process becomes mechanical and meaningless, lacking the depth of inquiry and generation of effective, targeted interventions that are monitored for fidelity and measured for results. Without training, “early intervention” strategies generated by teams are almost always shallow and have little effect. The team deteriorates and begins to become a “reactive” and crisis-driven dumping ground, moving students to special or alternative programs. In schools where training in problem-solving is intensive, and where training is evaluated, the results can be significant. Teachers, student support staff and administrators are frequently required to participate on a myriad of school teams. The decision to create problem-solving teams, or to integrate effective problem-solving processes and training into already existing team structures, including school improvement teams and quality review teams, should ideally be informed by a more comprehensive resource-mapping process (Adelman & Taylor, 2006) This process helps to identify and reduce redundant or duplicated efforts and helps with the reallocation and more efficient distribution of time and human resources. Collaboration with other school-based groups and teams can also help to facilitate task completion, reduce fragmentation and foster a sense of shared accountability and support for the implementation of school-wide strategies. The school leadership is critical for shaping the vision and mission of such teams, and providing ongoing guidance and support as needed.

School Mental Health: Prevention at All Levels 61 Successful Initiatives Are Coordinated by School, Community Agency, Non-Governmental Services, Students and their Families Agencies and schools are more successful when they coordinate their family-friendly interventions to ensure that interventions are carried out as designed and supported by all providers. Schools and agencies that blend resources, co-train staff, align services and actively involve families seem to be more effective than services that are merely housed in the same building, implementing parallel or duplicative services. Exemplary mental health programs build in assistance to schools and systems to integrate and coordinate mental health, health, social service and educational programs to maximize efficiency and effectiveness of service delivery. They also provide support for interagency and community programs that safeguard children’s mental health and safety. Successful Initiatives Are Responsive to the Cultural and Linguistic Values, Norms and Preferences of the Students and Families Served Too often we hear of well-supported mental health prevention and intervention programs and strategies that flop because they simply do not translate to the communities in which they are implemented. The selection criteria for prevention and intervention programming and strategies must include a successful track record of use with the cultural and linguistic populations served. Have evidence-based interventions been implemented, and protocols standardized using a meaningful percentage of students and families within the cultural and socioeconomic populations of focus? Are there mechanisms for adapting protocols to incorporate the values, traditions, norms and needs of families? And, just as importantly, are there feedback loops in place to ensure that family members provide ongoing feedback and input throughout the process of implementation? Developing culturally and linguistically responsive mental health services is a developmental process that should be infused in all aspects of program development and implementation (Cross, Bazron, Dennis, & Isaacs, 1989). Successful program models, such as federally funded systems of care communities (Eber, 1998; Stroul, 1996), are based on a set of guiding principles which include a youth-guided, family-driven and culturally and linguistically competent approach to service design and provision. School-based systems of care communities are growing in frequency throughout the country, and have developed innovative strategies for recruiting and retaining representative, community-based staff to work within the cultural frameworks and in the preferred languages of students and families they serve. Such services promote organizational infrastructure development that will support and sustain culturally and linguistically appropriate practice, and prioritize meaningful and respectful collaboration with youth, families and community members in meaningful and respectful ways. For instance, one school-based system of care community has made tremendous progress in outreach and engagement to the Latino community within its cachement area, and specifically with the Latino families and caregivers it serves. Youth and families are represented among their staff and major decision-making bodies. This system of care conducts ongoing dialogues and focus groups with families with the primary goal of engaging them in the development and implementation of service delivery, instilling family empowerment and leadership. Successful Initiatives Measure the Effectiveness of Training and Coaching Most trainings are evaluated immediately after the presentation, using participants’ desired outcomes rather than ongoing surveys of actual use and usefulness. Training research suggests

62 Kevin Dwyer and Erika Van Buren that needs-based training that involves engaged instruction followed by modeling, coaching, observations and evaluation is effective, whereas most didactic training is minimally effective in changing the behavior of the participants. Didactic workshops may be effective in increasing the participants’ awareness of what can be tried or modified to improve instruction or classroom behavior and increase interest in finding out more, but it is not professional development which implies developing, demonstrating and using new and effective skills High-quality professional development is a science that has been evaluated (Garet, Porter, Desimone, Birman, & Suk Yoon, 2001), showing that adequate training time, active participation, the attainment of new knowledge and support of study groups, same-school peer support and mentoring increases skill development and implementation. Successful Initiatives Align Best Practices Across Academic, Social-Emotional, Safety and Other Critical Areas Some schools use math and science vocabulary in their basic reading programs to enhance and reinforce meaning. Some high schools connect literature to history, and math to chemistry. Almost all schools use some standard sequential curriculum to teach reading and the foundations of math and written language. The language of social skills can also be part of the language of reading and writing. Some middle schools have developed creative writing skills using the theme of bullying, writing plays to dramatize the issue. The vocabulary used by staff in the cafeteria to remind students of respect and social responsibility should be the same as the language of the classroom and hallways. The therapist helping a student with impulse control strategies may be most effective using the social skills curriculum’s structure enabling the teacher to naturally reinforce that student’s therapy. Security personnel and hall monitors in a school that use the “Stop-and-Think” program must be trained in that model to ensure they reinforce that approach to behavior. Staff that yell (louder, longer and more frequently) at misbehaving students should be assisted in learning and replacing behavioral language to maximize the positive behavioral support effects. As managers of school-wide social skill promotion, instructional leaders (i.e., principals) should plan for this kind of alignment of academic, social skills, safety and mental health interventions on levels that support applied learning opportunities. Alignment across content areas as well as alignment among service intensities can make the classroom therapeutic and the therapy instructional. Family caregivers have reported that they too can participate in this process when given the tools and support to function as partners in learning. Successful Initiatives Are Planned, and Given the Time and Resources to Demonstrate Results Change takes time. Change, resulting in measurable results, takes even more time than its agents expect. Although this construct is acknowledged by policymakers, leaders and stakeholders, it is rarely concretely noted and accounted for in planning. Patience with “disrespectful, irresponsible children” is rare. Schools want children who are “ready to learn” or change agents who can quickly remedy problems that make teaching difficult. Most teachers are more patient with struggling students trying to phonetically de-code a new word than students struggling with learning appropriate classroom behaviors. Patience is almost always lost when students yell out vulgarities, hit or defy an authority’s command. Classroom control is lost, instruction is disrupted, unwanted feelings of anxiety, anger, even impotence surface and threats may be expressed, too frequently escalating the student’s negative behaviors.

School Mental Health: Prevention at All Levels 63 A structured, highly individualized intervention plan that requires positive comments when appropriate behaviors are observed, predicting outbursts and modifying or acknowledging the warning signs to help stimulate taught/learned replacement behaviors, as well as maintaining firm consistent consequences for serious infractions, will not immediately extinguish the inappropriate behavior, but should reduce its frequency. It takes time to teach new skills and, frequently, more time to extinguish previously learned and reinforced inappropriate behaviors. Likewise, school-wide prevention and mental health promotion plans and interventions will take time to implement and have measurable results. In fact, most school-wide interventions take more than three years to demonstrate significant measurable outcomes. Successful Initiatives Are Cost-Effective, and Use Multiple Funding Sources As noted earlier, public schools and mental health agencies are commonly managed and funded under different governmental systems. Even at the federal level, the funding streams come through different agencies as directed by various laws. Few laws, such as the Safe Schools Healthy Students grants, require interagency controls that can better ensure coordinated resource allocation. The Safe Schools Healthy Students demonstration grants make the school system the grantee but require interagency collaboration and are federally managed by the Departments of Education, Justice and Health and Human Services. Funding at the local level is best managed when it enables the blending and braiding of funds to address the complex barriers to learning, as well as support the prevention of problems. Blending funds requires school system and agencies to pool funds to address a specific agreedupon set of interventions or service. Unlike braided funding there is no tracking of each organization’s dollars. Braiding enables the pool of funds to be followed to specific components of service and is frequently favored over mere blending. Local management boards (such as a Child and Family Collaboration Council) that include representation from schools and agencies, using local, state and federal funds as well as charitable non-governmental organization funds, are becoming more common. The next step at the federal level will be for legislation to give incentives (or mandates) for merging demonstration grants, such as, for example, those that address serious mental illness in children from the Substance Abuse and Mental Health Services Administration with those that support social-emotional education through the U.S. Department of Education’s Leave No Child Behind and Medicaid. Successful Initiatives Are Shown to Quickly Relieve Some Elements of “Crisis” The state-of-the-art model for designing the comprehensive mental health promotion, prevention, early and intensive intervention approach for schools has been to plan for, train and implement the whole model, simultaneously. This construct enables services at different levels of intensity to support each other, to engage all staff in connecting with agreed-upon goals, to ensure interventions are aligned, and to enhance easy inclusion and transitions for youth. Many Safe Schools, Healthy Students federal grant recipients have followed this design, relying on the federal funds to accomplish this multi-year implementation effort. Another approach is to relieve some elements of a crisis. For example, a school that has identified several behaviorally troubling youth who are consuming significant resources and producing a contagion of disruption may find that the first step to change is to address the “fires” by

64 Kevin Dwyer and Erika Van Buren providing wraparound services to the youth and families with the most serious problems. Nevertheless, in this case, the establishment of an effective, school and home-based agencydriven mental health program may be the first step to enable the school to devote its energies to designing the prevention and early intervention components of the model. Regardless, even the intensive intervention component cannot stand alone for long. When youth have their complex social-emotional and behavioral needs addressed, the school’s climate improves best when it mirrors those supports. So the successful treatment reduces the crisis and enables the schools to develop supportive strategies to prevent other youth from following that trajectory. Furthermore, those elements that are shown to promote learning and prevent problems can then be made available to support all youth, including those who have serious, complex problems. Making the determination about where to start may be driven by the level of crisis in which the school finds itself. There is a noted danger in starting with intensive services and that is that those services, like special education, can become a separate entity consuming all the school’s mental health resources. Prevention and effective early intervention services are thus never resourced or implemented. In sum, it is critical that regardless of where one starts, each school establish its plan for a comprehensive prevention, intervention school mental health program and provide assurance that the full program will be implemented. Addressing the crises first can enable the school to begin building a mentally healthy climate and system of prevention and intervention. Successful Initiatives Combine a Spectrum of Comprehensive Prevention, Early and Intensive Interventions and Services Our identification of successful elements of school-based initiatives would not be complete without discussion of the evidence supporting implementation of the full continuum of prevention, early and intensive services, and the successful characteristics of these services. Earlier discussion within this chapter highlighted the general proclivity of US schools to rely on the provision of intensive services, and the negative implications associated with this pattern. The most effective programs appear to provide an excellent universal instructional method. When all students are carefully monitored in their responses to that curriculum and instruction, those who are falling behind are quickly identified and proven remedies are expeditiously applied. For youngsters who do not respond to such interventions, teams examine the “why” and prescribe appropriate intensive interventions, again monitoring results and modifying or maintaining effective interventions that improve the student’s reading. Everything is connected to the general curriculum and instructional measures; in essence, instruction is aligned. The same is necessary for social, emotional development. Behavioral remedies must be connected to the language of the classroom and the reinforcements of teachers, staff and peers. For example, in one school the agency-assigned clinical social worker learned the social skills language used across all the primary grades, and incorporated it into the “language of therapy.” This enabled both the therapist and the parent to shape replacement behaviors that the teacher and staff could naturally support and reinforce.

Conclusion and Summary Children who are mentally healthy, who have proven coping and problem-solving skills, and who have these skills promoted within schools, are far more likely to learn and achieve. Nevertheless, the school mental health promotion and prevention programs which promote

School Mental Health: Prevention at All Levels 65 the development of these important life skills cannot stand alone, nor can intensive intervention services enable children to maximize their learning in schools with corrosive classroom climates. This chapter has summarized the collective knowledge from both research and experience in the field to help practitioners and policymakers put the prevention and intervention pyramid in place. If a school can improve attendance, achievement and graduation rates, decrease suspensions and expulsions, as well as disproportionate special education placements, its initiatives will be seen as winners by most stakeholders. The mental health promotion and prevention-intervention paradigm is believed to be the most efficient and effective model schools can implement to ensure these positive educational outcomes communities seek. Demonstrating a connection between interventions and desired outcomes is the critical issue. What can we conclude about “what works” in schools? We can paint a relatively clear picture using what we know. First, effective programs have been supported by sound theory, including a clear understanding of the research-supported mechanisms for change (Fullen, 1991). We know that interventions that work are well resourced, sustained and celebrated by critical leadership as well as by teachers, students and families. They are connected to the academic mission of schools and their leadership, which provides the cornerstone of “mutual interest” that will sustain principal buy-in and support for the initiative. They are put in place with equal zeal and resources as that of academics. Leadership, policies and procedures related to such programmatic efforts must be system-wide and sustained, independent of personal initiatives or discretionary “soft money.” Programs that succeed are well coordinated, understood by staff, students and community. Like academics, they are evaluated for impact on individuals, classrooms, schools and system-wide. Identified best practices are used with fidelity and the staff is well trained, including such ancillary staff as cafeteria workers, school security personnel, mentors and other community partners. The school principal’s role as instructional leader includes mental health “instruction.” The numbers of student services personnel such as counselors, school psychologists and social workers are commensurate with the needs and such personnel support or provide coaching, consultation, monitoring and individualized services to ensure complete promotion, prevention and intervention services. They work in interdisciplinary and interagency teams that use effective planning and problem-solving techniques to maximize effectiveness and efficiency, always partnering with families. Schools look for incremental success, understanding that persistence of sustained effort and measured outputs will produce results. Although the breadth and depth of our understanding regarding how schools should function continues to grow, the educational system struggles to adopt this vision. Nevertheless, successful state and local initiatives are beginning to emerge which represent a shift from a “two-component policy framework,” which typically includes an agenda for improvements in instruction and school governance, to a three-component framework which addresses barriers to learning and uses language such as “an Enabling Component, a Learning Supports Component, and a Comprehensive Student Support System” (Adelman & Taylor, 2005; this volume). This third element combats marginalization of school mental health services, and supports a commitment to change. Although change is inconvenient, maintaining ineffective educational systems and interventions is more than dangerous to some. Schools can no longer afford to neglect paradigms of teaching and learning that embrace the richness and potential of the “whole child”; a richness that every child possesses, and that need only be cultivated and nurtured within the social worlds in which they live, learn and play. To truly educate is to draw out that richness and to ultimately promote the positive development of mentally healthy children and youth, which will serve to sustain them throughout their education and beyond.

66 Kevin Dwyer and Erika Van Buren

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4

Screening for Mental Health and Wellness Current School-Based Practices and Emerging Possibilities Erin Dowdy, Michael Furlong, Katie Eklund, Elina Saeki, and Kristin Ritchey, University of California, Santa Barbara The 20th century was about treating disease. The 21st century is about prevention —Sir William Castell, Former CEO, GE Healthcare

Research has documented the poor school-related outcomes for children with emotional and behavioral disorders (EBD)(Atkins, Graczyk, Frasier, & Abdul-Adil, 2002, 2003; Franca, Kerr, Reitz, & Lambert, 1990; Rones & Hoagwood, 2000; Wagner Kutash, Duchnowski, & Epstein, 2005). Students with EBD have low overall academic achievement (estimated to be at the 25th percentile in one meta analysis; Reid, Gonzalez, Nordness, Trout, & Epstein, 2004); are suspended or expelled from school at high rates (in secondary school 73% have a history of suspension or expulsion compared to 22% in the general population; Wagner et al., 2005); have high rates of absenteeism (Lane, Carter, Pierson, & Glaeser, 2006); have the highest incidence of contact with the justice system when compared to peers who have either other types of disabilities or no disability; and have low graduation rates (Wagner et al., 2005). As students with EBD transition into adulthood, they are at elevated risk of continued poor psychosocial outcomes (Greenbaum et al., 1996) and many are underemployed or unemployed after high school (Zigmond, 2006). However, the students identified with emotional and behavioral problems comprise less than 1% of the school-age population (Wagner et al., 2005) and, of those identified with EBD, approximately 65% are 12 years old or older (U.S. Department of Education, 2001). This suggests that children are likely under-identified for emotional and behavioral problems and, when they are identified, it is often later in the student’s educational career with missed opportunities for early intervention. The importance of the early identification of youth vulnerable to the complications associated with EBD cannot be overstated. In fact, Goal 4.3 of the President’s New Freedom Commission on Mental Health advocates for the screening of “. . . co-occurring mental and substance use disorders and link with integrated treatment strategies” (Hogan, 2003). Striving to implement Goal 4.3 is important because providing prevention and early identification services are humane—suffering is reduced by early intervention when problems are less complex or symptoms are prevented from developing at all (Desrochers, 2006). Nonetheless, despite what appears to be a logical need for early universal mental health screening, efforts to proactively identify these youth for early intervention services have been poor (Duncan, Forness, & Hartsough, 1995; Walker, Nishioka, Zeller, Severson, & Fell, 2000) and only 2% of schools engage in a systematic universal screening practice for mental health services (Romer & McIntosh, 2005).

Screening for Mental Health and Wellness 71 Fortunately, school mental health providers are trained to use problem-solving strategies to enhance the development of wellness, social, mental health, and life skills of all students, and particularly those with EBD. They often assume leadership roles as part of a school team effort that searches for and provides integrated services for children with mental health challenges (Adelman & Taylor, 2003). They do this while recognizing that positive behavioral and emotional health is associated with academic success (Atkins, Frazier, Adil, & Talbott, 2003; Catalano, Haggerty, Osterle, Fleming, & Hawkins, 2004). Strategies that help children with EBD to cope with immediate mental health symptoms are coupled with a focus on long-term efforts that bolster psychological well-being, which, in turn, result in better school attendance, participation, and school completion (Hazell, 2007). Although it may at first inspection seem to be an easy task to use universal screening procedures to identify students who are experiencing symptoms of mental health problems, these efforts are complicated by social and legal challenges that claim programs such as Teen Screen (see www.teenscreen.org) are funded by pharmaceutical companies with the intent of increasing the use of psychoactive medication with youth (see, for example, PsychSearch [www.psychsearch.net/teenscreen.html] and the Rhoads v. Penn-Harris-Madison School Corporation lawsuit now working its way through the federal courts [www.rutherford. org/articles_db/press_release.asp?article_id=723]). In addition, there is still substantial research to be done to validate the technical adequacy of the instruments and procedures employed in mental health screening procedures. With this as background, this chapter provides a rationale and description of screening and early identification for early intervention, procedural and ethical considerations for screening, an overview of screening instrumentation, and a proposed comprehensive approach integrating positive psychology assessments for mental “health” screening.

Preliminary Considerations Screening and Integrated Student Supports When school mental health providers set out to design and implement comprehensive mental health screening there are a number of core issues to consider. First, screening is not an activity that any individual school psychologist can undertake on his or her own. It needs to be incorporated into an integrated student services program (see Adelman & Taylor, 2006). There is limited benefit to mental health screening if there are no structures in place or disorganized service options available. Second, a number of studies have found that the referral process is complicated by the fact that certain symptoms might be harder to detect than others (Achenbach, McConaughy, & Howell, 1987; Bradshaw et al., 2008). Internalizing and externalizing behaviors are equally important to assess; however, internalizing symptoms might not be as readily apparent to teachers who make many referrals. Reliance on teachers, parents, and other school staff for referrals may overlook students with significant internalizing symptoms. A recent study found that students with externalizing symptoms, such as educational or behavior concerns, receive higher rates of services than children with internalizing symptoms (Bradshaw et al., 2008). This might help explain why boys are over-represented in many special education categories (Oswald, Best, Coutinho, & Nagle, 2003). The detection of both internalizing and externalizing problems, as well as academic difficulties during early elementary school years, is critical for effective prevention of subsequent mental health and educational problems (Bradshaw, Buckley, & Ialongo, 2008) and a systematic

72 Erin Dowdy et al. screening process might reduce some biases in the referral process (Kamphaus & Reynolds, 2007). Establishing Objectives for Screening One of the first issues to consider is to establish the core purpose of the mental health screening. Is the goal to monitor all students (population status) for program planning or to initiate procedures that lead to the identification of specific high-risk students for more intensive follow-up? From a strict clinical perspective, a primary goal of screening is to gather information via referrals/rating scales and youth self-reports to identify specific students who have enough symptoms of emotional problems that they merit additional assessment and possible intervention. This general strategy follows a public health model (Doll & Cummings, 2008; Kleiver & Cash, 2005; Nastasi, 2004; Short, 2003). With respect to screening for mental health needs among children and adolescents, the primary or universal level is what is usually considered to be “screening” for mental illness (Levitt, Saka, Romanelli, & Hoagwood, 2007). It involves the use of strategies in which an entire population (e.g., school, district, classroom) receives needs assessment with follow-up clinical assessments and services. Although the rationale for universal mental health screening to identify specific at-risk students is transparent, it is predicated on searching for symptoms of psychopathology and gathering information that is not anonymous. Given these considerations, it is also possible to decide to conduct screening not to identify specific at-risk youth, but to monitor the mental health needs of a population of students via youth self-reports. In fact, this is done nationally with the Youth Risk Behavior Surveillance Survey (YRBS) that has asked secondary school students about selected mental health experiences related to suicide ideation and sadness/depression. As shown in Table 4.1, data from the YRBS can be used at the national, state, or local school level to monitor mental health indicators for each successive cohort of entering secondary school students. If prevention and intervention efforts are successful, then each successive cohort should report lower rates of mental health symptoms. As these actual data show, a substantial proportion of high school students report at least one significant period of depression-like symptoms in the previous year. Even without knowing the identity of these students, it still provides evidence and a rationale to support the availability of preventive mental health services and a means to assess progress over time. Although this approach does not identify specific students for intervention, it provides information about students’ service needs. This approach requires surveys to be conducted annually with the goal of participation by all students; however, the reader is cautioned that YRBS items do not have the reliability or sensitivity to assess individual differences (see Benner et al., 2002). Even if the core purpose of screening is to identify risk status among students, screening data could be aggregated (with identifying information removed) across classrooms, schools, and districts to provide population-based needs-assessment information. In this way, both needs assessment and individual identification of students could occur.

Implementing a Screening Program Even after addressing core issues, school mental health providers are still faced with a multitude of issues when implementing mental health screening. A school or district that is interested in early identification needs to address the core issue of what to screen for (Stoep et al., 2005) and consider the pragmatics of screening. This section guides readers through a set of issues to consider when implementing a screening program.

Screening for Mental Health and Wellness 73 Table 4.1 Percentage of Students by Grade Level and Survey Cohort Year Who Felt So Sad or Hopeless Almost Every Day for Two or More Weeks in a Row That They Stopped Doing Some Usual Activities During the 12 Months Before the Survey from Youth Risk Behavior Surveillance Survey 1999–2007 Survey Year

Grade 9

Grade 11

Grade 10

Grade 12

1999

27.4

2001

29.4

28.7 (B)

27.2

27.0 (D)

2003

28.0

28.9

29.7

27.4

2005

29.0

28.8

28.9

26.4

2007

28.2

27.1

28.9

29.4

29.3 (C)

Note Survey results from 1999 (Kann et al., 2000; Table 12), 2001 (Grunbaum et al., 2001; Table 12), 2003 (Grunbaum et al., 2003; Table 16), 2005 (Eaton et al., 2006; Table 16), and 2007 (Brener et al., 2007;Table 16) Youth Risk Behavior Surveillance Summaries. Cell A shows the percentage of ninth graders in 1999 who reported being sad and Cell B shows the percentage of students in the same cohort two years later in grade 11 who reported being sad. Cells C and D show the same information for the 1999 cohort of 10th graders who were in the 12th grade when the 2001 YRBS survey was conducted. Efforts to prevent mood symptoms would be reflected in a lower percentage of vertically of ninth graders in each survey year reporting being sad. Successful intervention efforts would be reflected in lower rates (diagonally) as each cohort moves through the school system. This actual YRBS data show that since the tracking of this mental health-related item in 1999, there has been essentially no improvement of students’ experiencing of sadness over an extended period of time. These data also show the limitation of conducting surveillance screenings every two years because it is then possible to assess each ninth-grade cohort only twice while they are in high school.

What to Screen For? With respect to mental health screening Mills et al. (2006) ask, “screening for what?” What mental health problems should be included—depression, suicide, anxiety, or conduct problems? Many screening programs focus their efforts on one mental disorder to the exclusion of others. For example, a screening program that looks solely for symptoms of depression and risk for suicide (e.g., Teen Screen) might miss a student who is having significant difficulties with attention and hyperactivity. However, we do recognize that a school community might take such an approach because of the devastating impacts of teen suicide. Another approach draws upon research that has identified two broad dimensions of mental health problems—internalizing or overcontrolled symptoms and externalizing or undercontrolled symptoms (Reynolds, 1992). An argument can be made that when screening for psychopathology, then a focus could be on identifying internalizing and externalizing symptoms. However, a recent study by Najman and colleagues (2007) reported that a total problems score, such as the one derived from the Child Behavior Checklist (CBCL; Achenbach &

74 Erin Dowdy et al. Edelbrock, 1991) was more predictive of subsequent anxiety and depression problems than the internalizing subscale alone. A related study by Leon and colleagues (1999) lends further evidence to the potential importance of screening for general maladjustment rather than focusing solely on one disorder or a class of disorders and also highlights the need to take comorbidity into account when screening. The authors screened for depression and had the intriguing finding that many clients who were false positives on the screening procedure actually met diagnostic criteria for another mental disorder (Leon et al., 1999). This suggests that overlapping symptomatology might make it more difficult to screen for one particular disorder instead of focusing on screening for general maladjustment and symptomatology. Hence, given the current knowledge, an ideal screener might provide a measure of general maladjustment to be interpreted as a “red flag” for potential emotional and behavioral problems, whether the problems are internalizing or externalizing. The Pragmatics of Screening A substantial research base corroborates that schools often function as the de facto mental health system for many children and adolescents (Burns, Costello, Angold, Tweed, et al., 1995; Rones & Hoagwood, 2000). This is primarily due to accessibility, but also to perceived acceptability—children are more likely to seek help when school-based mental health services are available from adults whom they know and with whom they have been able to develop trust (Slade, 2002). Recent surveys report that the majority of schools offer some level of mental health or social service support, with 20% of all students receiving some type of schoolsupported mental health service (Kutash, Duchnowski, & Lynn, 2006). Sources of Screening Information. If schools are the primary setting for mental health services for children and adolescents, then a key consideration is who gathers screening information. Within the school setting, teachers are often seen as a key source for information about children’s social and emotional functioning. In fact, research has shown that teachers, with a great deal of accuracy, are able to identify children who are at risk of behavioral problems (Taylor et al., 2000). At the preschool, child, and adolescent levels, structured teacher ratings of child behavior have been found to be more reliable than parent ratings and different teachers rate the same child similarly (Reynolds & Kamphaus, 1992). Teachers are particularly adept at assessing problems with attention, a key temperamental variable associated with social and psychopathological outcomes (Molina & Pelham, 2003). Teacher ratings are one part of mental health screening; however, it is often suggested to collect ratings from multiple informants that include parents (when feasible), and students (when old enough to provide valid responses) to provide a comprehensive picture of a youth’s functioning (Kamphaus & Frick, 2002). Even when information from multiple sources is collected, there is often a lack of consistency among raters (Achenbach et al., 1987), suggesting that perhaps raters provide different, yet still valuable, information. However, a screening process that involves multiple informants is likely to be costly and time-consuming. In addition, several studies found that gathering information from a second informant does not significantly increase the amount of explained variance beyond that explained by the first informant (Biederman, Keenan, & Faraone, 1990; Jones et al., 2002; Lochman and the Conduct Problems Prevention Research Group, 1995). In general, there is no current consensus on the number of informants (in a multi-stage screening process) and the type of informants (parent, teacher, child) that should be included in the screening process. In particular, research is needed to evaluate “the value of different informants at various stages of the assessment process” (Johnston & Murray, 2003). In the school context, pragmatics suggests that the

Screening for Mental Health and Wellness 75 ease of obtaining teacher ratings supports their use with younger students and that self-reports be used with secondary school students because of their awareness of their own psychological experiences. Planning for Follow-Up Care. In addition to deciding who provides the screening information, there should be a process to provide follow-up assessment and intervention services for those students who screen positive for emotional or behavioral concerns. Screening without adequate and timely follow-up would be unethical and it is important to ensure that adequate resources are available prior to embarking on a screening program. The capacity to be responsive to student needs as identified through a screening process must be predetermined and should directly influence the program chosen; however, simultaneous work to build capacity for future and ongoing mental health needs should be undertaken. Some schools have successfully answered the question of who will provide follow-up services through the utilization of school-based mental health professionals within school-based mental health centers, while others have relied on linkages with community partners (Nagle & Gagnon, 2008). Initially, follow-up assessments will need to be conducted on all children who screen positively. If screening is to be used at a universal level (screening of all children) then the instrument should minimize the number of false negatives because these children would not have the opportunity to receive further services. However, setting cut scores is a matter of pragmatics because as the number of false negatives is reduced, it conversely increases the number of false positives. To ensure that students do not receive unnecessary services after providing a universal or first-gate assessment, additional assessments will be needed to decrease the number of false positives. A strategy for a second-gate assessment should be predetermined, considering false positives and false negatives, as well. When Should Screening Occur? Even within a universal approach to screening, a key issue is when the screening should occur. The most intensive option would be to collect screening information on every child at multiple time periods during the year. A child would first be screened upon entry into the new school or grade level. If teachers or other significant adults complete screening information, it will be important to ensure that the teachers have adequate time—at least one month—to get to know a student and his or her emotional and behavioral condition. If a child receives a positive screen and additional information leads the assessor to believe that there are no significant concerns, then the screening could be conducted again throughout the year to monitor the student’s symptoms. The child could be screened again prior to completing the school year to ensure that he or she was ready to move on to the next educational phase. An alternative, and perhaps more reasonable, approach would be to conduct screening at critical time periods. Stoep and colleagues (2005), for example, implemented the Developmental Pathways Screening Program (DPSP) in which students are targeted at the critical institutional and developmental transition period from elementary to middle school. The authors indicated that their program identified students at a time when the risk of emotional distress was high, yet possibly below the diagnostic threshold, and with sufficient time to prevent adverse outcomes (Stoep et al., 2005). Other critical time periods such as at entry to school (Spielberger, Haywood, Schuerman, & Richman, 2004) and prior to exit from school have been suggested. Still other programs have gathered information from all second- through fifth-grade students (Catron & Weiss, 1994) and arguments have been made that it might not be useful to gather information on internalizing symptoms of anxiety and depression until adolescence (Najman et al., 2007). Another approach to screening for emotional and behavioral problems would be to screen for these difficulties when other problems arise. For example, a school could implement a

76 Erin Dowdy et al. screening program in which all children that are referred to a student study/success/support team (SST), regardless of the reason for referral, would be screened. This could help make a differential diagnosis if the school-related problem were, in part, due to symptoms of emotional or behavioral distress. Perhaps the referred student is experiencing significant symptoms of inattention or anxiety that could be impacting his or her ability to perform in the reading classroom. However, it should be noted that if a student is already displaying significant signs of emotional or behavioral distress then a screening tool would not be warranted or needed and additional, more comprehensive, information should be obtained. Completing a short, timeefficient screener when a child is referred for a SST meeting, has excessive absences, or has been frequently sent to the office (signifying some problems within the classroom) might provide additional information that the team can use in forming appropriate, individualized interventions. Screening when critical behaviors occur is not as comprehensive as a universal screening approach and can lead to some children not being appropriately identified as needing additional services. However, this systematic process might allow for the identification and treatment of certain children that would not be identified through more traditional means of assessment. In addition, this approach, while confidential, is not anonymous and allows for rapid follow-up. The screening approach chosen should align with the target population and goals of interest (Stoep, 2005). Therefore, knowledge of the context and characteristics of the population to be screened is helpful in implementing an approach that will be most beneficial.

Mental Health Screening Instruments One initial decision that will surely impact other implementation decisions will be to determine which instrument to use. In this section of the chapter, we describe a set of criteria for evaluating instruments followed by a review of several instruments that have strong research support. Screener instruments provide practical benefits (Reise, Waller, & Comrey, 2000; Smith, McCarthy, & Anderson, 2000), but the challenge is to use measures with acceptable psychometric properties that are capable of predicting a wide range of behavioral and emotional outcomes. Glover and Albers (2007) delineated three characteristics of screening instruments that should be evaluated prior to use. First, the screening instrument must be appropriate for the intended use considering developmental and contextual variables and the ability of the instrument to be supportive and complementary of service delivery models. Second, screening instruments must demonstrate technical adequacy including acceptable levels of sensitivity (the proportion of individuals with the disorder who are correctly identified by the instrument as having the disorder; true positives) and specificity (the proportion of individuals without the disorder who are correctly identified as not having the disorder; true negatives). The positive predictive value (proportion of students correctly identified as at risk out of all students identified as at risk on the screener) and the negative predictive value (proportion of students correctly identified as not at risk out of all of the students identified as not at risk on the screener) of an instrument should also be examined. Third, the usability or practicality of the screening instrument should be evaluated. This includes evaluating the instrument on factors such as cost, time to administer and score, requirements for interpretation and data management, and whether it is an efficient use of school resources (Caldarella et al., 2008; Glover & Albers, 2007). The reader will be well served to further investigate how to evaluate screening instruments as they frequently change and the choice of instrument should depend on a variety of factors

Number of Items and Scale Content

11–17 years 3–4 years; 4–10 years; 11–17 years 3–4 years; 4–10 years; 11–17 years

25 for all versions • Hyperactivity-Inattention • Emotional Symptoms • Conduct Problems • Peer Problems • Prosocial Activities • Total Difficulties

Preschool: 3–5 years Child/Adolescent: Grades K–12 Preschool: 3–5 years Child/Adolescent: Grades K–12

Parent

Youth Parent

11 years and older 4–16 years

Pediatric Symptom Checklist (PSC)

Teacher

Grades 3–12

Student

35 35

Preschool: 27 Child/Adolescent: 25

30 for both versions

30

BASC-2 Behavioral and Emotional Screening System (BESS)

Teacher

Youth Parent

Strengths and Difficulties Questionnaire (SDQ)

Instruments/Forms Age Range

Test-retest reliability, 4 months: .77 Internal consistency: .89–.91 Sensitivity: .94 Specificity: .88

Split-half reliability: .90–.96 Test-retest reliability: .80–.91 Inter-rater reliability: .71–.83 Sensitivity: .44–.82 Specificity: .90–.97

Internal consistency: • Total Difficulties (.83) • Peer Problems (.46) • Remaining four subscales (.63–.77) Validity: Parent-reported service contact or special education for their child’s emotional/behavioral problems revealed significant differences (p = .0001) between youth identified by the SDQ compared with youth who were not identified by the SDQ: Sensitivity: .77; Specificity: .85

Reliability & Validity Sensitivity & Specificity

Table 4.2 Content and Psychometric Properties of Omnibus Youth Mental Health Screening Instruments

Jellinek et al. (1999) Murphy et al. (1989) Pagano et al. (2000) Simonian & Tarnowski (2001) Stoppelbeing et al. (2005)

Kamphaus & Reynolds (2007) Kamphaus et al. (2007) DiStefano & Kamphaus (2007)

Bourdon et al. (2005) Goodman (2001) Goodman & Scott (1999) Hysing et al. (2007) Mellor (2004) Shochet et al. (2006)

Research

78 Erin Dowdy et al. including the purpose of screening (for a more comprehensive review see Levitt, Saka, Romanelli, & Hoagwood, 2007). In our view, mental health screeners should be time-efficient, not so comprehensive that they would be better suited for clinical or diagnostic assessment, able to be used universally, sufficiently researched and validated, and capable of identifying an array of potential problems or strengths. With these parameters in mind, we identified the following instruments as potentially useful for school-based mental health screening: Strengths and Difficulties Questionnaire (SDQ); BASC-2 Behavioral and Emotional Screening System (BESS); Pediatric Symptom Checklist (PSC); and the Systematic Screening for Behavior Disorders (SSBD). The following section provides information on each screening instrument and process. See Table 4.2 for additional information. Strengths and Difficulties Questionnaire (SDQ) Overview. The SDQ (Goodman, 1997, 1999, 2001) is a brief, 25-item behavioral screening tool for youth 4–16 years old (more information is available at www.sdqinfo.com) that can be completed in approximately five minutes. The youth self-report, parent, and teacher versions of the SDQ ask about positive and negative attributes of the youth. There are three levels of the parent and teacher versions: 3- and 4-year-olds; 4- to 10-year-olds; and 11- to 17-year-olds. The youth self-report is for 11- to 17-year-olds. The items and subscales are based on the Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV; American Psychiatric Association, 1994) and were selected using factor analysis procedures (Goodman, 2001). Five subscales cover emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behavior. Each subscale has five items. Respondents rate items using a three-point scale ranging from 0 (not at all) to 2 (very much, all the time). The total difficulties score is generated by summing the items from all of the subscales except the prosocial behavior subscale. The total difficulties score, which ranges from 0 to 40, is then classified as normal, borderline, or abnormal based on the SDQ normative scoring bands. For example, the total difficulties scores on the parent version are: normal (0–13), borderline (14–16), and abnormal (17–40). Psychometric Properties. Normative data were obtained from the parents of 10,367 children between the ages of 4 and 17 participating in the 2001 National Health Interview Survey (Bourdon, Goodman, Rae, Simpson, & Koretz, 2005). Cronbach’s Alpha coefficients are good for the total difficulties (.83), impairment scores (.80), and its four subscales (.63–.77); but poor (.46) for the peer problems subscale. To examine the validity of the SDQ, service contact or use for a mental health reason was used as the criteria. Bourdon et al. (2005) found significant differences (p < .0001) between youth identified by the SDQ with service contact/use compared with youth who were not identified by the SDQ. Goodman and Scott (1999) examined the correlation of scores between the SDQ and the Child Behavior Checklist (CBCL; Achenbach & Edelbrock, 1983), which were completed by the mothers of 132 youth ages 4 to 7. One-half of the sample was recruited from dental clinics, a low psychiatric-risk population. The other half was recruited from child mental health clinics, a high psychiatric-risk population. The results showed that scores from the SDQ and CBCL subscales were highly correlated (.59–.87) and were equally able to discriminate between children recruited from high-risk and low-risk samples, a desirable outcome because the SDQ has more than 100 fewer items than the CBCL. Hysing, Elgen, Gillberg, Lie, and Lundervold (2007) evaluated the sensitivity and specificity of the SDQ in detecting emotional and behavioral problems among children with chronic illness (CI) in Norway. Parents and teachers of 7,007 children attending second to fourth grades

Screening for Mental Health and Wellness 79 (7 to 9 years old) in all public, private, and special schools in Bergen, Norway, completed the SDQ and a question about chronic illness. To calculate sensitivity and specificity, “abnormal” cases were counted as positive and “borderline” and “normal” cases on the SDQ were counted as negative. In the sample of children with CI the sensitivity was 77.3% and the specificity was 85.1%. Mellor (2004) investigated the reliability of younger children’s responses on the self-report version of the SDQ. Of the child participants, 359 were between ages 7 and 11 (younger children) and 558 were ages 11–17 (older children). Their parents and teachers also completed the SDQ. Results found that older children’s self-reports were significantly more consistent with those of their parents compared to those of younger children. Many screening instruments may not include youth self-reports for younger children due to the lower reliability rates for this age group compared to older children. Another possible explanation for these results might be that parents’ accuracy may vary when they report on the behaviors of younger versus older children (Mellor, 2004). BASC-2 Behavioral and Emotional Screening System (BESS) Overview. The Behavioral and Emotional Screening System (BESS) is an instrument used to identify behavioral and emotional strengths and weaknesses in youth ranging from preschool through high school (Kamphaus & Reynolds, 2007). It assesses a wide range of behavioral problems and strengths, such as internalizing and externalizing problems, school problems, and adaptive skills. There are three parallel forms with differing levels as shown in Table 4.2. The number of items ranges from 25 through 30, and each form can be completed in approximately five minutes or less. The majority of the BESS items were taken from the pool of items created during the development of the Behavior Assessment System for Children-2 (BASC-2; Reynolds & Kamphaus, 2004) Teacher Rating Scales (TRS), Parent Rating Scales (PRS), and Self-Report of Personality (SRP). There is one new item for the student form, three new items for the teacher (preschool) form, three new items for the teacher (child/adolescent) form, eight new items for the parent (preschool) form, and four new items for the parent (child/adolescent) form. Respondents are given four rating options—never, sometimes, often, or almost always for each item. The BESS may be scored by hand or with computer software. The report includes raw scores, T-scores and percentiles based on a normative sample that closely matches recent U.S. Census population characteristics. The sum of the items generates a total T-score with high scores reflecting more problems (Kamphaus & Reynolds, 2007). The scoring rubric, or risk level, for behavioral and emotional problems is as follows: (a) 20–60 suggests a “normal” level of risk; (b) 61–70 suggests an “elevated” level of risk; and (c) 71 or higher suggests an “extremely elevated” level of risk. Psychometric Properties. The BESS was developed using a normative sampling group of 12,350 teacher, parent, and student forms, collected from 233 cities in 40 states (Kamphaus & Reynolds, 2007). The split-half reliability estimates range from .90 to .96 across forms and ages. Test-retest reliability estimates are high for all forms and levels, ranging from .80 to .91. Inter-rater reliability estimates range from .71 to .83. The concurrent validity of the BESS was examined by co-administering the items with other social-emotional measures: Achenbach System of Empirically Based Assessment (ASEBA, .71–.77), Conners’ Rating Scales (CRS, .51–.78), Vineland Adaptive Behavior Scales (Vineland, .32–.69), Children’s Depression Inventory (CDI, .51), and the Revised Children’s Manifest Anxiety Scale (RCMAS, .55).

80 Erin Dowdy et al. The risk level classification cut scores were developed to maximize sensitivity and specificity. Results suggest that sensitivity, specificity, PPV and NPV were generally high. The sensitivity and PPV ranged from .30 to .82 and were highest when using the Total Score to predict the Behavioral Symptoms Index. Sensitivity and PPV values were lowest for predicting Internalizing Problems, especially on the parent and teacher forms. However, for the student form, sensitivity and PPV values were higher (.55 and .66, respectively) on Internalizing Problems, suggesting that the Total Score on the student form might be particularly useful for identifying internalizing problems among youth. Pediatric Symptom Checklist (PSC) Overview. The PSC (Jellinek et al., 1999; Little, Murphy, Jellinek, Bishop, & Arnett, 1994) is a screening instrument used to identify cognitive, emotional, and behavioral problems in children (Georgetown University, 2002). There are two versions of the PSC—the parent-completed version (PSC) and the youth self-report version (Y-PSC). The Y-PSC can be administered to youth ages 11 and older. Both versions have 35 items that are rated on a threepoint frequency scale—never, sometimes, or often. The total score is obtained by summing the score for each of the items. For children ages four and five, a total score of 24 or higher on the PSC is used to designate possible psychological impairment. For children ages 6 to 16, a score of 28 or higher indicates possible psychological impairment. The cut score for the Y-PSC is a total score of 30 or higher. Psychometric Properties. Pagano, Cassidy, Little, Murphy, and Jellinek (2000) examined the concurrent validity of the PSC and Y-PSC among 173 third- through eighth-grade students and their parents. When compared with teacher ratings of attention and behavior problems, the Y-PSC demonstrated a sensitivity of 94% and a specificity of 88%. Y-PSC scores and the Children’s Depression Inventory (CDI) scores found that children with positive scores on the Y-PSC were five times more likely to score in the clinical range on the CDI compared to children with negative scores on the Y-PSC. The Y-PSC also correlated significantly with teacher and parent measures of child dysfunction. In another study, Simonian and Tarnowski (2001) examined the correlation of scores between the PSC and the Child Behavior Checklist (CBCL). One hundred eighty-seven mothers of children ages 6 to 12 completed the parent-version of the PSC and the CBCL. The PSC total score was significantly correlated with the CBCL total behavior problem T-score and the externalizing and internalizing T-scores (r = .78, r = .76, and r = .71, respectively). Another validity study examined used a sample of 166 seventh- and eighth-grade students from a suburban, public middle school (Murphy, Jellinek, & Milinsky, 1989). The students completed the Y-PSC and 140 of the 166 students’ parents completed the PSC. The level of agreement between the PSC parent version and Y-PSC was 84%. More than half of the students who screened positive on the Y-PSC also screened positive on the PSC parent version. Results also suggest that students with a positive screen on the PSC parent version were more likely to have failed at least one course than the PSC negative students. The reliability and validity of the PSC was also examined in a population of chronically ill children (Stoppelbein et al., 2005). Parents of 404 children ages 6 to 17 diagnosed with insulindependent diabetes mellitus or sickle cell disease completed the PSC. Internal consistency for the PSC was high (␣ = .89) and test-retest reliability across four months was found to be acceptable (r = .77) for the diabetes sample. The prevalence of psychosocial problems identified by the PSC parent version has been shown to be consistent across different samples (Jellinek et al., 1999). In a nationally representative sample of 21,065 parents of 4- to 15-year-old children, the prevalence of

Screening for Mental Health and Wellness 81 psychosocial dysfunction in school-aged and preschool-aged children (13% and 10%, respectively) was virtually identical to rates of impairment reported in previous studies of smaller sample sizes (12–14% among school-aged children and 7–14% among preschoolaged children). Overall, the PSC meets criteria as a useful screener as it is brief to administer, evaluates a broad range of functioning, and has acceptable sensitivity, specificity, and predictive validity. Systematic Screening for Behavior Disorders (SSBD) Overview. Whereas the SDQ, BESS, and PSC are screening instruments, the SSBD is a general screening process. It identifies students in the primary grades (K–6) who may be at risk for developing externalizing and internalizing behavior disorders (U.S. Department of Education, 1995). The SSBD uses a multiple gating procedure with progressively more precise and specific screening instruments to identify youth who need help. The three stages in the SSBD screening process use teacher nominations, ratings, and observations. First, general education teachers rank order students in their class for both externalizing and internalizing behaviors. Teachers consider all students in their classrooms, nominate 10 students in each category and rank them according to the extent to which they exhibit characteristics of externalizing and internalizing behaviors. The three top-ranked students in each category are included in the stage two screening process. In the second stage, teachers complete the Critical Events Index (33 items) and Combined Frequency Index (11 items for adaptive behaviors; 12 items for maladaptive behaviors) for the three topranked students identified in stage one. Examples of items on the Critical Events Index include being cruel to animals or being physically aggressive with others. The Combined Frequency Index asks about adaptive behaviors such as following rules and also asks about maladaptive behaviors such as “manipulates other children and/or the situation to get his or her way.” During the third stage, students who exceed the normative cut-points on the Critical Events Index and Combined Frequency Index are systematically observed in the school setting. The observational measures used in stage three are academic engaged time, recorded in the classroom, and positive social behavior, recorded on the playground. Academic engaged time is presented as the percentage of observed time during two 20-minute classroom observations. Likewise, the positive social behavior measure is generated through two 20-minute playground observations. The observer records the level, quality, and distribution of the student’s playground behavior during the observations. Positive social behavior is presented as the percentage of intervals in which the target behavior is displayed and the overall rates of positive and negative social behavior. The normative cutoff points for stage three observations are used to determine next steps (e.g., referral to the school’s student study team; possible disability classification). In regards to the time-efficiency of this process, the stage one nomination and ranking of students requires approximately 45 minutes. Stage two, when teachers complete surveys for the top three internalizers and externalizers, also requires approximately 45 minutes. In 1.5 hours or less, the SSBD results in the nomination of six students in each class school-wide who warrant additional evaluation and possible interventions. At stage three, however, the student observations total 80 minutes, which can be quite costly. Psychometric Properties. A national normative sample of 4,500 cases on stage two measures (i.e., Critical Events Index and Combined Frequency Index) was used to develop the psychometric properties of the SSBD (Severson, Walker, Hope-Doolittle, Kratochwill, & Gresham,

82 Erin Dowdy et al. 2007). The inter-rater reliability coefficients for identifying students with externalizing behaviors were between .89 and .94. The inter-rater reliability coefficients for identifying students with internalizing behaviors were between .73 and .88. In addition, the test-retest reliability coefficients were .76 for externalizers and .74 for internalizers. These strong correlations support the SSBD as an objective method for identifying children who may need additional attention. Walker and Severson (1994) found that during a trial testing, 90% of a school’s students were correctly identified as internalizing, externalizing, or neither. Of the remaining 10%, only 1% were identified as having internalizing problems when they actually had externalizing problems, 5% had problems but were not identified (false negatives), and 4% did not have problems but were identified (false positives). This level of accuracy is comparable to other clinical instruments. For example, the Achenbach Teacher’s Report Form (Achenbach & Rescorla, 2001) correctly identified 85% of youth referred for mental health services, with 7% false negatives and 8% false positives. An independent evaluation of the SSBD in middle and junior high school provided further evidence for the reliability and validity of the SSBD ratings (Calderella et al., 2008).

Emerging Trends in Mental Health Screening Although traditional approaches to screening have taken psychopathology, mental illness foci, there is recognition that ultimately the objective is to gather information that promotes mental health. Kazdin (1993), for example, conceptualizes mental health in two broad domains— (a) the absence of dysfunction in psychological, emotional, behavioral, and social spheres; and (b) optimal functioning in psychological and social domains. This raises the question, “Is the absence of psychopathology the ultimate goal?” When considering implementing a screening program, would it be worthwhile to screen for well-being? Is it possible to forego the search for pathology and focus screening resources on measures of thriving and optimal development? This question is potentially important considering research suggesting that preventive interventions should consider the many risk, protective, and other environmental factors that deter negative outcomes for children experiencing mental health symptoms (Tomb & Hunter, 2004). Positive Psychology as a Possible Framework for Mental “Health” Screening “Positive psychology is the scientific study of what goes right in life, from birth to death and all stops in between . . . and that takes seriously those things in life that make life most worth living” (Peterson, 2006, p. 4). The three pillars of positive psychology are the study of positive emotion, the study of positive traits, specifically an individual’s strengths and virtues, and the study of positive institutions and communities (Seligman, 2002). These definitions provide a clear understanding of how determining individual emotions and traits, paired with environmental support, can promote improved individual functioning. Proponents of this approach demonstrate how a focus on positive psychology constructs not only integrates prevention efforts, it also builds upon individual strengths in promoting concepts such as subjective well-being (Diener, Suh, Lucas, & Smith, 1999), resilience (Glantz & Johnson, 1999), developmental assets (Scales & Leffert, 1999), and wellness (Cowen, 1991), which are aspirational developmental outcomes for all youths. Broadening a strengths-based approach to understanding youths’ functioning allows for a comprehensive understanding of psychological well-being. Capitalizing on one’s strengths and fostering positive attributes

Screening for Mental Health and Wellness 83 (e.g., gratitude and optimism) may buffer against negative outcomes and the development of psychological maladies (Masten, 2001; Seligman, 1995). An assessment of youths’ functioning reveals (a) the limits of the current medical model in mental health; (b) a primary focus on positive outcomes; and (c) the belief that building upon these positive outcomes may, in the long run, be the most efficacious way of reducing psychological dysfunction (Cowen & Kilmer, 2002). Strengths-Based Assessment Within the field of psychology, traditional assessment practices have focused primarily on the presence or absence of psychopathology. By determining levels of dysfunction that contribute to disorders such as depression, anxiety, or hyperactivity, individuals are provided a mental illness diagnosis. Clear definitions and measurement of the science of mental illness are provided in the current model and have helped to diminish the incidence of negative sequelae of mental disorders. However, this approach has failed to include an understanding of mental health or mental wellness, and has not incorporated strengths when evaluating an individual’s level of functioning. A different but complementary approach integrates both and shifts the focus slightly to prevent mental disorders by monitoring and supporting thriving and psychological well-being. This model integrates a strengths-based approach to assessment. As current research has discovered that human strengths act as a buffer against mental illness (Keyes & Lopez, 2002), identifying individual assets is a key factor in resiliency. As a result, promising research supports the integration of a dual-factor model, combining traditional assessment measures with strengths-based approaches. This practice provides a complementary perspective on children, assessing both mental wellness and mental illness. A thorough assessment requires the promotion of positive adaptive functioning in addition to the prevention and treatment of dysfunction (Kazdin, 1993). There is increasing support for movement away from deficit-focused service delivery toward more positive, ecological models that capitalize on the strengths of children. Psychologists do not have to pick one approach or the other, but are encouraged to gradually infuse positive psychology into current models of psychopathology and treatment (Lampropoulos, 2001) in order to develop an integrative psychology incorporating both wellness and psychopathological functioning, which more accurately reflects the full range of human functioning (Huebner, 2004; Joseph & Linley, 2006; Seligman & Csikszentmihalyi, 2000). Integrating strength-based assessments and measures of positive functioning into traditional models of assessing for psychopathology promotes a dual-factor model of individual functioning. This provides a potentially beneficial framework when conducting mental health screening in schools. Not only are students screened for at-risk behaviors, but strengths are also identified as potentially mediating factors in the area of mental illness. Although assessments of subjective well-being (SWB) and quality of life (QOL) began with adults, an assessment of these constructs among children and youth has been a recent advancement in the field. The measurement of subjective well-being in adults suggests that its facets and dimensions in youth may be more complex than current research indicates (Keyes, in press). The emphasis on assessment of children is on identifying at least minimal levels of positive subjective well-being and adaptive functioning against which to juxtapose symptom patterns. This research is in its early stages but, even within the field of school psychology, assessing children with emotional disabilities calls for assessments that respect the strengths of the child and their social contexts (Doll & Cummings, 2008).

84 Erin Dowdy et al. Consideration of a Dual-Factor Model of Mental Health Select studies have begun to conceptualize a dual-factor model of assessing not only for mental illness, but also for mental wellness. A study conducted by Suldo and Shaffer (2008), for example, examined a dual-factor model of mental health that pairs assessments of positive indicators of wellness with negative indicators of illness. They compared and evaluated low and high levels of psychopathology in conjunction with low and high levels of subjective wellbeing (SWB, see Figure 4.1). Students were organized into four categories: vulnerable (low on both factors), symptomatic but content (high on both factors), troubled (low SWB and high psychopathology), and complete mental health (high SWB and low psychopathology). This conceptualization argues that positive and negative indicators of mental health are not the opposite ends of the same continuum, but rather that assessment of positive and negative indicators provide separate but complementary perspectives on individual functioning. Only evaluating psychopathology can lead to an over-estimation or under-estimation of a student’s functioning in important areas of life (Suldo & Shaffer, 2008). Utilizing this type of strategy in screening methodologies could provide a more comprehensive picture of individual functioning and potentially reduce false positives and false negatives in the screening process. An ongoing investigation of the relations between positive psychology constructs (e.g., hope, gratitude, and grit) and clinical measures of psychological adjustment problems (e.g., BASC-2, Achenbach scales, etc.) could lead to a well-rounded assessment of individual functioning. Along these lines, one model for integrating positive psychology measures and traditional assessment practices is the integration of assessing quality of life (QOL) with traditional health assessments. Frisch (2006) recommends a model that assesses for symptoms of disorder or disease, combined with overall positive psychology indicators of well-being, quality of life, or life satisfaction. A similar critical component of positive psychology assessment is subjective well-being (SWB). SWB is described as an individual’s affective and cognitive evaluation of his or her life (Diener, 2000). SWB can be measured through an understanding of children’s perceived quality of life (PQOL). PQOL refers to “a person’s subjective evaluation of the degree to which his or her most important needs, goals, and wishes have been fulfilled” (Frisch, 2000, p. 220). In the use of the PQOL, a positive appraisal style (global PQOL) buffers against negative experiences and emotions and behaviors associated with psychopathological characteristics (Huebner, Suldo, Smith, & McKnight, 2004). As such, the PQOL attempts to identify and promote strengths that encourage positive adaptive functioning and simultaneously prevent or Subjective well-being

Positive functioning

Emotional well-being

Psychological well-being

Figure 4.1 Overview of Subjective Well-Being.

Social well-being

Screening for Mental Health and Wellness 85 ameliorate psychopathological conditions. Current research indicates that very high life satisfaction is associated with positive psychosocial functioning (Suldo & Huebner, 2004). A Complementary, Comprehensive Approach to Screening Research on the assessment of life satisfaction with children and adolescents is in the early stages, but the literature provides promising evidence that assessing levels of life satisfaction among children can provide predictive information around mental illness and mental wellness (Huebner, 2004). Specifically, studies have demonstrated that decreasing life satisfaction often precedes the occurrence of psychopathology (Lewinsohn, Redner, & Seeley, 1991); at the same time, life satisfaction measures different constructs than indices of psychopathology (Huebner, 2004). A conjoint factor analysis of Huebner’s Life Satisfaction Scale and the Youth Self Report (short form) of the Child Behavior Checklist (Achenbach & Edlebrock, 1991) found three distinct and independent constructs of internalizing disorders, externalizing disorders, and global life satisfaction (McKnight, Huebner, & Suldo, 2002). As such, it is suggested that screening assessment may be enhanced when employing measures of both domains. Research further indicates that a person can be dissatisfied with his or her life without showing psychopathological symptoms, and conversely that psychopathology could occur with moderate to high life satisfaction (Greenspoon & Saklofski, 2001; Suldo & Shaffer, 2008), showing that the relation between life dissatisfaction and clinical diagnosis is not concordant (Huebner, 2004). This emerging research points to the importance of a well-rounded assessment of an individual’s functioning including measures of mental illness and mental wellness. Well-being is not merely the absence of impairment; rather it refers to the presence of personal and interpersonal strengths that promote optimal functioning (Kazdin, 1993). Even further, recent research has established a robust link between access to external social resources and lower levels of psychopathology and mental health problems in adolescents (Arthur, Hawkins, Pollard, Catalano, & Baglioni, Jr., 2002; Robert, Hoge, Andrews, & Leschied, 2006). Among the social resources that have been singled out as being particularly important is school connectedness (McNeely, Nonnemaker, & Blum, 2002). The importance of this construct for adolescents’ positive mental health was emphasized in a series of studies using the National Longitudinal Study of Adolescent Health, beginning with the influential work by Resnick et al. (1997). In this study, it was found that students who reported higher levels of positive social attachment with their schools also had fewer psychopathology symptoms and engaged in fewer high-risk behaviors such as substance use and aggression. Subsequently, numerous other studies have replicated and extended this association (e.g., Anderman, 2002; Bonny, Britto, Klostermann, Hornung, & Slap, 2000; Klem & Connell, 2004). Most of this research, however, has been cross-sectional and correlational. In a study examining a school-based mental health program in Australia, Shochet, Dadds, Ham, and Montague (2006) administered the Psychological Sense of School Membership Scale (PSSM; Goodenow, 1993) as a measure of school connectedness in tandem with the Strengths and Difficulties Questionnaire (SDQ) and clinical scales of depression and anxiety to a sample of more than 2,000 students ages 12–14. Because the surveys were administered twice over a 12month period, it offers one of the few opportunities to assess the relative predictive power of a traditional symptom-focused screening instrument (SDQ) and a strength-focused instrument (the PSSM). The results of the Shochet et al. (2006) study provide evidence that strength-focused screeners have potential to identify future mental health functioning just as well as symptom-focused screeners. Using hierarchical linear modeling to control for Time 1 mental health symptom

86 Erin Dowdy et al. severity and for possible school-level influences, the authors found some surprising relations. For example, the correlations between PSSM and the Children’s Depression Inventory (–.62) and the SDQ (–.60) were significant and substantial. Even this cross-sectional finding suggested that scores on the PSSM are strongly and negatively associated with reported depression symptoms. Even more importantly, this association remained highly significant for the Time 2 CDI and SDQ scores. In fact, the correlations between the Time 1 PSSM and the Time 2 CDI and SDQ (both –.49) were nearly the same as for the Time 1–Time 2 correlations for the CDI (.53) and SDQ (.52). An intriguing finding was that the Time 1–Time 2 PSSM–CDI correlation (–.49) was larger than the SDQ–CDI correlation (.42). In other words, for this sample of adolescents, the PSSM school connectedness measure predicted mental health symptoms one year later as well as the SDQ. In a similar study using a sample of 10- to 14-year-old students from Texas, Loukas, Suzuki, and Horton (2006) reported smaller but significant one-year correletions between the Add Health School Connectedness Scale and measures of depression (–.29) and conduct problems (–.33). Although the Shochet et al. (2006) study supports efforts to integrate wellness content in mental health screening instruments, additional research is needed to identify which aspects of well-being and protective factors will be the most critical to include. For example, in another longitudinal study, Bond et al. (2007) administered measures of school and social connectedness to students in grade 8 and examined how they predicted mental health adjustment in grade 10. This study replicated the importance of positive school connectedness in grade 8 being associated with lower scores on a depression scale in grade 10. Interestingly, however, they found that the combination of positive school connectedness and positive social connectedness was by far the best predictor of positive mental health status with equally poorer mental health associated with poor school connectedness, social connectedness, or both. These are remarkable findings that merit further research and replication. They inspire creative thinking about the most effective strategies to screen for and monitor the mental health and well-being of youth. Although it can be challenging to motivate schools and communities to screen for depression and other mental health problems, it may be easier to convince them to ask students about well-being-focused constructs such as school connectedness. Not only may this diminish any inappropriate stigma that may be associated with mental health problems, it also focuses on matters that the schools may be better able and motivated to address. When schools screen for mental health symptoms, this might encourage perceptions that the screening primarily leads to mental health referrals and services. If a school inquires about the attachment or connectedness that its students have with the school, the locus of responsibility to respond to students in need gravitates more clearly to the school and its community. For example, Shochet et al. (2006) found that for both males and females differences of just one point on the Time 1 PSSM scale were associated with two fewer points on the Time 2 CDI score. Knowing this outcome, schools could screen for students with low PSSM scores, for example, and implement strategies to increase school connectedness. Such efforts that produce even modest improvement of connectedness could meaningfully decrease depression symptoms one year later. Although this research is in its early stages, it offers an example of how future mental health screening may be enhanced by well-being measures that address factors schools may be better prepared to address. In addition to providing a more comprehensive view of a student’s functioning, collecting information from a strengths-based perspective might be more palatable to the children, teachers, and parents involved in the screening process. For example, consider how parents who are hesitant to provide consent might respond to “screening for mental health problems”

Screening for Mental Health and Wellness 87 as opposed to “screening for happiness, hope, and wellness.” The potential importance of collecting information from a strengths-based perspective, including information on adaptive functioning, might be particularly useful when screening for early identification and early intervention. Can the same goal of early identification for early intervention be accomplished focusing solely on the presence or absence of positive indicators of well-being, utilizing a strength-based approach? This question remains, but surely should be considered.

Discussion and Concluding Comments Although it makes intuitive sense to identify mental health problems so they can be treated early, it is important to consider whether early identification is even possible. School mental health providers need to carefully scrutinize emerging research that informs best practices in mental health screening because previous studies have produced divergent findings about the accuracy of screening procedures. Some studies support the efficacy of school-based mental health screening. Jones and colleagues (2002), for example, used a brief conduct problems screener with 463 children in kindergarten and found that, six years later, they were able to strongly predict which children had EBD problems and were involved in mental health, special education, or juvenile justice services. In a longitudinal study, Kamphaus et al. (2007) used an abbreviated version of the Behavior Assessment System for Children Teacher Rating Scale—Child Version (BASC TRSC; Reynolds & Kamphaus, 1992) with a sample of 206 children. They found that the screener version of the BASC TRS-C (similar to the BESS) was able to predict a substantial range of outcomes one year later including conduct problems, social skills problems, depression, and academic achievement scores. A similar longitudinal study by DiStefano and Kamphaus (2007) found evidence of concurrent and predictive validity for a screener of the preschool version, the BASC TRS-P (Reynolds & Kamphaus, 1992). Specifically, the abbreviated BASC TRS-P was able to predict social readiness for the school environment, disciplinary infractions, academic problems, and counseling referrals in addition to other behavioral and emotional indicators. Further evidence for screening comes from a study conducted by Lane et al. (2007) in which 528 students were placed in low-, moderate-, and high-risk groups based on results of the Student Risk Screening Scale (SRSS; Drummond, 1994). Results indicated that students could be differentiated on behavioral outcomes, such as office discipline referrals and in-school suspensions, with the higher-risk group having more discipline referrals and suspensions. An additional study by Briggs-Gowan and Carter (2008) found that screening with a standardized tool in early childhood (12 to 36 months of age) identified the majority of children who exhibit significant emotional/behavioral problems in elementary school (kindergarten and grade 1) as measured by the Child Behavior Checklist and Teacher Report Form (Achenbach & Rescorla, 2001). In combination, these studies provide evidence that early identification is possible although additional research is warranted. The need for additional research is emphasized by other studies suggesting that schoolbased screening does not have predictive validity. For example, the results of a study investigating the ability of a mental health screener, the Diagnostic Interview Schedule for Children Predictive Scales 8 (DPS-8) to predict mental health diagnoses as identified by the National Institute of Mental Health Diagnostic Interview Schedule for Children-IV (NIMH-DISC-IV) indicated that the screener was unable to accurately predict diagnosis (Roberts, Stuart, & Lam, 2008). Specifically, with a Canadian sample of 153 high school students, the DPS-8 was only able to correctly identify 53.1% of the students with a psychiatric diagnosis. Furthermore,

88 Erin Dowdy et al. Najman and colleagues (2007) screened for risk of later problems with depression and anxiety and they were only able to detect a small proportion of the later cases with significant mental health problems. What may be the case is that youth cope with short-term life challenges that produce significant symptoms, but that over time these symptoms abate and/or the youth are able to cope with them because of internal assets and external social resources. The authors, in fact, challenged the “existing movement encouraging the detection and treatment of those with symptoms of mental illness in early childhood” (Najman et al., 2007, p. 694). We saved this discussion for the end of the chapter because we feel that interest in providing humane care for children mandates that every effort be taken to watch diligently for any signs of emotional distress. Whichever approach to screening discussed in this chapter a school implements, school mental health providers can help to ensure that the latest research evidence is available to modify screening practices so that information is interpreted properly so Table 4.3 Mental Health Screening Implementation Considerations 1. Establish a planning and implementation team …

Identify key stakeholders to assist in development and decision-making Staff, community health professional, parents, students c Consider integrating with pre-existing teams (e.g., school safety team) Assign roles for each member of the team c

…

2. Determine rationale and goals for screening … … … … … …

What is the purpose of the screening? What is the expected outcome? What is the school and community’s comfort level with undertaking this task? What will be done with the information obtained? What services are in place to address concerns? Is the focus on population-based needs assessment and/or screening to identify individual youth? Is the focus on assessing for mental health problems, well-being, or both? In what ways will screening benefit students, staff, school, and/or the community?

3. Identify resources … … … …

…

Identify data, resources, and services currently available at the school and in the community Design screening program to fit current capacity while working to build capacity for future needs Develop budget for screening Evaluate and make determinations about screening instrumentation based on intended use, technical adequacy, and usability Identify additional data, resources, and services that will be needed

4. Work out logistics …

Develop a timeline for screening, considering the following: Who will be involved in data collection (teacher, parent, student forms)? c How often and when will screening occur each year? c How will parent consent be obtained if identifying individual students? c What are the pragmatic time and space considerations for actual data collection? c How will students, staff, parents, and community be educated about screening prior to implementation? c

5. Follow-up … … … …

How will the data be analyzed and summarized in a way that facilitates open communication? Determine how information will be shared with students, families, staff, and community Link screening outcomes to services and interventions Critically evaluate screening process and monitor effectiveness of services provided

Screening for Mental Health and Wellness 89 as to respect the rights of parents and their children. Recognizing the need for school mental health providers to provide meaningful consultation and leadership for school-based screening, Table 4.3 provides a list of the essential considerations and steps for schools to consider prior to, and while, implementing a screening program for mental health and wellness. While advances in the field of early identification have been made possible through recent improvements in early identification technologies (Levitt et al., 2007), there is not yet a consensus on optimal screening procedures and/or instrumentation to be used. Additional research and development on screening instruments is being sought and longitudinal investigations are currently underway to provide additional reliability and validity evidence for screening instruments such as the BESS (United States Department of Education, Institute for Education Sciences, Grant # R32B060033B, awarded to Kamphaus and DiStefano). Although it is possible that more questions were raised than answered and that empirical validation awaits for some screening procedures, it would be premature to dismiss screening due to preliminary discrepant results and unknown answers. There is enough known now about early identification and early intervention that it is not recommended to wait to take action until systems are further refined. School mental health providers are in a unique position to take a leadership role to start the process of implementing a screening program that is appropriate for use in their schools and communities. Schools can begin to use the data that are already available to them (e.g., results of the periodic Youth Risk Behavior Surveillance Survey) and/or collect additional screening data to begin to monitor the mental health status of their students. These data could begin to inform service delivery and intervention approaches while building the capacity to serve students with unmet mental health needs. Service systems can continue to work together to provide more coordinated systems of care that are responsive to family and child needs. Early intervention and prevention efforts with documented evidence can continue to be implemented to children identified as in need. While there is still much to be done, it is hoped that the concepts and guidelines discussed here will stimulate conversations between school mental health providers, administrators, teachers, and parents about how to best accomplish screening for mental health and wellness. It is also hoped that, following these conversations, action will occur to undertake the vital task of watching for and purposefully responding to the many unmet mental health needs of children and adolescents.

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Implementing Universal Screening Systems Within an RtI-PBS Context Hill M. Walker and Herbert H. Severson, Oregon Research Institute and the University of Oregon Gale Naquin and Cynthia D’Atrio, University of New Orleans Edward G. Feil, Oregon Research Institute Leanne Hawken and Christian Sabey, University of Utah

Professionals in the field of school psychology, child mental health and special education are well aware of the positive impact that early screening, intervention and prevention efforts can have in successfully reducing later, disruptive behavior disorders among at-risk children and youth (Dodge, 2008; Weissberg, 2005). Current efforts by the National Association of School Psychologists to promote preventive interventions for children and youth on a universal basis are fueled by evidence pointing to the critical period of school entry and the need to ensure that every child achieves a good school beginning (NASP Position Statement on Early Childhood Care and Education, 2002). School success can foster school bonding, engagement, and attachment which, in turn, function as powerful protective influences against later destructive outcomes. That is, they have the potential to operate long-term much like a vaccine or inoculation (Embry, 2002). Comprehensive early intervention approaches involving parents, peers and teachers that are directed toward ensuring a successful start to a child’s school career are a proven method for developing these protective influences (Hawkins, Catalano, Kosterman, Abbott, & Hill, 1999). Well-conducted, longitudinal research suggests that much of problem behavior in adolescence has its origins in early childhood (Kazdin, 1987; Loeber & Farrington, 1998; Patterson, Reid, & Dishion, 1992; see also Eddy, Reid, & Curry, 2002). This longitudinal evidence provides empirical support for policies that can positively impact vulnerable children’s later lives (a) by addressing risk factors, and their associated behavioral correlates, early on in the child’s development; and (b) by developing the academic and social readiness skills that contribute to subsequent school success and social effectiveness (Dodge, 2008; Durlak, 1997; Diperna, Volpe, & Elliott, 2002; Gresham, 2004; Hunter, Hoagwood, Evans, Weist, Smith, Paternite, Horner, Osher, Jensen, & the School Mental Health Alliance, 2005). This early developmental period can be viewed as providing a pivotal window in which to intervene for preventing later potential problems, such as violence, substance abuse, educational failure, adolescent delinquency, and adult criminal involvement. Federal and state policies that support collaborative early intervention approaches, mounted within home, school, and community contexts, are perhaps one of the best hopes we have for preventing and remediating destructive behavior patterns before they become chronic and intractable (Zigler, Taussig, & Black, 1992). In a seminal policy piece on addressing youth violence, Dodge (2008) recently argued persuasively for the universal teaching of social skills and social competence as a promising means of accomplishing this important societal goal.

Implementing Universal Screening Systems Within an RtI-PBS Context 97 The RtI framework provides an important context for intervening early on in a child’s schooling career as well as early within a negative developmental trajectory—regardless of age. A primary goal of the three-tiered Response to Intervention (RtI) model is to detect those students as soon as possible who are at risk for learning disabilities, low achievement and possibly later school dropout and to provide an appropriate level of intervention for them based upon continuous progress monitoring (see Batsche, Elliott, Graden, Grimes, Kovaleski, & Prasse, 2005; Shinn, 2007). Although the three-tiered RtI logic seems to apply to the prevention and early remediation of behavioral as well as academic problems, RtI is most commonly mentioned in the context of academic supports and the prevention of learning disabilities (see Glover, Diperna & Vaughn, 2007). Experts in the field of Positive Behavior Support (PBS) have recommended adoption of a similar three-tiered model of positive behavior support to prevent and intervene with the full range of problem behavior typically seen in school settings (Frey, Lingo, & Nelson, in press; Sugai & Horner, 2002; Walker et al., 1996). With the advent of pressures to prevent destructive student outcomes through early intervention programs and supports, there has been a corresponding call for universal screening systems to detect at-risk students early in their school careers and to interrupt the trajectories on which so many find themselves (see Albers, Glover, & Kratochwill, 2007). In addition, a number of school-based professionals, who are working within 3-tier PBS systems and school contexts, have expressed interest in accessing such universal screening systems, for instance, in order to facilitate their efforts at targeting students, within tier 1, who will likely require tier 2 and 3 intervention approaches later on, based upon progress monitoring outcomes. The multi-gating Systematic Screening for Behavior Disorders procedure (Walker & Severson, 1990) has been a recent focus of much of this interest. Despite having been available for over a decade and longer, there have been relatively few demonstrations of how universal screening systems can be used effectively within such RtI-PBS contexts. The purpose of this chapter is twofold: (1) to describe ways in which the SSBD and similar universal screening systems can be effectively utilized within an RtI-PBS school context; and (2) to present a large-scale, case study application of the SSBD that illustrates its use within a court-mandated, child find initiative in a New Orleans regional school district (i.e., the Jefferson Parish Public Schools). The following topics are addressed herein: (a) current status of practices regarding universal screening for behavior problems in schools; (b) sample exemplars of approaches to universal screening for behavior problems in schools; (c) key features of the SSBD universal screening system; (d) recent applications of the SSBD in research contexts; (e) the utility of universal screening systems within RtI-PBS type contexts; and (f) illustration of a multi-year, case study application of the SSBD within a court-mandated initiative to improve services and supports for students experiencing emotional or behavioral disorders (EBD). The chapter concludes with some observations about needed directions for future research, practice and policy on universal screening and prevention.

Current Status of Practices Regarding Universal Screening for Behavior Problems in Schools It can be argued that school-based methods of identifying students with mental health needs are currently failing. In any given year, only about 1% of the public school population is identified, referred and actually certified as emotionally or behaviorally disordered (EBD) (Kauffman & Landrum, 2008). Yet estimates of the numbers of children and youth experiencing significant mental health problems in need of attention range upwards of 20% of the K–12, school population (see Burns & Hoagwood, 2002; Levitt, Saka, Romanelli, & Hoagwood,

98 Hill M. Walker et al. 2007). There is scant evidence that either of these percentages has shown appreciable movement in the past decade (Walker, Nishioka, Zeller, Severson, & Feil, 2000; Walker, Severson, & Seeley, in press). From the available evidence regarding current practices, it appears that the relative absence of school-based, proactive identification procedures and persistent inconsistencies in decision-making are key contributors to the low rates at which EBD students are identified and served. Further, the tepid investments in early detection initiatives by educational gatekeepers to date have often led to a reduced capacity to mount effective prevention efforts. As a result, children with EBD are identified much too late in their school careers at which point intervention efforts are likely to be less successful and also come at increasing cost. The primary outcome of these practices is very often “too little, too late” (Albers, Glover, & Kratochwill, 2007). Kauffman (1999; 2005) has insightfully examined barriers to the mounting of universal screening systems for detecting behaviorally at-risk students in school settings. Obstacles he has identified include: (1) the stigma of being identified as behaviorally at-risk and labeled EBD in order to qualify for services; (2) cost savings in order to avoid the expense of referral and certification as EBD; (3) the possibility of lawsuits by parties dissatisfied with available or provided services; and (4) the often nettlesome burdens of complying with IDEA bureaucratic requirements associated with certification as EBD. Another likely restraint on the early detection of EBD students concerns the reluctance of many teachers to refer behaviorally at-risk students as such referral may be interpreted as an indication of weak classroom management skills. Research on teacher referral practices indicates that teachers are the primary gatekeepers of both academic and behavioral referrals and while they are prone to make academic referrals early, they tend to delay behavioral referrals well into the upper elementary and the middle school grades (see Walker, Nishioka, et al., 2000). In addition, general education teachers typically under refer externalizing students and rarely refer internalizing students (Lloyd, Kauffman, Landrum, & Roe, 1991; Walker et al., 2000). These trends and practices coalesce to validate Kauffman’s claim that we actually “prevent prevention rather efficiently” (Kauffman, 1999).

Sample Exemplars of Approaches to Universal Screening for Behavior Problems in Schools Although Albers et al. (2007) note that additional research is needed on the feasibility, efficacy, cost, and consumer acceptance of universal screening approaches, a substantial amount of research and development work has been invested in universal screening approaches since the mid-1990s. In our view, a strong level of innovation is apparent in these efforts and the field of education greatly underutilizes the potential benefits of many of these advances. Some examples of these universal screening methods are briefly described below. These methods are based upon a diverse array of information sources including teacher informants, archival school records, and in vivo behavioral observations. Teachers’ Use of Nominations and Likert Rating Scales Teacher appraisal of student behavior, based on Likert rating scales, have been a relatively popular approach in the evaluation of students referred for social, emotional, and behavioral problems (see Merrell, 1999, 2001). Such Likert scales typically ask the rater to assess students’ behavior along three-, five-, or seven-point dimensions of problem frequency or severity.

Implementing Universal Screening Systems Within an RtI-PBS Context 99 Hundreds of such scales are in use and evaluations of many of them can be accessed through the Buros Mental Measurements Yearbook (Geisinger, Spies, Carlson, & Plake, 2007), which annually reviews newly developed scales. The Child Behavior Checklist (Achenbach, 1991) has become the rating scale gold standard for measuring child and youth psychopathology and is, by far, the most widely used instrument for this purpose. Merrell (1999) has contributed a comprehensive analysis of assessment instruments for use in social, emotional, and behavioral domains. In addition to their relatively unsystematic use, critics of teacher rating instruments point to their global and relatively crude assessment properties (e.g., “How many fidgets are there in pretty much?”). Others argue that the sensitivity of teacher ratings pales in comparison to more direct measures such as in vivo behavioral observations. In spite of these criticisms, teacher ratings continue to be a widely used and important source of information in child screening, identification, and evaluation processes. Teacher ratings have the advantage of defining and pinpointing the behavioral content of a student’s perceived adjustment problems, are based upon many hours of teachers making social comparisons among students, and can be standardized so as to enable valid comparisons as referenced to normative age and gender scores. Merrell (2001) has pointed out that Likert behavioral ratings have a number of additional advantages. For example, they (a) are relatively inexpensive; (b) provide essential information on low-frequency behavioral events of potential importance; (c) are relatively objective and reliable, especially when compared to interview and projective assessment methods; (d) can assess individuals who are unable to contribute self-reports; (e) take into account the many observations and judgments of child behavior made by social agents within natural settings over the long term; and (f) reflect the judgments of expert social informants who are familiar with the student’s behavioral characteristics (i.e., parents, teachers, peers). In school-based practice, general education teachers are typically asked to nominate behaviorally at-risk students (often in the absence of specific criteria for nomination) and then to follow up with Likert ratings for each nominated student on a scale such as the Achenbach Behavior Checklist or the Social Skills Rating System (Gresham & Elliott, 1990). If a student’s profile is within the clinical range on the Likert scale being used, he or she is then considered for further evaluation, possible certification and/or services and supports. Drummond (1993) has developed an intriguing matrix system, based on Likert teacher ratings, that is designed to screen entire classrooms of students for their risk status in relation to antisocial behavior patterns. In this universal screening approach, every student in the teacher’s classroom is rated on a set of seven indicators of antisocial behavior. Drummond’s Student Risk Screening Scale (SRSS) is a cost-efficient and popular procedure for quickly screening entire classrooms. A matrix format is used that has seven behavioral descriptors across the top of the rating form and students’ names down the left side (see Figure 5.1). The classroom teacher assigns every student a Likert rating, ranging from 0 = never to 3 = frequently, for each of these seven items of the SSRS: (1) stealing; (2) lying, cheating, sneaking; (3) behavior problems; (4) peer rejection; (5) low academic achievement; (6) negative attitude; and (7) aggressive behavior. Teachers compare each student against all other students in the classroom as they rate each item. The SRSS is a brief, research-based, easily understood, reliable, valid, and cost-efficient instrument. The SRSS was recently investigated in a study of 674 high school students by Lane, Kalberg, Parks, and Carter (2008) and found to have acceptable psychometric characteristics. The major advantages of the SRSS are that (a) all students are systematically screened and evaluated; (b) it accomplishes universal screening; and (c) normative social comparisons are facilitated by requiring the teacher to evaluate all students on each item at the same time rather

100 Hill M. Walker et al. Sample student risk screening scale (SRSS) Screening form for an entire class Items Names

Stealing

Lying, cheating, sneaking

Behavior problems

Peer rejection

Low academic achievement

Negative attitude

Aggressive behavior

Susan

0

0

1

1

1

0

0

3

Low

Jamie

3

0

1

2

0

1

1

8

Moderate

Fred

1

1

3

3

2

3

3

16

High

Totals

Levels of Risk: High risk = 9–21; Moderate risk = 4–8; Low risk = 0–3 Scale: 0 = Never to 3 = Frequently

Figure 5.1 Sample Student Risk Screening Scale (SRSS) Screening Form for an Entire Class.

than rating individual students on a series of items on a case-by-case basis. The SRSS thus affords every student an equal chance to be evaluated in relation to the seven SRSS items. A matrix system of this type is also ideally suited for the classwide assessment and pre-post evaluation of instruction for all students in a series of social skills. A more complete description of the SRSS and its potential applications is provided in Walker, Colvin, and Ramsey (1995) and Sprague and Walker (in press). Critical Behavioral Events Critical behavioral events refer to episodes having great intensity, salience and social impact; they include but are not limited to assault, fire -setting, self-injury, exposing oneself, stealing, cheating, bullying, and so on (Todis, Severson, & Walker, 1990). Research by Walker and his colleagues on mainstreaming and social integration shows that such behavioral events (1) are unacceptable to teachers in the extreme; (2) are likely to prompt teacher efforts to have the student displaying them permanently removed from the classroom; and (3) differentiate atrisk students in terms of the severity of their behavioral adjustment problems (Walker, 1986). These critical behavioral events have been characterized as “behavioral earthquakes” because of their disruptive influence on classroom ecology. The importance of critical events is derived from their severity and their potential destructiveness to the individual and other social agents. The impact of critical events is not dependent upon their frequency of occurrence but rather determined by the fact that they occur at all. These are very rare occurrences in the behavioral repertoires of typically developing children and youth but are not all that infrequent in the lives of some behaviorally at-risk individuals. Gresham, MacMillan, and Bocian (1996) conducted a study of the Critical Events Index (CEI) in their use of the SSBD screening procedure within a larger study of the social-affective status of at-risk students. The CEI was used to identify three groups of students from an elementary-aged student sample based upon the groups’ total number of critical events: (a) highrisk (n = 30), (b) moderate-risk (n = 55), and (c) low-risk (n = 30). These groups were then contrasted on a series of cognitive/achievement, social competence, externalizing behavior, and school history variables as derived from searches of archival school records of individual students. Multivariate and univariate analysis procedures showed that the three at-risk groups were differentiated primarily on social competence and externalizing behavioral measures.

Implementing Universal Screening Systems Within an RtI-PBS Context 101 However, a series of cross-validated, stepwise discriminant function analyses, contrasting the high- and low-risk groups only, and using combinations of social competence, externalizing, internalizing, and school history variables, correctly identified over 85% of the high-risk group and 78% of the low-risk group. Gresham et al. (1996) recommend inclusion of critical events measures within multi-method assessments of at-risk, behavioral status and they view these events as “vital signs” indicative of childhood psychopathology. Blechman and Hile (in press) make the following observations regarding critical events: (1) Student involvement in critical events provides a bias-free screen for the detection of at-risk students in the general or universal student population; and (2) Systematic documentation of all critical events provides the most effective and least expensive method of screening for at-risk students. They note further that reliance upon readily available information from archival student records within screening efforts reduces costs, increases feasibility, and avoids extraordinarily adverse consequences to students. Blechman and Hile (in press) define critical events in their work as including school and criminal offenses, threats of violence or suicide, suicide attempts, and caregiver requests for assistance with behavior management. They argue that these events offer a useful and inexpensive predictor of future and more serious critical events. The work of these authors reflects an increasing trend toward using critical behavioral events in screening practices, as either rated by knowledgeable informants or culled from existing archival records. Archival School Records If the early preschool detection of behavior problems is not possible, school records can provide an additional valuable source of screening information as students move through the primary and intermediate grades. Archival school records that accumulate as a natural part of the schooling process provide a rich and inexpensive information source regarding a range of school adjustment problems and also provide a record of the manner in which schools try to cope with such problems. Because these records build naturally as an ordinary part of the schooling process, they are relatively unobtrusive and far less reactive than typically recorded assessments (e.g., teacher ratings, in vivo behavioral observations, sociometric measures). Our experience shows that they become more complete as students progress through the intermediate grades and into middle school settings. Walker and his colleagues developed the School Archival Records Search (SARS) procedure (see Walker, Block-Pedego, Todis, & Severson, 1991) to accomplish the coding, analysis, and aggregation of archival school records. SARS provides for the systematic coding of 11 archival variables, which can then be analyzed individually or aggregated into domain scores that provide profiles of student status in three areas of school adjustment: disruption, needs assistance, and low achievement. The individual SARS variables that are coded comprise the following: number of different schools attended, days absent, low achievement, grades retained, academic/behavioral referrals, current Individualized Educational Program (IEP), nonregular classroom placement, Title I, referrals out of school, negative narrative comments, and school discipline referrals. In the context of schooling, archival school records are the closest proxy we have for police contacts and juvenile records that are used in evaluating delinquency prevention programs and in validating measures that purport to predict later delinquent acts. Disciplinary referrals of students to the front office, as reflected in archival school records,

102 Hill M. Walker et al. have emerged as a very useful measure for assessing overall school climate and for identifying student groups and individuals who are in need of behavioral supports and intervention (see Irvin, Tobin, Sprague, Sugai, & Vincent, 2004; Tobin & Sugai, 1999; Walker, Stieber, Ramsey, & O’Neill, 1993). Sugai, Horner, and their colleagues have conducted extensive research on this topic in the past five years. Sugai et al. (2000) reported normative data profiles on disciplinary referrals involving a sample of 11 elementary schools and nine middle/junior high schools. These elementary schools averaged 0.5 disciplinary referrals per student per school year. At the middle/junior high school level, however, students being referred to the principal’s office due to disciplinary infractions was a very common occurrence. In the Sugai et al. study, the elementary schools averaged 566 students enrolled and 283 disciplinary referrals within a school year; in contrast, the middle/junior high schools averaged 635 students and 1,535 disciplinary referrals within a school year. These authors also analyzed some of the patterns that existed within this pool of disciplinary referrals. Based on their analysis, they argue that such patterns can guide the direction and focus of intervention approaches for addressing chronic behavior problems within the school setting (i.e., targeting the whole school, small groups, and/or individual students). For example, at the elementary level, Sugai. Sprague, Horner, & Walker (2000) found that the top 5% of students with the most discipline referrals also accounted for 59% of total disciplinary referrals within the school; at the middle/junior high level, the top 5% accounted for 40% of all discipline referrals. These figures closely parallel outcomes for juvenile crime where 6 to 8% of juveniles typically account for 60 to 65% of all delinquent acts (Loeber & Farrington, 1998). According to Sugai et al., elementary-aged students with five or more disciplinary referrals within a school year are considered to be behaviorally at risk; those with 10 or more such referrals are considered to be chronic discipline problems who may be severely at risk for both inschool and out-of-school destructive outcomes. Recording and utilizing disciplinary referrals to identify at-risk students and to guide intervention applications requires the computerization of school records. Horner and his associates have developed the School Wide Information System (SWIS) procedure, which is a web-based computer application for entering, organizing, and reporting office discipline referrals found within schools (May, Ard, Todd, Horner, Glasgow, & Sugai, 2001). SWIS computerizes discipline referrals and is a valuable tool for use by teachers and school administrators in collecting and analyzing discipline-related information. Perhaps the advantage of the SWIS procedure is that it systematizes and standardizes the process of documenting, recording and reporting on disciplinary referrals. Figure 5.2 illustrates the SWIS Office Referral Form, which is completed for each disciplinary referral made by a teacher to the school office. This referral form documents each disciplinary episode for which a front office referral is initiated by the teacher. The SWIS referral form describes the location, specific problem behavior, and possible motivation for the behavior, the resulting administrative decision, and other persons who were involved in the incident. Parents are asked to sign and date the referral form to indicate that they have knowledge of the incident, the referral, and its disposition. SWIS is an important advance in the computerization of archival school records that allows individual schools to profile themselves in relation to disciplinary practices and their resulting effects. It can be used also as a measure of certain aspects of school reform efforts, as a measure of the school’s climate, as a pre-post measure of schoolwide interventions, and as a vehicle for guiding and targeting allocation of intervention resources to small groups and individuals. It is also recommended as a schoolwide, behavioral screening device to identify those students who are experiencing serious to chronic school adjustment problems.

Implementing Universal Screening Systems Within an RtI-PBS Context 103

SWISTM OFFICE DISCIPLINE REFERRAL FORM Grade Level

Referring Staff

Student(s)

Time

Date

Location Classroom

Cafeteria

Bus loading zone

Playground

Bathroom/restroom

Parking lot

Commons/common area

Gym

On bus

Hallway/breezeway

Library

Special event/assembly/field trip

Other

Problem Behaviors (check the most intrusive) MINOR

MAJOR

Skip class/truancy

Vandalism

Inappropriate lang.

Abusive lang./inapprop. lang.

Forgery/theft

Property damage

Physical contact

Fighting/physical aggression

Dress code

Bomb threat

Defiance/disrespect/ non-compliance

Defiance/disrespect/insubordination/non-compliant

Lying/cheating violation

Arson

Tobacco

Weapons

Disruption

Harassment/ tease/taunt

Alcohol/drugs

Other

Property misuse

Disruption

Other

Tardy

Combustibles

Possible Motivation Obtain peer attention

Avoid tasks/activities

Don’t know

Obtain adult attention

Avoid peer(s)

Other

Obtain items/ activities

Avoid adult(s)

Others Involved None

Peers

Staff

Teacher

Substitute

Unknown

Other

Administrative Decision Time in office

Detention

Saturday School

In-school suspension

Loss of privilege

Parent contact

Individualized instruction

Out-of-school suspension

Conference with student

Other

Comments: Follow up comments:

Figure 5.2 SWIS Office Discipline Referral Form.

Behavioral Observations Behavioral observations recorded in natural settings (e.g., homes, classrooms, playgrounds, hallways) remain the preferred assessment method of most behavior analysts for assessing the behavior problems of students. In typical school usage, the teacher referral process requires that a school psychologist, or other related-services professional, directly observe the target student in a setting or context in which the problem behavior occurs (i.e., the referral setting). A wide range of coding systems and recording procedures are used for this purpose. However, the vast majority of them do not have adequate technical data or information to support their use(s). In addition, most of these codes lack local, state, or national norms that are appropriate for making social comparisons among students (see Leff & Lakin, 2005, for a recent review). It should be noted that behavioral observations are rarely used for universal screening purposes due to their labor-intensity and high cost. Much more typically they are used in

104 Hill M. Walker et al. conjunction with teacher nominations and ratings to develop a more complete picture of the target student, to verify teacher impressions or ratings of the student, and to document the nature, estimated frequency, and topography of the behavior problems cited in a referral. Teacher referrals are often based upon discrete behavioral events of high intensity or salience (e.g., insubordination, teacher defiance) that may be missed within the narrow window of time and occasions sampling that most such observations involve. Naturalistic behavioral observations are also vulnerable to observer bias and expectancy effects that can be induced by the observer’s prior knowledge of the case. Further, direct observations are timeconsuming and labor-intensive in that they usually require considerable planning and careful monitoring if they are conducted effectively (see Merrell, 2001). In spite of these downsides, naturalistic behavioral observations remain popular among school professionals and they do have an important role to play in the screening-identification process if they are incorporated into a comprehensive assessment process that involves other, less expensive measures (e.g., teacher nominations, rankings, ratings, archival records searches, etc.). We do not recommend their use in isolation; but rather that they constitute an important component of a multi-agent, multi-method, and multi-setting assessment approach to the screening-identification process (Merrell, 1999). All of the approaches described above have some degree of applicability to universal screening within an RtI-PBS context—some more than others. Behavioral observations and Likert teacher ratings have the most relevance in progress monitoring and decision-making regarding moving a student into more intensive levels of intervention and remediation. Archival school records, critical behavioral events and office disciplinary referrals, because of their low frequency and high salience, are much less sensitive and are more useful for initially identifying those students who are less likely to respond to a universal intervention. Some guidelines and recommendations for the use of universal screening procedures within RtI-PBS type contexts are discussed in the sections that follow.

Key Features of the SSBD Universal Screening System Walker and Severson (1990) developed the Systematic Screening for Behavior Disorders (SSBD) screening procedure for use with elementary-age children (K–6 grades) based upon a conceptual model, and corresponding empirical findings, documenting that children’s problem behavioral characteristics could be divided reliably into “externalizing” (e.g., aggressive, hyperactive, noncompliant, antisocial, etc.) and “internalizing” dimensions (e.g., shy, phobic, depressed, anxious, isolated from peers, etc.) (see Achenbach, 1991; Ross, 1980). The SSBD was patterned after screening models developed and validated by Greenwood, Walker, Todd, and Hops (1979) for the preschool screening of children at risk for social withdrawal and by Loeber, Dishion, and Patterson (1984) for the screening of adolescents at risk for later delinquency. The SSBD is a proactive, universal screening procedure that provides each student with an equal chance to be screened and identified for either externalizing or internalizing behavior disorders. The SSBD procedure consists of three interconnected screening stages or gates where movement through each gate is required for consideration at the next gate. Most students are screened out in the initial SSBD gates (i.e., gates 1 or 2) because they do not meet the behavioral criteria necessary to proceed to the next phase of screening. Walker and Severson began their first trial testing of the SSBD in the mid-1980s and conducted extensive research, supported by a series of federal grants, on this screening system prior to its publication in 1990 (Walker, Severson, Stiller, Williams, Haring, Shinn, & Todis, 1988; Walker, Severson, Todis, Block-Pedego, Williams, Haring, & Barckley, 1990).

Implementing Universal Screening Systems Within an RtI-PBS Context 105 Figure 5.3 illustrates the three screening gates of the SSBD. In gate 1, teachers are asked to think about all students in their class and to nominate those students whose characteristic behavior patterns most closely match either the externalizing or internalizing behavioral definitions provided for them. The three highest-ranked externalizing students and the three highest-ranked internalizers then move to screening gate 2 where their behavior is more specifically rated by the teacher on a 33-item Critical Events Index (CEI) and on an Adaptive (11 items) as well as a Maladaptive (12 items) Likert rating scale that requires estimates of frequency of occurrence. Students who exceed national, normative cutoff scores on these measures move on to an optional screening gate 3 where they are observed in classroom and playground settings. Using a direct observation procedure, a school professional (school psychologist, counselor, or behavioral specialist) observes and codes each target child’s behavior for two 20-minute sessions in the regular classroom. A stopwatch measure of Academic Engaged Time (AET) is used for this purpose. The Peer Social Behavior (PSB) code is used to record the level, quality, and distribution of the target student’s peer-related, social behavior at recess during two 20minute, observation sessions. Multiple-gating assessment procedure for identification Pool of regular classroom preschoolers

STAGE I:

Teacher ranking on internalizing & externalizing behavioral dimentions 3 Highest ranked children on externalizing & internalizing behaviral criteria Pass gate 1

STAGE II:

Teacher ranting on critical events checklist (CEI) & combined frequency index (CFI) Exceed normative criteria on CEI or CFI Pass gate 2

STAGE III:

Direct observations & parent questionnarire direct observation in freeplay & structured activities & parent rating Exceed normative criteria

Classroom interventions

Pass gate 3

Referral to multidisciplinary evalution

Adapted from: Feil, E., Severson, H. and Walker, H. (1994). Eary screening project: Identifying preschool children with adjustment problems. The Oregon Conference Monograph. Vol. 6.

Figure 5.3 SSPD/ESP Multiple Gating Procedure.

106 Hill M. Walker et al. Normative data for the SSBD gate 2 instruments consist of over 4,000 cases representing the four U.S. census zones. The SSBD user’s manual also provides normative observation data for the AET and PSB codes involving over 1,300 cases for each code—also collected across the four census zones. Those students who exceed national, normative cutoff points on the AET and PSB codes are considered to have serious problems and are usually referred for further evaluation to specialized, school-based services. As a rule, an archival records search is conducted at this point to provide further confirmation of results of the screening-identification process and serves as a further source of information for decision-making. The first two screening gates of the SSBD can be completed by the classroom teacher in approximately one to 1½ hours. Completion of the gate 2 screening tasks typically identifies one externalizer in every classroom and one internalizer in every two or three classrooms. The time involved in conducting SSBD screening assessments increases as one moves through the screening stages; however, the number of students who are the targets of those assessments is greatly reduced from gates 1 to 3. It is recommended that universal SSBD screenings be conducted twice a year—once in the fall and once in the spring (e.g., in October and February) to identify students in need of intervention supports and services, to maximize the sensitivity of school staff to initial behavior problems in the fall of the school year, and to detect emerging behavior problems later in the school year. The SSBD has been extensively researched and has excellent psychometrics (see Severson, Walker, Hope-Doolittle, Kratochwill, & Gresham, 2007; Walker & Severson, 1990; Walker, Severson, Nicholson, Kehle, Jenson, & Clark, 1994).

Recent Applications of the SSBD in Research Contexts The SSBD has been used as an initial behavior screen in two multi-site federal initiatives funded by the U.S. Office of Special Education Programs within the past decade. These initiatives are: (1) the National Reading and Behavior Centers Program, and (2) the National Behavior Research Centers Program. Coordination and technical assistance supports were provided for these two initiatives respectively by the University of Wisconsin (Elliott & Kratochwill) and by SRI (Wagner, Woodbridge, & Sumi). In these applications, the SSBD was used primarily as a universal screening instrument to identify appropriate candidates for inclusion in RtI-PBS interventions and, in some cases, as measures of pre-post outcomes. Use of the SSBD in a standardized fashion across the varied implementation-data collection sites comprising these programs of research provided substantial advantages in identifying comparable sample populations and in improving the external generalizability of empirical findings. There have been some recently reported SSBD studies by investigators who are engaged in individual programs of research and development involving student populations that are behaviorally and/or academically at risk. For example, Walker, Cheney, Stage, Blum and Horner (2005) used the SSBD as a primary means of identifying their intervention sample of target students (n=1,540). They concluded that students at risk of school failure are best identified by monitoring school discipline referrals and use of the SSBD screening process. Kathleen Lane and her associates have conducted an extensive series of studies using the SSBD as a primary vehicle of instrumentation with this same at-risk population and found it to be an effective instrument for the identification of students from this population in need of intervention supports and services (see, for example, Lane 2007; Lane, Parks, Kalberg, & Carter, 2008). To date, Lane and her associates have assembled an SSBD data base of approximately 7,000 cases at elementary and middle school levels.

Implementing Universal Screening Systems Within an RtI-PBS Context 107 Caldarella, Young, Richardson, Young, and Young (2008) recently reported an investigation in which they evaluated the SSBD screening procedure for use with middle and junior high school students (n=123). They reported that their study provided evidence for the reliability and validity of SSBD teacher ratings of early adolescent students who are behaviorally at risk. Collectively, these studies are encouraging in that they validate the SSBD’s use as a research instrument in identifying target student populations who share behavioral characteristics across diverse participating sites. They also serve to extend the SSBD’s effective range of application with adolescent student populations and validate its use as a criterion in evaluating other screening instruments (see Lane et al., 2008).

Applications of Universal Screening Systems Within RtI-PBS-Type Contexts In 1996, Walker and his colleagues adapted the U.S. Public Health classification system governing prevention outcomes for use as a scaffold in coordinating the integrated delivery of behavioral interventions within school settings (Walker, Horner, Sugai, Bullis, Sprague, Bricker, & Kaufman, 1996). The goal of this adaptation was to enable the coordination and integration of differing intervention approaches (i.e., primary, secondary, tertiary) and to maximize the resources needed to respond to the needs and problems of the three groups of students found within any school or preschool setting: (1) those who are not at risk and are progressing normally; (2) those who have mild to moderate risk status; and (3) those who are severely at risk. This classification schema allows schools to (1) address all three types of prevention goals and outcomes (primary, secondary, tertiary) in a coordinated fashion across these three groupings of students; (2) deliver positive behavioral supports and services in a cost-effective manner to all who need them; and (3) create and sustain a positive school climate and social ecology that provide a supportive context for teaching and strengthening school expectations governing student conduct (e.g., be safe, be respectful, be responsible). Figure 5.4 illustrates this classification schema along with some of the universal, selected and indicated interventions that are used to achieve respectively primary, secondary and tertiary prevention outcomes. This model of service delivery defines prevention as a goal or outcome of intervention rather than as an approach (Walker, Ramsey, & Gresham, 2004). Differing intervention types are used to achieve specific prevention goals and outcomes. For example, primary interventions are directed toward achieving outcomes in which the goal is to keep problems from emerging. Secondary interventions are used to achieve goals and outcomes where the focus is on reducing or eliminating the emergent problems of already behaviorally at-risk students. Finally, tertiary interventions produce focused outcomes for student populations having severe risk status. This delivery model allows for a more cost-effective use of available, schoolbased resources. The schema or heuristic in Figure 5.4, sometimes referred to as the teaching pyramid, has also been adapted for use in coordinating interventions for very young preschool children by Fox, Dunlap, Hemmeter, Joseph, and Strain (2003). The SSBD screening system parallels and lines up seamlessly with the three-tiered model of increasingly intensive intervention approaches that are built into the RtI model. For instance, as one moves through the sequential SSBD gates, those students who remain in the screening pool (i.e., are not screened out) are more likely to be candidates for secondary and tertiarylevel PBS interventions and supports (Walker, Severson, & Seeley, in press). In particular, those students who have positive profiles on the Critical Events Index at screening gate 2 may be especially at risk and in need of intensive, indicated interventions at the tertiary level. Further, the screening tools and procedures of the three SSBD screening gates also parallel assessment needs at each of the three-tiered levels within the RtI model.

108 Hill M. Walker et al. Preventing violent and destructive behavior in schools: Integrated systems of intervention Assessment

Intervention

Full risk and protective factor assessment Suspension and expulsion data Youth services contacts

Students with chonic/ intense problem behavior (1–7%)

Students at-risk for problem behavior (5–15%)

Students without serious for problem behaviors (80–90%)

Tertiary prevention

• •

Secondary prevention

• •

Primary Universal screening prevention Academic progress

• •

Brief functional assessment School records review Discipline referrals

Specialized individual interventions Individual student servies Wraparound services (family, community) Specialized targeted interventions First step to success Treatment packages ♦ Self management ♦ Scheduling ♦ Academic support

Universal interventions Social skills training High academic expectations • School-wide rules • ATOD education

Figure 5.4 Preventing Violent and Destructive Behavior in Schools.

There are at least five potential applications of a universal screening system like the SSBD within an RtI-PBS context. They include: (1) universal screening to identify candidates who will likely need exposure to secondary and tertiary-level interventions; (2) assessments to monitor progress within PBS tiers and for determining whether the student should move to a different level of intervention and support; (3) as a source of information for designing interventions at both group and individual levels; (4) as a tool for planning staff development training and technical assistance initiatives based on analysis of teacher ratings of behavioral deficits and excesses collected at screening gate 2 for at risk students; and (5) as one index of how well a school-wide intervention is functioning based on the severity of gate 2 SSBD behavioral profiles of teacher-nominated students within a single classroom or a series of classrooms in a school. That is, those students who pass out of screening gate 2 should be fewer in number and have less problematic behavioral profiles, compared to national norms, if a schoolwide behavioral intervention is operating effectively. The case study described below is an example of the SSBD being used within an RtI-PBS context and nicely illustrates this type of SSBD application. Naquin and D’Atrio of the University of New Orleans describe how they integrated the SSBD screening system into a larger assessment battery that provides multiple information sources on the impact of their Pupil Assistance Model in addressing the needs and challenges of behaviorally at-risk students and their teachers.

Illustration of a Multi-Year, Case Study Application of the SSBD Within a Court-Mandated Initiative to Improve Services and Supports for EBD Students The material herein describes an actual application of the SSBD screening procedure within an RtI-PBS context for the purpose of responding to the mandates of a court-ordered child find process. Specifically, this court-ordered initiative was prompted by a continuing failure of

Implementing Universal Screening Systems Within an RtI-PBS Context 109 a Louisiana School District (Jefferson Parish Public Schools) to adequately screen, identify, place and serve students who are at risk for emotional and behavioral disorders (EBD) within general education settings. The SSBD fulfilled a number of purposes in this multi-year process including: (1) serving as a child find-screening process; (2) providing a template for developing staff training regimens for participating schools; and (3) evaluating outcomes. Details of this initiative follow. Description of Jefferson Parish Public Schools The Jefferson Parish Public School System (JPPSS) is a local education agency in the Greater New Orleans Region and forms the largest metropolitan area in Louisiana. According to the U.S. Census Bureau, as of July 1, 2007, the Greater New Orleans population was 704,010. At that time, the population in Jefferson Parish was reported to be 423,520 persons based upon approximately 98% of the population reported by the Census in 2000. In addition to being the largest metropolitan area in Louisiana, JPPSS ranks as the largest school district in the state. JPPSS consists of 88 schools located on the east and west banks of the Mississippi River in Jefferson Parish. Student enrollment during the 2007–2008 school year was 44,019 students enrolled in kindergarten through the 12th grade. JPPSS student enrollment currently is comprised of 33% Caucasian and 67% minority students with 61.9% of the students participating in the free or reduced lunch program. JPPSS is recognized as having the largest limited English-proficient student population in Louisiana with more than 2,500 students eligible for services. These students represent more than 68 countries with 52 spoken languages. There are 36 non-public schools located in Jefferson Parish that serve an additional 18,883 students in the community. Background of the CAP Initiative The Pupil Assistance Model (PAM) team at the University of New Orleans (Naquin & D’Atrio) began working in JPPSS elementary schools in 2000 providing staff development and consultation services. By 2003, the PAM team began to focus consultative services on the implementation of a Response to Intervention (RtI) model. At that time, services were provided in 10 identified “vanguard” schools and assistance focused on the identification of students needing intervention in the areas of reading and mathematics. JPPSS Corrective Action Plan—2005. Implementation of the current PAM-RtI/PBS model in JPPSS was facilitated by the issuance of a Corrective Action Plan (CAP) that resulted from monitoring of the district conducted by the Louisiana Department of Education during 2005. In the Mediated Settlement Agreement, the administrators of the CAP identified nine areas in need of remediation as described below. The first goal stipulated that teachers and administrators in JPPSS be urged to comply with all conditions of the CAP and that parish personnel be educated about provisions of each goal. Second, and central to the core findings of the Settlement Agreement, was that JPPSS “develop, devise, and implement an effective positive behavioral intervention and support program for all students.” The stated intent of implementing such a program would also indirectly increase the access that students with EBD would have to the general education setting. Thus, in response to the requirements of goal 2, JPPSS adopted a Positive Behavior Support model for the entire school district and the associated training initiatives recommended by the Louisiana Department of Education.

110 Hill M. Walker et al. Goal 3 pertained to restructuring of the special education pre-referral process. While Louisiana had established School Building Level Committee (SBLC) teams at every school (as mandated by law), their intended functions as problem-solving bodies were never fully executed. Historically, the SBLC system resulted in a “test and place” process that simply facilitated the placement of students into special education. At the time that the CAP was initiated, a very high percentage of referrals to the existing SBLCs resulted in identification and placement into a special education setting. Research conducted by Algozzine, Ysseldyke, and their colleagues has indicated that approximately 97% of all referrals to SBLCs have resulted in identification and placement into a special education setting (Algozzine, Ysseldyke, & Christenson, 1983; Ysseldyke, Vanderwood & Shriner, 1997). Additionally, and perhaps just as alarming, was the finding that JPPSS had over 2½ times as many students with EBD (12.8%) as the state average (4.5%) (Louisiana Department of Education, 2005). Goal 3 of the CAP also required that the existing SBLC process be redefined and reorganized into a true problem-solving model that used evidence-based interventions. Thus, the SBLC teams in JPPSS were renamed as the Academic and Behavior Intervention Teams (ABITs), with school-level participation being required of at least three regular education teachers, two special education experts (which could include members of the Pupil Appraisal Unit) and a school administrator. The CAP also called for implementation of a three-tiered Response to Intervention (RtI) model of service delivery that operated within the context of general rather than special education. The purpose of this change was to create an effective, cost-efficient and responsive process to manage and oversee the assessment and intervention of student needs on every level within each participating school site. The University of New Orleans PAM team has been most directly involved with JPPSS in addressing this goal. The fourth provision of the CAP as specified in goal 4 pertained to students in JPPSS with emotional or behavioral disorders (EBD). This goal required that alternative methods be developed to handle behavior and discipline concerns of students with EBD and that behavior management methods other than school suspensions and expulsions be used to manage student problem behavior. This goal also focused on how the provisions of student Individual Education Plans (IEPs) were developed and implemented. The five remaining goals pertained to providing services for students identified as having EBD. These goals were included to increase the quality of related services delivered by qualified service providers, to improve overall accountability measures of tracking services for students with EBD, and to enhance the knowledge base of school personnel and those who work directly with these students. Another CAP goal called for the provision of higher-quality and more programmatic transition services to students with EBD. Finally, the CAP obligated JPPSS to review the access that students with EBD had to the general education setting and provide assurances that these students received special education programming in the Least Restrictive Environment (LRE). The initial implementation template of the RtI model proposed by the CAP delayed referral to the school-based problem-solving team until the third tier of the three-tiered intervention process was reached. The PAM team revised this proposed rule to include the oversight of students needing intervention at all levels, not just the third or tertiary-level tier. The involvement of the ABIT problem-solving team in the PAM version of the RtI/PBS model was essential at all tiers of the intervention model so that appropriate interventions could be delivered via a seamless continuum of service delivery. Another key component of the PAM model was the emphasis it placed on the key role of the school-based, problem-solving team (ABIT) within each school. The ABIT is consolidated so

Implementing Universal Screening Systems Within an RtI-PBS Context 111 that communications across departments are enhanced and all students are considered in team decision-making processes. The problem-solving team (ABIT) at each school is comprised of members from the school’s administration, general education staff, pupil appraisal staff, and representatives from other compensatory programs such as Section 504, and special education. At the district level, a Consolidated Team consisted of representatives from essential and compensatory district programs including representatives from general education, special education, Positive Behavior Support, Section 504, Technology, Accountability, Safe and Drug Free Schools and consultants from the University of New Orleans. The purpose of creating a consolidated team on the district level and in each school was to foster development of a true seamless model of service delivery across departments, service areas, and educational entities with the entire school system, and eventually the community. JPPSS is at the time of this writing in the third year of implementing the PAM-RtI/PBS model in its 53 elementary schools and is expanding the model to middle and high schools for the 2008–2009 school year. The universal screening procedures utilized within the PAM model are described below. Using the Systematic Screening for Behavior Disorders (SSBD) to Identify EBD Students 2006–2007 SSBD Screenings. During the 2006–2007 school year, the SSBD was used to screen general education students respectively in grades 1–5 in all 53 elementary schools within JPPSS. Of the students included in the screenings, 49.3% were African American, 33.7% were Caucasian, and 17.0% were distributed across an Other category. For the first year of screening using the SSBD in JPPSS, the PAM team conducted all screenings in the 53 elementary schools. This team was comprised of Master’s and Doctoral-level staff with a primary background or certification as either an Educational Diagnostician or School Psychologist. All screening staff had at least 30 hours of graduate coursework in assessment and diagnostic methods. In January of 2007, training was conducted with the 53 elementary school Academic and Behavior Intervention Teams (ABITs) regarding the utilization and philosophy behind using the SSBD, the administration of the SSBD, and the scoring and interpretation of data. During the second semester of the 2006–2007 year, SSBD screenings were conducted at each school in either a faculty meeting (typically one to 1½ hours) or in grade-level meetings (i.e., during teacher planning periods). To assist the school system, all gate 1 and gate 2 protocols were collected by UNO personnel. SSBD gate 2 protocols were scored and data were entered into a Microsoft Excel spreadsheet for each school. The Excel spreadsheet for each school included a summary page of all SSBD data for the school, and summaries of at-risk students according to name, teacher, grade and Critical Events Index score. Finally, each spreadsheet included a grade-by-grade roster of all students who were either nominated or considered to be behaviorally “at-risk.” Spreadsheets were disseminated to schools by members of the UNO PAM team and results were discussed with school administrators and members of the ABIT. Of the 22,101 students screened in spring, 2007, a total of 3,488 students were nominated by their teachers (approximately 15.8% of all students screened) for further screening using gate 2 SSBD measures. Across the 53 schools, the percentage of students nominated ranged from 3.7% to 34.3%. Of those students nominated, 1,533 students were determined to be at-risk according to the SSBD criteria and cutoff scores. The percentage of at-risk students ranged from 1.3% to 14.6% of the students screened, which was 6.9% of the total student population. Of the 1,533 identified at-risk students, 71.7% were male and 28.3% were female; 66.6% of the

112 Hill M. Walker et al. at-risk students were Externalizers and 33.4% were Internalizers. These statistics are consistent with findings reported by Walker and Severson (1990) for the SSBD standardization data (Walker & Severson, 1990). 2007–2008 SSBD Screenings. In the fall of 2007, JPPSS Pupil Appraisal staff members, comprised of Educational Diagnosticians, School Psychologists, and Social Workers, were trained in the SSBD’s administration, scoring and data interpretation procedures. For the second year of behavioral screening, Pupil Appraisal staff assisted the UNO PAM team in the administration of the SSBD in the 53 participating elementary schools. To enhance the likelihood that administration procedures would be similar across the school district, each member of the Pupil Appraisal team was given a Coach Card (developed by UNO) that outlined the step-bystep administration of the SSBD. During the 2007–2008 school year, SSBD screenings began in mid-October and data for most schools were collected by the end of November. UNO personnel collected gate 1 and gate 2 protocols and data were entered into spreadsheets as they were during the 2006–2007 school year. Spreadsheets for the SSBD data collected during the second year of screening were expanded and refined. Specifically, more descriptive and demographic data were provided on the school summary sheet and two additional pages were added to each file. UNO staff developed an SSBD Item Analysis process that provided a frequency count for each item on the Critical Events Index (CEI) of the SSBD according to gender and grade. Each time one of the 33 CEI items was checked by a teacher it was tallied on the Item Analysis sheet. Each tally was placed into a box that specified the grade of the student and whether the behavior was produced by a male or female student and whether he or she was an Externalizer or Internalizer. This process was followed for all protocols received from each school, regardless of whether the student was simply nominated or at-risk. A similar format was used to analyze scores on the Adaptive and Maladaptive Behavior Scales. The Adaptive Behavior Scale of the SSBD assesses the frequency of behaviors that are considered to be socially desirable and appropriate to the classroom. Each of the 12 items on this scale is assigned a score ranging from 1 to 5, depending on frequency of occurrence. A score of 1 indicates that the student “Never” exhibits the behavior in question and a rating of 5 indicates that the student “Frequently” exhibits the behavior. Students can receive a maximum score of 60 on the Adaptive Behavior Scale, with higher scores indicating that students frequently exhibit socially desirable or appropriate forms of expected classroom behavior. For the Item Analysis procedure, a tally was kept for an item on the Adaptive Behavior Scale that received either a score of 1 or 2, indicating that a student never or seldom exhibited that desired behavior. Tallies were entered into boxes that specified student grade, gender and whether the student was identified as an Externalizer or Internalizer. On the Maladaptive Behavior Scale, students were rated on 11 items according to the frequency with which they exhibited undesirable behaviors. Student ratings range from 1 to 5, with a score of 1 representing “Never,” and 5 representing “Frequently.” Students can receive a maximum score of 55 on the Maladaptive scale, with higher scores indicating that students frequently exhibit inappropriate or socially undesirable behaviors. For the Item Analysis, a tally was developed for an item on the Maladaptive Behavior Scale if a student was assigned a score of 3 or higher. Each tally was entered into boxes that again specified student grade, gender and whether he or she was identified as an Externalizer or Internalizer. As noted, the Item Analysis procedure was developed by the UNO PAM team to assist with the specification of areas of concern identified within each school. Positive Behavior Support teams were able to utilize data from the Item Analysis to more accurately develop school-wide training plans for each participating school. Information from the Item Analyses for the

Implementing Universal Screening Systems Within an RtI-PBS Context 113 Adaptive and Maladaptive Behavior scales was also used to guide social skills training in areas needing more intensive attention. By using the Item Analysis, administrators and members of the ABIT and PBS teams were able to obtain information (per teacher report) about specific behaviors and/or areas in their respective schools as well as subgroups of students needing more focused intervention. The Item Analysis for the CEI provided information about the behaviors most frequently reported by teachers to be seriously problematic (e.g., “Ignores teacher warnings”). Further examination of the Item Analysis also provided information about whether intervention should be directed toward a specific grade (e.g., fourth grade), gender, or at-risk subgroup (e.g., Externalizer or Internalizer). An additional component pertaining to administration of the SSBD in 2007–2008 was the manner in which information from three items on the Critical Events Index was handled. Discussion between UNO personnel and JPPSS administrators led to a determination that items #18, #22 and #24 required mandatory reporting by the school administration and UNO personnel if checked off by a teacher. These items refer to the presence or evidence of a student having suicidal (or death-related) thoughts and the suspicion of a student having been either physically or sexually abused. The district decided that if a protocol was found to have a check by any of the three items, a report was to be made immediately to the school principal. During the 2007–2008 school year, the SSBD was used to screen 16,634 JPPSS students in grades 1–5. Five sixth-grade classes were also included. Of the total number of students screened, 3,521 (approximately 21% of the screened population) were nominated for gate 2 assessments by general education teachers. Of those students nominated, 1,299 were determined to be behaviorally at-risk based on their SSBD gate 2 profiles. Across the 53 schools, the percentage of students nominated ranged from 5.1% to 33.8% and the percentage of students judged to be at-risk in each school ranged from 2.0% to 21.7%. Overall, 7.8% of all students screened were at-risk as compared to 6.9% of all students in the previous school year. The percentages of males and females identified as at-risk were identical to the percentages identified during the first year of screening (i.e., 71.7% males, 28.3% females). The distribution of Externalizers and Internalizers was also quite similar (i.e., 66.6% Externalizers, 33.4% Internalizers). Operating Details of the Pupil Assistance Model (PAM) at Tiers I, II, and III The PAM applies a comprehensive Response to Intervention (RtI) and Positive Behavior Support (PBS) framework with the goal of incorporating research-based assessment tools and evidence-based interventions into a systematic approach that yields positive results for students. PAM is a multi-tiered academic and behavior-focused model in which both assessment and intervention processes increase in intensity and measurement precision at each successive tier. The Pupil Assistance Model includes the following components: • • •

Tier I: General Education—Universal screening, school and/or class-wide interventions Tier II: Standard Protocol—Small group and targeted interventions Tier III: Problem-Solving—Individual and more intensive services

The PAM model is based on the best practices of providing high-quality instruction and interventions carefully matched to student need, monitoring progress frequently to make needed changes in instruction, and applying student response data in making important educational decisions. PAM promotes the use of Curriculum-Based Measurement (CBM) for academic universal screening and frequent progress monitoring. The behavioral component of the

114 Hill M. Walker et al. model incorporates the use of Office Discipline Referral (ODR) data and the Systematic Screening for Behavior Disorders procedure as measures to align student needs with effective behavioral interventions. The integrated PAM model has produced improved academic and behavioral outcomes for JPPSS students. PAM also encourages evaluating school climate by using a measure such as the Safe School Assessment and Resource Bank (SSARB) as an initial assessment when developing a culture of positive behavioral support. Below is a brief description of the PAM process at Tiers I, II, and III. Central to high-fidelity implementation of the PAM model is the establishment of a school-based problem-solving team that functions to oversee and guide assessment and interventions on each level of the tiered model. Tier I: General Education—Universal Screening, School and/or Class-Wide Interventions. In Tier I, all students are screened in reading, math and behavior to determine who has satisfactory competence levels in specific academic areas and demonstrates expected levels of appropriate behavior. This information provides teachers and administrators with the necessary information to select curricula, design instruction, and implement behavior strategies to prevent school failure. After screening in reading and math, assessments are scored, graphed, and the data are analyzed by grade, class and student. If at least 50% of the students in a classroom are not performing at an appropriate level as determined by normative comparisons, then the possibility of inappropriate instruction, ineffective classroom management, or a potential curriculum change is considered. If at least 50% of the students have performed at a level consistent with grade-level norms and expectation, yet some students are still performing within the “at-risk” range, those students are further assessed to determine if poor motivation or a lack of skills or both are factors in their performance problems. If either or both are determined to be contributing factors, these students are referred on to Tier II. In the behavioral area, screening is accomplished by examining Office Discipline Referral (ODR) data and administering the Systematic Screening for Behavior Disorders (SSBD). Office Discipline Referral data are analyzed to identify areas of concern that may impact the entire school, or large numbers of students, such as the time of day of behavioral incidents, location, and type of offense and so forth. If 20% of the student population is “at risk” for a particular offense, or 20% of the students repeatedly get in trouble in a particular setting or time of day, the School-Wide Positive Behavior Support Plan is revised to address these concerns. Next, the SSBD is administered to all students (with the exception of those already identified as receiving special education) during the fall semester. Any target student meeting gate 2 SSBD exit criteria is then considered for referral to Tier II or possibly Tier III interventions and/or assessments. PAM encourages the use of item analyses on the gate 2 rating scales to determine whether school-wide, grade-level or individual classes need a specific intervention focus. Tier II: Standard Protocol—Small Group and Targeted Interventions. Tier II is designed for small groups and individual students who may need more targeted and intensive intervention. Systematic interventions with standard Coach Cards are generally used at this level. Interventions may include differentiated instruction, a different type of program, or targeted behavior strategies such as Check-In/Check-Out (CICO). Additionally, peer tutoring, peer mentoring, psycho-educational groups and/or a parent training component aspect may be added as intervention support in Tier II. For academic interventions, aim lines are set using Curriculum-Based Measurement (CBM) procedures. Data are collected daily and examined after every five to seven data points by calculating the level and rate of student progress. Decisions are made based on the students’

Implementing Universal Screening Systems Within an RtI-PBS Context 115 success or failure to respond to appropriate interventions that have been implemented with solid treatment fidelity. These decisions may include continuing the intervention, modifying the intervention and/or strategy; moving back to Tier I or intensifying the intervention at a Tier III level. Behavior interventions follow similar procedures. Goals are set, behavior is monitored carefully using teacher ratings, observations, work completion, attendance, and/or Office Discipline Referral data. Aggregated data are also examined at least every five to seven data points to determine the students’ success or failure in responding to interventions that have been implemented with treatment fidelity. The decision options are to continue the intervention, change the intervention and/or strategy, return to Tier I or move the student on to Tier III. Tier III: Problem-Solving—Individual and More Intensive Services. An individualized problem-solving approach is generally used at Tier III for the persistent and atypical problems which are not resolved by the more standard and manualized interventions at Tier II. Generally, these procedures are applied with individual students. The design of Tier III interventions is guided by systematic assessment and review of the problem; hypothesis development is intended to target specific academic and/or behavior skill deficits. Examples of academic interventions may include instructing students on a lower grade level or teaching a prerequisite skill set. Typically, students assigned to Tier III due to behavioral issues will have a functional behavior assessment (FBA) completed to assist in the problem-solving process. The FBA helps identify significant, pupil-specific, social, affective, cognitive and/or environmental factors associated with the occurrence and non-occurrence of specific behaviors. On the Tier III level, data are collected daily and examined every five to seven data points to determine the students’ success or failure to respond to the intervention. The resulting decisions may be to continue the intervention, change the intervention and/or strategy, or in rare cases to move a student back to Tier I or Tier II. Students who are not successful at this level may eventually be referred for special education services and may involve tertiary resources such as child mental health and/or other social service agencies. The Pupil Assistance Model application of RtI and PBS, as described herein, can be conceptualized as a continuum of programs and services for students having moderate to severe academic and behavior difficulties. PAM is an RtI/PBS multi-tiered service delivery system critical to meeting NCLB and IDEA regulations. The continuous monitoring of the adequacy of student responses to well-implemented, universal curriculum and positive behavior support procedures is particularly relevant to the PAM approach as a means of determining whether existing instruction and support are sufficient. The overarching goals of the PAM are: (1) to ensure that quality instruction, good teaching practices, differentiated instruction, remedial opportunities and evidence-based, behavior strategies are available and accessible within general education; and (2) that special education is provided for students with disabilities who require more specialized services than can be provided in a general education context. PAM appears to be a promising model for jointly addressing academic and behavior problem domains within an integrated framework. Future research on its outcomes with at-risk student populations, and their sustainability, will help establish the parameters of its efficacy.

Directions for Future Research, Practice and Policy The origins of antisocial behavior patterns appear to be in evidence at an increasingly early age and it is known that these behaviors can be prevented from escalating into more serious and

116 Hill M. Walker et al. intractable problems (Strain & Timm, 2001). Effective practices for this student population in schools should include universal screening procedures to provide early detection, schoolbased interventions, training in parenting skills, and teacher in-service training, all of which have been empirically demonstrated to increase prosocial behavior and reduce aggressive behavior problems (Reid, 1993; Walker, Ramsey, & Gresham, 2004). Prevention begins with early detection and as RtI-PBS approaches continue to be adopted by schools, it is imperative that a standard universal screening component be adopted as a continuing part of their routine procedures. The movement toward using an RtI-PBS approach, as illustrated by the PAM model described above, can address both academic and behavior concerns but it needs to begin with increased awareness of the scope and extent of the problem. Teachers, administrators, policymakers, legislators, staff development specialists, and researchers alike need to become aware of the integral relationships that exist between academic and behavioral performance. Continuing to address them as independent issues can lead to stunted progress for many students and continued frustration for teachers (Dodge, 2008). Along with increased awareness, there needs to be a more robust knowledge base developed to support the potential of the RtIPBS heuristic to positively influence student performance across these domains and using a school-based, team approach (Albers, Glover, & Kratochwill, 2007; Elliott, Huai, & Roach, 2007). This team should include, at a minimum, general and special education teachers, school psychologists, behavior specialists, reading specialists, and administrators. In order for RtI to effectively address behavioral and academic domains, currently available technology needs to be improved and expanded. Specifically, a system for integrating academic and behavior data needs to be developed so that it can be aggregated and accessed for rapid, high-quality decision-making. In this regard, the authors of the SSBD procedure are currently in the process of developing a web-based, electronic version of the instrument that will allow for administration, scoring and profiling of student outcomes. Existing software such as Discipline Tracker or SWIS may be able to be modified to integrate academic data. It will also be important to develop behavioral measures that can be given repeatedly and remain sensitive to change over time similar to the DIBELS measures for reading performance (see Gresham, in press). To do so will require the identification of meaningful behavioral benchmarks and normative criteria for grade levels by gender and developmental stages. We believe the risk factors that result in children coming to school suffering from palpable, environmentally induced damage can be effectively addressed by a coordinated continuum of effective early interventions, including the PAM approach illustrated herein, that address the needs of severely at-risk children from the beginning of their school careers. Compelling longitudinal research by Hawkins et al. (1999) shows that comprehensive early intervention, delivered in the first three grades of school and that involves parents, teachers, peers, and the target child, provides long-term protection against a number of health-risk behaviors at age 18. These risks include delinquent acts, school failure and dropout, teenage pregnancy, heavy drinking, school behavior problems, and having multiple sex partners. We cannot afford to ignore the enormous policy implications of these and similar robust findings. We currently have the knowledge and available expertise to implement these prevention initiatives with good integrity using an RtI-PBS framework. For example, Walker, Seeley, Small, Severson, Graham, Feil, Serna, Golly, and Forness (2009) recently reported results of a four-year, randomized controlled trial that illustrates the combined use of the SSBD screening system with the First Step to Success early intervention program which addresses secondary prevention goals in school and home contexts (Walker, Kavanagh, Stiller, Golly, Severson, & Feil, 1998). Prevention initiatives of this type for at-risk students, mounted early in their

Implementing Universal Screening Systems Within an RtI-PBS Context 117 school careers, appear to be increasingly in evidence within the educational and psychological literatures. However, as yet, we have not demonstrated the will, on a broad-based scale, to (a) assume ownership of the problems that these prevention efforts are designed to address; (b) invest the resources necessary to support their high quality implementation; and (c) provide the long-term supports that will ensure their maintenance and durability. We are hopeful that the next decade will see positive changes in the policies of schools, mental health systems, social services agencies, and legislative bodies that will allow these important goals to be realized.

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Implementing Universal Screening Systems Within an RtI-PBS Context 119 Loeber, R., & Farrington, D. P. (Eds.). (1998). Serious and violent juvenile offenders: Risk factors and successful interventions. Thousand Oaks, CA: Sage Publications. Louisiana Department of Education (2005). State special education data profile—2005. Baton Rouge, LA. Louisiana Department of Education (2008). District composite report—Jefferson Parish. Baton Rouge, LA. May, S., Ard, W., Todd, A., Horner, R., Glasgow, A., & Sugai, G. (2001). School-wide information system. Eugene, OR: University of Oregon, Educational and Community Supports Merrell, K. W. (1999). Behavioral, social, and emotional assessment of children & adolescents. Mahwah, NJ: Lawrence Erlbaum Associates. Merrell, K. W. (2001). Assessment of children’s social skills: Recent developments, best practices, and new directions. Exceptionality, 9(1 & 2), 3–18. NASP Position Statement on Early Childhood Care and Education (2002). http://www.naspoline.org/ about_nasp/pospater_earlychild.aspx. Bethesda, MD: National Association of School Psychologists. Patterson, G. R., Reid, J. B., & Dishion, T. J. (1992). Antisocial boys. Eugene, OR: Castalia. Reid, J. B. (1993). Prevention of conduct disorder before and after school entry: Relating interventions to developmental findings. Development & Psychopathology, 5, 311–319. Ross, A. (1980). Psychological disorders of children: A behavioral approach to theory, research and therapy (2nd ed.). New York: McGraw-Hill. Severson, H., Walker, H. M., Hope-Doolittle, J., Kratochwill, T., & Gresham, F. M. (2007). Proactive, early screening to detect behaviorally at-risk students: Issues, approaches, emerging innovations, and professional practices. Journal of School Psychology, 45(2), 193–224. Shinn, M. (2007). Identifying students at risk, monitoring performance, and determining eligibility within response to intervention: Research on educational need and benefit from academic intervention. School Psychology Review, 36(4), 601–617. Sprague, J. & Walker, H. M. (in press). Building safe and healthy schools to promote school success: Critical issues, current challenges, and promising practices. In M. Shinn & H. Walker (Eds.), Interventions for achievement and behavior problems in a three-tier model including Response to Intervention. Bethesda, MD: National Association of School Psychologists. Strain, P. S., & Timm, M. A. (2001). Remediation and prevention of aggression: An evaluation of the Regional Intervention Program over a quarter century. Behavioral Disorders, 26(4), 297–313. Sugai, G. & Horner, R. (2002). The evolution of discipline practices: School-wide positive behavior supports. Child and Family Therapy, 24(1/2), 23–50. Sugai, G., Sprague, J., Horner, R., & Walker, H. (2000). Preventing school violence: The use of office discipline referrals to assess and monitor school-wide discipline interventions. Journal of Emotional and Behavioral Disorders, 8, 94–101. Tobin, T. & Sugai, G. (1999). Using sixth-grade school records to predict violence, chronic discipline problems, and high school outcomes. Journal of Emotional and Behavioral Disorders, 7, 40–53. Todis, B., Severson, H., & Walker, H. M. (1990). The critical events scale: Behavioral profiles of students with externalizing and internalizing behavior disorders. Behavioral Disorders, 15(2), 75–86. Walker, B., Cheney, D., Stage, S., Blum, C., & Horner, R. (2005). School-wide screening and positive behavior supports: Identifying and supporting students at risk for school failure. Journal of Emotional and Behavioral Disorders, 7, 194–204. Walker, H. M., Block-Pedego, A., Todis, B., & Severson, H. (1991). School archival records search (SARS): User’s guide and technical manual. Longmont, CO: Sopris West. Walker, H. M., Colvin, G., & Ramsey, E. (1995). Antisocial behavior in schools: Strategies and best practices. Pacific Grove, CA: Brooks/Cole. Walker, H. M., Horner, R. H., Sugai, G., Bullis, M., Sprague, J. R., Bricker, D., & Kaufman, M. J. (1996). Integrated approaches to preventing antisocial behavior patterns among school-age children and youth. Journal of Emotional and Behavioral Disorders, 4, 194–209. Walker, H. M., Kavanagh, K., Stiller, B., Golly, A., Severson, S., & Feil, E. (1998). First step to success: An early intervention approach for preventing school antisocial behavior. Journal of Emotional and Behavioral Disorders, 6(2), 66–80.

120 Hill M. Walker et al. Walker, H. M., Nishioka, V. M., Zeller, R., Severson, H. H., & Feil, E. G. (2000). Causal factors and potential solutions for the persistent under-identification of students having emotional or behavioral disorders in the context of schooling. Assessment for Effective Intervention, 26(1), 29–40. Walker, H. M., Ramsey, E., & Gresham, F. M. (2004). Antisocial behavior in school: Evidence-based practices (2nd ed.). Belmont, CA: Wadsworth/Thomson Learning. Walker, H. M., Seeley, J., Small, J., Severson, H., Graham, B., Feil, E., Serna, L., Golly, A., & Forness, S. R. (2009). A randomized controlled trial of the First Step to Success early intervention: Demonstration of program efficacy outcomes in a diverse, urban school district. Journal of Emotional and Behavioral Disorders, 17(4), 197–212. Walker, H. M., & Severson, H. H. (1990). Systematic Screening for Behavior Disorders (SSBD): User’s guide and technical manual. Longmont, CO: Sopris West. Walker, H. M., Severson, H., Nicholson, F., Kehle, T., Jenson, W., & Clark, E. (1994). Replication of the Systematic Screening for Behavior Disorders (SSBD) procedure for the identification of at-risk children. Journal of Emotional and Behavioral Disroders, 2(2), 66–77. Walker, H. M., Severson, H., & Seeley, J. (in press). Universal, school-based screening for the early detection of behavioral problems contributing to later destructive outcomes. In M. Shinn & H. Walker (Eds.), Interventions for achievement and behavior problems in a three-tier model including Response to Intervention. Bethesda, MD: National Association of School Psychologists. Walker, H. M., Severson, H., Stiller, B., Williams, G., Haring, N., Shinn, M., & Todis, B. (1988). Systematic screening of pupils in the elementary age range at risk for behavior disorders: Development and trial testing of a multiple gating model. Remedial and Special Education, 9(3), 8–14. Walker, H. M., Severson, H. H., Todis, B. J., Block-Pedego, A. E., Williams, G. J., Haring, N. G., & Barckley, M. (1990). Systematic screening for behavior disorders (SSBD): Further validation, replication and normative data. Remedial and Special Education, 11(2), 32–46. Walker, H. M., Stieber, S., Ramsey, E., & O’Neill, R. (1993). Fifth grade school adjustment and later arrest rate: A longitudinal study of middle school antisocial boys. Journal of Child and Family Studies, 2(4), 295–315. Weissberg, R. (2005, August). Social and emotional learning for life success. Paper presented at the annual meeting of the American Psychological Association, Washington, DC. Ysseldyke, J., Vanderwood, M., & Shriner, J. (1997). Changes over the past decade in special education referral to placement probability: An incredibly reliable practice. Diagnostique, 23(1), 193–201. Zigler, E., Taussig, C., & Black, K. (1992). Early childhood intervention: A promising preventative for juvenile delinquency. American Psychologist, 47, 997–1006.

6

Building Conditions for Learning and Healthy Adolescent Development A Strategic Approach David Osher and Kimberly Kendziora American Institutes for Research

Schools can play a key role in promotion, prevention, and adolescent development. In schools, students confront major developmental challenges, are judged by important natural raters, interact with prosocial and antisocial peers, and acquire (or fail to acquire) important capacities and credentials that may enable them persevere or thrive. From the perspective of risk and protection, schools may function as a protective factor, creating a safe harbor and sense of safety, offering challenges and a sense of mission, fostering positive relationships with adults and prosocial peers, developing competencies and a sense of efficacy, and providing students with access to social capital, mental health supports and youth-development opportunities. Unfortunately, schools—particularly many serving students of color—may also be stressful places that function as a risk factor, as youth adapt their behaviors to relatively inflexible bureaucratic structures and adult-driven demands within a high-stakes environment (e.g., Eccles & Midgely, 1989). Instead of safe harbors, schools can be treacherous whirlpools, exposing students to physical and emotional violence, boredom, alienation, and academic frustration, negative relationships with adults and peers, teasing, bullying, gangs, public humiliation and failure, segregation with antisocial peers, harsh punishment, and expulsion from the school community and its resources. In this chapter, we outline the role of schools in student development, describe an intervention that has been implemented successfully, describe the intervention’s development, and discuss the implications of the intervention, including research findings that emphasize the importance of improving the social and emotional conditions for learning to prevention initiatives.

Conditions for Learning in Schools Schools can be “lifelines . . . opportunities which potentially lead from pathways associated with deviant or destructive outcomes” (Cairns & Cairns, 1994, p. 6). For schools to play this role they need to protect and hold on to all their students and to help them develop the academic, social, and emotional competencies that they will need to succeed as adults. Longitudinal analyses from the Woodlawn study of 1,000 African American children found that early school performance and school (as well as family) attachment were related to adolescent substance use, delinquency, and school dropout (Ensminger, Kellam, & Rubin, 1983; Ensminger & Slusarcick, 1992) and that strong school (as well as family) bonds were protective for African American children who grew up in poverty in Chicago (Ensminger & Juon, 1998) Successful schools create and maintain what we refer to as effective conditions for learning. Such conditions typically include high-quality pedagogy, well-trained teachers, adequate resources, and effective leadership. Another equally, if not more, important set of conditions

122 David Osher and Kimberly Kendziora is called the social and emotional conditions for learning. Students who feel “connected” to school across these social/emotional indicators are more likely to have improved attitudes towards school, learning, and teachers; heightened academic aspirations, motivation, and achievement; and more positive social attitudes, values, and behavior (Allensworth & Easton, 2007; McNeeley, Nonnemaker, & Blum, 2002; Resnick et al., 1997). Recent research emphasizes the view that learning is optimized only after students’ social, emotional, and physical needs have been successfully met (CASEL, 2003; Learning First Alliance, 2001; Osher, Dwyer, & Jackson, 2004). The social and emotional conditions for learning include a number of factors that are essential for ensuring that students feel safe and supported in school. Specifically, four components define the social and emotional conditions for learning: (a) school safety, (b) challenge, (c) student support, and (d) social and emotional learning. Research and practical experience demonstrate that a high level of school safety and student discipline, that comes as a result of challenging curriculum and instruction and effective supports, reduces administrative burdens and allows teachers to spend more time on the task of raising academic performance (Osher, Dwyer, & Jackson, 2004). School safety refers to an overall school climate in which students feel physically and emotionally safe. There is little to no violence, fighting, bullying, crime, substance abuse, or gang presence. Overall, there is a climate of mutual respect and trust among all members of the school community, and students feel comfortable in taking personal and academic risks. Failure to support academic achievement is related to students’ disengagement from school and increased risk-taking behavior (Blum, Beuhring, & Rinehard, 2000). A safe and supportive learning environment fulfills students’ basic psychological needs for belonging, autonomy, influence, competence, and physical security. As these needs are met, students tend to become increasingly committed to the school community’s norms, rules, and values (Learning First Alliance, 2001). Research also shows that the physical environment can have a profound effect on the ability of students to learn efficiently (National School Boards Association, 1996). An analysis in schools of such areas as bathrooms, cafeterias, hallways, and isolated areas can determine if safety “hot spots” exist. With this information, a school improvement team can change the environment to minimize opportunities for inappropriate behavior (Osher, Dwyer, & Jimerson, 2006). In addition, providing teachers and other staff with opportunities to influence decisions on school safety policy (as well as other student connection indicators) can help to create a more cohesive, well functioning professional community (Smylie, 1994). Finally, students who participate in structured extracurricular activities are less likely to engage in negative and risky behaviors and have better attendance, lower dropout rates, lower rates of drug use, higher academic achievement, and higher aspirations than non-participants (Brown & Theobald, 1998; Mahoney, 2000). A second key component to the social and emotional conditions of learning is challenge. Schools may be safe and orderly, but if they fail to build a supportive, engaging community and press for high academic expectations, students learn little (Learning First Alliance, 2001; Lee & Smith, 1999). Teachers should challenge students in terms of the level of effort they put forth, as well as the academic and behavioral standards to which they are expected to achieve. Ideally, teachers and other school staff provide rigorous academic support to all students, and work to ensure that the curriculum has direct relevance to students’ life goals. In addition, all students should have access to high-level, demanding courses, as well as service learning opportunities, extra-curricular activities, and internships that allow them to explore their post-secondary options. Research has shown that when students feel that teachers and other

Building Conditions for Learning and Healthy Adolescent Development 123 adults challenge them, they are likely to do better in school (Catalano, Berglund, Ryan, Lonczak, & Hawkins, 2004). Examinations of the NELS: 88 survey, which collected data from students in high school, established that students were more likely to perform well on tests when they believed that their teachers cared about them and that this relationship was stronger for students who were judged to be at risk for dropping out of high school (Muller, 2001; Ryan & Patrick, 2001). In the classroom, cooperative learning strategies (e.g., group discussions, presentations, projects) have been shown to promote students’ development of social learning, sense of the classroom as a community, and academic achievement (Johnson & Johnson, 1989; Slavin, 1990). Finally, students who perceived their teachers as warm, caring and supportive had higher classroom participation rates which in turn positively affected their academic achievement (Voelkl, 1995). In addition to appropriately challenging students, an overall sense of student support is a critical condition for learning. When coupled with a consistent emphasis on academic performance, a strong sense of support and school community boosts academic achievement (Lee, Smith, Perry, & Smylie, 1999). There is also evidence that these effects may be most pronounced for students who are considered to be at-risk (Shouse, 1996). Establishing effective student supports involves ensuring that children’s basic needs are met and that the significant adults in their lives work collaboratively to encourage, support, and nurture them. Students work with and receive support from teachers who are able to establish a connection with them, personalize their experience, and engage them in the learning process. For example, examinations of national data have shown that positive student beliefs about how much their teachers support their efforts to succeed in school can reduce the probability of their dropping out by half (Croninger & Lee, 2001). A study of 167 sixth-grade students found that student support was associated with increased grade-point averages, through its effects on students’ interest in class, interest in school, and social responsibility (Wentzel, 1998). Goodenow (1993) found teacher support to be predictive of students’ expectancy of success, which in turn predicted their class effort and resulting grades. Other studies of interventions designed to build relationships between adults and students in school have also shown a positive impact of these programs on school-related attitudes and motives (Battistich, 2001; Sinclair, Christenson, & Thurlow, 2005). Finally, schools that provide sufficient conditions for learning ensure that students learn and exhibit the social and emotional learning they need to succeed. Social and emotional learning is the process of developing the ability to recognize and manage emotions, develop caring and concern for others, make responsible decisions, establish positive relationships, and handle challenging situations effectively. Related skills that can be developed and reinforced in schools include relationship-building, anger management, and responsible decision-making (CASEL, 2003; U.S. Department of Education, 2000). Students with strong social and emotional learning are able to maintain healthy interpersonal relationships with peers and adults, and have access to a multitude of coping strategies to manage stress and difficult situations. Moreover, social and emotional learning is strongly tied to learning and performance at school. Evidence from social and emotional learning programs suggests that social and emotional learning increase students’ capacity to cope with emotional experiences that interfere with learning, to work effectively with other students in the classroom, to face barriers to academic achievement, and to set and strive towards academic goals. Several studies have found that early evidence of prosocial behaviors (e.g., effective problem-solving, effective decision-making, and effective interpersonal relationships) predicts better academic outcomes later on. For example, one study found that good behavioral conduct during late childhood (ages eight to 12) had a direct positive impact on academic success during late

124 David Osher and Kimberly Kendziora adolescence (ages 17–23) (Masten et al., 1995). Another recent study found that higher levels of social and emotional learning in seventh grade significantly predicted higher academic achievement in 10th grade (Fleming et al., 2005). This research strongly suggests that social and emotional learning has an enduring impact on outcomes related to school achievement. Schools can promote social and emotional learning through regular practice and modeling among adults and students in the school, and by placing a high value on conflict resolution, communication, caring, appreciation for diversity, problem-solving, and teamwork. Research on social and emotional programming suggests that maximizing students’ opportunities for participation and choice is essential for fostering student decision-making abilities, selfefficacy, self-expression, personal responsibility and accountability (Greenberg et al., 2003; Zins, Weissberg, Wang, & Walberg, 2004). School interventions that have focused on creating a caring learning environment and providing students with the skills and supports to manage school transitions have proven effective in increasing attendance, GPA and stability of self-concept, and decreasing dropout, emotional and behavioral problems (Felner & Adan, 1988; Reyes & Jason, 1991). Mentoring programs provide the relational context for promoting social and academic competence, and in research have resulted in improvements in school attendance, parental relations, academic performance and peer emotional support, as well as decreases in conduct problems in youth (Catalano et al., 2004). Diverse instructional and classroom management practices, including cooperative group learning, service learning strategies and positive behavioral supports, can have a significant impact on improving student relations, academic gains, prosocial attitudes and behavior, cognitive problem-solving abilities, empathy, sense of civic responsibility and sense of contributing to the community (Billig, 2000; Elias, Zins, Graczyk, & Weissberg, 2003; Johnson, Johnson, & Maruyama, 1983). In addition, social and emotional learning can significantly improve learning in the classroom when they are integrated into different subject matter (CASEL). Involving parents and community members in teaching and reinforcing the development of social and emotional learning appears to be a cornerstone of many effective youth development programs (Zins et al., 2004). Specifically, research suggests that when parents are involved in implementing social and emotional interventions, students benefit more and the effects of participation are more lasting and pervasive (Elias et al., 2003). Building a comprehensive plan for improving student connection across the critical components of safety, challenge, student support, and social and emotional learning takes time and requires input, planning, and commitment from students, teachers, administrators, school counselors, psychologists, parents, and relevant community agencies (CASEL, 2003). There are barriers to addressing the social and emotional conditions for learning in schools, including inadequate resources; lack of information about the importance of social emotional development and how to address it; as well as the enormous pressure on staff, principals, superintendents, and school board members to produce short-term gains on high-stakes tests. We believe the maxim that what gets assessed gets addressed, and unless social and emotional conditions for learning are assessed and acted upon, it is unlikely that schools will become a protective factor for all students. A fully functioning process for addressing these factors includes three parts: (a) an efficient assessment process; (b) transformation of data into actionable information, which connects to key school metrics; and (c) tools to respond to that information in a deliberate manner.

Building Conditions for Learning and Healthy Adolescent Development 125

The Intervention: Surveying the Social and Emotional Conditions for Learning for Performance Management and Continuous Improvement The intervention has five objectives: (a) changing discourse within districts, as school and district staff discuss the conditions for learning; (b) orienting the behavior of principals, school staff, and district personnel to addressing the social and emotional conditions for learning in a strategic manner; (c) monitoring how subgroups of students and schools are doing; (d) providing data for continuous improvement; and (e) creating conditions where schools, districts, and states can learn about the importance of addressing the social and emotional conditions for learning. The intervention has three components. The first is a psychometrically robust survey of the social and emotional conditions for learning, which can be incorporated into school, district, or state score cards. This contains four scales and takes 15–25 minutes to complete. The toolkit, which is linked to the individual school reports, is designed to help school teams use the survey information by presenting the school’s survey results; housing a database of evidence-based programs and strategies for addressing student connection issues; providing advice for how to look at data, implement programs, and take the next steps; and providing a forum for offering comments or quotes about personal experiences with a program or strategy. The programs and strategies identified include universal, selective/targeted, and indicated/intensive programs, which have been demonstrated to work with similar groups of students in similar contexts.

Developing the Intervention An increasing body of research provides the basis for conceptualizing the social and emotional conditions for learning (Osher et al., 2008). In 2003, AIR joined with the Collaborative for

Figure 6.1 “Student Connection Survey” (aka Conditions for Learning Survey) Score Report to Schools.

126 David Osher and Kimberly Kendziora Academic, Social, and Emotional Learning (CASEL) and the Learning First Alliance (LFA) to develop a strategy for overcoming the barriers to creating safe and supportive schools. This led to an invitational conference sponsored by the Fetzer Institute in 2004, which brought together nationally recognized experts in youth development, cognitive psychology, and education. The basic premise was that the approach to standards-based reform, which has been effective in focusing educators on advancing academic achievement—establishing standards, measuring progress toward meeting those standards, and supporting a continuous improvement process for making corrections and aligning resources to meet standards—should be expanded to explicitly address personal, social, and organizational facilitators of learning, and that doing so would, in turn, support greater academic and life success for more students. Our initiative progressed substantially in July 2005 when Chicago Public Schools (CPS) asked AIR to demonstrate that conditions for learning could be assessed and to identify three or four indicators for CPS’ high school scorecards that reflect the conditions for learning in a school, such as school climate or student engagement. The indicators had to be (a) practical to measure, (b) scientifically valid, (c) easy to communicate to diverse audiences, and (d) actionable by school personnel. In August 2005, AIR convened a meeting with many of the same national experts and CPS staff. Again, their paradigms were diverse, and their work focused on a variety of factors (social emotional learning, positive behavioral support, student connection, drug prevention, violence prevention, dropout prevention, engagement, educational intervention, clinical psychology, survey research, and measurement). The group came to a strong, clear consensus on the most important factors that schools should address if they wanted to improve student attendance, achievement, graduation, and post-secondary success, as well as actionable indicators of these factors. The indicators identified as most critical were (a) students are safe, (b) students are challenged, (c) students are supported, and (d) students are socially and emotionally skilled. Each of these indicators has extensive research to support its importance for schools and its link to students’ academic success, graduation, and postsecondary success. Together they constitute what has been conceptualized as the social and emotional conditions for learning (Osher et al., 2008). AIR conducted 22 structured focus groups with students, parents, and teachers to inform the survey with the authentic input of stakeholders. Based on expert consensus, stakeholder input, and a review of existing school climate surveys, the AIR team developed the survey. The initial high school survey was pilot-tested with 1,700 students in 24 high schools to identify psychometrically strong scales. We administered the survey to students in 115 Chicago high schools and attained a response rate (77%; 74,602 valid surveys) approaching the average daily attendance rate of 84%. For the 2006–2007 school year, Chicago asked AIR to include students in grades 6–8 in the survey and to modify some of the items to increase the range of responses on items and to attain greater between-school variance. AIR revised items and tested the new scales in cognitive laboratory interviews with diverse students in grades 6–12. We pilottested the new version in 21 high schools (n = 1,359) and 24 elementary schools (n = 1,685), resulting in a stronger survey instrument. The 2007 operational administration, which was combined in the same form with the Consortium on Chicago School Research’s survey, obtained 60,802 valid surveys from 119 high schools (at a 64% response rate) and 76,187 valid surveys from 484 elementary schools (at an 83% response rate). CPS administered the survey in 2008 on its own. The results of the survey are published annually, and are intended to be used by parents and youth to choose schools. These reports have started to change discourse in the district. For example, they have been used by district staff for planning, and are drawn upon for discussion at regional meetings with principals and at district training events.

Building Conditions for Learning and Healthy Adolescent Development 127

Research on the Survey While the survey can be viewed as an intervention, it also provides data that can be used to understand more about the social and emotional conditions for learning. With support from the Spencer Foundation and collaboration from CPS and the Consortium on Chicago School Research, AIR studied the conditions for learning in Chicago middle and high schools, analyzing data from the 136,989 students who completed the 2007 survey to understand which student and school characteristics are associated with conditions for learning, including achievement. Analytic Strategy We combined data from different sources and years: at the student level the 2007 survey data were combined with district data on each student’s race, gender, English language learner (ELL) status, grade level, cumulative GPA, and achievement scores. Additionally, we obtained a constructed “on track” variable that the Consortium developed and CPS uses to predict timely graduation (the variable indicates whether a student has earned at least five full-year course credits and no more than one semester F in a core course in their first year of high school). At the school level, data were merged from different sources. First, some of the 2007 studentlevel variables were aggregated to the school level, such as achievement test results, to obtain indicators of the context of schoolwide achievement. Second, some data were retrieved from the CPS website, such as high school dropout and graduation rates, enrollment data, suspension, and average class size. Third, 2005 data from Common Core of Data were used to obtain some descriptive characteristics of the schools such as school type. Finally, some neighborhood data were also obtained from the Consortium on Chicago School Research at the University of Chicago. These variables include various measures of crime from the school or students census block (which summarizes information from November 2005 to October 2007), and poverty indicators from the census. Characteristics of the Conditions for Learning Survey Participants Table 6.1 presents demographic characteristics for the CPS students overall, compared to those who completed the survey. Because not every eligible student participated, it is important to understand the ways in which the surveyed sample is different from the district overall. Results indicate that the two groups are very similar on some, but not all, demographic characteristics. Both groups have similar percentage of females; White students; Asian and Hispanic students who are classified as having limited English proficiency (LEP); and students in the Free or Reduced Price Lunch Program. In the surveyed sample, however, African American students are slightly under-represented (47%) compared to the whole population (52%). Also there is a minor tendency for students in the middle grades to be slightly over-represented and for high school students to be somewhat under-represented. In addition, the proportion of grade 9 students “on track” is over-represented in the surveyed sample. While in Chicago this percentage is around 53%, in the surveyed sample it is 66%. This result might be expected in a survey which depended upon students being in school. Still, it is important to consider that the surveyed sample—at least for grade 9—is more representative of “better” or “on track” academic achievement students.

128 David Osher and Kimberly Kendziora Table 6.1 Number and Percentage of Students in the Chicago School District and in the Surveyed Sample by Student Demographics All Middle Grade & High School Students

Surveyed Students

N

%

N

%

Gender

Male Female

100,508 101,611

50% 50%

63,174 68,558

48% 52%

Ethnicity

White African American Native American Asian/Pacific Hispanic

17,946 104,190 308 7,186 72,482

9% 52% 0.2% 4% 36%

13,033 62,007 210 5,563 50,919

10% 47% 0.2% 4% 39%

Grade level

Grade 6 Grade 7 Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

32,371 32,424 30,991 38,639 30,073 24,166 20,675

15% 15% 15% 18% 14% 12% 10%

25,088 25,074 22,883 20,301 16,074 12,633 9,679

19% 19% 17% 15% 12% 10% 7%

LEP status

Not LEP LEP

192,916 9,203

95% 5%

126,514 5,218

96% 4%

Special Ed

No disability With disability

166,880 35,239

83% 17%

112,721 19,011

86% 14%

Lunch status

Not in lunch In lunch

33,837 168,282

17% 83%

20,326 111,406

15% 85%

On track status*

Off track to graduate On track to graduate Total

14,223 16,169 202,339

47% 53%

6,432 12,238 131,732

34% 66%

Source: 2007 Chicago Public School district data * Note: On track information is available only for grade 9 students.

Correlations Among the Conditions for Learning Survey Constructs What is the relationship between the four conditions for learning? Table 6.2 presents the correlations among the Conditions for Learning survey for middle-grade students; Table 6.3 presents results for high school students. As expected, all the scales were positively correlated with each other. In the middle grades, the correlations between School Safety and Challenge and School Safety and Student Support

Table 6.2 Middle Grades: Correlations Among Conditions for Learning Constructs

1 2 3 4

Middle level

1

2

3

4

School Safety Challenge Student Support Social & Emotional Learning

1 0.17 0.25 0.48

1 0.62 0.20

1 0.34

1

Note: All the correlations are statistically significant.

Building Conditions for Learning and Healthy Adolescent Development 129 Table 6.3 High School: Correlations Among Conditions for Learning Constructs

1 2 3 4

High school

1

2

3

4

School Safety Challenge Student Support Social & Emotional Learning

1 0.25 0.26 0.52

1 0.65 0.25

1 0.32

1

Note: All the correlations are statistically significant.

were small, which suggests that these are separate constructs. The correlation between School Safety and Social Emotional Learning was moderate, suggesting that students who felt safer at school tended to rate the social emotional learning of their peers more highly. This makes sense because some of the items on the Safety Scale assess “emotional safety”—the extent to which students are picked on or bullied. Challenge is highly correlated with Student Support (r = .62), which is logical because both scales ask students to rate teacher or school factors such as whether teachers “Think all students can do challenging school work” and whether they “Often connect what I am learning to life outside the classroom.” Challenge was less correlated with Social Emotional Learning (r = .20) which focuses on the behavior of other students. Finally, Student Support had a small correlation with Social Emotional Learning (r = .34). The same pattern of results was observed among high school students, although on average the magnitude of the correlations was slightly higher. Conditions for Learning in Middle Grades vs. High School Are there differences in how middle-grade and high school-grade students experience the conditions for learning? The following figures present the mean values on the scales by different student demographic characteristics. It is important to remember when examining these figures that the ranges of the constructs were to some extent different. Therefore, comparisons are more meaningful when done within the same construct. The results show that middle-grade students tended to provide higher scores on all the Conditions for Learning scales than high school students. All differences were statistically significant. It may be that older students’ perceptions of their surroundings were more critical than those of their younger peers; on the other hand, it may be that the actual conditions for learning were worse in high schools. Perhaps high schools in Chicago were less safe, teachers were less challenging, students received less support, and they were less likely to have socially and emotionally skilled peers compared to middle-grade students. Additional data would need to be collected to explain these effects. Nonetheless, the finding of lower ratings for variables, such as school climate, that are similar to conditions for learning is consistent with a broader body of research and experience that shows that both students and staff report progressively less satisfaction as they advance higher in grade levels (Eccles & Midgley, 1989; Spier, Cai, Kendziora, & Osher, 2007). Correlations Between Achievement and Conditions for Learning What are the relationships between the conditions for learning and academic achievement? In the following figures, we present the correlation coefficients between Prairie State Achievement Examination (PSAE) scores and conditions for learning. The PSAE measures the

130 David Osher and Kimberly Kendziora 340

Mean scale score

320

300

280

260

240 School safety

Challenge

Stud. support

Middle school

Soc. & Emot. skills

High school

Figure 6.2 Mean Conditions for Learning Scores for Middle Grades and High School.

Correlation between PSAE tests & conditions for learning

achievement of grade 11 students relative to the Illinois Learning Standards for Reading, Mathematics, and Science. Each correlation value is illustrated using a bar, where the size of the bar represents the linear strength between the two variables and the vertical axis shows the range of the correlation. Therefore, the taller the bar, the larger the correlation. The School Safety scale showed the highest correlations with the subscales from the PSAE test. All the correlations were statistically significant and suggest the importance of school safety and academic achievement. Although not presented here, results of achievement tests administered to students in grades 8, 9, and 10 showed a similar pattern of results, with safety having the highest correlation with achievement.

Grade 11 Writing

Math

Reading

Science

0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 School safety

Challenge

Student support

Social emotional skills

SCS Constructs

Figure 6.3 Correlation Between PSAE Subscales and Conditions for Learning Scales (Grade 11).

Building Conditions for Learning and Healthy Adolescent Development 131 Exploring Correlations Between GPA and Conditions for Learning Cumulative GPA data from the spring semester of 2007 were obtained to cross with the Conditions for Learning survey responses. In contrast to the pattern of correlations observed between achievement scores and the conditions for learning scales, the students’ GPAs were highly correlated with the Challenge construct. These correlations were above .23 for the four grade levels, whereas the correlations between the School Safety and GPA across the different grades were approximately .18. It may be the case that GPA was more strongly related to Challenge than achievement test scores because Challenge taps the proximal teacher behaviors that are hypothesized to be associated with greater academic success (e.g., “My teachers often assign homework that helps me learn,” “In my classes, we often discuss different interpretations of things we read”). Because grades were a more proximal indicator of teacher ratings of this success than achievement test scores, the higher correlation with GPA may indicate that a student is meeting a teacher’s expectations, whether or not these are aligned with the standards assessed on the achievement tests. Conditions for Learning by On Track Index (Ninth Grade) What are the relationships between the conditions for learning and students being on track to graduate? The Chicago on Track to Graduate index is computed for ninth-grade students to identify students who are at risk of not graduating on time. The index was developed by the Consortium on Chicago School Research, and is thought to be a better indicator of graduation than achievement test scores or student demographic characteristics. Students are “on track to graduate” if they have earned at least five full-year course credits in their freshman year, and received no more than one semester F in a core course. Figure 6.5 shows that “on track” stu-

High school

Correlation scores between GPA & conditions for learning

0.30 0.25 0.20 0.15 0.10 0.05 0.00 School safety

Grade 9

Challenge

Grade 10

Student support Grade 11

Social and emotional skills Grade 12

Figure 6.4 Correlation Between Conditions for Learning and GPA Scores (High School).

132 David Osher and Kimberly Kendziora High school Mean scale score

320 300 280 260 240 School safety

Challenge Off track

Student support On track

Social and emotional skills

Figure 6.5 Conditions for Learning Scores by On Track Status (Grade 9 Students).

dents tended to score higher than “off track” students on the School Safety, Challenge, and Student Support. No differences are observed between the two groups in the Social Emotional Learning scale. Conditions for Learning and School Enrollment Does school size appear to affect the conditions for learning? Enrollment size ranged from 41 to 2,005 students in schools with middle grades and from 67 to 4,248 students in high schools. The distributions of enrollment in both levels were highly positively skewed; schools with more than 2,000 students were not very common. Because of the skewness, simple Pearson correlations are not very meaningful. To approximate the relation between enrollment size and the responses on the Conditions for Learning survey, the enrollment distribution was divided into quintile categories: the lowest 20%, between 20% and 40%; between 40% and 60%; between 60% and 80% and above 80%. Especially in high school, there seemed to be a tendency for Conditions for Learning to be negatively associated with enrollment. These negative relations were more evident for the Challenge, Student Support Scales, and Social Emotional Learning scores. The correlations between the Conditions for Learning constructs and the logarithm of enrollment are shown in Table 6.4. Particularly for students in high school, larger enrollment is associated with worse conditions for learning, especially student ratings of support from teachers. These specific data, which are consistent with data that we analyzed for another city (Lampron, Brown, Osher, & Table 6.4 School Level Correlations Between Conditions for Learning Constructs and the Logarithm of Enrollment

School Safety Challenge Student Support Social & Emotional Learning Note: Values in bold are statistically significant.

Middle School log(enrollment)

High School log(enrollment)

0.01 –0.07 –0.15 0.02

–0.21 –0.35 –0.59 –0.37

Building Conditions for Learning and Healthy Adolescent Development 133 Poirier, 2008), suggest that smaller high schools are perceived by students as being safer, more challenging, much more supportive, and having a more socially and emotionally skilled peer group. Specific Correlations for Conditions for Learning Scales What are the relationships between student perceptions and other data that can be collected at schools? To examine the utility of Conditions for Learning for performance management, we investigated the extent to which student perceptions of the conditions for learning in their school related to objective data available to principals for decision-making. Answering this question involved testing the distributions of variables (and transforming where necessary) and calculating bivariate correlations. Results for each of the scales follow. School Safety, School Disruption, and Neighborhood Crime. Students’ ratings of perceived Safety in their schools should be associated with objective measures of school disruption. Results for correlations with school suspensions and number of suspended students are presented in Table 6.5. Both suspension variables needed to be logarithmically transformed. The high school variable was still somewhat skewed, which likely attenuated the very high correlations presented below. The numbers of school suspensions and suspended students were almost perfectly correlated; both were very strongly associated with School Safety as assessed on the Conditions for Learning survey. Additionally, we examined the relationship between School Safety and neighborhood crime, testing the logic that schools in neighborhoods with more crime would feel less safe to students. Crime in students’ home census blocks was more strongly related to school safety than crime in the school’s own census block (“imported disorder”), supporting the finding by Clark and Lab (2000) that the school’s neighborhood characteristics do not strongly influence in-school crime. However, Wayne, Welsh, Stokes, and Greene (2000) have reported findings that the school’s surrounding neighborhood is more influential for in-school victimization than students’ home neighborhoods. Recent analyses conducted by the lead author using data from Cleveland may help illuminate some of the relationships among student and school environments, the conditions for learning, and school academic achievement. Along with colleagues from Kent State University’s Institute for the Study and Prevention of Violence, we linked conditions for learning with data on neighborhood disadvantage and school performance. Results are described Table 6.5 Correlations Between School Safety, School Suspensions, and the Number of Suspended Students

1 2 3

1 2 3

Middle Level

1

2

3

School Safety ln school suspensions ln N suspended students

1 –0.43 –0.43

1 0.99

1

High School

1

2

3

School Safety ln school suspensions ln N suspended students

1 –0.65 –0.62

1 0.99

1

Note: All correlations are statistically significant.

134 David Osher and Kimberly Kendziora Table 6.6 Correlations Between School Safety and Neighborhood Crime

1 2 3 4

1 2 3 4

Middle Grades

1

2

3

4

School Safety logarithm of crime count in school’s census block ln of per capita crime in school’s own census block Average ln of per capita crime from students’ home census blocks

1 –0.47 –0.61 –0.70

0.65 0.58

1 0.78

1

High School

1

2

3

4

School Safety logarithm of crime count in school’s census block ln of per capita crime in school’s own census block Average ln of per capita crime from students’ home census blocks

1 –0.26 –0.39 –0.45

1 0.56 0.46

1 0.70

1

Note: All correlations are statistically significant.

in Table 6.6. We found that neighborhood disadvantage predicted the School Performance Index (PI) score for elementary and high schools and that school safety explained the neighborhood disadvantage effect: more disadvantaged neighborhoods had less-safe schools, accounting for significant variation in lower academic performance scores. Further, when we controlled for neighborhood disadvantage, attendance and discipline events, we found that student perception of school safety alone predicted the PI score for both K–8 schools and high schools (Osher et al., 2008). Challenge and Dropout. To examine the strength of the relationship between perceptions of high expectations and school variables, we sought to correlate Conditions for Learning scores for Challenge with dropout in the Chicago data. Dropout data were available for high school students. Our hypothesis was that the schools that were perceived as the most challenging and engaging would have the lowest levels of dropout. This is in fact what we found. We log-transformed the dropout variable to improve its distribution, and found that dropout correlated significantly with Challenge, but correlations between dropout and other Conditions for Learning scores (Safety and Social Emotional Learning) were also significant. Results are presented in Table 6.7. Student Support and Class Size. In the previous section on school characteristics, we explored the relationship between Conditions for Learning constructs and school enrollment. We found that Student Support was significantly related to the log-transformation of student enrollment in both middle grades and in high schools (–0.15 for middle grades and –0.59 for high school). This indicates that smaller enrollment is strongly associated with higher perceptions of support, especially in high school. Table 6.7 Correlations Between Challenge (and other Conditions for Learning Constructs) and Dropout High School

ln dropout percent

School Safety Challenge Student Support Social & Emotional Learning

–0.31 –0.33 –0.13 –0.32

Note: Values in bold are statistically significant. Note: Dropout percent data are from 2006.

Building Conditions for Learning and Healthy Adolescent Development 135 Table 6.8 School Level Correlation Between Conditions for Learning Constructs and the Average Class Size in High School Average Class Size HS School Safety Challenge Student Support Social & Emotional Learning

0.15 0.01 –0.24 0.13

Note: Values in bold are statistically significant.

The Student Support construct did not covary with many other educational variables, but it related strongly to proxy measures of personalization (as it should). We had data on average class size for high schools; the only Conditions for Learning construct with which it was significantly associated was Student Support. (See Table 6.8.) Peer Social Emotional Learning and Graduation. For the 2007 Conditions for Learning survey, students rated their peers’ levels of social emotional learning. We hypothesized that students’ collective social emotional learning should be associated with measures of persistence in school, which could be indexed by the graduation rate. Graduation rates for high schools ranged from 21 to 100. Correlations are presented in Table 6.9. As expected, the correlation between social emotional learning and graduation was significant. However, it was not quite as high as the correlation between safety and graduation. Overall, the constructs that compose the Conditions for Learning survey have demonstrated evidence of validity. They are associated with variables that they should be associated with, and are not associated with variables that they should not be.

The Implications of This Work The challenge for schools and communities is to create environments where students are supported in developing the skills and capacities to thrive. This involves interventions in three domains: health; social/emotional/behavioral; and cognitive/academic (Kendziora, Osher, & Schmitt-Carey, 2007). Schools play a key role in this process. For example, a meta-analysis of more than 200 universal school-based studies showed significant improvements in students’ social-emotional learning; attitudes towards self, school, and others; social behaviors; conduct problems; emotional distress; and academic performance. Notably, SEL programming yielded an average gain on achievement test scores of 11 to 17 percentile points (Payton et al., 2008). Schools can develop the social emotional and cognitive capacities that enable students not just to survive but to thrive. Doing this successfully involves leveraging and disseminating Table 6.9 Correlations Between Graduation Rates and Conditions for Learning Constructs High School

Graduation Rate

School Safety Challenge Student Support Social & Emotional Learning

0.41 0.25 –0.03 0.36

Correlations obtained for graduation rates higher than 40 Note: Graduation percent from 2006 dataset. Note: Values in bold are statistically significant.

136 David Osher and Kimberly Kendziora information about the connections between learning and academic performance. For example, in Alaska, where AIR developed a survey to assess the impact of a youth development initiative across 18 school districts, we found that not only were several aspects of school climate and connectedness related to student achievement, but positive change in school climate and connectedness was related to significant gains in student scores on statewide achievement tests. Our findings showed that whether a school started with high or low school climate and connectedness, or high or low achievement scores, changing that school’s climate and connectedness for the better was associated with increases in student performance in reading, writing, and mathematics (Spier et al., 2007). Similarly work we have done in previously troubled schools in the South Bronx suggested that mental health supports, when coupled with family engagement and organizational efficacy, were effective in creating conditions for learning in these schools (Kendziora et al., 2008). The Chicago work does not stand alone. Work on the measurement of conditions for learning, by AIR and others, has extended to additional sites: • •

• •

• •

•

•

•

The survey and school report are likely to be piloted in a large state to help monitor its progress toward educational equity; Two of the four survey scales will be used in 2008 and 2009 in San Diego to help monitor its Gates-funded small high schools (it already collected survey information on challenge and support); A number of large cities are currently exploring use of the AIR survey; New York City has used a similar student survey to monitor how students (among other stakeholders) are experiencing schools, and will use these surveys as part of their principal accountability process; Individuals connected with school mental health have identified the report card strategy as a vehicle for promoting school mental health (Hunter et al., 2005); The state of Illinois and the city of Anchorage, Alaska, have developed social emotional learning standards, and New York State legislation calls for its Departments of Education and Mental Health, create voluntary social emotional learning standards; Say Yes to Education, an organization focused on dramatically increasing high school and college graduation rates for our nation’s inner-city youth, has targeted social emotional and behavioral factors and health, along with cognitive and financial factors as key to realizing its goals; The Association for Supervision and Curriculum Development has initiated a major campaign that focuses on addressing the whole child, which focuses on health, physical and emotional safety, caring adults, engagement, and challenge; and United Voices for Education, an organization of over 42 educational stakeholder organizations, has called for school report cards that focus on the conditions for learning.

In spite of the examples of progress in this area noted above, barriers remain. The first is to demonstrate the importance of social-emotional factors in education. Although more research is accumulating, it will be important to demonstrate dramatically the impact of the social emotional conditions for learning and of addressing these conditions effectively. The second is financial; doing good surveys and providing support for responding to these surveys is not inexpensive. Paradoxically, as long as social emotional factors are marginalized, it will be hard to generate the resources for such surveys. The third barrier is making sure that interventions enter the classroom, affect the learning process, and reach the individual child. This is a struggle in all systems change, including education. Steps are being taken in this direction. For

Building Conditions for Learning and Healthy Adolescent Development 137 example, Say Yes to Education has developed, and is piloting in five Harlem Elementary Schools, a monitoring process, linked to individual growth plans, that look at whether students are off track, on track, or on track to thrive in the domains of health, social-emotional, and cognitive/academic development. Although these barriers exist, the middle school and high school “crises” and the continuing inability to transform educational outcomes for disadvantaged students create a window of opportunity for scaling up an intervention that can contribute significantly to improving attendance, learning, performance, graduation rates, and post-secondary outcomes in multiple cities and states. In conclusion, there are three major recommendations or action steps that should be taken to create emotionally safe and supportive school that promote students’ positive social, emotional, and academic learning: (a) help districts and states develop the capacity to assess and monitor the social and emotional conditions for learning; (b) ensure that stakeholders understand the importance of the social and emotional conditions for learning; and (c) and provide schools and communities with effective tools and strategies to improve the social and emotional conditions for learning.

Notes The research presented in this chapter was funded by a grant from the Spencer Foundation. The development and initial implementation of the school survey was funded by the Children First Fund: Chicago Public Schools Foundation and the American Institutes for Research. We are also grateful for the collaboration and support of our colleagues at the Collaborative for Academic, Social, and Emotional Learning and the Consortium on Chicago School Research. The work of many of our colleagues at AIR—especially Drs. Marjorie Chinen and Allison Gandhi—is reflected in this report. 1 http://research.cps.k12.il.us/cps/accountweb/Reports/download.html 2 In this report, we refer to “middle grade” students rather than “middle school” students because approximately 80% of the students in grades 6, 7, and 8 who completed the survey were in fact enrolled in K–8 elementary schools.

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7

Assessment for Integrated Screening and Prevention Using the Resiliency Scales for Children and Adolescents Sandra Prince-Embury, The Resiliency Institute of Allenhurst, LLC

Introduction: Need for Universal Mental Health Screening Psychologists who work in schools have identified the need for universal screening for social and emotional disturbances in the schools (Doll & Cummings, 2008). Epidemiological research conducted in the 1980s showed that at least 20% of school-aged youth had a diagnosable psychiatric disorder, and only a fourth of these children were receiving therapeutic services (Doll, 1996; U.S. Department of Health and Human Services, 1999). Others have also identified high rates of unidentified and untreated youth with mental disorders (Katoaka, Zhang, & Wells, 2002; Costello, Mustillo, Erkanli, Keeler, & Angold, 2003). About 20% of children are estimated to have mental disorders with at least mild functional impairment. Due to a significant gap between the need for and availability of mental health services, schools have become, by default, the mental health provider for most school-aged children and adolescents (Hoagwood & Johnson, 2003). The role of schools in monitoring mental health is supported by developmental research of the past three decades which demonstrated that mental health and psychological wellness are not ancillary to school success but are integral to it (Haertel, Walberg, & Weinstein, 1983; Masten et al., 2005). Doll and Cummings (2008) recommend universal screening in the schools that will both identify vulnerable children earlier than would otherwise be the case, and guide the planned development of services to match children’s needs. Universal mental health screening programs have also been suggested by national task forces and mental health commissions (New Freedom Commission on Mental Health, 2003). The consensus is that early identification is desirable in that children who are identified and treated early may avoid the development of psychopathology and associated impairments. Pre-symptom identification is desirable because once psychological symptoms have occurred there is a greater chance that the symptoms have already interfered with the youths’ functioning as well as a greater likelihood that the symptoms might crystallize into a psychological disorder. Early identification is also beneficial because more fully developed psychopathology is more difficult and more costly to treat. A special issue of the Journal of School Psychology highlighted the need for universal screening in the schools as well as the need to inform school personnel about the use and utility of tools for universal screening (Albers, Glover, & Kratochwill, 2007a; 2007b). However, as Levitt, Saka, Romanelli, and Hoagwood (2007) point out, the implementation of universal screening for social and emotional problems is different from universal screening for other conditions in that it is not yet possible to identify asymptomatic youth and offer interventions that would prevent the onset of symptoms. According to these authors, current screening methods rely on youth having some level of symptoms or impairment in functioning in order

142 Sandra Prince-Embury to identify them as being at-risk. In this chapter we present an alternative model of universal screening that is based on resilience and vulnerability as a framework for prevention-focused screening. The Resiliency Scales for Children and Adolescents (RSCA) (Prince-Embury, 2007) are presented as theoretically based and psychometrically sound tools for the identification of vulnerability and resilience in youth who may be asymptomatic but at risk for the development of symptoms. The sections below discuss the constructs underlying the RSCA, its psychometric properties and validity evidence, and sample models for screening and intervention using the RSCA.

Resiliency Framework Traditional approaches to mental health services have not necessarily integrated the processes of screening and prevention. For example, symptom-based screening may not be accompanied by more comprehensive assessment and suggestions for appropriate intervention. Conversely, preventive programs may be selected for targeted risk populations without specific assessment tools to evaluate the pre- and post-intervention conditions of the participants. Consequently, there is a need for a framework that systematically links screening with specific intervention decisions and which provides a method for evaluating the specific impact of these interventions. Resilience theory presents a model for integration of screening, intervention and outcome assessment. It considers strengths as well as vulnerability. Strength-based screening is advantageous because it is associated with less stigma and may provide more information for designing preventive interventions. Resilience in the face of adversity has been studied extensively by developmental psychopathologists for the past 50 years. This body of work has defined resiliency as the ability to weather adversity or to bounce back from a negative experience. Research of resilience suggests that mental disorders and psychological symptoms may be based in part on less personal resiliency or greater vulnerability that the child may have experienced in a variety of ways. See the work of Rutter, Garmezy, Masten and others for more information (Garmezy, 1971; 1985; 1991; Garmezy, Masten, & Tellegren, 1984; Luthar, 1991; Luthar, Cicchetti, & Beckev, 2000; Luthar & Zieglar, 1991, 1992; Masten, 2001; Masten & Coatsworth, 1998; Masten & Curtis, 2000; Masten & Powell, 2003; Masten et al., 2005; Rutter, 1987; 1993). Resilience has been recognized by school psychologists as a concept that is very consistent with overall educational goals and well suited for application in educational settings. The National Association of School Psychologists embraced the theme of “Resilience: Building Strength for Life” for its 2008 Conference in New Orleans. This theme was part of a year-long initiative aimed at integrating resilience concepts into the practice of school psychology and presenting practices to build resiliency within the school setting. Application of resilience constructs in educational environments makes sense for many reasons. The constructs are based on relative strength and vulnerability as opposed to clinical pathology. The constructs relate to academic achievement and positive educational environments as well as avoidance of pathology and dysfunction. The constructs are developmental and normative and may be applied universally to guide system-level practice as well as individually to screen for children and youth who may be at risk. For example, principles of resilience have been applied in describing resilient classrooms (Doll, Zucker, & Brehm, 2004), in coaching parents (Brooks & Goldstein, 2001), and in coaching teachers (Brooks & Goldstein, 2008). What is currently needed is a way to summarize, quantify and standardize resiliency in a way that is reliable and easily understood. The Resiliency Scales for Children and Adolescents (Prince-Embury, 2006; 2007) are based on three theoretical constructs that emerge as cornerstones of developmental resiliency

Assessment for Integrated Screening and Prevention 143 theory—Sense of Mastery, Sense of Relatedness, and Emotional Reactivity—and the relationship of these factors to one another. Together these constructs form a framework for understanding a child’s balance between strength and vulnerability in the potential development of symptoms. These core constructs are represented by three scales collectively comprised of 10 subscales. Sense of Mastery One core mechanism that has been consistently identified in general developmental research and resilience research is the development of a sense of mastery or self-efficacy. White (1959) introduced the construct as a sense of mastery/efficacy in children and youth that provides them with the opportunity to interact with and enjoy cause and effect relationships in the environment. According to White, a sense of competence, mastery, or efficacy is driven by an innate curiosity, which is intrinsically rewarding and is the source of problem-solving skills. Bandura (1977) has studied the importance of self-efficacy in children on various aspects of behavior and achievement. Subsequently, the study of competence was incorporated into the third wave of resilience studies that focused on intervention and in particular the promotion of competence as a strategy for preventing or ameliorating behavioral and emotional problems (Masten & Coatsworth, 1998; Masten, Burt, & Coatsworth, 2006). Consistent with this focus, the Project Competence group (Masten & Obravadic, 2006) examined competence criteria for positive adaptation in their studies of resilience and particularly on competence in age-salient developmental tasks (Masten & Powell, 2003). Several studies conducted as part of the Rochester Child Resilience Project supported the hypothesis that positive efficacy expectation is related to resilience. Positive efficacy expectations in 10- to 12-year-olds predicted better behavioral adaptation and resilience to stress (Cowen, Pryor-Brown, Hightower, & Lotyczewski, 1991). Positive expectations about their future predicted lower anxiety, higher school achievement and better classroom behavior control (Wyman, Cowen, Work, & Kerley, 1993). The RSCA Sense of Mastery scale operationalizes the constructs mentioned above in three subscales; Optimism, Self-Efficacy and Adaptability. The Optimism subscale includes items describing positive attitudes about the world and life in general and about an individual’s life specifically, currently, and in the future. The Self-Efficacy subscale includes items describing belief in one’s own general and/or specific competence and control. The Adaptability subscale includes items describing one’s personal receptivity to feedback, to learning from his or her own mistakes, and willingness to ask others for assistance. Previous research and theory suggests that children and youth who have less of a sense of mastery may be more vulnerable to the development of psychological symptoms. Consistent with this assumption, preventive interventions may target increasing general or specific sense of mastery by increasing children’s problem-solving skills and sense of self-efficacy. The Sense of Mastery scale and subscales may be used to screen for low sense of mastery in youth or to assess whether preventive interventions designed to increase sense of mastery have accomplished this goal. Sense of Relatedness A second body of literature links youths’ relational experience and abilities with resilience against adversity. The implication of this body of literature is that social relatedness provides external buffering in two ways. First, youth may view relationships as providing specific

144 Sandra Prince-Embury supports in specific situations. Second, internal mechanisms reflecting the cumulative experience of previous support may shield youth from negative psychological impact. The RSCA Sense of Relatedness scale operationalizes sense of relatedness in four subscales: perceived access to support, trust, comfort with others and tolerance of difference with others. The importance of relationships and relational ability as mediators of resilience has been supported in research by developmental psychopathologists such as Werner and Smith (1982). Throughout her writing, Werner has stressed the importance of children having relationships with caring adults other than, or in addition to, their parents. Werner and Smith noted that resilient youth sought support from non-parental adults more often than nonresilient youth, especially from teachers, ministers, and neighbors. These supports were influential in fostering resilience. The RSCA Access to Support subscale was designed to assess this construct. The significance of trust has been identified most clearly by Erik Erikson (1963) as the first stage of social emotional development, upon which all social development is built. Erikson defined basic trust as the ability to receive and accept what is given. He believed that basic trust was initially based in infancy with the oral mode of functioning, and then emerged from aggregated experiences with the primary caregiver to establish children’s balance of trust versus mistrust. The RSCA Trust subscale was designed to tap this construct. Early differences in physiologically based temperament have frequently been cited by developmental and personality theorists as influencing the relationships that individuals have with others, which in turn influence development of the capacity to trust. Constructs such as “slow to warm up to others” (Thomas & Chess, 1977) or internality (Eysenck, 1967) have been used to describe discomfort with others. The RSCA Comfort subscale was designed to tap the opposite of these: manifested by meeting people and making friends easily and generally feeling calm in the presence of others. The ability to have one’s own thoughts and express them even though they may differ from the thoughts of others has been conceptualized by Bowen (1978) as one manifestation of “differentiation within a family system.” It may be hypothesized that this ability would be part of the child’s balancing dependency and striving for autonomy and would become increasingly important with age. The Tolerance subscale was designed to tap this construct. Screening that employs the Sense of Relatedness scale of the RSCA might identify youth who feel isolated or disconnected from others. Interventions designed to increase sense of relatedness might be implemented and pre-post comparisons of scores from the Sense of Relatedness scale could evaluate the effectiveness of these interventions. Emotional Reactivity Research in the field of developmental psychopathology has found that children’s development of pathology in the presence of adversity is related in some way to their emotional reactivity and ability to regulate this reactivity. Strong emotional reactivity and the associated difficulty with emotional self-regulation have been associated with behavioral difficulty and vulnerability to pathology. Conversely, the ability to modulate emotional reactivity is a significant factor in fostering resilience (Cicchetti, Ganaban, & Barnett, 1991; Cicchetti & Tucker, 1994). Consistent with these findings, pre-symptom universal screening might consider youths’ personal vulnerability associated with emotional reactivity. Emotional reactivity may be defined as arousability or threshold of tolerance that exists prior to the occurrence of adverse events or circumstances. Rothbart and Derryberry (1981) have defined emotional reactivity as the speed and intensity

Assessment for Integrated Screening and Prevention 145 of a child’s negative emotional response. Emotion regulation is defined as the child’s capacity to modulate emotional responses (Eisenberg, Champion, & Ma, 2004). Emotional regulation is further defined as the intra- and extra-organismic factors by which emotional arousal is redirected, controlled, modulated, and modified so that an individual can function adaptively in emotionally challenging situations (Cicchetti et al., 1991; Thompson, 1990). Siegel (1999) identified aspects of reactivity as intensity, sensitivity, specificity, windows of tolerance, and recovery. The RSCA Emotional Reactivity scale operationalizes emotional reactivity in three subscales: The Sensitivity subscale addresses how easily the individual is aroused; the Recovery subscale assesses how long it takes the individual to recover after arousal; and the Impairment subscale describes the degree to which youths’ functioning is impaired by emotional reactivity. Thus pre-symptom screening using the RSCA Emotional Reactivity scale incorporates identification of high sensitivity to emotional triggers, difficulty recovering when emotionally triggered, and significant impairment of functioning when triggered. Preventive interventions offered in response to high scores on this scale might incorporate relaxation exercises to decrease sensitivity, or instruction in coping skills to speed recovery and minimize impairment. Description of the Resiliency Scales for Children and Adolescents (RSCA) The RSCA is a self-report instrument and describes the experience of the child as reported by the child. It consists of three global scales: Sense of Mastery, Sense of Relatedness, and Emotional Reactivity. T-scores on these three global scales comprise the Resiliency Profile which displays the child’s relative strengths and vulnerabilities. Two composite scores, the Resource Index and the Vulnerability Index, are summary scores that quantify the child’s relative strength and vulnerability. The three global scales are comprised of 10 subscales that can be used to understand the child’s specific strengths and vulnerabilities in more depth. The Sense of Mastery scale is a 20-item self-report questionnaire written at a third-grade reading level. Response options are ordered on a five-point Likert scale: 0 (Never), 1 (Rarely), 2 (Sometimes), 3 (Often), and 4 (Almost Always). The Sense of Mastery scale consists of three conceptually related content areas: optimism about life and one’s own competence; self-efficacy associated with developing problem-solving attitudes and strategies; and adaptability, which is flexibility, being personally receptive to criticism, and learning from one’s mistakes. Internal consistencies for the Sense of Mastery Scale are good with an alpha of .85 for youth ages nine to 11, .89 for youth ages 12–14 and .95 for youth ages 15–18. Test-retest reliability coefficients were .79 for youth ages nine to 14 and .86 for youth ages 15–18 (PrinceEmbury, 2007). The Sense of Relatedness scale is a 24-item self-report questionnaire written at a third-grade reading level. Response options are frequency-based, ordered on a five-point Likert scale: 0 (Never), 1 (Rarely), 2 (Sometimes), 3 (Often), and 4 (Almost Always). Within this scale, a sense of relatedness refers to comfort with others, trust in others, sense of access to support by others when in need, and tolerance of differences with others. Internal consistency was good to excellent for the Sense of Relatedness scale: .89 for children ages nine to 11, .91 for children ages 12–14 and .95 for youth ages 15–18. Test-retest reliability coefficients were good; .84 for youth ages nine to 14 and .86 for youth ages 15–18 (Prince-Embury, 2008). The Emotional Reactivity scale is a 20-item self-report questionnaire written at the thirdgrade reading level. Response options are ordered on a five-point Likert scale: 0 (Never), 1 (Rarely), 2 (Sometimes), 3 (Often), and 4 (Almost Always). Unlike the Sense of Mastery and

146 Sandra Prince-Embury Sense of Relatedness scales, lower scores on the Emotional Reactivity scale are indicative of resilience and high scores are indicative of vulnerability. This scale consists of three related content areas: the Sensitivity subscale assesses the child’s threshold for emotional reaction and the intensity of the reaction, the Recovery subscale describes the length of time required for recovering from emotional upset, and the Impairment subscale describes the degree to which functioning is disrupted while upset. Internal consistency for the Emotional Reactivity scale was excellent with alphas of .90 for youth ages nine to 11, .91 for youth ages 12–14 and .94 for youth ages 15–18. Test-retest reliability coefficient was .88 for youth ages nine to 14 and youth ages 15–18 (Prince-Embury, 2007). Summary Index Scores. Universal screening requires as much simplification of scoring as possible along with the potential to unfold the scores into deeper levels of information as needed. The RSCA Summary Index scores are useful for screening purposes because they combine information into two scores for initial screening, but also may be unfolded to provide more detailed information at the global and subscale levels. The Index scores were developed based on empirical analyses of RSCA scale score profiles, factor analytic studies and validity studies (Prince-Embury, 2006, 2007; Prince-Embury & Courville, 2008a; 2008b). A first index score, the Resource Index, is the standardized average of the Sense of Mastery and Sense of Relatedness scales. This average is an estimate of students’ personal strength or resources, weighting Sense of Mastery and Sense of Relatedness equally. Factor analytic studies indicate that although the three RSCA scales represent three distinct factors, two of these factors are highly related and thus may be considered together for screening purposes (PrinceEmbury & Courville, 2008a). Internal consistency for the Resource Index was excellent with alphas of .93 for youth ages nine to 11, .94 for youth ages 12–14 and .97 for youth ages 15–18. Test-retest reliability coefficient was .90 for youth ages nine to 14 and .77 for youth ages 15–18 (Prince-Embury, 2007). Resilience theory suggests that youth who perceive themselves as having sufficient personal resources will be more resilient and less likely to develop psychopathology than those who experience themselves as having insufficient personal resources. Students with significantly below-average scores on the Resource Index require preventive interventions to increase personal resources. The Vulnerability Index score is the standardized difference between the Emotional Reactivity T-score and the Resource Index score. It quantifies children’s personal vulnerability as the relative discrepancy between their combined self-perceived resources (the Resource Index) and their fragility as described by emotional reactivity (the Emotional Reactivity Scale; Prince-Embury, 2007). Internal consistency for the Vulnerability Index score is excellent with alpha coefficients of .93 for youth ages nine to 11, .94 for youth ages 12–14 and .97 for youth ages 15–18. Test-retest reliability coefficient was .83 for youth ages nine to 14 and .90 for youth ages 15–18. Sensitivity and Specificity using the Vulnerability Index cut score in adolescent sample with a Vulnerability Index cut score of T54 yielded Sensitivity of 81.2% and specificity of 75.5% and a total hit rate of 78% (Prince-Embury, 2008). The Vulnerability Index score is consistent with theories that define vulnerability as the result when internal fragility or external challenge is not balanced by personal resources. Personal vulnerability would be indicated by a high Vulnerability Index score which would indicate that students’ personal resources were significantly below their level of emotional reactivity.

Psychometric Properties and Validity of the RCSA Despite consensus on the need for universal pre-symptom screening that employs a resilience framework, there are few psychometrically sound screening tools. Glover and Albers (2007)

Assessment for Integrated Screening and Prevention 147 provide criteria for universal screening tools, arguing that these should demonstrate appropriateness, usability, and technically adequate norms, reliability, and validity. They suggest that few current screening tools have adequate norms, reliability, or sensitivity and specificity. In their review of currently available assessment tools, Levitt et al. (2006) also conclude that few of the currently available screening instruments demonstrate high levels of both sensitivity and specificity. These authors also call for assessment tools with demonstrated reliability and validity. Adequate reliability is particularly important if follow-up testing is needed. Screening tools require sufficient internal and test-retest reliability for different age groups and in different populations in order to be sensitive to change in pre-post testing for different groups. Lack of internal consistency indicates that the measured characteristic is not clearly defined and may include excessive error variance. Such measures are in turn likely to have poor test-retest reliability. This is important because if measures do not assess a construct consistently in an untreated or control sample over time, they are unlikely to detect the effect of interventions over time in treatment groups. Measures that are sensitive enough to measure change at a group and individual level over time are necessary to determine whether or not preventive interventions are effective. In response to the call for efficacious and effective screening tools the sections below examine evidence of the reliability and validity of the RSCA. Reliability Cicchetti (1994) suggests that coefficient alphas at or above .70 are adequate, at or above .80 are good, and at or above .90 are excellent. Alphas of .90 are thought of as adequate for tracking individual scores over time. Alphas of .80 or more are considered adequate for tracking group scores over time. Using these criteria, reliability evidence was excellent for the RSCA Index Scores, good for the Global Score, and adequate for most subscales. The RSCA index and global scale scores show good or excellent internal consistency across age and gender groups and, as expected, greater internal consistency was evidenced with increased age (Prince-Embury, 2007). For children ages nine to 11, the RSCA Index scores and the Emotional Reactivity Scale score meet the criterion of alpha > .90 for individual-level tracking. The Sense of Mastery and Sense of Relatedness Scale scores meet the criterion of .alpha > .80 for grouplevel tracking. For children ages 12–14, the RSCA Index scores and all three global scores meet the criterion for individual level tracking. Six of the RSCA subscales met criterion for grouplevel tracking. For youth ages 15–18, both Index scores, three global scale scores, and three subscale scores meet the criterion for individual level tracking. All scores meet the criterion for group-level tracking. Hence the RSCA demonstrates good internal consistency, confirming the conceptual and theoretical derivation of the scale, subscales and indices. Test-Retest Reliability. Most RSCA scales have at least adequate test-retest consistency across two weeks for both the child and adolescent sample. Test-retest reliability is good for the Index and global scale scores. As expected, adolescents evidenced more consistency over time than children (Prince-Embury, 2007). Test-retest reliability is important if a score is to be used as pre-post measures of the effectiveness of an intervention. High test-retest reliability indicates that changes in the score are more likely due to change in circumstances and are not due to error variance or chance alone. Low test-retest reliability would make it difficult to use the measure for pre-post testing as one would not know whether change in score was due to error variance or intervention. For children ages nine to 14, only the Resource Index meets the criterion for individual longitudinal tracking while the Vulnerability Index, Emotional Reactivity and Sense of Relatedness scale scores meet the criteria for group-level tracking. For

148 Sandra Prince-Embury youth ages 15–18 the Vulnerability Index may be used for individual-level tracking while all of the global scales and five of the subscale scores may be used for group-level tracking. It should be noted that the test-retest samples described are relatively small and these test-retest coefficients should be replicated in additional larger samples. Validity Evidence Validity evidence of a measurement tool is not provided by any single study or statistic, but rather consists of a body of findings demonstrating that the instrument measures what it says it does and may be used in the way that it is intended. One form of validity evidence is concurrent validity which is demonstrated by high correlations between scores on the instrument and scores on relevant other measures. Concurrent validity for the RSCA as a screening tool would be demonstrated by high correlations between the RSCA and other measures of psychological symptoms and other difficulties. Predictive validity is demonstrated by the ability of a cut score to accurately classify cases into relevant criterion groups. Sensitivity is defined as the ability of a score to accurately predict those individuals who have an identified problem. Specificity is defined as the ability of a score to accurately identify those individuals who do not have the problem of concern. False negatives are those students who are missed or who have the problem but who have not been identified as having the problem. False positives are those individuals who are falsely identified and who do not have the problem in question. “Hit rate” is the term used to describe the overall number of individual’s accurately identified. Evidence of Concurrent Validity. Prince-Embury (2007; 2008) reported strong positive correlations between the Vulnerability Index Score and the Emotional Reactivity score and all Beck Youth Inventory—Second Edition (BYI-II; Beck, Beck, Jolly, & Steer, 2004) scores of Negative Affect and Behavior for a non-clinical group of adolescents. The Vulnerability Index score had significant positive correlations with the BYI–II scores of Negative Affect and Behavior: .65 (Anxiety), .66 (Disruptive Behavior), .75 (Depression), and .77 (Anger). Similarly, high positive correlations between the Emotional Reactivity score and scores on all BYI–II scores of Negative Affect and Behavior: .65 with Anxiety, .67 with Disruptive Behavior, .74 with Depression, and .76 with Anger support the hypothesis that a high degree of emotional reactivity is associated with Negative Affect and Behavior. In addition the Vulnerability Index score and the Emotional Reactivity scores correlate with BYI-II scores across affective domains in adolescents and are not limited to any one affective domain. There are also high negative correlations between the Resource Index score and the BYI–II scores of Negative Affect and Behavior: –.51 with Disruptive Behavior, –.53 with Anxiety, –.61 with Depression, and –.62 with Anger. Similarly, high negative correlations were found between the Sense of Mastery (–.51 to –.61) and the Sense of Relatedness (–.45 to –.57) scales and all BYI–II scores of Negative Affect and Behavior. Evidence of Criterion Group Differences. Prince-Embury (2007) reported significant differences between mean scores of clinical groups and matched control groups for children and adolescents. The non-clinical group scored higher on the Resource Index score, Sense of Mastery, Sense of Relatedness scales and subscales. The clinical sample scored higher on the Vulnerability Index, Emotional Reactivity scale and subscale scores. Effect sizes were large for all differences. Discriminant Function Analysis and Classification. Discriminant function analysis reported elsewhere (Prince-Embury, 2008) examined the relative predictive validity of the RSCA Index and scale scores, demographic variables and the psychological symptoms assessed by the

Assessment for Integrated Screening and Prevention 149 BYI-II (Beck et al., 2004). Variables entered as independent variable included the following: (1) parent level of education, (2) gender, (3) RSCA Scale scores (Sense of Mastery T-score, Sense of Relatedness T and Emotional Reactivity T-scores), Index scores (Vulnerability and Resource) and Beck Youth Inventory II scores for Anxiety, Depression, Anger, and Disruptive Behavior. Groups to be discriminated were coded according to clinical status as 0 (non-clinical) or 1 (clinical). The structure matrix reported by Prince-Embury ( 2008) indicated a high loading for both the Vulnerability Index and the BYI-II Anxiety scores on the final discriminant function. The standardized canonical coefficients indicated that the Vulnerability Index contributed more unique variance to the function than did the BYI-II Anxiety score. The canonical correlation for the function was .599 indicating that this function accounted for 36% of the variance in clinical membership. Seventy-seven percent of the original cases were accurately classified. The classification sensitivity was 73% and specificity was 81%. When the Vulnerability Index was entered alone into the discriminant function sensitivity was 79% and specificity was 78%, while the percentage of original cases accurately classified was 78.6%. Therefore, removing the BYI-II from the discriminant function decreased specificity, but increased sensitivity by 6% or 11 clinical cases. In addition, regression analyses conducted with the Vulnerability Index score removed indicate that the Emotional Reactivity Score accounted for most of the variance of the Vulnerability Index when predicting symptoms. The Resource Index score accounted for 1% to 3% of the variance, suggesting a slight ameliorative effect. In summary, validity evidence was the following. 1

2

Significant and high correlations were found between Negative Affect and Behavior (BYI–II scores) and all of the Resiliency Scales and Index scores. The strongest correlations were between the Resiliency Vulnerability Index and Emotional Reactivity scores and the BYI–II scores. Discriminant Function analysis using gender, parent education level, Resilience Scale and Index scores and BYI–II Negative Affect and Behavior scores to predict membership in the clinical versus non-clinical sample indicated the RSCA Vulnerability Index was the best predictor followed by the BYI–II Anxiety score accurately predicting 73% of cases. Emotional Reactivity accounted for most of the predictive variance of the Vulnerability Index score with the Resource Index score accounting for only 1–3% of the variance.

Linking Screening with Preventive Intervention Personal Resiliency Profiles In the previous sections we have discussed the need for universal screening in the schools, and the challenge posed by the task of screening students in a way that does not focus on psychological symptoms alone. We have also discussed the need for screening tools to have adequate psychometric properties while at the same time being easy to use. The Resiliency Scales for Children and Adolescents (RSCA) was presented for use in screening as an instrument that is based on resilience theory and has good psychometric properties. The Personal Resilience Profile is based on RSCA global scale scores (Sense of Mastery, Sense of Relatedness and Emotional Reactivity) and provides a visual tool for better understanding the multiple aspects of personal resilience. The profile presents three scores standardized using the same T metric that, when viewed together, emphasize relative

150 Sandra Prince-Embury 70 60 50 Bobby

40

Linda 30

Joe

20 10 0 Mastery

Relatedness

Reactivity

Figure 7.1 Resiliency Profiles for Bobby, Linda and Joe.

perceived resources and vulnerabilities of children and adolescents. Figure 7.1 displays individual Resiliency Profiles for three students, Bobby, Linda and Joe. Joe’s profile reveals relative vulnerability associated with an Emotional Reactivity T-score of T60 which is high relative to the standardization sample. However, Joe’s Sense of Relatedness and Sense of Mastery are in the average and above-average range. Linda’s Resilience Profile shows that she, like Joe, has high Emotional Reactivity (T60). Linda’s Sense of Mastery and Sense of Relatedness T-scores are both in the average range, close to T50. Bobby’s profile shows a high Emotional Reactivity score also (T60). Unlike Joe and Linda, however, Bobby’s Sense of Mastery and Sense of Relatedness T-scores are low (T37) and below-average (T42). Comparing these three personal Resiliency Profiles, Bobby appears to be most at risk in that his profile suggests high Emotional Reactivity and below-average perceived personal resources with which to manage his emotional reactivity. Joe and Linda, who indicate the same degree of emotional reactivity, also indicate higher levels of Sense of Mastery and Sense of Relatedness with which to manage their reactivity. This example illustrates that individual Personal Resiliency Profiles may be used to examine the relative strengths and weakness of individuals, in order to identify students who are most at risk and/most in need of preventive intervention. Aggregate Resiliency Profiles. In addition to use of the Personal Resiliency Profile to examine individual profiles, aggregate Resiliency Profiles may be compiled to represent the relative strengths and vulnerabilities of groups of youth that share one or more characteristics in common. Figure 7.2 displays aggregate Resiliency Profiles for six groups of adolescents: non-clinical, Anxiety Disorder, Depression, Conduct Disorder, Bipolar Disorder and a group that had been in therapy previously. The Resiliency Profile of the non-clinical group approximates a straight line around a T-score of 50. The Resiliency Profiles of the four clinical groups vary somewhat but share these characteristics in common: high Emotional Reactivity, low Sense of Mastery and low Sense of Relatedness. These similarities suggest that in spite of differences in disorder, youth with clinical disorders have similar Personal Resiliency Profiles.

Assessment for Integrated Screening and Prevention 151 70 60 50

Non Anxiety Depression Conduct Bipolar Nonspec

40 30 20 10 0 Mastery

Relatedness Reactivity

Figure 7.2 Resiliency Profiles for Clinical and Non-Clinical Adolescent Groups. Resiliency Scales for Children and Adolescents—A Profile of Personal Strengths. Copyright 2006 NCS Pearson, Inc. Reproduced with permission. All rights reserved.

Multi-Tiered Universal Screening Using the RSCA Index Scores The relationships between the three global RSCA scores illustrated in the profiles above may be quantified and expressed in two Index scores. The Resource Index combines the Sense of Mastery and Sense of Relatedness scale scores. The Vulnerability Index score quantifies the difference between the Emotional Reactivity scale score and the Resource Index score. As illustrated in Figure 7.2, this discrepancy is visible across clinical groups. Validity evidence suggests that the Vulnerability Index may be used as a first-line predictor in accurately classifying clinical status (Prince-Embury, 2007). Therefore, Tier 1 screening first uses the Vulnerability Index to identify students who may be at-risk for developing clinical symptoms and other difficulties. Students who have Vulnerability Index T-scores in the above-average or higher ranges are screened for further examination. Table 7.1 shows three Vulnerability Index score ranges, above-average, high and very high, for three age bands. The cut score for screening ultimately depends on how wide a screening net is desired as well as the relative resources available to the school. Table 7.1 shows that a cut score of T55 on the Vulnerability Index score would identify approximately 30% of a normative sample as above-average. A cut score of T60 would identify approximately 16% of the sample as high and a cut score of T65 would identify approximately 9% of the sample as very high. A multi-tiered screening model for identification of individual students at-risk for social and emotional problems is illustrated in Table 7.2. This model begins with the Vulnerability Index score and follows up with the Emotional Reactivity scale score and Resource Index scores taking the steps indicated in Table 7.2. Table 7.1 T-Score Ranges for the Vulnerability Index and Cumulative Percentages by Age Band Range

T-Score Range

9–11

12–14

15–18

Very High High Above Average

>64 60–64 55–59

91+ 84–90 70–83

92+ 83–91 70–82

91+ 87–90 76–86

152 Sandra Prince-Embury Table 7.2 RSCA Screening Model 1: Individual Level 1. First Tier: using the Vulnerability Index score. If the Vulnerability Index score is T55 (above average) or higher, monitor by retesting in three to six months. 2. Second Tier: If the Vulnerability Index score is T60 (high) or higher and if Emotional Reactivity Score is T60 or higher, then students may be identified for preventive intervention addressing management of emotional reactivity. 3. Third Tier: If the Resource Index is T40 or below, examine the Sense of Mastery and Sense of Relatedness scale scores to determine specific areas for preventive intervention. If the Sense of Mastery Score is T40 or below refer for preventive intervention pertaining to Sense of Mastery, self-efficacy, adaptability. If the Sense of Relatedness is T40 or below, refer for preventive intervention pertaining to Sense of Relatedness, social skills, communication skills, etc.

To illustrate how the RSCA Index and scale scores might be used for universal screening, the following example is provided. If the RSCA was administered to a school population of 1,000 at the beginning of the academic year, Tier 1 might identify 300 students as above-average in vulnerability, using a Vulnerability Index score equal to or greater than T55. This group would be monitored by retesting in three months. Of these 300 students, approximately 150 students might have Vulnerability Index scores equal to or greater than T60, identifying them as potentially high in vulnerability and warranting preventive intervention. Of this identified group of 150 students, approximately 135 students would also have Emotional Reactivity T-scores equal to or greater than T60. This group would be identified as potentially high in emotional reactivity and potentially in need of preventive intervention aimed at reducing emotional reactivity. In addition, 110 students would have Resource Index scores equal to or less than T40, suggesting that these students are low in resources and warrant preventive intervention to enhance resources. Approximately 85 of the 150 students would meet these criteria: Vulnerability Index and Emotional Reactivity scores of T60 or above, as well as Resource Index scores of T40 or below. Preventive intervention based on the hypothetical screening for the 1,000 students provided above might include the following: intervention on managing emotional reactivity for 40 students; intervention for enhancing personal resources for 25 students; both interventions for 85 students. Outcome Assessment One advantage of systematic screening and targeted intervention is that outcome assessment can be implemented to determine whether a preventive intervention had a positive impact on the targeted aspect of personal resiliency. The importance of outcomes assessment is underlined by the fact that it is easier to convince policymakers of the importance of implementing preventive interventions that are empirically supported. To the extent that preventive interventions are implemented prior to the appearance of symptoms, positive outcomes often need to be inferred from the observation that symptoms did not occur for a specified period of time during and after the intervention. Screening with the RSCA provides another option for the assessment of impact. For example, interventions designed to assist with lowering emotional reactivity could be evaluated based on their effectiveness in producing lower scores on the RSCA Emotional Reactivity scale. Similarly, interventions designed to increase sense of mastery could be evaluated based on their effectiveness in producing higher scores on the RSCA Sense of Mastery

Assessment for Integrated Screening and Prevention 153 scale while those designed to increase sense of relatedness could be evaluated based on their effectiveness in producing higher scores on the RSCA Sense of Relatedness scales. Using the hypothetical example, the above outcome assessment could proceed as follows. The 300 students identified as above-average on the Vulnerability Index score (> T55) during universal screening would be retested three months later. If the first testing occurred at the end of September, the second testing might occur in the middle of December. During this three months, preventive interventions would be implemented in October and November for the group of 150 students identified with Vulnerability Index and Emotional Reactivity scores equal to or above T60 (the high vulnerability range). In January and February, RSCA scores at first and second testing would be compared for students in the identified group who had not received the intervention as well as for students who had received the intervention. Analysis of preventive screening and intervention would address the following questions: 1

2

Did any significant changes occur over time in the group of students who had scored above-average on the Vulnerability Index but had not participated in any intervention? Significant changes that may have occurred at the aggregate level for the nonintervention group could be examined to determine whether these changes were related to school-wide events or circumstances, or whether these changes reflected changes for a few specific individuals in this group. Students in the non-intervention group whose Vulnerability Index score had increased by five T-score points or entered the high range might be considered for preventive intervention in April or May of the academic year. Did any significant changes occur between pre- and post-intervention testing of the intervention group? For example, did the average Vulnerability Index score for the Intervention group decrease by five or more T-score points? Were these changes consistent with the specific intervention received? For example, did students receiving preventive intervention to manage emotional reactivity manifest lower Emotional Reactivity scale scores post-intervention? Students in the intervention group whose Vulnerability Index scores remained high might be considered for additional intervention.

The importance of psychometrically sound outcome assessment is highlighted by the fact that solid evidence of statistically significant change underlies empirical support for preventive interventions. Empirically supported interventions provide solid argument in favor of funding for widespread implementation of such interventions. Classroom Screening Applications Although applications discussed thus far have focused on identification of individual students who might benefit from preventive intervention, there are other potential benefits of universal screening using an instrument such as the RSCA. For example, Doll et al. (2004) describe the use of the ClassMaps survey for the purpose of selecting classrooms and interventions to enhance classrooms’ resilience. The RSCA scores may be used in a similar way to supplement the ClassMaps approach. Schools that administer the RSCA may aggregate RSCA scores within classrooms rather than focusing on individual scores. In this way classrooms rather than individual students may be identified as high in Vulnerability or Emotional Reactivity, or low in Sense of Mastery or Sense of Relatedness and classroom interventions could be designed accordingly. Use of the RSCA with an aggregated classroom approach as suggested by Doll, Zucker and Brehm in the Resilient Classroom (2004) is described in Table 7.3.

154 Sandra Prince-Embury Table 7.3 Screening Model II for Aggregate Classroom Level a b c d e

At-risk classroom identified by aggregate class scores on RSCA using Vulnerability Index score T60 or higher. At-risk classrooms examined to see if high score are due to presence of individual at-risk outlier score (individual RSCA Vulnerability Index scores T65 or higher). ClassMaps administered to all at-risk classrooms to determine classroom characteristics that need attention. RSCA class Resiliency Profiles and ClassMaps profiles examined for high risk classrooms to determine appropriate interventions. Pre- and post-intervention comparisons made to determine if intervention was successful.

Impact of Unanticipated Events Another application of universal screening that occurs regularly at the beginning and end of the academic year is describing the potential impact of anticipated and unanticipated circumstances. For example, much work has been done to assess the impact of a disaster on children and adolescents after its occurrence. Due to the absence of pre-disaster measurement, impact of the event is a matter of inference. If universal screening data had been collected, it would be possible to compare pre- and post-event status of a school’s students. Resiliency and Academic Achievement Finally, resilience in children and adolescents is related to many aspects of their functioning in an educational setting including academic achievement, school attendance, dropout rate, etc. Universal resilience screening would make it possible to assess variables other than cognitive ability that influence the ability of students to benefit from the education that is provided for them.

Assessment-Based Preventive Interventions Preventive interventions are many and a comprehensive discussion of these interventions is clearly beyond the focus of this paper. This section is intended to provide examples of preventive interventions that are linked with specific RSCA scores and the developmental constructs underlying these scales. RSCA prevention strategies may be based on the personal resiliency profiles of children and individuals, and may be focused on groups or individuals, across age band. Such theoretical consistency allows for more systematic assessment of whether interventions targeted to foster resiliency in a certain way do in fact impact children and adolescents in that way. This moves assessment of efficacy of intervention from a simple “Does it work or doesn’t it?” to the more complex assessment of “How does it work?” It is recognized that personal resiliency, although based on core developmental constructs, is highly complex in nature. For this reason, interventions designed to achieve one type of goal may impact other aspects of youth functioning. It is for this reason that a robust multi-factored assessment tool is needed. According to the RSCA framework provided above, child-focused preventive interventions may be broadly considered as vulnerability-reducing, resource-enhancing or some combination of both. The RSCA research mentioned above (Prince-Embury, 2008) suggests that prevention of psychopathology should first address psychological vulnerability associated with high emotional reactivity. In the clinical experience of the author, efforts at enhancing resources while emotional reactivity is high may be less effective than when emotional reactivity is lower.

Assessment for Integrated Screening and Prevention 155 Interventions to Reduce Personal Vulnerability: Emotional Reactivity

Scaled scores

Youth who have very high Emotional Reactivity scores (T65 or higher) should be referred for further assessment. Such youth are usually found to have very high BYI-II Anger, Depression and/or Anxiety Scaled scores and may benefit from psychoactive medication to reduce their reactivity. For these students, intervention may include referral for medication evaluation as well as training in monitoring and controlling emotional reactivity. Re-administration of the RSCA Emotional Reactivity scale two or three weeks after beginning medication may indicate whether the medication is helping. For youth who have higher-than-average emotional reactivity, but who are not in the highest range (T55–T64), preventive intervention may focus initially on intentional management of emotional reactivity. This preventive strategy might start by helping the youth to identify emotional reactivity as a potential source of vulnerability. Some youth may already be aware of this, but others may need time to fully understand the connection. Awareness may be enhanced by breaking emotional reactivity down into the more discrete and observable components of sensitivity, recovery and impairment. Once these constructs are understood by the youth in terms of his or her own experience, strategies for self-monitoring and eventual self-management are possible. This section discusses some assessment-linked tools for helping children and adolescents to identify and manage emotional reactivity. These tools may be used individually and in groups. The techniques focus on identifying triggers for emotional reactivity and helping youth quantify and communicate the difficulty they have in various types of situations. Figure 7.3 illustrates that clinical groups of adolescents are above-average or high on the three subscales of the Emotional Reactivity Scale. Intervention tools described below address the personal attributes addressed by each subscale separately although the tools may be administered simultaneously. Pre- and post-intervention comparison of each subscale score might help in evaluating the specific impact of prevention strategies designed to address aspects of emotional reactivity. 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

Non clinical Depression Anxiety Conduct disorder Bipolar disorder Non specific

Sensitivity

Recovery

Impairment

Figure 7.3 Emotional Reactivity Subscale Profiles. Resiliency Scales for Children and Adolescents—A Profile of Personal Strengths. Copyright 2006 NCS Pearson, Inc. Reproduced with permission. All rights reserved.

156 Sandra Prince-Embury Interventions to Enhance Resources

Scaled scores

Interventions targeting Resource Enhancement would be implemented when Resource Index scores are low (T44 or lower). The specific type of intervention implemented would be determined by the Personal Resiliency Profile which distinguishes between Personal Resources associated with Sense of Mastery and Personal Resources associated with Sense of Relatedness. Low Sense of Mastery scale scores might trigger interventions focused on increasing students’ sense of mastery. Interventions Targeting Sense of Mastery. Low Sense of Mastery scale scores (T44 or lower) might trigger interventions focused on increasing students’ sense of mastery. Research, theory and interventions for children have tended to combine the constructs of Optimism and SelfEfficacy (i.e., Seligman’s Optimistic Child, 1995). The Resilience program at the University of Pennsylvania employs cognitive behavioral techniques to overcome depression in children (Reivich, Gilham, Chaplin, & Seligman, 2005). Cognitive behavior treatments for depression are based on the belief that depression is based in part on a triad of hopelessness about the future, oneself and the world in general. Consistent with this assumption, many treatments focus on challenging negative assumptions and encouraging more positive reframing of beliefs. Approaches that are based on increasing self-efficacy may increase opportunities for success, so that youth can be reinforced by the experience of themselves as successful. The RSCA Sense of Mastery scale score makes it possible to assess whether preventive interventions designed to promote optimism and self-efficacy in youth actually do so. Subscales that make up the Sense of Mastery scale make it possible to estimate impact on specific aspects of sense of mastery represented as subscales tapping optimism, self-efficacy and adaptiveness. Figure 7.4 displays aggregate Sense of Mastery subscale profiles for non-clinical and clinical adolescent samples. These profiles suggest that all subscales are low for most of the clinical groups particularly the depressed group. 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

Non clinical Depression Anxiety Conduct disorder Bipolar disorder Non specific

Optimism

Self-efficacy

Adaptability

Figure 7.4 Sense of Mastery Subscale Profiles. Resiliency Scales for Children and Adolescents—A Profile of Personal Strengths. Copyright 2006 NCS Pearson, Inc. Reproduced with permission. All rights reserved.

Assessment for Integrated Screening and Prevention 157 The following interventions, described in the RSCA manual (Prince-Embury, 2007) are examples of preventive interventions targeting increased Sense of Mastery. Although these interventions might effect change in all three subscales of the Sense of Mastery scale they are specifically targeted to improve sense of self-efficacy. Strength Identification. Adolescents who have experienced more failure than success in their lives may have lost the ability to identify their own strengths. For such youth, it is helpful to provide preventive interventions that help them remember and identify positive experiences associated with hidden, forgotten, buried, or uncultivated strengths. Block and Block (1980) originally coined the term “islands of competence” and Brooks and Goldstein (2001) have recently expanded this concept with numerous clinical examples of identifying islands of competence to enhance resilience in youth. In addition, once areas of strength are identified, preventive intervention may further identify, elaborate, enhance, and generalize these strengths. These interventions can help youth generalize their strengths to other areas where they may not feel as successful. Structured preventive interventions might also help youth identify specific skills and think about how these skills could be employed in other areas. Preventive Interventions Targeting Sense of Relationship Preventive interventions focusing on relatedness frequently emphasize increasing social skills, assuming that low sense of relatedness is related to lack of social skill. The ability to relate to others and to gain strength and resilience from these relationships is a multi-faceted and complex process. Relating to others is particularly important in adolescence as teenagers prepare for adulthood and attempt to be more independent of parents. Those who have worked with adolescents hear reports of feeling lonely, isolated, unpopular, popular but fearful of losing their status with a particular group of peers, or being the victim of bullying behavior by others. These relationship difficulties are frequently reported in relatively simple terms and often reflect the outcome of more complex interpersonal processes. Subscales of the Sense of Relatedness scale suggest aspects of relatedness that might be identified and targeted as outcomes when designing preventive interventions. Figure 7.5 displays Sense of Relatedness subscale scores for adolescent clinical groups as compared with a non-clinical sample. Two of the clinical groups display mean subscale scores in the below-average or low range, the Depression and Conduct Disorder groups. The Relationship scale and subscale scores may be used to track impact of interventions designed to improve sense of relatedness in children and adolescents by comparison of pre- and postintervention scores on these subscales. For example, adolescents who score low in access to support subscale scores frequently have difficulty communicating with their peers and or their parents. Interventions targeting these specific areas might yield changes in this specific subscale score. Perceived Social Support. Developmental theorists have acknowledged the significance of perceived social support for intervention. Research has indicated that an individual’s perception that social support is available and accessible is the most important dimension of social support (see Hogan, Linden, & Najarian, 2002). Thompson, Flood, and Goodvin (2006) suggest that it is sometimes more important to focus on youths’ subjective experience of supportiveness by carefully examining their expectations of support and how these are or are not discrepant from the support that they perceive to be provided by others. Thompson et al. also suggest that troubled youth may be less capable of viewing others as sources of available support because of their emotional turmoil, and youth in difficulty may be less able to mobilize supportive networks when they are needed. These ideas highlight the need to

Scaled scores

158 Sandra Prince-Embury 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0

Non clinical Depression Anxiety Conduct disorder Bipolar disorder Non specific

Trust

Support

Comfort

Tolerence

Figure 7.5 Sense of Relatedness Subscale Profiles. Resiliency Scales for Children and Adolescents—A Profile of Personal Strengths. Copyright 2006 NCS Pearson, Inc. Reproduced with permission. All rights reserved.

explore the supports identified by children and adolescents before a time of crisis, so that they can objectively reflect on the support and think of how they might ask for help in different circumstances.

Summary: Implications for Screening and Intervention Programs In summary, the premise of this chapter asserts that screening in schools should be proactive, detecting personal vulnerability or those who may lack personal resiliency, as opposed to symptom-based which is reactive and pathologizing. A proactive method provides the opportunity to screen and intervene with children before psychological symptoms have occurred. From a classroom management perspective, once symptoms occur they have already had a chance to disrupt the learning of a symptomatic youth as well as others in the classroom environment. From the perspective of family and community relationships, screening based on personal vulnerability is non-stigmatizing and thus may be shared with youth and their parents in useful and non-alienating ways. From a broader child mental health perspective, allowing symptoms to occur runs the risk of allowing these symptoms to crystallize and to further compromise normal development. As we know, symptoms which have crystallized into psychological disorders are more difficult and costly to treat. In addition, screening based on symptoms alone runs the risk of only identifying children with externalizing symptoms or those symptoms that are most visible to others. Thus a symptom-based model of screening may run the risk of allowing youth with internalizing symptoms to slip through the cracks. If, as educators, we propose that the purpose of screening is not just identification but also intervention we should consider the utility of the screening tools that we employ for this

Assessment for Integrated Screening and Prevention 159 purpose. Screening based on personal vulnerability and personal resources bypasses symptoms and is based on etiology of problems. Screening using the RSCA has inherent in it the identification of areas of personal strength (sense of mastery and sense of relatedness) and personal vulnerability (emotional reactivity) which are rooted in developmental theory. In this way, well established principles of development may guide universal and individual levels of preventive intervention. To aid in this process, examples of specific interventions for strengthening sense of mastery and sense of relatedness and minimizing emotional reactivity have been provided in this chapter. Finally, assuming agreement on all of the principles above, the bottom line for school administrators and funding sources is, “Does it work?” For this reason we need screening tools that are simple to administer and score and which have excellent psychometric properties of reliability and validity. This chapter presents the Resiliency Scales for Children and Adolescents as a nationally normed assessment tool with demonstrated reliability and validity. The advantages of psychometric strength are multiple: credibility in obtaining funding, integrity of assessment and the ability to effectively assess relative success of targeted interventions. In closing we are well aware of the daunting nature of translating good ideas into practical application. For this reason we have included in this chapter two examples of screening programs using the RSCA: one based on screening for individual children who are at risk and one based on classroom screening. We have also included an example for using the RSCA to assess outcomes of preventive interventions. These examples are provided as helpful guidelines to be modified to fit the specific needs of individual school systems.

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8

Social Support How to Assess and Include It in Research on Prevention and Youth Outcomes Michelle K. Demaray, Christine K. Malecki, Lyndsay N. Jenkins, and Christy M. Cunningham, Northern Illinois University

Even for people not well-versed in research, intuitively social support seems essential for a positive, healthy life for people throughout the lifespan. Focusing on childhood and adolescence, it is easy to see how having a loving, caring parent; a supportive, honest, and sharing friend; or an information-sharing, accessible teacher would be building blocks for positive well-being. Researchers seem to agree, and have conducted a great deal of work examining the relationship between social support and many positive outcomes in youth (Demaray & Malecki, 2002; Demaray, Malecki, Davidson, Hodgson, & Rebus, 2005; DuBois, Felner, Brand, Adan, & Evans, 1992; Jackson & Warren, 2000) as well as the negative relationship of social support with distress and problem behaviors (Sheeber, Hops, Davis, & Andrews, 1997; Wills & Cleary, 1996; Windle, 1992; Zimmerman, Ramirez-Valles, Zapert, & Maton, 2000). Thus, social support is a critical variable for prevention research and programming to focus on when developing or evaluating prevention efforts. Many types of interventions can be perceived as increasing social support, even though different terminology may be used. For example, a group intervention with victims of bullying in schools may focus on teaching specific social skills to victims for dealing with future bullying. However, an important outcome of this group may also be increased emotional social support among the group members and the development of supportive networks or bonds. In addition, from the broad definition of social support presented above, providing information, such as concrete skills to deal with bullying, is also a type of informational social support. Social support is often present in many prevention efforts and assessing students’ perceptions of social support is important for understanding the specific mechanisms in the interventions that are making a difference. For example, in the victim group described above, is it the teaching of specific skills to victims for dealing with bullying or is it the building of emotional bonds with fellow victims that may be related to positive outcomes for victims? The focus of prevention programming to prevent youth problems and promote healthy children and youth has increased in recent years (Small & Memmo, 2004). Especially with the 2004 reauthorization of the Individuals with Disabilities Education Act, the use of prevention practices in schools has received increased attention (Glover & DiPerna, 2007). Following a three-tiered model of prevention, the Response to Intervention (RtI) model is actively being applied to school settings. Although there is a renewed interest in prevention, prevention efforts and three-tiered models of prevention have been around for a long time and have strong support (Kratochwill, Volpiansky, Clements, & Ball, 2007). This chapter will not go into depth on the definition and models of prevention which are presented thoroughly in the first chapter (and other chapters) of this book. However, the importance of social support and how it relates to prevention and youth outcomes will be highlighted. Furthermore, assessment methodologies for including social support in prevention research will be detailed,

164 Michelle K. Demaray et al. and a few current prevention studies that have included the construct of social support will be reviewed.

Social Support Based on the work of House (1981), Tardy (1985) developed a comprehensive framework to conceptualize the construct of social support. His model describes five different issues that are theoretically and operationally relevant in defining the multi-dimensional construct of social support: direction, disposition, description/evaluation, content, and network. While Tardy conceded that these five issues are not exhaustive, he did suggest that they encompass the primary elements of social support, and recommended explicit discussions of these issues in each study so that conceptualizations of social support are clear. In addition, recent developers of children’s social support measures have cited Tardy’s work and highlighted the need to consider the usefulness of these five key dimensions (Malecki & Demaray, 2002; Zimet, Dahlem, Zimet, & Farley, 1988).

Tardy’s Model of Social Support Direction. Are you more interested in the social support someone is giving or the social support someone is receiving? The concept of direction focuses on who is giving the social support and who is receiving it. Disposition. What matters most, whether social support is available to you or if you have actually used that social support? A common situation that reflects available support is when a young woman takes her first babysitting job at a neighbor’s house. If her mother is at home, a few doors away, she knows that she can call her mom for advice or help if something were to happen. Knowing that this support is available provides a sense of security in the new, unfamiliar situation. There may be benefits to knowing that support is available even if it is not utilized. Taking available support one step further, is it important to measure the perceptions of available support, which may be different from what support is really available. For example, teachers may be available to students after school and during prep times but if a student is not aware of that, they will perceive less support than is really available to them. It could be argued that it is the perception of the support that truly matters in the lives of youth. An adult may believe a child/student is supported, but if youth do not perceive being supported, that will influence the outcomes related to social support. Description/Evaluation. Students may be asked to describe the social support they perceive or they may be asked to evaluate that support. What is most important, the amount of support a student perceives/receives or how satisfied he or she is with it? Content (Type). The four types of support (emotional, instrumental, informational, and appraisal) that Tardy (1985) compiled from the literature are important to consider when defining and studying social support. Emotional support is what is typically thought of when asked about social support; it is feeling loved, cared for, and a sense of belonging. Instrumental support consists of providing resources to support the youth (e.g., time, money, physical resources). Providing youth with necessary and important information and knowledge is informational support and providing them with feedback about their performance and functioning is appraisal support. Network (Source). The people who are providing (or are available for providing) social support are an important factor to consider. For example, the research is clear that there are

Social Support 165 differences in the support that children and adolescents perceive from parents versus friends, or that they perceive from teachers versus parents. Who, then, are the key players in students’ lives? There are developmental changes in the people that children and adolescents rely on for support over time (Cauce, Felner, Primavera, & Ginter, 1982; Furman & Buhrmester, 1985). Thus, if global social support is examined rather than support from specific sources, we may miss potentially important information. Theoretical Basis of Social Support Cohen and Wills (1985) noted that there is a vast amount of evidence documenting the positive correlation between social support and well-being, but speculated that there were different processes at work. Two primary theoretical orientations drive the majority of the literature on social support: the stress-buffering theory and the main effect theory (Cohen, Gottlieb, & Underwood, 2000). When the positive effects of social support are in response to stress or risk status, the stress-buffering theory is at work (Barrera, 1986; Cohen et al., 2000; Cohen & Wills, 1985). The stress-buffering theory makes sense when studying resilience. For students to be considered “resilient,” by definition they must have conditions in their lives that place them at-risk. Social support may be one protective factor that contributes to their resilience. If a youth’s baseline level of social support is not adequate to provide beneficial effects, maybe because the child has extra stressors in his or her life, focusing on increasing social support via group interventions (i.e., secondary prevention) or individualized interventions (i.e., tertiary prevention) may buffer the child or adolescent from negative outcomes. Alternatively, the main-effect theory advocates that social support has a positive benefit for all children and adolescents regardless of any specific stressors or risk circumstances (Cohen et al., 2000; Cohen & Wills, 1985). The main-effect model suggests that access to social support improves students’ overall psychological well-being and therefore reduces psychological problems (Cohen et al., 2000). Thus, it logically follows that having sufficient levels of social support for all students (i.e., primary prevention) is an important goal for schools and prevention programs. There is evidence for both the stress-buffering and the main effect models depending on the way that support is measured. Measures of social support that assessed available interpersonal resources during times of stress provided evidence for the buffering model. But when the measure of social support measured the degree to which a person was a part of a social network, there was evidence for the main effect model. Generally, both models may be used as tools in explaining the role of social support in the lives of resilient children, but most typically, the stress-buffering model will be most relevant.

Social Support and Youth Outcomes The goal of this section of the chapter is to review selected studies that investigated the relationship of social support to various youth outcomes. Some of these studies found main effect relations for social support—that social support was associated with beneficial outcomes regardless of a specified risk—while the remaining studies investigated the stress-buffering role that social support serves for youth identified as at-risk. These selected studies have examined the impact of social support on outcomes for specific groups of children and adolescents, including those victimized by peers, HIV-positive youth, adolescents experiencing acculturation related to immigration into the United States, and students considered at-risk based on socioeconomic status.

166 Michelle K. Demaray et al. Social Support and Main Effect Role Social support has been found to be positively related to a wide variety of outcomes, thus demonstrating a main effect role. A study by Demaray et al. (2005) investigated a longitudinal relationship between social support and student adjustment in a sample of 82 middle school students. Student adjustment was measured by the Behavior Assessment System for Children, Self Report of Personality (BASC; Reynolds & Kamphaus, 1998), a broad-band measure of student’s emotional well-being. The results showed significant relationships between parent support and clinical maladjustment (a measure of internalizing problems) and emotional symptoms (assesses anxiety, social stress, depression, sense of inadequacy, interpersonal relations, and self-esteem); classmate support was related to emotional symptoms; and school support was related to school maladjustment (a measure of dissatisfaction with school). Social support was measured via the Child and Adolescent Social Support Scale (Malecki, Demaray, & Elliott, 2000), which measures the frequency of social support from five different sources (parents, teachers, classmates, close friend, and school) as well as four different types of social support (emotional, appraisal, instrumental, and informational). The relationship between social support and academic well-being was investigated in a large sample of Latino youth (DeGarmo & Martinez, 2006). This study demonstrated a main effect as well as a stress-buffering role of social support. Academic well-being was assessed by four variables: GPA, frequency of homework completion, students’ evaluations of their own academic performance, and a question about how likely the student was to drop out of school. Via structural equation modeling, they found that social support was significantly related to higher levels of academic well-being. This main effect relationship was found for overall support and for support from parents and the school. The researchers also found that social support from parents buffered the relationship between discrimination and poor academic well-being. This study demonstrates that social support may play a main effect role as in the relationship between social support and academic-well-being and also play a buffering role, as in the relationship between discrimination and academic well-being. A study by Atri, Sharma, and Cottrell (2007) examined the main effect role of social support, along with acculturation and hardiness on mental health, in Asian Indian international students. These researchers found that social support, primarily the belonging aspect of social support, was positively related to mental health. They concluded that belonging support, also referred to as companionship support, is important for the mental health of individuals migrating to different countries; thus experiencing empathy, caring, trust, and reassurance is important for these individuals. Atri et al. (2007) assessed social support via a version of the Interpersonal Support Evaluation List, which is a 12-item scale that assesses the perceived availability of three types of social support: belonging support, appraisal support, and instrumental support. In addition to the main effect role of social support in the health of individuals who have immigrated to a new country, a stress-buffering role may be present and is described in an additional study below. Social Support and the Stress-Buffering Role Davidson and Demaray (2007) examined social support as a moderator in the relationship between peer victimization and internalizing and externalizing distress from bullying. They found that social support buffered the relationship between peer victimization and internalizing distress, but not externalizing distress. Thus, Davidson and Demaray concluded that social support may be serving a protective function against internalizing problems for individuals

Social Support 167 who are victims of bullying. Children and adolescents who are victims of bullying often report many mental health difficulties, including anxiety and depression, poor school adjustment and poor social competence, so these findings are promising. For the study, social support was measured via the Child and Adolescent Social Support Scale (Malecki et al., 2000), which is a comprehensive 60-item measure that measures the frequency of social support from different sources (parents, teachers, classmates, close friend, and school) as well as different types of social support (emotional, appraisal, instrumental, and informational). Another study examined the relationship of social support with the mental health of HIVpositive adolescents and young adults. Research has shown that these individuals are at risk for mental health problems that can impact their quality of life and physical well-being (Lam, Naar-King, & Wright, 2007). This study found that social support buffered against the negative mental health concerns that commonly arise in HIV-positive youth, such as depression and anxiety. The researchers used an abbreviated version of the Social Provision Scale (Cutrona & Russell, 1987), which consisted of 12 items that asked participants about relationships with people in their lives. A recent study examined the stress-buffering relationship of social support on the mental health of adolescents and young adults that had immigrated to a different country. Oppedal, Roysamb, and Sam (2004) in Norway examined the impact of social support in the relationship between acculturation and mental health among junior high students who immigrated to Norway. They found that low support from family or friends along with low ethnic competence (low levels of acculturation) yielded significant negative effects on mental health. Additionally, when individuals experienced discrimination as well as low family and class support, mental health was negatively affected. They determined that there were complex interactions between social support, acculturation status, discrimination, and mental health, but that low levels of social support were consistently linked to mental health difficulties. This study used an unpublished scale of social support that was based on research by Cohen and Wills (1985), Hesbacher, Rickels, Morris, Newman, and Rosenfield (1980), and Ystgaard (1997). The scale measured two types of social support (emotional and instrumental) from four different sources (family, class, friend, and teacher). Another study examining the protective factors of social support was conducted by Malecki and Demaray (2006). Although weak and mixed findings were found for a main effect relationship between social support and academic performance, these researchers found that social support buffered the relationship between low socioeconomic status (SES) and academic performance. Therefore, students who lived in poverty were less likely to have poor academic outcomes if they had higher levels of social support than those students who lived in poverty and had lower levels of social support. In this study social support was assessed via a rating scale, the Child and Adolescent Social Support Scale (CASSS; Malecki, Demaray, & Elliott, 2000) that assessed four types of support (emotional, appraisal, instrumental, and informational) from five sources (parents, teachers, classmates, close friends, and school). The above summarized research is a small sample of the research that focuses on the role of social support in the lives of children and adolescents. The studies clearly indicate that for some outcomes there are beneficial aspects of social support for all individuals (main effect). This is important as one thinks about the role of social support in primary prevention programs. The research also demonstrates that, at times, social support may play more of a buffering role and aid children and adolescents who are at risk. Again, this is important as researchers think about the roles of social support in secondary and tertiary intervention programs. There were also several examples where social support played both a main effect and stress-buffering role; indicating that social support may be important at various levels of prevention. Although

168 Michelle K. Demaray et al. the research reviewed was not necessarily focused on prevention programming, it demonstrates the important relationships that may be present between social support and the wide variety of youth problems practitioners are trying to prevent. In order to assess students’ perceptions of social support in research focused on prevention programs and youth outcomes, the researcher needs to adequately assess the construct. A brief overview of assessment of social support is provided next.

Measurement of Social Support Social support can be measured via four primary orientations. Researchers or clinicians can use measures of one’s network size, measures of social integration, measures of functional support, and/or measures of social status. Network Analysis Using network analysis, participants are asked to provide the number of people that are in their social support network (i.e., Tardy’s sources). Additionally, some network analyses measure the density of a network by measuring how someone’s network members know each other. For example, if there are five people who support an individual, and those five people know each other, that network will be stronger than a network of five people who do not know each other. If they are connected with one another, network members can collaborate and combine efforts in providing support. Network analysis is done simply by asking questions such as “List all of the people in your life that are there for you when you need them.” or “How many people do you have in your life that support you?” For network density, participants can be prompted to draw lines connecting the people who know one another on the list. Often network analysis (if measuring density) can be done graphically (Gottlieb, 2000). Social Integration One can also measure social support via social integration which is measured by the number of different types of social relationships one has in their life. For example, a parent–child relationship, a coach–player relationship, a brother–sister relationship, and a mentor–mentee relationship would make up four distinct types of relationship roles, all supportive. Additionally, researchers interested in social integration as a way of measuring social support would want to know how frequently someone participates in the social activities with which they are involved (Brissette, Cohen, & Seeman, 2000). If a child is in softball and violin, how often do those organizations meet? How many people are involved in those activities? How connected does the child feel to those activities? Does participation in those activities provide the child with support? Social Status Sociometric analysis compiles techniques and methods for determining social networks and the social status of individuals. Several methods to determine this have been established, including a process in which respondents are asked to write names of people in the class whom they like and dislike (Kaya, 2007). Another method, derived from Newcomb and Bukowski (1983), includes nominating each person for both sympathy and antipathy nominations.

Social Support 169 From the scores obtained for either of these methods, a classification for each respondent is derived: popular, rejected, neglected, controversial, and average (Maassen, Akkermans, & Van Der Linden, 1996). These status categories enable researchers to determine other characteristics, which often coincide with these labels. For instance, Kaya (2007) noted that children identified as popular often exhibit higher self-esteem, better academic and social skills, and are less likely to use substances, while children identified as neglected tended to be more aggressive and less compliant. One might surmise that students of popular or average status perceive higher levels of support than students of neglected or controversial status. While such status information can be helpful in determining which students may need further attention, sociometry has been a controversial technique due to its very nature. According to Bell-Dolan and Wessler (1994), despite the usefulness of sociometry, many parents, teachers, ethics review committees, and researchers are concerned about the nominating nature of this method. In the process of obtaining the information, participants may disclose with each other their choices of who they like or dislike in the class. This could yield conflict or distress between the participants if one was chosen over another. While respect and understanding must be given at all times while performing a sociometric survey, the data from a survey this controversial can produce significant information regarding a student’s social and emotional growth. Another form of peer evaluation includes teacher nomination which can avoid possibly negative associations related to peer assessments (Kaya, 2007). Direct Observations Observations can be used to measure the functional aspects of social support. Researchers could observe interactions and code the frequency, quality, type, source, and level of support. This is advantageous in that the researcher is not relying on student perceptions and can view supportive behaviors in the natural environment. However, there are practical reasons why this is not typically feasible (e.g., time-intensive, expensive). Additionally, it may be important to still measure perceptions of support. If one observes a great number of supportive interactions, but the recipient does not perceive those interactions as supportive, that is important information to gather. Functional Support Approaches By far the most common method of measuring social support is through a functional approach using rating scales. Typically rating scales adhere in some form to Tardy’s model (1985) of social support and tap some or all of the dimensions in the model (i.e., source, type, direction, evaluation, etc.). Often rating scales measure only the frequency of one or two types of support. However, influenced by the stress-buffering model of social support, rating scales can have items that ask about support in response to a specific stressor. For example, if researchers are investigating how social support might buffer a student’s response to being bullied, they could use a rating scale that asks about specific supportive behaviors that are available to them regarding being bullied. Some measures also include ratings of negative interactions (Brissette et al., 2000). Although someone may have a supportive relationship in his or her life, it may be important to look at the ratio of positive to negative interactions in that relationship. If the same person who provides someone with a great deal of support is also someone that causes conflict or stress in his or her life, that is important information to consider.

170 Michelle K. Demaray et al. Frequently Used Measures of Social Support with Children and Adolescents A review of the social support literature from 2000 to 2003 (four years; 290 studies reviewed) listed the six most commonly used measures in the child and adolescent research (Malecki, Demaray, & Rueger, 2008). These measures included the Child and Adolescent Social Support Scale (Malecki, Demaray, & Elliott, 2000), the Multidimensional Scale of Perceived Social Support (Zimet, Dahlem, Zimet, & Farley, 1988), the Network of Relationships Inventory (Furman & Buhrmester, 1985), the Social Support Questionnaire (Sarason, Levine, Basham, & Sarason, 1983), the Perceived Social Support Scale (Procidano & Heller, 1983), and the Social Support Scale for Children (Harter, 1985). Table 8.1 provides a summary of the measures of social support that were used most frequently in this four-year time frame. Included are the age range of the validation sample or age of intended population, the number of times it was used in the literature from 2000 to 2003, the number of items and response format of the measure, and the type and source of support measured. Table 8.2 summarizes the reliability and validity evidence provided in their respective validation studies/manuals. Overall, all measures provided evidence of excellent reliability, although some of the subscales on the Network of Relationships Inventory were below the commonly accepted standard of .70 (Nunnally & Bernstein, 1994). Validity standards among the measures varied considerably, but in general, all measures evidenced support for their validity. One measure described in Table 8.1 was created using Tardy’s (1985) model. Specifically, the current authors have further developed the Child and Adolescent Social Support Scale (CASSS) to assess perceived, available social support from parents, teachers, classmates, a close friend, and people in the school. Within each source of support, four types of support are assessed: emotional, informational, appraisal, and instrumental. As noted in Table 8.1, there are 12 items per source (three items representing each type of support). Given that there are five sources tapped, there are a total of 60 items. Students rate each item on frequency and importance. A total frequency score and a total importance score can both be computed. Additionally, a frequency and an importance score can be computed for each source of support (e.g., Parent Frequency and Parent Importance). Therefore, a total of 12 scores are available. Finally, some work has been done suggesting that type scores can be adequately reliable within each source (e.g., Parent Emotional Support, Parent Informational Support, etc.). The benefits of the CASSS are that (1) it was developed specifically with children and adolescents in mind (appropriate for use with third- through 12th-graders); (2) it taps several sources and types of support; (3) it taps evaluation of and frequency of support; and (4) there is strong evidence that the scores produced are reliable and valid. Specifically, all frequency scores on the CASSS have demonstrated internal consistencies in the high .90s consistently across studies where it has been used. Additionally, the results of factor analyses consistently yielded a clear five factor structure representing the five sources of support (Rueger, Malecki, & Demaray, 2010). There are some limitations of the CASSS. Currently, it does not produce a network size score, a measure of network density, or a measure of social integration. However, the overwhelming majority of studies on child and adolescent social support take a functional approach and use a rating scale of the frequency of social support. So, the CASSS is representative in its approach. However, it could be improved by including an optional network density and social integration measure. How Can Social Support Assessment Data Be Utilized? Currently in childhood and adolescent realms, social support is measured primarily as a construct of interest to researchers. The social support measures described in Table 8.1 are used by

Developed for adults

Validated on sample of 5th and 6th graders

Developed for adults

Developed for adults

Validated on a sample of 3rd to 8th graders

Multidimensional Scale of Perceived Social Support (MSPSS; Zimet et al., 1988)

Network of Relationships Inventory (NRI; Furman & Buhrmester, 1985)

Social Support Questionnaireb (SSQ; Sarason et al., 1983)

Perceived Social Support Scale (PSSS; Procidano & Heller, 1983)

Social Support Scale for Children (SSSC; Harter, 1985)

36 items using two-part answer strategy

40 items (20 items for Family, 20 items for Friend) using agreement ratings (Yes, No, Do not know)

27 items used to determine network density as well as satisfaction that is based on a 6-point Likert scale

241 items on a Likert-type scale; 29 items for each of 8 relationship figures plus 9 general questions

12 items on a Likert-type scale

120 items; 12 each of Frequency and Importance items on a Likert-type scale for each source

Items and Response Format

Notes a 3 of the 7 studies used an earlier version of the CASSS. b 7 of the 13 studies used the “Short form” of the SSQ, which uses 6 items.

Developed for 3 to 12th graders

Child and Adolescent Social Support Scalea (CASSS; Malecki, Demaray, & Elliott, 2000)

rd

Ages

Scale and Author

Table 8.1 Six Commonly Used Measures of Social Support in Youth

No subscales based on type, but items appear to be emotional and instrumental

No subscales based on type, but items appear to be emotional and informational

No subscales based on type, but items appear to be emotional, instrumental, informational, and appraisal

Social support subscales for Companionship, Instrumental Aid, Intimacy, Nurturance, Affection, Admiration, and Reliable Alliance

No subscales based on type, but items appear to be emotional and instrumental

Possible to score emotional, instrumental, informational, appraisal subscales for each source or across all sources

Subscales Based on Type

Parent, Teacher, Classmate, Friend subscales; Total/Global Support score

Friend, Family subscales

Overall Network Number Score; Family Network Score; Overall Support Satisfaction Score;

Type of support for: Mother, Father, Sibling, Relative, Boy/Girl Friend, Same-Sex Friend, Other-Sex Friend, Extra Person

Family, Friend, Significant Other subscales; Total/Global Support Score

Parent, Teacher, Classmate, Close Friend, School subscales; Total/Global Support Score

Subscales Based on Source

Frequency items: .90 to .96 range for various source subscales and .96 to .98 for global scores Importance items: .88 to .96 range for source

.85 to .91 range for various source subscale and global score

All but two subscales were above .60

Network Score = .97 Satisfaction Score = .94

Friend Support = .88 Family Support = .90

.72 – .88 range for subscales

CASSS

MSPSS

NRI

SSQ

PSSS

SSSC

Not provided in manual

Total Scale .83 (Interval of 1 month)

Network Score .90 Satisfaction Score .83 (Interval of 4 weeks)

Not provided in manual

r’s in the .72 to .85 range for various source subscale and global score (2–3 months)

Global Frequency: .75 to .78 Subscales: .58 to .74 (Frequency) .45 to .65 (Importance) (Interval of 8–10 weeks)

Test-Retest Reliability

SPPC: Global self-worth (.28 to .49 range for various sources)

Langner symptom scores: Friend (–.27); Family (–.29) —Also correlated with measures of psychiatric symptoms, self-statements, and observed interactions

MAACL (Depression) with Satisfaction Score —Men (–.22) and Women (–.43) EPI: Personality (significant for women only) —Extraversion with Network (.35) and Neuroticism with Satisfaction Score (–.37)

Adolescent friends’ descriptions of their relationships are moderately to highly related (r’s = .34 to .63 range)b

HSCL Depression Subscale (r = –.24 for both Family and Friends subscale; –.13 for Significant Other) HSCL Anxiety Subscale (r = –.18 for Family)

SSRS: Social skills (r = .62) SSCS: Self-confidence composite (r = .49) SSSC: Self esteem (r = .56) SSAS: Social Support measure Total Score (r = .55) BASC: Behavior Ratings—Maladjustment (r in the –.20 to –.41 range); Personal Adjustment (r in the .36 to .43 range)

Convergent and Divergent Validitya

Note. Unless otherwise stated, the following reliability and validity evidence were found in the validation study or manual cited in the first column of the table. a The following instruments, cited in the respective validation studies and manuals, were used to test the convergent/discriminant validity of the support measures: Social Skills Rating System (SSRS; Gresham & Elliott, 1990), Student Self-Concept Scale (SSCS; Gresham, Elliott, & Evans-Fernandez, 1993), Social Support Scale for Children (SSSC; Harter, 1985), Social Support Appraisals Scale (SSAS; Dubow & Ullman, 1989), Behavior Assessment Scale for Children Self Report of Personality (BASC; Reynolds & Kamphaus, 1998), Hopkins Symptom Checklist (HSCL; Derogatis, Lipman, Rickels, Uhlenhuth, & Covi, 1974), Multiple Affect Adjective Check List (MAACL; Zuckerman & Lubin, 1965), Eysenck Personality Inventory (EPI; Eysenck & Eysenck, 1968), Langner screening instrument (Langner, 1962), Self Perception Profile for Children (SPPC; Harter, 1985). b Validity evidence was taken from Furman (1996), which was cited in the scoring manual of the NRI.

Internal Consistency

Test Name

Table 8.2 Reliability and Validity Evidence for the Six Most Commonly Used Social Support Scales in Youth

Social Support 173 researchers in the health, development, psychology, social policy, and education fields for a variety of purposes. Social support could be included in schools as they focus on prevention efforts in programming or in research evaluating the outcomes of prevention efforts. This chapter clearly articulates the important dimensions of social support to consider in either programming or research efforts and the methods to conduct reliable and valid assessments of the construct. School psychologists, administrators, social workers, and other school personnel could use a brief measure of social support (a brief functional rating scale, a social network questionnaire, etc.) to determine whether the majority of their students perceive adequate levels of support in their lives. This type of assessment would fit nicely into a Tier 1 assessment approach in a problem-solving or Response to Intervention model. If 80% to 85% of students do not perceive adequate levels of support from teachers, classmates, or people in their school, a largescale prevention/intervention program addressing the relevant source of support could be implemented. Using a brief measure of social support to evaluate the effectiveness of large scale curricular changes or the implementation of a new prevention program in schools could be very useful. Students also could be screened to select students for small-group intervention. Perhaps small groups of same-age peers with low perceived classmate support could be furthered assessed to determine the extent and nature of the problem. For example, a school psychologist may determine that the students who perceive low classmate support have poor social skills or do not know how to seek support from their classmates. This problem could be addressed in a small group intervention. Small group or individual interventions targeting social support also could be evaluated with a brief, reliable measure of support. Of course, if initial interventions are not effective, more in-depth assessment methodologies would be helpful. A network analysis, social integration analysis, and/or direct behavioral observations would be useful tools in determining the availability and quality of the social support in a student’s life. These data would help guide intervention while the brief rating scale methodologies could be used for evaluation of those interventions.

Conclusions With a renewed focus on prevention, researchers and practitioners are attempting to create a wide range of approaches to preventing negative youth outcomes and promoting positive outcomes. Given that social support has a long history of being related to many positive outcomes for youth, it should not be ignored in the prevention research and literature. Although many variables are important to consider, youths’ perceptions of social support should also be included as part of a comprehensive protocol to the assessment of prevention programming and as a potential variable related to positive outcomes for children and youth.

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176 Michelle K. Demaray et al. Zimmerman, M. A., Ramirez-Valles, J., Zapert, K. M., & Maton, K. I. (2000). A longitudinal study of stress-buffering effects for urban African-American male adolescent problem behaviors and mental health. Journal of Community Psychology, 28, 17–33. Zuckerman, M., & Lubin, B. (1965). Manual for the Multiple Affect Adjective Check List. San Diego, CA: Educational and Industrial Testing Service.

9

Peer Support as a Means of Improving School Safety and Reducing Bullying and Violence Helen Cowie, University of Surrey Peter K. Smith, Goldsmiths, University of London

Introduction One could argue that the root cause of the problems endemic in modern society is the abuse of power, particularly by those who are unaware that they are doing it. School, as a microcosm of society, creates a context where children experience power and become familiar with oppression and bullying. The victims of oppression often come to accept that some peers’ privileged position is natural, and form constructions of themselves as “subordinates.” They internalise a sense of low self-esteem and a heavy weight of resentment. The dominant ones can also be damaged emotionally. They cannot risk being too aware of their own feelings or those of others, since they might become unbearably sensitive to the pain of those whom they abuse. In between these two groups lie the bystanders—those who observe the oppression though they do not directly take part. A majority of bystanders usually dislike bullying, and a proportion would like altruistically to help; but they are often unsure of what they should do to intervene, or are too confused or embarrassed to be able to offer practical support (Schulman, 2002). Some ignore the problem, becoming (in the terminology of Salmivalli, Lagerspetz, Björkqvist, Österman, & Kaukiainen, 1996) “outsiders” who ostensibly bear no responsibility for what is happening in their school community. Some assist or reinforce the perpetrators of violence and social exclusion. A few become defenders of the victims; Batson, Ahmad, Lishner, and Tsang (2002) identify the source of this kind of altruistic behaviour as “empathic emotion towards a person in need.” They argue that empathy-based socialisation practices can encourage perspective-taking and enhance prosocial behaviour, leading to more satisfying relationships and a greater tolerance of stigmatized outsider groups. Defenders are usually friends of a victim who help them by intervening on their behalf, comforting them, confronting bullies, or seeking adult help, thus making future attacks less likely (Boulton, Trueman, Chau, Whitehand, & Amatya, 1999). A critical way of extending such protectiveness beyond the immediate friendship group is to create contexts where there is optimism and hope that relationships need not be abusive, violent or exploitative (Seligman, Reivich, Jaycox, & Gillham, 1995). Schools can do this by developing a moral community founded on principles of equality, concern for others and empathy for others’ feelings (Schulman, 2002). Spanish educators call such community spirit convivencia—living and working together in harmony. One method that can play a critical part in this is peer support, an approach originally grounded in counselling. Essentially peer support systems in schools are defined as flexible frameworks within which children and young people are trained to offer emotional and social support to fellow pupils in distress (Cowie & Jennifer, 2008). Although counselling is often

178 Helen Cowie and Peter K. Smith considered to be an intervention for the privileged individual few, its real importance is that “it could point to a new understanding of morality and be the harbinger of a different kind of culture” (Kitwood, 1990, p. 221). Peer support can go beyond the help offered by one person to another, useful though that is; its greater strength lies in its potential to create a cooperative community based on mutual trust, respect, open communication and a willingness to explore past hurts as well as present needs and desires. Peer support is a form of lived morality and encompasses a range of activities and systems within which young people’s potential to be helpful to one another can be fostered through appropriate training in such skills as mentoring, active listening, conflict resolution, befriending and the promotion of children’s rights to work and learn in a safe environment. For examples of peer support training and practice in action, see Cowie and Jennifer (2007) and Cowie and Jennifer (2008). The method of peer support has been quite widely used in Canada and Australia since the 1980s. It has grown in popularity in recent years and is now used in many countries; for example in the UK the existence of such systems is reported in around 50% of both primary and secondary schools (Smith & Samara, 2003), where it is now a widely used intervention to promote emotional health and well-being (Cowie, Boardman, Dawkins, & Jennifer, 2004). Varieties of peer support programs have become more popular internationally in schools as anti-bullying interventions that promote the United Nations Convention on the Rights of the Child (UN, 1991) and have the potential to improve pupil safety, emotional health and wellbeing (Cowie et al., 2004). They often get funding as an intervention to combat school bullying, violence and social exclusion. Studies of such programs have been reported in Australia (Ellis, Marsh, & Craven, 2005; Peterson & Rigby, 1999; Rigby, 1996; Slee, 1997), Canada (Carr, 1994; Cole, 1987; Cunningham et al., 1998; Gougeon, 1989; Pepler, Craig, & Roberts, 1995; Rosenroll, 1989), Finland (Salmivalli, 2001), Italy (Menesini, Codecasa, Benelli, & Cowie, 2003), Japan (Taki, 2005; Toda, 2005), New Zealand (Sullivan, 2000), Spain (Andrès, 2007; Fernandez, Villaoslada, & Funes, 2002) and the USA (Benson & Benson, 1993; Boehm, Chessare, Valko, & Sager, 1991; Lane-Garon & Richardson, 2003; Sanders & Phye, 2004).

Types of Peer Support Programs Primary school schemes generally involve training certain pupils as buddies or befrienders (DfES, 1994, 2000; Smith & Sharp, 1994). They look out for pupils that appear lonely, often in the playground, and offer to play with them or help them (Cartwright, 1996); they also report serious fights and conflicts to adults (Cremin, 2007). There may be a “buddy bench” or “friendship stop” where pupils can go in the playground if they would like peer support (Cowie, Boardman, Dawkins, & Jennifer, 2004). In addition, some primary schools incorporate other activities for peer supporters such as leading structured games activities, supporting learning at a homework club (Demetriades, 1996), and one-to-one work with very young pupils who needed support in learning how to play with others (Cowie & Wallace, 2000). For further examples, see Cowie and Jennifer (2008). Secondary school peer support schemes usually involve peer mentors, who may offer support to pupils with difficulties in a “drop-in” room, do group work with a tutor group, offer one-to-one contact with a pupil in need over a period of time, or run a lunchtime club for younger pupils (Andrès, 2007; Cowie & Wallace, 2000; Smith & Watson, 2004). Peer supporters can also be elected by their peers to deal with interpersonal issues (Andrès, 2007). Some secondary school pupils work as mediators to resolve conflicts through a structured process in which a bystander in the role of neutral third party assists voluntary participants to resolve their dispute (Fernandez, Villaoslada, & Funes, 2002).

Peer Support as a Means of Improving School Safety 179 Peer support schemes have evolved over time and change in line with local needs and pupil perceptions of the effectiveness and acceptability of this type of intervention (Cowie, Naylor, Talamelli, Chauhan, & Smith, 2002). An example of this is the Question and Answer Handout method, developed in Japan, to preserve the anonymity of pupils seeking help (Toda, 2005). With advances in technology, peer support methods now take account of distance-learning types of support, including use of the internet and email support (Cartwright, 2005; Cowie & Hutson, 2005; Hutson & Cowie, 2007). Training in peer support generally involves active listening and the skill to respond genuinely and authentically to the needs and feelings of those seeking help. But peer supporters need to go beyond empathy to a rational problem-solving stance so that the participants can move into some form of resolution or restitution. This is where good communication skills are essential. The peer supporters must show, through their choice of words, the tone of their voice, the rhythm of their speech, and their confidence, that they believe in the real possibility of a solution to the problem. There should also be some form of supervision and debriefing to allow time for peer supporters to process what they do and collectively to address the issues that they encounter.

Key Aspects and Aims of Peer Support Programs Peer support programs have laudable intentions, but do they deliver? How effective are they? We consider three key aspects of peer support to be fundamental, and conceptually useful in distinguishing the main aims, and possible outcomes. We will assess the outcomes of peer support schemes in these three main areas. Various specific outcomes might be looked for in each domain. (1) Selected peers are trained to be peer supporters. An immediate aim here is to give peer supporters certain skills, often including opportunities to learn good communication skills; to share information and to reflect on their achievements in supervision groups; to develop perspective-taking and empathic abilities; and to deal with interpersonal conflicts, social exclusion, violence and bullying in proactive and non-violent ways. Outcomes: are useful skills acquired, and are they used? Do they gain self-esteem and heightened self-awareness? Do peer supporters gain status with peers or are they sometimes made fun of? Is gender an issue? (2) Certain peers will be users of the peer support scheme(s). The aim here—ostensibly the usual immediate aim of such schemes—is that they will be helped in this way, either directly by the peer supporter, or by the peer supporter arranging or encouraging other forms of help to be sought and/or given to them. Outcomes: do users feel they gained from the peer support system? Would they use it again? Did problems of bullying or violence reduce for them, after using a peer support scheme? (3) An ostensible longer-term aim of such schemes, through the training of peer supporters and the help and advice given to users, is to reduce rates of unresolved conflicts and bullying among pupils in the school. In this way, the workings of the peer support scheme(s) in a school might generally raise the profile of the school as a caring and moral institution, and have effects on school climate or ethos. Outcomes: Is the existence of the peer support system known through the school? Do pupils know how to use it? Is it used? Is it seen favourably, by pupils, teachers, by parents and others in the school? Is it thought to have positive effects, either generally on school climate, or more specifically on liking of school and perceived safety in school? Do peer relationships improve? Do general levels of school bullying or violence decline in the school?

180 Helen Cowie and Peter K. Smith In this review, we look at the main sources of evidence regarding these outcomes and conclude with a discussion of what such evidence tells us so far, what further research is called for, and implications for the running of peer support systems in schools.

Research findings Evaluations of peer support schemes are as varied as the different types. Many are impressionistic “in-house” case studies, often carried out by the enthusiastic practitioners who introduced and implemented the intervention. Others have used independent evaluation, but varied in the nature of the data base, from generalised summaries backed up by supportive quotes, through to quantitative surveys of attitudes and outcomes. These studies have, however, rather consistently indicated advantages of peer support. Peer supporters usually report that they benefit from the helping process, that they feel more confident in themselves and that they learn to value other people more. For vulnerable pupils, use of the peer support system can be a critical part of the process of feeling more positive about themselves and dealing with difficulties such as victimisation. Teachers frequently report that the school environment becomes safer and more caring following the introduction of a peer support scheme, and that peer relationships in general improve (Cowie et al., 2002; Cowie & Sharp, 1996; Cremin, 2007; Hurst, 2001; Mental Health Foundation, 2002; Naylor & Cowie, 1999). Some studies have reported some independent evaluation, including quantified findings: Cowie (1998), Naylor and Cowie (1999), Cowie, Naylor, Talamelli, Chauhan, and Smith (2002), Smith and Watson (2004), Cowie and Oztug (2008) and Cowie, Hutson, Oztug, and Myers (2008). A few studies have used a longitudinal pre-post test design to assess effects: Andrès (2007), Cowie and Olafsson (2001), Ellis, Marsh, and Craven (2005), Houlston and Smith (2008), Lane-Garon and Richardson (2003), Menesini, Codecasa, Benelli, and Cowie (2003), Peterson and Rigby (1999) and Salmivalli (2001). We will describe and review the outcomes of these studies under the three main headings given above.

The Outcomes of Peer Support Schemes Those Trained in Peer Support Cowie (1998) conducted interviews to capture the experiences of peer supporters and the members of staff in charge of peer support services with 25 peer supporters and nine teachers in nine UK schools (two primary, seven secondary), where peer support systems had been established for at least one year (range one to four years, mean 2.4 years). She asked about the perceived benefits of the peer support scheme for the peer helpers and for the school as a whole; about any problems that been encountered, and ways in which these might be overcome. She found that all of the interviewees (both teachers and young people) claimed that peer supporters gained personal benefit from taking part in the scheme. The most frequently mentioned benefits were: an increase in self-confidence, a sense of responsibility, and a belief that they were contributing positively to the life of the school community. Sixty percent of the peer supporters reported that these benefits arose directly from the training that they had received. At the same time, the peer helpers reported some degree of derision or hostility from members of the peer group in the form of “adverse comments,” “jealousy at all the attention,” or “doubts about the capacity of the service to offer help.” This played a part in the striking gender difference in the balance of boys and girls at all stages of recruitment, application, training and implementation. Even when boys were recruited, their dropout rate was high,

Peer Support as a Means of Improving School Safety 181 apparently because of “peer pressure from other boys” (secondary school teacher) or “macho values in the school” (peer supporters). In a larger-scale study, Naylor and Cowie (1999) surveyed 2,313 secondary school pupils and 234 teachers about their experiences of and attitudes towards peer support in 51 schools where there was a well-established system of peer support. They questioned peer supporters, groups of service users or potential users, teachers involved in managing the systems and a sample of teachers not involved in running the systems. They found that 78% of peer supporters reported that they had gained useful social and interpersonal skills, such as active listening, from the training and 58% reported that, through the practice of peer support, they had become able to demonstrate that they cared for peers in need and so were able to put into action their altruistic wish to do something about the problem of bullying in their schools. Cowie, Naylor, Talamelli, Chauhan, and Smith (2002), in a follow-up study two years later from 35 of the original 51 schools (413 pupils of whom 204 were former victims and 209 former non-victims), found that peer supporters appreciated the opportunity of addressing a real problem in their school community and being given the skills and structures to tackle it. Peer supporters commented favorably on the usefulness of the communication skills that they learned in the course of training, especially “active listening.” All peer helpers reported that there were personal benefits for them through their involvement in the schemes, highlighting active listening (100%), developing the capacity for empathy towards a person in need (81%), and experiencing a gratifying sense of responsibility (74%), for example in helping to make the school a safer place and in being able to make changes to the systems on the basis of their experience. A frequent comment was that the experience of participating in the peer support scheme had led them to decide on one of the caring professions for a career. Cowie et al. (2002) noticed that peer supporters in their study changed over time. There were transformations in confidence and a growing identity as peer supporters. There were also differences that often related to the extent and degree of help that the peer supporters received from others, including the quality of teacher facilitation, parental approval, the extent and relevance of training and debriefing groups, and feedback from other pupils, whether users or potential users. Some boy peer supporters struggled with the issue of gender identity; others managed to find compatibility between the role of peer supporter and being “manly.” There were also useful links with other systems, such as external training agencies (for example, ChildLine: http://www.childline.org.uk/), national groups (for example, the UK Observatory: www.ukobservatory.com), and higher education (for example, a university-based research project), each of which had potential for wider dissemination of the work of the peer supporters, for example, through presentations at conferences, publication of achievements in newsletters, and opportunities to meet and learn from peer supporters from other schools. Cowie and Olafsson (2000) studied the workings of a peer support scheme in one innerLondon secondary school (population 420), which, unusually for a state school, was single-sex (boys) and which was set in a disadvantaged inner city catchment area. Because of its poor reputation in the neighborhood, parents tended to send their children to other schools if possible, and the school had a large number of empty places and a large intake of boys who had been suspended from other schools because of their disruptive behaviour. The seven peer supporters were interviewed, and the Olweus bullying questionnaire was given before the introduction of a peer support service, and 7.5 months after. They found that, despite the extremely difficult circumstances in which they worked, the seven peer supporters in the school, which had high levels of aggression, expressed belief in and commitment to the peer support principles in which they had been trained. All reported the positive impact on them of being trusted to take some responsibility for the issue of bullying and gave a strong sense that the peer support

182 Helen Cowie and Peter K. Smith intervention had made a difference. At the same time, they all commented that a greater number of peer supporters would be needed to service the 420 boys in the whole school and were resigned to the fact that some victims did not seek out help for fear of reprisals or anxiety about appearing to be weak. Smith and Watson (2004) obtained data from 20 schools in England (10 primary, two middle, eight secondary) that had taken part in ChildLine in Partnership with Schools (CHIPS) training. They interviewed the head teacher and the CHIPS co-ordinator(s), held discussion group meeting(s) with pupils who had been trained in peer support; and collected questionnaires focusing on knowledge of the school’s peer support system and opinions about its effectiveness. Questionnaires were obtained from 455 primary and 379 secondary pupils (including 178 trained as peer supporters), plus 109 primary and 95 secondary staff. They found that, generally, peer mentors felt very enthusiastic about their work. It was appropriate to the training they had received, and they felt that they had developed useful skills as peer mentors. While most were well occupied, some had little to do; this was more likely in some playground or drop-in room schemes. Also, some schemes where peer mentors worked with younger-aged tutor groups ran the risk of the peer mentors being assigned routine or administrative tasks. Houlston and Smith (2008) carried out a one-year longitudinal study in an all-girls secondary school, evaluating the effects of a peer counseling scheme which was implemented as part of a whole-school anti-bullying intervention. Both qualitative and quantitative findings indicated that peer counselors did benefit from their involvement in the school’s peer support project. Students were largely very positive about the training they had received and displayed an awareness that it had led to the acquisition of transferable communication and interpersonal skills. Pre- and post-tests were carried out on the 14 peer supporters, and 14 agematched control pupils. There were no differences from controls in shame acknowledgement and displacement, or social skills, but the peer counselors did show a significant differential increase in social self-esteem. Andrès (2007) carried out a longitudinal study in two secondary schools in Spain: an experimental school with 778 pupils, which had developed a system of peer support to enhance convivencia, and a control school (462 pupils) in the same catchment area. This area had a large influx of families from other cultures and an ongoing concern about rising levels of violence, both in the community and in schools. In the experimental school, pupils in each class elected classmates to act as peer helpers; these pupils were given training to intervene directly to resolve peer conflicts, with a particular brief to intervene in cases of bullying and the abuse of power. The program had positive effects on the social development of those who participated as peer helpers. The girls showed significant gains on measures of empathy, prosocial behaviour, self-efficacy and problem-solving ability; the boys, who started from a lower level on these measures, also made progress but at a slower pace. In summary, there is consistent qualitative evidence that the practice of peer support gives direction to some young people’s altruistic wishes to address injustices such as bullying and deliberate social exclusion in their school community, and that the training enhances their communication and problem-solving skills and their capacity to feel empathy for peers in distress. To date the best quantified findings concern improvements in social self-esteem, self-efficacy and the capacity to resolve interpersonal difficulties. Users of Peer Support Naylor and Cowie (1999), in their survey of secondary school pupils, found that of 65 pupils who were users of the peer support systems, 82% reported that they found them “useful” or

Peer Support as a Means of Improving School Safety 183 “very useful”; 82% said that they found these helpful in giving them the strength to cope with bullying; and 80% said that they would recommend the system to a friend. Only 18% had negative thoughts about the systems—for example, that they were “not helpful” or “off-putting.” The reasons users gave as to why a peer support system was perceived as beneficial were: the service provided someone who listens; users were helped to overcome the problem; and the existence of the peer support system indicated that the school cared about their well-being. In the follow-up study two years later, Cowie et al. (2002) confirmed these findings; overall, 87% of bullied pupils said that the system had been useful or very useful to them, the most frequent reason given being that it helps to talk to a peer. Cowie and Olafsson (2001) interviewed seven users of the peer support service in one secondary school with severe problems; of these, four found peer support helpful and would recommend it to a peer. They valued having a peer to listen to their problems and found it helpful to have the protection of a peer supporter’s presence. But three reported that they did not feel any safer at school and resented the fact that the bullies had not been punished for their actions. Smith and Watson (2004), in their evaluation of the ChildLine peer support schemes in the UK, found that usage of schemes varied between schools, and often depended on the role and “hours” of the peer mentors. Just over half of all pupils (53% primary, 52% secondary) were aware of someone that had made use of the peer support scheme. When asked if they themselves had used the scheme, in primary schools 22% had used it once and 11% more than once; in secondary schools, 9% had used it once and 8% more than once. The pupils that had used the scheme were asked whether they had found the peer support to be helpful. Overall, 44% of primary school users said it helped a lot, 50% said it helped a bit, and 6% that it did not help. In secondary schools, 47% said it helped a lot, 42% said it helped a bit, and 11% said it did not help. There was no gender difference in primary schools, but in secondary schools more boys said it did not help than girls (18% versus 7%). When asked if they would use the peer support scheme again should they have a problem, 75% of primary school users said yes, 22% not sure, and 3% no. In secondary schools, 67% said yes, 22% not sure, and 11% no. There was no significant difference between primary and secondary users, or between boys and girls. Houlston and Smith (2008) found that over 30% of year 7 students in an all-girls secondary school indicated that they had used the peer support service at least once, but only 11% in year 8 (the two years the program was targeted at). In year 7, 33% of pupils said the scheme had helped “a lot,” and 50% “a bit”; 17% said it had not helped. In year 8, no-one said it had helped “a lot,” 75% said “a bit” and 25% said it had not helped. In summary, most users report finding the schemes helpful; in successful schemes this is likely to be a large majority, but it may not always be so in schools with severe problems or a poorly implemented scheme. School Ethos A survey of schools across England by Smith and Samara (2003) asked them to rate how satisfied they were with different anti-bullying interventions on a five-point scale (1, not at all satisfied, to 5, extremely satisfied). Active listening/counselling-based approaches had a rating of 3.9, Befriending a rating of 3.6, and Mediation by peers a rating of 3.5; these indicate moderate satisfaction and are similar ratings to those given to many other interventions (e.g., whole school policy 3.9; support group approach 3.5; training playground supervisors 3.5; see Smith & Samara, 2003, for a full table).

184 Helen Cowie and Peter K. Smith Pupil and teacher reports, often qualitative in nature, also generally indicate satisfaction, but with some exceptions. Cowie et al. (2002) found that teachers reported that a school’s reputation was enhanced in the local community through the presence of a peer support system since it indicated that the school cared for its young people’s well-being. The Mental Health Foundation (2002) developed and evaluated peer support schemes in six secondary schools and one further education college in England. The quantifiable data is limited to responses by staff (n=34) to six items; 61% of staff felt that the peer support systems had reduced incidents of bullying. Grossman and Tierney (1998) had similar positive outcomes in their evaluation of the Big Brothers, Big Sisters peer mentoring program in the US. Naylor and Cowie (1999) found that pupils generally, and teachers, reported that the presence of a peer support system was reassuring; it was perceived as beneficial even by pupils who had never actually used the service. But some adults and pupils were not aware that a peer support program was in place, indicating some serious shortcomings in dissemination of information about the schemes and a failure to provide induction on the topic of peer support to new staff, new pupils and key staff such as lunchtime supervisors. Cowie et al. (2004) observed some difficulties in keeping up the momentum of a peer support program in a school with high levels of racial tension and aggression. In fact, the peer supporters themselves identified poor communication and lack of commitment on the part of staff and pupils as key barriers to success in implementation. Naylor and Cowie (1999) found that peer support systems did not appear to reduce bullying, since its incidence as measured by an anonymous questionnaire was similar to that reported in other surveys at that time (e.g., Whitney & Smith, 1993). Nevertheless, on the basis of users’ responses to their questionnaire, they argued that the presence of a peer support system reduced the negative impact of bullying on victims and made it more acceptable for them to report it, especially as it was perceived by both users and potential users that peers are able to detect bullying at a much earlier stage than adults could. Lane-Garon and Richardson (2003) studied the impact of a peer mediation scheme on school climate in a sample of 300 elementary school pupils in the US. Both mediators and nonmediators perceived the school climate to be safer than had been reported in the year prior to the introduction of the peer mediation scheme. This represented an increase from 56% (in 1999) to 66% (in 2001) of pupils who either agreed or strongly agreed that they felt safe on campus. Responses to the items Other students treat me with respect at school and I feel like I belong here both increased from 47% to 58% over this period. Smith and Watson (2004) found that almost all staff (94%) and a significant majority of pupils (72%) felt it had been a good idea to implement a peer support scheme within their school. Pupil support was highest in primary girls (82%) and lowest in secondary boys (59%). Generally, support was higher in those trained in peer support (90%), and also in those who had used the peer support system (84%). Only 4% of pupils generally were opposed to the idea (highest in secondary boys at 7%); not a single member of staff questioned felt the scheme had been a bad idea. Many teachers reported on how the environment felt safer. Despite a lack of direct, objective evidence, staff often reported reductions in the occurrence of smaller, petty bullying incidents, with pupils more capable of resolving situations by themselves in a peaceful way. Vulnerable pupils could be spotted more easily, in part due to the increased awareness of pupils, and could be supported quickly by the existing network. When asked whether the peer support scheme was helping to stop bullying in school, 52% of staff and 43% of pupils said yes, but roughly 45% of all participants were unsure, and 11% of pupils (highest in secondary boys at 19%) and 3% of staff said no.

Peer Support as a Means of Improving School Safety 185 Houlston and Smith (2008) found that most staff members in an all-girls secondary school (79% from 34 responses) thought that it was a good idea to have the peer support scheme in the school; and a majority (62%) thought that it helped to stop bullying. Over half of pupils thought that the peer support scheme was a good idea, but there was a significant age difference (74% in year 7, 53% in year 8, 51% in year 9). A minority of students in each year group (5% to 8%) did not think the scheme was a good idea. There was also a significant age difference in whether students thought that the scheme helped to stop bullying, this being highest in year 7 at 28%. Many students in each year group were unsure (44% to 70%), and a substantial minority (16% to 38%) thought it did not stop bullying. These are all rather subjective reports; do they hold up when some controlled comparisons are used? One study compared two secondary schools with a peer support system with two schools without; Cowie, Hutson, Oztug, and Myers (2008) surveyed around 900 secondary school pupils in total, and found that the pupils in the non-peer support control schools actually reported that they felt safer in the toilets, the playground, corridors and in lessons than their counterparts in schools with peer support. However, within the peer support schools there were significant differences in perceptions of safety between the substantial minority of pupils who were unaware that their school had a peer support system and those who were aware of it. The pupils who were aware felt safer in lessons, perceived school as a friendlier place to be, and worried significantly less about being bullied in comparison with those who were unaware. They were also much more likely to tell someone when bad things happened at school. A small number of studies have used pre- and post-tests to assess outcomes such as levels of victimization. Two have assessed peer-led interventions of a general nature. Peterson and Rigby (1999) measured victimization rates in years 7, 9, 10 and 11 over a two-year period in one Australian secondary school, following a multi-faceted intervention which included student-led activities and a peer helping scheme. There was no overall decrease in victimization, but there was a significant interaction with year group: victimization was less for year 7 pupils, but greater for year 9 pupils, at the post-test. Salmivalli (2001) reported effects of a small-scale (eight peer counsellors; one week) peer-led intervention in a Finnish secondary school, of a general awareness-raising nature. Seventh- and eight-graders (13–15 years) were assessed. For seventh-grade girls there were positive outcomes (reduction in self- and peer-reports of victimization), but these were not found in eighth-grade girls, or either year group of boys. Girls showed an increase in willingness to influence bullying problems; but boys actually increased in pro-bullying attitudes. However, this intervention was very short and there was no control group comparison. Cowie and Olafsson (2000), in their study of one secondary school with high levels of violence, administered the Olweus bullying questionnaire (Olweus, 1993) before the introduction of the peer support service and 7.5 months after. The high incidence of bullying in the school showed little change over the period when the peer support service was in operation. The pupils’ estimate of the number of victims in their own class was 2.64 in November and 2.63 in the following June; the estimated number of bullies in own class was 2.39 in November and 2.46 in June. When asked how often teachers, peer supporters and other young people tried to put a stop to someone being bullied, pupils tended to perceive all three parties as intervening less in June than in the previous November. On the positive side, those who had told no-one about being bullied decreased from 39% in November to 30% in June; the number of victims who had told teachers, peer supporters or someone at home increased from November (n=12) to June (n=18). The authors concluded that it was unrealistic to expect that a small

186 Helen Cowie and Peter K. Smith number of peer supporters could solve the problem of bullying in such a challenging context, especially when there was little evidence of supervision, monitoring, and support from adults in order to sustain the intervention over time. The school staff did not appear to apply sanctions against known bullies following the activities of the peer supporters, and this was noted negatively by the pupils (users and potential users alike). Houlston and Smith (2008), in their study of an all-girls secondary school, used pre- and post-test assessments of levels of bullying, and perceptions about it, with 416 pupils in grades 7, 8 and 9. There was no significant change in levels of reported victimization or bullying. However, perceptions of levels of bullying generally did decline, and perceptions that the school was doing something about bullying did increase. These improvements were most notable in year 7 pupils. Three studies have included non-intervention control classes. Menesini et al. (2003) assessed a class-based befriending intervention in sixth, seventh and eighth grades of two Italian middle schools over one year (October to May). Across the two schools, nine classes were in the program condition, and five classes were controls. Experimental classes showed a decrease in bullying and outsider roles, and less of an increase in reinforcer and assistant bullies, compared to controls; especially for boys. There were no significant changes in defender or victim roles overall, but both increased at sixth grade only. Attitudinal data showed an increase in anti-bullying attitudes only in sixth-graders. Ellis, Marsh, and Craven (2005) evaluated effects of a peer support program for year 7 students in three Australian secondary schools. Students in the year of implementation (experimental group) were compared with year 7 students from the year before implementation (control group). Improvements were found in experimental compared to control pupils in some aspects, including self-concept, and lower pro-bully attitudes, and these outcomes were retained over time; but there were no significant changes for pro-victim attitudes, or global self-esteem. This study did not assess levels of bullying. Andrès (2007) found mixed results in her longitudinal study of two Spanish schools, in one of which there was a well-established system of peer support. The perception of teachers and pupils in the experimental school was that convivencia increased during the period of the intervention. However, although social exclusion decreased in the experimental school, rates of physical bullying increased as did the incidence of aggressive conflicts. In summary, the outcomes of peer support schemes for school ethos are mixed. Well-controlled quantitative studies are few, and by no means give uniformly positive outcomes. Where studied, variations by year group are often reported. So, although many positive benefits have been documented about the effectiveness of peer support schemes, there is a clear need to explore in more detail how peer support systems work, which pupils benefit from them and which pupils do not benefit and why. In a climate in which it is widely held that peer support systems are effective, and many schools are implementing them with high hopes for their impact on the reduction of school violence and bullying, it is timely to ask whether their presence leads to an increased sense of safety at school and a greater willingness to tell another person about being bullied.

Discussion of Findings We first consider the findings so far regarding the three main outcomes of peer support systems, together with how these interact with details of how these operate. We discuss what further research is needed, and implications for practice given our present state of knowledge.

Peer Support as a Means of Improving School Safety 187 Those Trained in Peer Support The most well established finding is the benefit of peer support systems for the peer supporters. There is evidence for improved self-esteem; increased social and communication skills; greater empathy; and a sense of responsibility and doing something worthwhile in the school. These have been nearly universally reported in the studies reviewed. These benefits probably stem from the quality of training received (in most of the studies quoted the peer supporters rated their training very highly), continuing supervision, and the practice of skills in a context generally valued by other pupils and the school. These data are based on self-reports from peer supporters and observations by teachers, so there may be an element of social desirability present in responses; this is true even for Houlston and Smith (2008), where pupils used a selfreport self-esteem questionnaire. It would be useful to complement this with other measures of (for example) social competence and social skills, perhaps from other pupils and teachers, before and after training; and compared with control children (such as those on the waiting list for training at a later date). This is not to say that there are no problems for peer supporters. We have identified three main ones: status, gender imbalance, and anxieties about lack of usage. Regarding status, while many peer supporters feel valued and respected in their role, in some cases they can feel an isolated minority; as in the schools surveyed by Cowie and Olafsson (2000) and Naylor and Cowie (1999); or they can encounter some teasing about their role. This is clearly more likely in schools where the peer support system is not embedded in the school ethos. It is also more likely from secondary boys, who tend to have less positive attitudes towards peer support systems (Smith & Watson, 2004). A good gender balance is important here, and this is the second issue—it is widely reported to be easier to recruit girls as peer supporters than boys. Naylor and Cowie (1999) concluded that boys are perfectly capable of demonstrating the caring qualities of the peer supporter but that they often choose not to use these abilities unless they perceive that the circumstances in which they are to do so will not threaten their perception of what it is to be manly. One explanation might be their representations of masculinity and femininity as portrayed by parents, peers and the media. Important steps here are likely to be the way peer supporters are selected (e.g., should there be quotas for boys and girls?), and having a few high-status boy peer supporters who can help advertise the scheme through publicity and assemblies. Finally, peer supporters feel more respect and worth in their role if they are engaged and can put their skills to use. The dangers are, for example, that no-one comes to “buddy benches” in the playground, or peer support rooms in the school, through fear of being stigmatized; or that peer supporters get given more routine tasks when assigned to tutor groups. Such under-use or misuse can be avoided. Peer befrienders and supporters can play a more active role in organizing games and activities, or lunchtime clubs (as described in Demetriades, 1996), which can be attractive to many pupils while still allowing timid or bullied children to get support in a less obvious way. Counselling schemes can make use of a school intranet, or can be geared widely to support in a range of areas (academic as well as interpersonal), to avoid risks of stigmatizing those seeking it (Cartwright, 2005; Cowie & Hutson, 2005; Smith & Watson, 2004).

Users of Peer Support Most users of peer support systems report it as being helpful. The degree of helpfulness varies, but a majority would use the service again and would recommend it to a friend in need. A

188 Helen Cowie and Peter K. Smith minority, ranging from 6% in Smith and Watson (2004), and 18% in Naylor and Cowie (1999), up to 3/7 or 43% in the challenging school surveyed by Cowie and Olafsson (2000), report not finding it helpful. We lack detail on how often use of the peer support service has actually stopped experiences of being bullied. We also need to know more about why some users had negative experiences, but some at least blame the attitude of peer supporters, or lack of follow-through to deal with bullying—issues that can potentially be dealt with by good training and supervision of the peer supporters, and integration of the system in the wider school ethos and anti-bullying policy. School Ethos In comparison with the data on benefits for peer supporters and for users—where data based on self-report have some face validity—the findings for more general impact on school ethos are more indirect. Many staff and pupils think that there are positive effects on general levels of school bullying or peer relationships, but many are also unsure. There are anecdotal reports of how individual pupils have benefited, but lack of more general data based on more objective measures. Only a few studies have done the latter. Of these, no study comes out with across-the-board positive findings. Only Menesini et al. (2003) and Ellis, Marsh, and Craven (2005) came out with some positive and no negative findings; Menesini et al. used a rather atypical class-based program, and Ellis, Marsh and Craven did not assess bullying levels. Two studies, Peterson and Rigby (1999) and Salmivalli (2001), assessed peer-led interventions of a general nature. They report a mixture of positive and negative findings, with interactions by year group (generally, younger pupils showing more positive changes), as did Andrès (2007). Cowie, Hutson, Oztug, and Myers (2008) found that pupils who were not aware that their school had a peer support system in place (NAPS) were significantly more likely to worry a lot about being bullied than the pupils who were aware (APS). Although there were no differences in terms of perceptions of safety in corridors, toilets and playground, the APS felt significantly safer in lessons and were significantly more likely to report that school was a friendly place to be. The APS pupils were significantly more likely to tell someone when bad things happened to them and, if they did something bad themselves to another person, to confide that to someone else, usually a member of their family or a close friend. Cowie and Olafsson (2001) actually found some increase in bullying, although there was no control group comparison. The fact that rates of bullying remained similar over the period of study in this troubled school might indicate that the peer support intervention prevented the situation from becoming worse rather than failing to make a difference. Finally, Houlston and Smith (2008) found no changes in personal experiences of being a bully or victim, despite general perceptions shifting positively. At one level, it is clear that in many schools, peer support systems are both known by most pupils (in terms of how they function, how to use them), and they are seen in a generally favorable way. However, there is also evidence that a substantial minority in some schools remain unaware of the school peer support system and that these pupils are significantly more likely to feel unsafe at school and to worry about being bullied, and are less likely to tell someone when bad things happen to them (Cowie, Hutson, Oztug, & Myers, 2008). Most pupils (even secondary school boys!) do dislike bullying, and most pupils do see peer support systems as a good idea. But a minority of pupils (presumably including many who do the bullying) appear skeptical of or resistant to peer support schemes (Andrès, 2007; Smith & Watson, 2004). Roberts, Liabo, Lucas, DuBois, and Sheldon (2004) argued that peer mentoring interventions may fail to reach certain groups of disaffected young people. At a deeper level, we lack

Peer Support as a Means of Improving School Safety 189 objective evidence of the effects on school climate, liking of school and perceived safety in school. As with levels of bullying, we generally have positive staff and pupil perceptions on these issues, but a lack of pre-post data using more objective measures of school liking, school climate, or school safety.

Issues Affecting the Success of Peer Support Schemes The degree to which the peer support strategy has been integrated into the whole school policy or “ethos” is often a contributing factor to its success. From the studies reviewed, schools that make pupils aware of the scheme, through the use of assemblies, newsletters, posters and presentations, often find that the scheme becomes more accepted, and the peer mentors earn respect and credibility from fellow pupils. However, it is rare to find any interaction between schools in terms of planned sharing of expertise, experience and examples of good practice. Few schools appear to offer mentors the opportunity to exchange ideas with pupils from other schools. Furthermore, there is little continuity between primary and secondary sectors, with secondary schools failing to capitalize on the skills of peer mentors moving up from primary education. The active support of the head teacher or a senior leadership figure also appears to be a crucial factor, either directly, or through clearly providing time and resources for dedicated scheme co-ordinator(s). The latter can undertake the day-to-day overseeing of the program, and provide support and continued training for the peer mentors, thus reducing the pressure and responsibilities placed on them. Additionally, it may be that it is the awareness that peer supporters are there to help that enables pupils to create a social construction that school is a safer place to be. The findings from the Cowie, Hutson, Oztug, and Myers (2008) study suggest that for those pupils who know about their school’s peer support system one important outcome is that they feel empowered to talk about negative things that happen to them, or that they do to others, with someone else, not necessarily a peer supporter. In other words, the observation (and in some cases the experience) of the helpfulness of sharing worries and anxieties with another has become an accepted method for coping with issues of concern in that particular school context. Given the extensive evidence from earlier studies of the impact of peer support training on the young people who take part, there is a case for arguing that peer supporters should be given additional training on how to challenge bullies and bystander apathy as well as the more traditional forms of training in active listening and empathy. A range of different strategies is essential if schools and their pupils are to be successful in the ongoing effort to create and sustain social and learning environments that are friendly and safe. There may also be an argument for the training to be carried out with a much wider population of young people. Research into peer mediation programs indicates that training only a small number of mediators is not preventative of violence or favorable to optimum developmental outcomes and that, as the number of trained mediators grows in proportion to the school population, so school climate effects are more likely (Lane-Garon & Richardson, 2003). In other words, the extent to which peer support is actually available seems to influence these outcomes.

Suggestions for Future Research We now have enough information of a general nature to point us toward the major likely outcomes, benefits and difficulties of peer support systems in schools. We have suggested

190 Helen Cowie and Peter K. Smith three main outcome areas, with sub-divisions within each of these. Evidence is stronger in the first two areas (benefits for peer supporters and users) than in the latter area (benefits for school ethos, including peer relationships and bullying), but in all areas there is now scope for more focused and objective research. A part of such a research effort should be more quantitative, pre- and post-test designs, measuring aspects of peer supporter skills and characteristics, user experiences, peer relationship issues including levels of bullying, and school climate. This “traditional” kind of approach has been noticeably lacking in the area, with the exception of the half-dozen studies reviewed above, each of which has shortcomings. There are of course difficulties with this approach. Peer support systems usually run alongside other anti-bullying initiatives. Control groups (such as pupils selected for training at a later date; or non-users of peer support systems) may not be fully equivalent. Nevertheless, without some supportive evidence from such measures, “objective” in the sense of being at one remove from direct reports vulnerable to self-report biases and social desirability of responses, the benefits of peer support systems may remain unconvincing to sceptics. We certainly do not advocate a wholesale switch to these quantitative experimental approaches. Precisely because of their own vulnerabilities, such methods need to be complemented by qualitative methods and case study approaches that give insight into processes and the complexity of interactions in individual schools and contexts (Smith, 2003). Another suggestion is to make more use of young people as researchers (Greene & Hogan, 2005). In the past decade, there has been a shift in the ways in which children in our culture are perceived. This can be seen through policies to enhance the rights of the child, as in the U.K. Children Act (2005), which recognize that children are people, that they should be consulted on decisions that affect them, and that they play an active part in the creation of their social worlds. The principle of the child’s right to participate in decision-making is stated in Article 12.1 of the U.N. Convention on the Rights of the Child (United Nations, 1991) where it is recommended that “the views of the child be given due weight, in accordance with the age and maturity of the child.” The way in which researchers see children has a powerful impact on how they study them— for example, in the methods they choose, the research population under investigation and the interpretation of the data gathered (James & Prout, 1997). Traditionally, adults as experts in child development gathered evidence about children “objectively.” More recently, researchers have seen the need to record children’s own perspectives on the grounds that children themselves are the most important source of evidence on how they experience their lives (Jennifer & Cowie, 2009). In other words there is a movement to engage in research with rather than on children (e.g., Veale, 2005) where children are active participants in the project. In our culture, we do not have a strong track record of listening to children or of understanding children’s lives in their own terms and as a primary source of evidence. Nevertheless most of the studies reported here relied extensively on the perspectives of the young people involved in peer support both as users and as practitioners, and this particular field seems to be an ideal arena in which to put more innovative child-centered methods to the test, for example with the peer supporters in the role of active researchers into their own experience. More use could be made of anonymous feedback on the part of users while still respecting the confidentiality of their interactions with peer supporters. Recent developments in the use of the internet as a vehicle for peer support could be mobilized in this domain. This brings us back to the moral issues raised in the introduction. Qualitative responses by peer supporters have indicated their awareness of the moral dilemmas faced by bystanders when they observe pupils being mistreated and abused by peers. The practice of peer support

Peer Support as a Means of Improving School Safety 191 appears to give direction to young people’s altruistic wishes to address injustices such as bullying and deliberate social exclusion in their school community. This is the moral stance taken by those bystanders who—unlike the silent majority—are prepared to demonstrate publicly their stance against injustice (Cowie & Hutson, 2005). In England recently, peer support has contributed to government strategies and initiatives that demonstrate commitment to involving young people in decisions that affect their lives, for example Every Child Matters (DfES, 2003) and Working Together: Giving Children and Young People a Say (DfES, 2004). The opportunity to be a peer supporter is viewed by some as an important pathway for the inclusion of children and young people in policymaking, and is central to the vision of initiatives by major charities such as ChildLine, National Society for the Prevention of Cruelty to Children (NSPCC) and the National Children’s Bureau (NCB). There is great scope within these initiatives, and corresponding initiatives in other countries, for researchers to build on the framework available from the research to date, and embark now on a variety of more thorough studies on the effectiveness of this kind of involvement and participation on the part of young people and to evaluate successes and failures in their implementation.

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10 The Developmental Implications of Classroom Social Relationships and Strategies for Improving Them Jan N. Hughes and Lisa K. Barrois, Texas A&M University

Classrooms are, by definition, social contexts. Students’ social relationships at school impact their social and emotional adjustment as well as their academic motivation and learning (Connell & Wellborn, 1991; Hughes & Kwok, 2007; Ladd, 1990; Wentzel, 1999). Affectively positive and supportive relationships with teachers and classmates promote a sense of school belonging and a positive student identity (Furrer & Skinner, 2003; Connell & Wellborn, 1991), which engender the will to participate cooperatively in classroom activities and to try hard and persist in the face of challenges (Anderman & Anderman, 1999; Birch & Ladd, 1997; Hughes, Luo, Kwok, & Loyd, 2008; Skinner & Belmont, 1993). In addition to the motivational aspects of positive social relatedness at school, close and supportive school relationships may impact achievement through more responsive and appropriate instruction (Hughes, Gleason, & Zhang, 2005; Itskowitz, Navon, & Strauss, 1988; Jussim, 1986; Matsumura, Slater, & Crosson, 2008) and greater access to learning resources (Gest, Domitrovich, & Welsh, 2005; Hughes, Dyer, Luo, & Kwok, in press; Plummer & Graziano, 1987). The overall level of social support provided to students in a classroom, an aspect of classroom climate, has also been linked to students’ motivation and learning (Battistich, Solomon, Watson, & Schaps, 1997; Hamre & Pianta, 2005). Given the importance of classroom social relationships to social and academic outcomes, it is important to identify interventions that promote supportive relationships between teachers and students and among students. Such efforts are most likely to be successful when based on a good understanding of the dimensions of social relationships in a classroom and the processes by which they impact student motivation and learning. Therefore, this chapter first provides an integrative summary of the literature on the links between classroom social relationships and students’ academic and social competencies before critically reviewing research on the efficacy of interventions that attempt to promote supportive, positive classroom relationships. We begin our summary of the empirical literature on classroom social relationships with teacher–student relationships before addressing peer relationships and, finally, the overall social-emotional climate. Teacher–Student Relationships and School Adjustment An extensive research documents links between the quality of students’ relationships with their teachers and children’s concurrent and future academic and social outcomes (for review see Hamre & Pianta, 2006; Pianta, 1999). Children who experience supportive, positive relationships with their teachers have more positive attitudes toward school (Ryan Stiller, & Lynch, 1994), are more academically engaged and achieve more (Hughes & Kwok, 2007; Hughes et al., 2008; Skinner, Zimmer-Gembeck, & Connell, 1998), enjoy higher levels of peer

The Developmental Implications of Classroom Social Relationships 195 acceptance (Hughes, Cavell, & Willson, 2001; Hughes & Kwok, 2006), and are less likely to engage in substance abuse, early sex, and other risky behaviors (Resnick et al., 1997). Conversely, students whose relationships with teachers are characterized by conflict are more likely to drop out of school, be retained in grade, experience peer rejection, and increase externalizing behaviors (Ladd, Birch, & Buhs, 1999; Pianta, Steinberg, & Rollins, 1995; Silver, Measelle, Armstrong, & Essex, 2005). Importantly, the association between teacher–student relationship quality and subsequent adjustment holds when previous levels of adjustment are statistically controlled (Ladd et al., 1999; Hughes, Cavell, & Jackson, 1999; Hughes & Kwok, 2006; Hughes et al., 2008; Meehan, Hughes, & Cavell, 2003). The benefits of a supportive, low-conflict relationship with one’s teacher are important from the earliest school years (Birch & Ladd, 1998; Howes, Hamilton, & Matheson, 1994) through adolescence (Resnick et al., 1997; Ryan et al., 1994; Wentzel, 1998). The dimensions of teacher–student relationships and the mechanisms by which the relationship affects adjustment, however, may differ at different developmental periods. Researchers studying younger children have drawn from attachment theory (Bowlby, 1980) in positing that a close and supportive relationship with one’s teacher promotes a child’s emotional security and confidence (Howes et al., 1994; Pianta & Steinberg, 1992). A secure relationship with the teacher may serve as a resource that permits young students to actively explore their environment and to cope more effectively with novel academic and social demands (Little & Koback, 2003). Researchers studying pre-adolescent and adolescent students draw on social-cognitive and motivational theories in explaining the impact of teacher–student relationship qualities on student social and academic outcomes (Ryan et al., 1994; Skinner et al., 1998). According to self system motivational theory (Connell & Wellborn, 1991), students who perceive their teachers as meeting their basic psychological needs for competence, autonomy, and relatedness are most likely to identify with school, invest in the school’s agenda, and achieve more. Longitudinal studies support tenets of this theory, finding that middle school students who perceive their teachers as more involved with them and as granting them greater autonomy are more effortfully engaged in classroom activities and achieve more (Skinner et al., 1998). Although the teacher–student relationship is important at all ages, early relationships may be especially important to long-term adjustment. Hamre and Pianta (2001) found an effect for teacher–student relationship conflict assessed in first grade on achievement seven years later, controlling for relevant baseline child characteristics. The long-term effect may be due to the reciprocal causal processes between relationship quality and student engagement and achievement. Support for this interpretation comes from a study that assessed teacher–student relationship support, student effortful engagement, and academic achievement each year for three consecutive years from first to third grade (Hughes, Loyd, & Buss, 2007). In addition to reciprocal effects among the variables and mediational effects (with first-grade teacher support predicting third-grade achievement via its effect on second-grade effortful engagement), first-grade teacher support had a direct effect on third-grade achievement, and teacher– student relationship support, above the year-to-year stability of the variables. This lag effect suggests that teacher–student relationship quality in first grade shapes children’s patterns of engagement in learning that, in turn, leads both to more supportive relationships with subsequent teachers and to higher levels of achievement. Although most of the research on teacher–student relationships focuses on the motivational aspects of a supportive relationship, it is likely that relationship quality also influences the quality of the instruction the child receives. For example, first-grade teachers are likely to hold higher achievement expectations for students, relative to students’ assessed achievement, when they have a positive, supportive relationship with the student (Hughes et al., 2005).

196 Jan N. Hughes and Lisa K. Barrois Teacher expectations, in turn, influence the quality of teachers’ instruction to students (for reviews see Brophy, 1983; Jussim, 1986). For example, in interacting with students for whom they hold high expectations, relative to students for whom they hold low expectations, teachers wait longer for answers to a question, assign more challenging work, attribute poor performance to effort rather than to ability, and provide more praise and less correction for the same behaviors. Drawing from resiliency literature, several researchers have documented that a positive teacher–student relationship can function as a protective factor that buffers children from the effects of known risk factors (Copeland-Mitchell, Denham, & DeMulder, 1997; Fallu & Janosz, 2001; Hughes et al., 1999; Ladd & Burgess, 2001). Reasoning that children with poor self-regulatory control might especially benefit from a warm relationship with their teachers due to their greater reliance on external resources to regulate their attention and behavior, Chen, Liew, and Hughes (2007) found that first-grade children with poor self-regulatory control improved more in reading the following year when they experienced a warm relationship with their first-grade teacher. A similar benefit was not found for children with average and high levels of regulatory control. Similarly, children with externalizing problems benefit more from the provision of a close teacher–student relationship than do children without such difficulties (Silver, Measelle, Armstrong, & Essex, 2005). Classroom Peer Relationships and School Adjustment Every child in a classroom can be thought of as having a position within that class on various dimensions of peer relatedness. The most extensively researched peer group status variable is one’s level of acceptance, or liking, within the group (for reviews see Ladd, 1990; Parker, Rubin, Erath, Wojslawowicz, & Buskirk, 2006). Low peer acceptance (or high peer rejection) and its associated lack of a sense of belonging to school forecasts school avoidance and disaffected patterns of engagement from kindergarten through the middle grades (Buhs & Ladd, 2001; Furrer & Skinner, 2003; Guay, Boivin, & Hodges, 1999; Ladd et al., 1999; Wentzel, 1998). In a longitudinal sample from kindergarten to grade 5, the effect of early negative peer experiences on achievement was mediated by declining classroom participation and school avoidance (Buhs, Ladd, & Herald, 2006). An additional longitudinal study has found that the direct effect of peer acceptance on achievement may be mediated by the effect of peer acceptance on self-perceived academic competence (Flook, Repetti, & Ullman, 2005). In addition to one’s level of social acceptance in the classroom, students develop reputations among their classmates based on their behaviors, traits, and interactions with other students that also predict academic outcomes (Hamm & Faircloth, 2005). In the elementary grades, a student’s peer reputation for academic competence has been found to predict changes in children’s academic motivation and achievement, above levels of their peer acceptance (Gest et al., 2005). Children who are perceived as academically competent by their peers develop greater confidence in their academic abilities, which leads, in turn, to greater engagement and achievement (Hughes et al., in press). Classrooms differ in the degree to which social comparison cues regarding children’s achievement are available to students and, therefore, the salience of individual differences in academic abilities (Mac Iver, 1988). Teacher practices that provide more social comparison cues include providing more frequent and public feedback on performance, grouping students of similar ability for instruction, grading students in reference to comparison with others rather than in relation to personal improvement, and providing more response opportunities to higher-ability students than to lower-ability students (Ames, 1992; Marsh & Craven, 2002; Urdan & Midgley, 2003). Lower-performing

The Developmental Implications of Classroom Social Relationships 197 children may fare worse when placed in classrooms where such social comparison cues are more available than in classrooms in which such cues are less available (Hughes & Zhang, 2007). Classroom Emotional Climate and School Adjustment Conceptually, a given student’s relationship with the teacher and with peers can be distinguished from the typical, or normative, level of teacher and peer support in the classroom. For example, each child in a classroom has a unique relationship with the teacher; yet, every child in the classroom experiences the same classroom climate, defined in terms of the teacher’s general tendency to interact with students in a warm and respectful manner. Similarly, each child has a particular peer status; yet, the classroom can also be described in terms of the general provision of peer social support to students. The distinction between individual and normative levels of teacher warmth was supported in a study finding that normative or typical teacher warmth made an independent, or unique, contribution to students’ peer acceptance and motivated patterns of engagement, above students’ individual level of teacher warmth (Hughes, Zhang, & Hill, 2006). Additional studies have found that when teachers provide a positive classroom climate, children are more engaged in learning and are more successful in meeting classroom challenges (Rimm-Kaufman, La Paro, Downer, & Pianta, 2005). Furthermore, a positive emotional climate, defined in terms of teacher sensitivity, child-centered instruction, warmth and fun, and low punitive control, is particularly important to the academic and social adjustment of children at risk for school failure due to background variables (Hamre & Pianta, 2005). Importantly, a positive social and emotional climate does not come at the expense of instructional quality. In preschool and early grades, observed measures of emotional and instructional quality are moderately and significantly correlated (Mashburn et al., 2008; Hamre & Pianta, 2005). Matsumura et al. (2008) found that middle school teachers who are respectful of and caring toward students also tend to provide more rigorous instruction, defined in terms of presenting students with a challenging curriculum and pressing students to engage in critical thinking. In these classrooms, interactions among students are more respectful and supportive and students are more engaged (Matsumura et al., 2008). Thus, social and instructional processes seem to support each other, creating learning environments that promote student social and academic adjustment (Urdan & Schoenfelder, 2006). Teachers establish caring classroom communities not only by providing supportive and positive relationships with students but also by respecting student autonomy and providing students with challenging, relevant work (Battistich & Horn, 1997). The concept of pedagogical caring (Noddings, 1992) emphasizes that teachers communicate caring for students by interest in student input, clarity in classroom rules and expectations, adjusting instruction to individual differences, and providing effective feedback. In such a classroom environment, students develop the intrinsic motivation necessary for continued learning and feel a sense of safety that allows them to take the academic risks necessary for learning to occur (Urdan & Schoenfelder, 2006). Several studies have demonstrated that classrooms characterized by caring and supportive interpersonal relationships and opportunities for autonomy and challenge are associated with student academic motivation and engagement, prosocial behaviors, higher achievement, and low levels of problem behaviors (for reviews see Brophy, 2004; Schaps, Battistich, & Solomon, 2004).

198 Jan N. Hughes and Lisa K. Barrois

Promoting Positive Classroom Social Relationships Progress has been made in identifying teacher practices and behaviors that promote respectful and supportive relationships between teachers and students and among students. Recently developed observational tools, such as the Classroom Assessment Scoring System (CLASS; Pianta, La Paro, & Hamre, 2008), that are based on the body of research on effective teacher practices for promoting positive learning environments, have become available to educators and researchers. The CLASS assesses those dimensions of classroom social-emotional and instructional quality (e.g., teacher sensitivity, positive climate, encouragement of child responsibility, positive discipline practices) that have been found to promote student academic and social competencies. The next step in making such classroom environments available to more students is to identify prevention interventions that improve teachers’ abilities to engage in these behaviors and to create caring classroom environments. In considering this challenge, it is important to acknowledge that teacher–student interactions are embedded in larger systems that may promote or impede teachers’ efforts to create positive teacher–student relationships and classroom environments. For example, when teachers have limited time to get to know students individually due to frequent class changes or large class sizes, or when teachers work under stressful conditions with little emotional support and guidance, they may be less able to create positive emotional climates for their students (Pianta, 1999). Classrooms with higher numbers of students with problematic behaviors pose an additional risk for poor teacher– student relationships, especially for students with higher levels of externalizing behaviors (Buyse, Verschueren, Doumen, Damme, & Maes, 2008). Teachers often receive little pre-service or in-service training in how to create a positive social and emotional climate. Recent research on teacher retention suggests that difficulty in creating a positive social environment in the classroom, including relating to students, maintaining discipline, creating a sense of community among students, and motivating students, is one of the primary reasons that new teachers leave the teaching profession (Kersaint, Lewis, Potter, & Meisels, 2007; Mitchell & Arnold, 2004; Murray, 2005). Nationally as many as onethird of teachers leave the teaching profession within their first three years; nearly half leave within five years (Ingersoll, 2001). Interventions that improve teachers’ abilities to connect positively with students and to create positive classroom environments are expected to improve teachers’ well-being while promoting students’ school adjustment. Prevention Interventions to Improve Classroom Social and Emotional Climate A majority of schools in the United States implement programs designed to improve relationships and the social and emotional climate of classrooms. However, only a minority of schools select programs supported by sound evidence of efficacy and effectiveness (Greenberg, 2004). The lack of accessible information on evidence-based programs is one of several factors that contribute to the under-adoption of empirically supported prevention programs. To meet this need, governmental agencies and research organizations have created guidelines for evaluating effectiveness claims. Examples include the Collaborative for Academic, Social, and Emotional Learning’s Safe and Sound: An Educational Leader’s Guide to SEL Programs (2003), the U.S. Department of Education’s (2003) guide to identifying effective programs, and the Substance Abuse and Mental Health Services Administration’s (SAMSHA) Registry of Evidence-based Programs and Practices (2008). Because these guides use different criteria for the selection of effective programs and different terms to indicate level of rigor (e.g.,

The Developmental Implications of Classroom Social Relationships 199 promising, exemplary, model, probably efficacious, efficacious), they yield low correspondence when evaluating the same program (Flay et al., 2005). To reduce the resulting confusion, the Society for Prevention Research’s Task Force on Standards of Evidence determined the most appropriate criteria for prevention programs and policies to be judged efficacious, effective, or ready for dissemination (Flay et al., 2005): “Efficacy refers to the beneficial effects of a program or policy under optimal conditions of delivery, whereas effectiveness refers to effects of a program or policy under more real-world conditions” (Flay et al., 2005, p. 153). These classifications are hierarchical in that for a program to be found “effective” it must also meet the criteria for efficacious programs, and for a program to be ready for broad dissemination (going to scale), it must also meet all the criteria for effective programs. Selection of Programs to Review In this review we applied criteria for efficacy to school-based, universal prevention programs that have as a primary aim increasing positive social interactions in the classroom and/or improving the social-emotional climate of the classroom. Programs that assessed only negative behaviors (e.g., aggression, delinquency) were excluded, as were programs that provided the intervention only to students selected on the basis of risk rather than to intact classrooms. Only programs that were implemented by teachers and that utilized a comparison or control group and for which at least one efficacy study appeared in a peer-reviewed journal were considered. The following steps were followed to identify programs that met these criteria. The PsycINFO and ERIC databases were searched for available programs. Additionally, existing reviews such as Safe and Sound: An Educational Leader’s Guide to Social and Emotional Learning Programs (CASEL, 2003) were consulted. A total of eight programs met the inclusionary criteria. These programs, the articles that formed the basis of the review, and descriptive information about each program are included in Table 10.1. These eight programs vary considerably in their emphasis on teachers’ behaviors and interactions with students. Typically the emphasis in these programs is on the impact of the curriculum on students’ prosocial behaviors and skills rather than on teacher–student relationships or the classroom social-emotional context, per se. Thus, in addition to evaluating program efficacy, we describe each study’s approach to improving teachers’ abilities to improve classroom social relationships, including the level of teacher training and support and the program’s focus on preparing teachers to teach, model, prompt, and reinforce targeted skills outside the formal lessons. In other words, we address the study’s relative emphasis on program implementation versus teacher professional development. Criteria for Efficacy We applied the efficacy standards created by the Society for Prevention Research (Flay et al., 2005) to identify programs with the greatest support for efficacious use in the schools. Following is a brief description of how we applied each standard to the eight prevention programs that met our inclusionary criteria. Details for coding each standard are provided in Flay et al. In order to meet the criteria for the “Intervention Description and Outcomes” standard, a detailed description of the intervention including such details as intended population, content, organization, goals and duration must be available. The study must measure those behaviors and outcomes that the program intends to alter. The use of quality measures must

200 Jan N. Hughes and Lisa K. Barrois Table 10.1 Study Characteristics Program

Study

PATHS

Domitrovich et al., 2007 Pre-K

SSDP

ICPS

PreK-K PreK-K 2nd–4th

Social skills; Pro-social behavior, Academic engagement and performance Social skills; Problem behavior Social skills; Problem behavior; Academic engagement and performance; Peer acceptance; Classroom climate Social skills; Problem behavior Problem behavior; Academic engagement and performance; School attitudes and belonging Problem behavior; Academic engagement and performance Academic engagement and performance; School attitudes and belonging Social skills; Problem behavior; Pro-social behavior Social skills; Problem behavior; Pro-social behavior Social skills; Problem behavior; Pro-social behavior Social skills; Problem behavior; School attitudes and belonging; Academic engagement and performance Problem behavior; School attitudes and belonging Social skills; Problem behavior Social skills; Problem behavior Academic engagement and performance

3rd 4th

Social skills; Problem behavior Social skills; Problem behavior

Kam et al., 2004 CPPRG, 1999

1st–3rd 1st

Greenburg et al., 1995 Hawkins et al., 2001

2nd–3rd 1st–6th

Hawkins et al., 1992

1st–5th

Abbott et al., 1998

5th–6th

Second step Frey et al., 2005

CDP

Age at Intervention Outcomes Assessed Delivery

2nd–5th

Taub, 2001

3rd–6th

Grossman et al., 1997

2nd–3rd

Battistich et al., 2004

3rd–6th

Battistich et al., 2000

3rd–6th

Shure & Spivack, 1980 Shure & Spivack, 1979 Responsive Rimm-Kauffman et al., Classroom 2007 T.J. Dilworth et al., 2002 Open Circle Hennessey, 2007

be demonstrated by evidence of construct validity and reliability of measures. Additionally, to avoid potential demand characteristics, at least one form of data must be collected by individuals not involved in the delivery of the intervention. To meet criteria for the “Clarity of Causal Inference” standard, the study design must include a control condition. The assignment to the control versus intervention group must be done in a way that reduces the possibility of biased results and allows for confidence in results. This assignment may be random with processes for group assignment well explained. Or, the use of match control designs with demonstrated pre-test equivalence is acceptable assuming group assignment does not result in unintended relationships between group and other variables. The “Generalizability of Findings” standard was met if the authors specified the study sample and how it was obtained. For our purposes, a study sample is considered specified if descriptions of race, socioeconomic status and age are provided. For school-wide interventions, we did not consider report of gender necessary as most schools are gender-balanced. In order to meet the standard for the “Statistical Analysis” category, the analysis must include all intended participants and take into account the level at which participants were randomized to study condition in order to avoid violating the assumption of statistical

The Developmental Implications of Classroom Social Relationships 201 independence of observations. In the case of school-wide interventions, accounting for the clustering of students into classroom groups is necessary. When multiple outcomes are assessed, it is necessary to adjust for multiple comparisons to ensure that findings exceed what would be expected by chance. Also, measurement of attrition is necessary to insure there are no differences in the nature or extent of attrition between groups which may bias results. To meet the “Statistically Significant Effects” standard, results must be reported for every measured outcome and a pattern of statistically significant positive effects should be demonstrated. We considered a pattern to be evident if over 50% of findings were in the expected direction with at least one statistically significant finding that was free of demand characteristics. There must be no negative effects on critical study outcomes. Additionally, we noted if at least one outcome was assessed at least six months following the end of the intervention with statistically significant findings, indicating that the program has some effect on developmental trajectories.

Results Each study was independently rated by each author with respect to each standard. Disagreements were resolved by discussion. Rating results are reported in Table 10.2. Below a summary evaluation is provided for each program. Promoting Alternative Thinking Strategies (PATHS) The PATHS curriculum (Kusché & Greenberg, 1994) aspires to increase social and emotional competence in students by targeting social problem-solving, social skills, emotional awareness and understanding as well as self-control. The PATHS program is based on an ecobehavioral systems orientation which posits that by changing the child, change will be created in the ecology and in their interaction. Ideally, the PATHS program is implemented as students start grade school and continues through the fifth grade. The PATHS curriculum instructs students on the expression, understanding and regulation of emotions using an affective-behavioralcognitive-dynamic framework. Sixty lessons are taught three times per week for 20–30 minutes throughout the school year. Lessons are organized into units such as self-control, emotions and problem-solving. Teachers present lessons using a variety of didactic instruction, class discussion, modeling, role play, social and self-reinforcement, and worksheets. In addition to teaching lessons, teachers are encouraged to generalize their use of PATHS concepts across the school day. Teachers receive two and one-half days of training just before school starts and then receive weekly consultations throughout the year, including observations by staff members followed by feedback (Conduct Problems Prevention Group, 1999). The Conduct Problems Prevention Research Group (1999) evaluated the effects of the program after one year on a group of first-grade students. On sociometric measures, the program students showed less hyperactive-disruptive behavior and less aggression than control students, with no statistically significant differences in exhibited prosocial behavior. No differences were evidenced on teacher-report measures. Additionally, observers rated PATHS classrooms as having a more positive atmosphere than control classrooms (i.e., following rules, ability to express feelings appropriately, level of interest and enthusiasm, and ability to stay focused and on-task). Greenberg, Kusché, Cook, and Quamma (1995) found that thirdgraders exposed to the PATHS program for one to two years were able to generate more words to describe positive and negative feelings, were better able to describe negative feelings,

Domitrovich et al., 2007 Kam et al., 2004 CPPRG, 1999 Greenburg et al., 1995 Hawkins et al., 2001 Abbott et al., 1998 Hawkins et al., 1992 Frey et al., 2005 Taub, 2001 Grossman et al., 1997 Battistich et al., 2004 Battistich et al., 2000 Shure & Spivack, 1980 Shure & Spivack, 1979 Rimm-Kauffman et al., 2007 Dilworth et al., 2002 Hennessey, 2007

PATHSa Y YY Y Y Y Y Y Y Y YY Y Y Y Y Y

Replicability Y Y Y Y Y Y Y Y N Y Y N Y Y Y N Y

Measurement

Description and Outcome

Y Y Y Y Y N Y Y N Y N N N N N N N

Causal Inference

Y N Y Y Y Y Y Y Y Y N N Y Y Y N Y

Generalizability

N N Y N N N N Y N Y N N N N N N N

Statistical Analysis

Y Y Y Y Y N Y Y N Y Y N Y Y Y Y N

Significant Effects

N Y N N Y N N Y N Y Y N N N N N N

Duration

Precision of Outcomes

Note: a Indicates a program for which positive outcomes have been replicated with a different sample. A Y– indicates that criteria were met with reservations.

Responsive Class T.J. Open Circle

ICPS

CDP

Second stepa

SSDP

Study

Program

Table 10.2 Evaluation of Studies

The Developmental Implications of Classroom Social Relationships 203 provide examples of their own feelings and understand the emotions can be hidden or changed. However, no differences were found in students’ explanations of how to recognize emotions or understand simultaneous feelings. Kam, Greenberg, and Kusché (2004) reported that, at two years post-intervention, the special education program students had a reduced growth rate of depressive symptoms and increased growth rate of vocabulary words to describe negative feelings compared to special education control students. There were no differences in growth rates of positive feeling vocabulary words or social problem-solving skills. Additionally, Domitrovich, Cortes, and Greenberg (2007) evaluated a modified PATHS program for preschool students and found that, compared to control students, intervention students were rated by teachers as more socially competent and as less socially withdrawn and demonstrated better emotion knowledge skills. Overall, the studies of the PATHS program demonstrate methodological rigor. All allowed for replicability by describing the program and targeted outcomes well and using valid and reliable measures for program analysis. All but one study utilized random assignment to intervention or control group status. Kam et al. (2004) demonstrated pre-test equivalence on study variables, thus allowing for causal inference. Additionally, all but Kam et al. provided adequate description of the study sample allowing for generalizability. The Conduct Problems Prevention Research Group (1999) used multi-level analysis to account for the nesting of the data into classroom groups, thus ensuring precision of outcomes. The remaining three studies did not account for nesting of the data. All studies showed a pattern of statistically significant positive effects. Seattle Social Development Project (SSDP) The SSDP (Hawkins, Kosterman, Catalano, Hill, & Abbott, 2005) aims to promote positive social development while strengthening bonds between school, home and family, thereby preventing adolescent behavior and health problems, such as drug abuse or delinquency. Based on social development theory, the intervention aims to increase protective factors and decrease risk factors associated with behavior and health problems. Implementation of the SSDP involves the combination of three component parts: utilizing classroom management and instructional practices; implementing a cognitive behavioral instructional program addressing self-control and social competencies; and providing parent workshops. Teachers attended a five-day training program addressing classroom management, direct instruction strategies and cooperative learning. In addition, teachers implemented the Interpersonal Cognitive Problem Solving program to teach ways to effectively handle conflict with peers. The SSDP places a greater emphasis on teachers’ application of targeted teacher practices throughout the day than on teaching lessons on problem-solving. Teachers are taught to establish classroom routines at the beginning of the year, to develop clear and explicit expectations for behavior, to praise positive behavior, and to provide frequent assessments of student learning and feedback. Teachers’ use of targeted practices throughout the day is observed periodically, followed by feedback to teachers (Abbott et al., 1998). Thus SSDP places a greater emphasis on teacher professional development than on implementation of a packaged curriculum. In sixth grade, the program was supplemented with a four-hour training from SSDP staff about resisting negative peer influences. Finally, developmentally appropriate parent training programs were offered to parents. In a series of quasi-experimental studies (Hawkins, Kosterman, Catalano, Hill, & Abbott, 2005), a group of students receiving the intervention from first to sixth grade were compared to a matched control group of students on a variety of outcomes from second grade to age 21.

204 Jan N. Hughes and Lisa K. Barrois At the end of second grade, males in the experimental group demonstrated less aggressive and externalizing antisocial behavior than those males in the control group. Girls in the intervention group were reported to be less self-destructive than control group girls. At the beginning of fifth grade, initiation of alcohol use and delinquency was higher amongst control group students than intervention group students. California Achievement Test scores were higher amongst control females versus intervention group females, although these differences were not demonstrated in sixth grade. At the end of sixth grade, analyses of program effects on lowincome students revealed that low-income males had fewer antisocial friends, demonstrated better social and schoolwork skills, obtained higher achievement test scores and reported more commitment to school. Low-income females in the intervention group participated more in the classroom environment, reported more bonding and commitment to school as well as classroom and team learning opportunities. Low-income females were less likely to use cigarettes or have exposure to marijuana than controls. At age 18, six years after the conclusion of the intervention, program group students reported better school grades and achievement, more commitment and attachment to school and less misbehavior at school than control group students. Additionally, program students reported less violent behavior, sexual activity and involvement with multiple sexual partners across their lifetimes than controls. Intervention group students were also less likely to have engaged in heavy alcohol use in the past year. At age 21, nine years after the completion of the intervention, former program students were more likely to be high school graduates, have completed some college, be employed in the past month and had been employed longer at their present jobs. Reported mental health was better among former program students than controls. Although no statistically significant effects were evident for substance use, other findings evidenced a decrease in risky behavior. Former program students had fewer sexual partners and were more likely to use a condom when engaging in sexual activity. Females reported fewer pregnancies and births. Additionally, former program students were less likely to have a court charge ever, to commit a high variety of crimes or to sell drugs in the past year. The one study that assessed the effect of SSDP on teachers’ behaviors failed to find an effect (Abbott et al.,1998). Control teachers and program teachers did not differ on observed measures of teacher use of the targeted practices. Consistent with the program’s theory, students in classrooms in which teachers engaged in these practices, whether these teachers were in the control group or the experimental group, demonstrated greater school bonding. Overall, the intervention program and outcome goals are well described in these studies. Valid and reliable measures are utilized to measure program outcomes. Ability to make causal inferences is adequate as assignment of schools to conditions was largely random. The sample was well described. Across the years, the studies generally demonstrated a consistent pattern of effects on students over time. However, some studies demonstrated methodological issues leading to reduced confidence in results. The single study that took into account the nesting of data into classes (e.g., Hawkins et al., 1992) did not result in a consistent pattern of statistically significant findings. Additionally, some analyses did not address the potential effects of attrition on measurement of study variables (e.g., Abbott et al., 1998; Hawkins et al., 1992). Talking with TJ The Talking with TJ program (The Hallmark Corporate Foundation, 1994a; The Hallmark Corporate Foundation, 1994b) consists of two components: “Teamwork Building” intended for second- and third-grade students and “Conflict Resolution” intended for fourth- and fifth-grade students. Both components aim to increase social and emotional competence in

The Developmental Implications of Classroom Social Relationships 205 program participants by developing skills in the areas of group planning, diversity and inclusion as well as cooperative team play. Each component consists of six lessons, lasting 45–60 minutes each. Lessons are led by project staff instead of teachers. Each lesson plan includes a 15-minute video clip providing examples of successful and unsuccessful skill use, an in-class discussion of the skills from the video and guided practice activities to promote generalization of learned skills. In addition, posters with “TJ Teamwork Tips” are displayed throughout the classroom and comic books addressing program goals are sent home to be shared with parents. Although teachers do not deliver the lessons, they are provided with a list of activities for teaching social and emotional competencies and are encouraged to incorporate teamworkbuilding activities into school activities, such as games or problem-solving discussions. The level of teacher training is not described in the studies reviewed. Dilworth, Mokrue, and Elias (2002) evaluated the effectiveness of the Teamwork-Building component with an urban sample of third-grade students. The Social Skills Rating System— teacher (SSRS) form was utilized as a measure of students’ social skills and problem behaviors. Students completed the self-concept scale as well as the anger inventory. Overall, no significant differences were observed on the teacher-reported SSRS ratings. However, program students reported higher self-concept than control students. No differences were indicated between groups on anger. The intervention and outcome goals were well described by the authors. Reliable and valid measures were used for outcome analysis. Student self-report measures demonstrate a pattern of positive effects. However, obstacles to causal reasoning do not allow clear interpretation of results on these outcomes. Data were collected from teachers who were involved in delivering the intervention and from students by undergraduate and graduate students. Because it was unclear whether the undergraduate and graduate students collecting the student measures were different from those delivering the intervention, these data cannot be considered free of demand characteristics. Outcomes measures were administered two to three weeks after the conclusion of the intervention; thus, the potential decay of effects over time cannot be assessed. Schools were not randomly assigned to conditions, and pre-test equivalence was not demonstrated. It was not specified how participating schools were obtained. Furthermore, analysis of the data did not account for the level of randomization or adjust for pre-test differences. Open Circle The Open Circle program (Hennessey, 2007) strives to increase social and emotional competence with grade-differentiated curriculum for kindergarten through fifth grade. Specifically, the intervention targets peer relations, decision-making and problem-solving. Open Circle is intended to be implemented throughout the school year, across years. There are 35 core lessons and several supplemental topics. Throughout the year, teachers have bi-weekly circle time for 15 minutes to address curriculum objectives and allow students to express ideas and opinions and engage in joint problem-solving. Hennessey (2007) evaluated the effects of the Open Circle program on a sample of fourthgrade students. The Social Skills Rating System teacher and student forms were utilized as a measure of students’ social skills and problem behaviors. Teachers’ ratings of program students were higher than those not exposed to intervention. Students’ ratings did not indicate a statistically significant difference. The level of teacher training is not described in this study, which states that teachers received consultation and “peer observation” to encourage teachers to reflect on their practices.

206 Jan N. Hughes and Lisa K. Barrois The lessons were well described and outcome goals were clearly stated. Reliable and valid measures were selected to assess outcomes. The sample was well defined. Yet, some methodological challenges reduce confidence in the results from this study. Program and control schools were not randomly assigned to conditions. Schools implementing the program were selected on the basis of their experience with the program, with some having up to 10 years’ experience of implementing the intervention. Only four classrooms were included in each condition and analyses did not account for the nested nature of the data (i.e., students nested in classrooms). No data were provided to assess the long-term effects of the intervention. Also, it is important to note that all findings were based on the reports of teachers delivering the intervention and may be prone to demand effects. Responsive Classroom Approach The Responsive Classroom Approach (Northeast Foundation for Children, 1996) is intended to incorporate social and academic learning to bolster students’ academic achievement. The program is not a packaged curriculum; rather, it relies on the teacher’s implementation of principles and practices. There are seven principles that include the themes of a focus on the importance of how students learn, relationships inside and outside school, and sensitivity to diversity. Teachers implement the practices including: a daily morning meeting to create community, classroom rules with logical consequences and emphasizing students’ efforts rather than products. The program is less focused on teaching specific skills than on applying the seven principles throughout the day. By implementing these principles and practices, children are expected to have academically motivating experiences in caring environments. These experiences are expected to increase students’ engagement in learning and use of social and self-regulatory strategies, thereby increasing academic achievement. Rimm-Kaufman, Fan, Chiu, and You (2006) investigated the effects of the Responsive Classroom Approach over three years on a sample of second- through fifth-grade students. Scores on two district-administered standardized assessments, the Degrees of Reading Power test and the CMT-Math test, were used to compare intervention and control students. On the Degrees of Reading Power test and CMT-Math test, the second- and third-graders who received the intervention for three and two years, respectively, performed better than control students, while the fourth-graders receiving the intervention for one year did not perform different than controls. Additionally, comparison of the predicted attainment of math proficiency levels (Goal, Proficient, Remedial) of program students versus controls at fifth grade indicated that program students were more likely to achieve above the remedial level. Importantly, program teachers were observed to implement the seven principles more frequently throughout the day than were control teachers. The article provides a thorough program description and utilizes valid and reliable outcome measures. Also, the study sample is well defined. The article also demonstrated that the program is able to produce a consistent, reliable pattern of effects. Yet, some issues with methodological rigor lead to reduced confidence in these results. Participating schools were not randomly assigned and differences between the intervention and control group students were evident on pre-test measures of study variables. Outcomes were only assessed immediately after the intervention, so it is not possible to assess the longevity of effects. Also, the authors did not employ necessary multi-level analysis of the data to account for the level of randomization of participants.

The Developmental Implications of Classroom Social Relationships 207 Child Development Project (CDP)/Caring School Community The CDP (Battistich, Schaps, Watson, Solomon, and Lewis, 2000; renamed Caring School Community in 2007) strives to make elementary schools “caring communities of learners” characterized by caring collaborations between students, schools and parents; a commitment to caring, justice, responsibility and learning; responding to developmental and sociocultural needs of students; use of engaging curriculum; and opportunities for students to be involved mentally and socially in the school environment. Stable, warm, and supportive relationships between teachers and students and among students are key program goals. The caring community of learners is expected to facilitate students’ intellectual and sociomoral development as well as fulfill students’ need for belonging, autonomy and competence, thereby ensuring students’ attachment to the school environment. To implement the project, schools follow four program principles: establish supportive, warm, stable relationships; teach students to be moral, caring and disciplined; follow a constructivist approach to learning by allowing students to learn in the most suitable way while learning from the diversity of others; and foster intrinsic motivation within students. Schools develop individualized approaches to implement these four program principles. Battistich, Schaps, Watson, Solomon, and Lewis (2000) evaluated the effects of the program on developed sense of community and presence of problem behaviors in fifth- and sixth-grade students. Sense of community was evaluated via a student report scale. Results showed that students at intervention schools exhibiting high levels of program implementation were more likely to report higher levels of sense of community than controls. However, reports of sense of school community by students at intervention schools exhibiting low levels of implementation declined compared to control students. Problem behaviors were assessed using a student self-report of use of alcohol, cigarettes and marijuana as well as 10 antisocial behaviors such as skipping school, damaging property or hurting someone. Taken as a whole, there was no statistically significant difference between the reports of problem behaviors by intervention and control group students. However, students at schools exhibiting high levels of implementation did see a difference in reported alcohol and marijuana use compared to controls. Battistich, Schaps, and Wilson (2004) reported program-related outcomes two years after the conclusion of the intervention. Outcomes included student-reported school-related attitudes, social and personal attitudes as well as the positive and negative behaviors of students and their friends. Teachers reported on students’ behaviors related to assertiveness, aggressiveness, involvement in school and concern for others. Finally, grades in core subjects were obtained from school records. Overall, four of the 40 comparisons favored program students over control students with no comparisons favoring control students. Specifically, program students reported better teacher–student relationships, liking for school, sense of efficacy and involvement in youth activities. No teacher-reported outcomes statistically significantly favored program students versus control students. Both studies provide rich discussion of the program and description of outcome goals. However, both studies of the effects of the Child Development Program evidence obstacles to causal reasoning which lessen confidence in these results. The Battistich et al. (2000) study provided no evidence that targeted outcome measures were reliable or valid. Schools were assigned to the intervention group based on faculty interest and ability to successfully implement the program rather than being randomly assigned. Furthermore, there was no test of the equivalence of the intervention and control group student on outcome measures at pre-test. Although the gender and racial composition of the sample was adequately described, there was no description of the socioeconomic status of the sample. The authors did not use multi-level

208 Jan N. Hughes and Lisa K. Barrois analysis to account for the level of randomization. There was not a consistent pattern of results and the findings reported were not greater than what would be expected by chance. Additionally, some negative effects of the program were evident. For example, studentreported sense of school community actually declined at some schools. The follow-up study by Battistich et al. (2004) showed some additional obstacles. Reliability information was reported for most measures, but was not reported for any teacher variables or two student variables. Analysis only included the three program schools with high implementation. The authors did not report whether attrition differed by intervention status. A consistent pattern of effects was not evident; only four of 82 tested outcomes were statistically significant. Second Step Violence Prevention Program (Second Step) Although the Second Step program (Committee for Children, 2002) has a strong emphasis on decreasing antisocial behavior, it was included in this review because it also seeks to increase students’ social competence by improving their competencies in the areas of social problemsolving, perspective-taking, impulse control, and anger management. The age-differentiated curriculum for Preschool to eighth-grade students contains 13 to 25 lessons, depending on student grade level, that last around 30 minutes each. The curriculum set provides teachers with photo lesson cards with age-appropriate skills printed on the front for use with students; some lessons including a video portion as well. The back of the card provides teachers with directions for class generalization activities such as modeling, practicing, and reinforcing newly learned skills. The level of teacher training varies from study to study. The Frey et al. (2005) study provided two days of training at the beginning of the school year and twicemonthly teacher consultations with program staff. The Second Step website (www.cfchildren.org) has many teacher resources, including tips for teachers on how to transfer program skills to other areas of academic learning. Additionally, the website contains information on workshops held at several locations across the country for teachers (one day) and for trainers of teachers (two days). Grossman et al. (1997) and Frey, Nolen, Edstrom, and Hirschstein (2005) conducted rigorous evaluations of the Second Step program. Both used random assignment, multi-level models, and psychometrically sound measures. Grossman found positive effects of the program on observed levels of second- and third-graders’ classroom aggressive and prosocial behavior, but no effects on teacher- or parent-rated behaviors. Furthermore, these effects persisted for six months after the treatment ended. Using a more extensive assessment protocol that assessed program effects on students’ social cognition as well as behavior, Frey et al. (2005) evaluated the effects of the Second Step program on a sample of second- through fifthgrade students over two years. Teachers rated students’ pro- and anti-social behaviors on the School Social Behavior Scale (SSBS). Additionally, students’ prosocial goals and beliefs were assessed via questionnaires and observations of students as they engaged in various games and vignettes involving peer interactions. Students reported on the hostility of the peer in the vignette and the extent to which they would react to the peer’s actions using a violent means. Students also engaged in a prisoner’s dilemma game from which behavioral observations and further student surveys were conducted. Finally, students were asked to negotiate a prize division in pairs and, then, asked to rate their satisfaction with the results. Teacher reports on the SSBS indicated that the program students exhibited more socially competent behaviors than control students. Also, unbiased observations of students indicated that program students were more likely to prefer prosocial goals and to solve conflicts cooperatively. Generally intervention effects were stronger for the first year than for the second year. In conclusion, the

The Developmental Implications of Classroom Social Relationships 209 intervention indicated a broad range of positive effects both on behavior and on students’ social cognition. In a study with significant methodological limitations, Taub (2001) evaluated the Second Step program with a group of third- through fifth-grade students. To measure students’ use of social skills, teachers completed the School Social Behavior Scale (SSBS) for each student and observers conducted observations of four student behaviors: responding to directions from adults, engaging appropriately with peers, following classroom rules and bothering/annoying/distracting other students. Teachers rated intervention students higher than control students on the SSBS Social Competence and Antisocial Behavior scales. Behavioral observations indicated that intervention group students were observed to appropriately engage with peers and follow directions from adults more often than control students, with no statistically significant differences between groups on other observed variables. However, methodological aspects of the Taub et al. study decrease confidence in their findings. The collected data may be affected by demand characteristics as all outcomes measures were completed by teachers as well as the project coordinator and trained undergraduate assistants who were responsible for delivering the intervention. The authors did not describe how schools were selected for either the intervention or comparison condition, and pre-test equivalence of these groups was not demonstrated. The data analysis did not use multi-level techniques to adjust for possible cluster effects. A pattern of positive effects was not demonstrated, and possible negative effects were reported. In light of the positive findings from the Frey et al. and Grossman et al. studies, which are methodologically sound, and the equivocal results of the less rigorous Taub study, a conclusion that the Second Step program is efficacious appears warranted. Interpersonal Problem Solving (ICPS) The ICPS curriculum (Shure, 2000; Shure, 2001) for nursery and kindergarten students attempts to teach students a problem-solving style of thinking. The curriculum is delivered by teachers in daily 20-minute lessons over the course of about three months. Teachers promote problem-solving by teaching students to develop alternative solutions, consequential thinking and causal thinking through lessons using pictures, puppets and role play techniques. Additionally, teachers are trained to implement program concepts when students exhibit problems in the school environment. Information on the level and type of training provided to teachers was not included in the efficacy studies. Shure and Spivack (1979, 1980, 1982) reported the effects of the ICPS program in a group of preschool and kindergarten students. Program students exhibited better interpersonal problem-solving skills at the conclusion of the intervention than control group students. Specifically, program students were able to generate more alternative solutions and displayed more consequential and causal thinking than control group students. Behavioral observations indicated that program students were less impulsive and inhibited than control group students. The program is well described and reliable; valid measures are used to assess program outcomes. Additionally, the study sample was well defined. The study also demonstrated that the program produced a pattern of positive effects. However, methodological difficulties reduce confidence in the results of this study. It was not explained how students were assigned to intervention or control group status. However, pre-test equivalence on measures was demonstrated. Analysis did not account for randomization by group with multi-level techniques. Also, no analysis was provided of those students who attrited to ensure that observed effects were not biased by differential measurement attrition.

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Discussion The Flay et al. (2005) criteria for efficacy are rigorous, and some of the standards for efficacy are especially challenging for universal school-based prevention interventions. Specifically, analysis at the level of randomization requires a large number of classrooms or schools for interventions that are delivered at the classroom or school level. Only three programs met this criterion for at least one efficacy study (PATHS, Second Step, and SSDP). A second challenge is the requirement for assessment of outcomes at least six months after the intervention. Interventions that occur for multiple years are most highly recommended (Collaborative for Academic, Social, and Emotional Learning, 2003). For example, the PATHS program is intended to be used for grades K–5. Longitudinal designs of this duration are not only expensive and difficult to implement; problems with selective attrition of subjects over several years may threaten the internal validity of results. Nevertheless, PATHS, SSDP, Second Step, and CDP found that intervention effects persisted for at least six months after the intervention. Despite the rigorous standards for efficacy applied to these programs, Paths and Second Step met all standards and are, therefore, deemed “efficacious.” A determination of efficacious means that the program produces benefits when delivered under conditions of high experimenter control. It is important to remember that a finding of efficacy does not mean that a given school will obtain the same results when they implement the program, under conditions and with populations that may differ substantially from those of the efficacy trials. However, a finding that program benefits are attributable to the program and not to some other factor, such as pre-existing differences between intervention and comparison schools or demand characteristics in the measurement of outcomes, is a necessary “first step” in the search for effective programs. Fortunately, both PATHS and Second Step have been adopted by many schools (Frey et al., 2005; The Prevention Research Center at Penn State: http://prevention.psu.edu/projects/PATHS.html), demonstrating that the interventions are acceptable to schools and feasible. Additionally, studies have identified mediators responsible for program benefits (Frey et al., 2005, for Second Step and Riggs, Greenberg, Kusché, & Pentz, 2006, for PATHS), thereby increasing confidence in the theory that underlies the interventions. Therefore schools considering programs to improve classroom climate are advised to give careful consideration to these programs. The eight programs reviewed all aim to improve the social interactions in classrooms in the direction of more cooperation and task engagement and less conflict and aggression. However, few studies assessed classroom-level effects; rather studies assessed the effect of the intervention on individual students; exceptions are PATHS, Second Step, and SSDP. Of the three programs that analyzed classroom effects, PATHS and Second Step found an effect for the intervention. Both studies found intervention effects for observed classroom interactions. These same two programs meet standards for efficacy. Thus one can conclude that they “deliver” on the goal of improving classroom social interactions. Some studies only examined intervention effects for high-implementation teachers. Such designs likely overestimate intervention effects, because teachers who implement interventions effectively may just be more effective teachers in general. In support of this argument, Battistich et al. (2000) found a negative effect of the CDP on students’ sense of school belonging for low implementation schools, relative to control schools. Perhaps schools that were unsuccessful in implementing the CDP were generally less effective schools. Designs that include all teachers assigned to the intervention condition, regardless of whether they implemented the program or not, are referred to as “intent to intervene” designs and offer high external validity regarding the likely outcome of the intervention in a given school.

The Developmental Implications of Classroom Social Relationships 211 Each program involves a teacher-delivered curriculum, and most programs assessed teacher implementation of the curriculum. The programs differ, however, in their emphasis on changing teacher behaviors beyond delivering the “packaged” curriculum. If one is interested in changing classroom climate, one would want to know whether the intervention had an effect on teachers’ everyday interactions in the classroom. The distinction between compliance with the scripted curriculum versus putting intervention principles into action throughout the day has been referred to as “talking the talk” versus “walking the talk” (Hirschstein, Van Schoiack Edstrom, Frey, Snell, & MacKenzie, 2007). Surprisingly, only one study (SSDP; Abbott et al., 1998) actually examined whether intervention teachers engaged in the targeted teacher behaviors more than control teachers. Targeted teacher behaviors included proactive classroom management and frequent recognition of student positive behaviors. Although these researchers found the expected relationships across intervention and control conditions between targeted teacher behaviors and student outcomes (i.e., student bonding to school and perceived opportunities for involvement and reinforcement), they found no effect of the intervention on teacher behaviors. That is, children in both intervention and control classrooms in which teachers engaged in more of the targeted behaviors improved more on measures of school bonding and social competence, but the intervention did not effect these teaching behaviors, nor was an intervention effect found for theoretically relevant student outcomes. The authors concluded that their theory was correct (i.e., the relationship between teaching strategies and student outcomes), but their intervention was not successful in changing the targeted teacher behaviors. Conclusions and Future Directions A growing body of research has identified teaching behaviors that promote student academic and social competencies (for review see Brophy, 2004). In a series of studies, Pianta and colleagues have found an effect for two dimensions of the quality of teacher–student interactions on children’s social and academic learning (Mashburn et al., 2008 ). Specifically, children who are in classrooms in which teacher–student interactions are more sensitive, responsive, affectively positive, and respectful gain more in social competencies, and this effect is stronger for children at-risk for poor school adjustment due to behavioral or family background factors. It is fair to say that we know a great deal more about which teacher behaviors promote positive classroom environments and student social and academic learning than we do about how to promote these teaching behaviors. Teachers report receiving very little training in how to relate to students, maintain discipline, create a sense of community among students, and motivate students (Murray, 2005). Furthermore, teachers generally report that in-service professional development programs are of little value, perhaps due to the relatively brief nature of instruction, little to no opportunity for teachers to practice new approaches with ongoing support, and poor integration of in-service content into school practices (Neff, 1990). The programs reviewed in this chapter provide some insights regarding effective strategies for promoting teachers’ abilities to create positive classroom climates. The PATHS program, which had an effect on classroom climate (and presumably on teachers’ behaviors, although this was not assessed) included intensive teacher preparation and ongoing individual consultation support during teachers’ first year of the program, with frequent and non-evaluative feedback. The consultation was embedded in the teacher’s classroom context and was personalized, collaborative, and interactive. School psychologists and other school mental health professionals have a potentially important role to play in promoting teachers’ abilities to create positive classroom climates

212 Jan N. Hughes and Lisa K. Barrois and to relate to and motivate students. Consultee-centered consultation, in which the consultant responds sensitively and respectfully to the teacher’s request for assistance, is particularly well suited to this task (Caplan, 1970; Hughes et al., 2007). The consultant’s role in this model is to establish an egalitarian and interactive relationship with the teacher in which the consultant provides information and emotional support to the teacher who wishes to implement new classroom approaches to improve classroom climate and student motivation. The individual nature of consultee-centered consultation helps to ensure that strategies that emerge from the consultation are not only based on good science but are also consistent with the teacher’s beliefs and philosophy of teaching and well-suited to that particular classroom context. The importance of matching the approach to the teacher’s beliefs is supported by a study finding that teachers were more likely to implement the PATHS program when their pre-existing beliefs about teaching emotions were consistent with the program (Buss & Hughes, 2007). The consultant also assists the teacher in implementing new approaches through the collection and analysis of data, including observations and the provision of nonevaluative feedback. The consultant and the teacher reflect together on the teacher’s experiences, thereby increasing the teacher’s self-awareness and problem-solving skills. The development and evaluation of such individualized and responsive approaches to assisting teachers in creating positive social environments, fostering positive relationships among students and with students, and motivating students to become engaged learners represents a critical direction for future research.

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11 Factors Influencing Teacher Interventions in Bullying Situations Implications for Research and Practice Jodi Burrus Newman, Karin S. Frey, and Diane Carlson Jones, University of Washington Students at school are often involved in and harmed by peer bullying (Hanish & Guerra, 2002; Nishina, Juvonen, & Witkow, 2005; O’Connell, Pepler, & Craig, 1999; Pepler, Jiang, Craig, & Connolly, 2008). Bullying is the act of intentionally and repeatedly hurting someone with less power (Solberg & Olweus, 2003). It can be physical (kicking, slapping, taking belongings), verbal (ridiculing, insulting, mean-spirited teasing), or social/relational (ostracizing, spreading rumors). Large-scale studies in the US indicate that peer bullying increases during the late elementary years and peaks in middle school (Espelage, Bosworth, & Simon, 2001; Frey, Hirschstein, Edstrom, & Snell, 2009; Nansel, Overpeck, Pilla, Ruan, Simon-Morton, et al., 2001). Approximately 30% to 40% of students claim regular victimization at school (Davidson & Demaray, 2007; Swearer & Cary, 2003). Teachers are key players in efforts to prevent bullying and limit its negative effects (Doll, Song, & Siemers, 2004; Hirschstein, Edstrom, Frey, Snell, & MacKenzie, 2007; Kallestad & Olweus, 2003; Kochenderfer-Ladd & Pelletier, 2008; Orpinas, Horne, & Staniszewski, 2003). In fact, teachers are frequently relied upon as “first responders” to instances of bullying both in the classroom (Atlas & Pepler, 1998) and in the larger school context (Parault, Davis, & Pelligrini, 2007). Teacher intervention plays a critical role in representing adult authority and guidance so that the power differential between bullies and targets can be addressed. However, teacher interventions in student bullying vary widely in terms of likelihood, specific strategies employed, and effectiveness (Bradshaw, Sawyer, & O’Brennan, 2007; Craig, Henderson, & Murphy, 2000; Mishna, Scarcello, Pepler, & Wiener, 2005). This chapter critically reviews the literature on teacher interventions in bullying and examines specific factors that account for differences in intervention practice and outcomes. We first provide background information on the effects of student bullying. We then propose a theoretical model depicting teacher interventions in relation to students, classes, and the school community. Our analysis of the literature reveals that these interrelated social contexts influence interventions, and that supportive relationships and practices may effectively reduce bullying behavior.

Outcomes Associated with Bullying Involvement Students who are targets of bullying endure frequent harassment from their peers and often lack the assertiveness and power to end a bullying episode (Olweus, 1993). The negative effects of being targeted may be considerable. Targets often experience decreased psychological wellbeing, disrupted social connections, and compromised opportunities to learn. Psychological problems experienced by some targets include depression, lower self-worth, social anxiety, and loneliness (Hanish & Guerra, 2002; Juvonen, Nishina, & Graham, 2000; Kochenderfer & Ladd, 1996; Nishina et al., 2005). Students who are targets may develop maladaptive ways of

Factors Influencing Teacher Interventions in Bullying Situations 219 coping with the emotional trauma (Hanish & Guerra, 2002; Juvonen et al., 2000). With continued victimization, behavior problems increase and worsen over time (Schwartz, McFaydem-Ketchum, Dodge, Pettit, & Bates, 1998). Class participation (Buhs, Ladd, & Herald, 2006), school attendance (Kochenderfer & Ladd, 1996; Smith, Talamelli, Cowie, Naylor, & Chauhan, 2004), and academic performance decline among the chronically bullied (Nishina et al., 2005; Schwartz, Gorman, Nakamoto, & Toblin, 2005). Some students may attempt to escape daily harassment by dropping out of school (Slee, 1994). Students who bully use aggressive tactics against their peers to secure resources and attain power (Nishina, 2004; Olweus, 1993; Pellegrini, 2002). Some bullies have social-cognitive skill deficits, including hostile biases and poor perspective-taking (Crick & Dodge, 1994; Jolliffe & Farrington, 2006). Others appear to be socially skilled manipulators who understand, but do not care, that their actions hurt people (Gianluca, 2006; Hawley, 2003; Sutton, Smith, & Swettenham, 1999). Short-term outcomes associated with bullying behavior are varied. Some bullies are rejected by their classmates. Others, however, are accepted, supported, and even regarded as popular among peers (Demaray & Malecki, 2003; Rodkin, 2004). In the long run, students who bully may become reliant on coercion, learn to use aggression to harm others, and fail to develop positive relationship skills (Pepler et al., 2008)—failure that may be played out in dating relationships (Pepler et al., 2006), families (Duncan, 1999), and student-teacher relationships (Frey & Nolen, in press). Students who witness bullying may also be adversely affected. Emboldened by the success of others, some student bystanders may become bullies themselves (Bradshaw et al., 2007). Schools may develop “cultures of bullying” (Unnever & Cornell, 2003) in which abusive treatment is expected and tolerated. Other bystanders may be disturbed by fear, guilt, and moral confusion (O’Connell et al., 1999; Jeffrey, Miller, & Linn, 2001). Anxiety and dislike of school increase on days in which middle school students witness others being bullied (Nishina & Juvonen, 2005). Although some adults argue that bullying is beneficial because it strengthens students’ abilities to handle adverse situations (Hazler, Miller, Carney, & Green, 2001), the evidence strongly suggests otherwise. Research clearly indicates that bullying can have a detrimental impact on the emotional, social, and academic lives of targets, bullies, and witnesses.

Teachers Play a Pivotal Role When teacher interventions occur in the context of a comprehensive school-wide bullying prevention program, they can effectively aid students by providing emotional support (Hunter, Boyle, & Warden, 2004), facilitating student problem-solving (Hirschstein et al., 2007), fostering prosocial interactions (Frey et al., 2005), and decreasing bullying behavior (Frey et al., 2005; Olweus, 1993). Unfortunately, this potential is often unrealized because teachers vary in the degree to which they acknowledge, interpret, and intervene to limit bullying behavior (Bradshaw et al., 2007; Craig et al., 2000; Mishna et al., 2005). Many teachers choose not to intervene or express uncertainty about how to respond to bullying (Mishna et al., 2005; Rigby & Bagshaw, 2003). In fact, observations of classrooms indicated that teachers only intervened to stop 18% of bullying interactions (Atlas & Pepler, 1998). The effectiveness of specific intervention strategies is also variable. Nearly 45% of British students who reported that a teacher intervened to stop bullying, felt that the intervention either changed nothing, or made the situation worse (Smith & Shu, 2000). Furthermore, commonly employed “zero-tolerance policies” and exclusionary punishments such as suspension appear to have little merit. These practices are related to negative school climate, unruly student behavior, and lowered achievement (Skiba, Ritter, Simmons, Peterson, & Miller 2006).

220 Jodi Burrus Newman et al. Finally, teachers at schools that are attempting to implement school-wide prevention programs may see few, if any, beneficial outcomes (Smith, Schneider, Smith, & Ananiadou, 2004). What factors account for variation in the likelihood and effectiveness of teachers’ bullying interventions? We propose a model to address this question that is grounded conceptually in socioecological theory (Swearer & Espelage, 2004), a relational perspective on motivation (Baumeister & Leary, 1995; Birch & Ladd, 1996; Connell & Wellborn, 1991; Pianta & Stuhlman, 2004), and a transactional model of development (Sameroff & MacKenzie, 2003). These approaches stress that social interactions and relationships are contextually situated and that individuals and social contexts powerfully affect one another.

The Social Ecology Socioecological theory provides an organizing framework of the multiple embedded contexts, from direct interactions among individuals to the broader culture, which interrelate to influence bullying behavior and intervention practice (Bronfenbrenner, 1979; Swearer & Espelage, 2004). This theory has informed our understanding of who is involved in bullying at school and the relational contexts in which bullying prevention efforts occur. The model presented in Figure 11.1 draws from the work of Frey and Nolen (in press), but we position the teacher at the center of the social ecology. In addressing the role of the teacher, we specify three overlapping relational contexts. The first relational context entails dyadic interactions between teachers and targets, and teachers and bullies. The second context includes the teacher’s interactions with the entire class, as when facilitating peer relationships or establishing classroom norms. Teacher– student and class-wide interactions are situated within and are further influenced by the third context, which includes the support that teachers receive for their bullying interventions from School community School-wide consistency Support for teacher interventions Classroom norms and relationships

Teacher characteristics • Empathy • Efficacy • Seriousness of bullying

Teaching practices with individuals • Recognition • Coaching • Monitoring

Supportive teacherstudent relationships • Responsive • Caring • Authoritative guidance

Teaching practices class-wide • Promoting positive peer relationships • Norm setting • Group instruction • Monitoring

Bully Target Peer relationships Reward or repudiate bullying

Figure 11.1 Relationships Affecting Teacher Interventions in Bullying Situations.

Factors Influencing Teacher Interventions in Bullying Situations 221 school community members such as administrators and other teachers. In each of these multiple contexts, we describe practices that are recommended for bullying prevention. Although it is likely that family influences and the broader culture also affect intervention efforts, we are restricting our analysis to interactions that occur within the school context.

Teacher–Student Relationships that Motivate Socially Responsible Behavior The quality of teacher–student relationships is a central component of our model, a contextual factor that may interact with specific intervention practices. In particular, we propose that supportive teacher–student relationships serve as models for future relationships and help motivate students to adopt prosocial norms (Birch & Ladd, 1996; Connell & Wellborn, 1991; Crosnoe, Johnson, & Elder, 2004; Doll et al., 2004; Hamre & Pianta, 2005; Hughes & Kwok, 2006; Hughes, Lou, Kwok, & Loyd, 2008; Pianta & Stuhlman, 2004; Wentzel, 1997). Supportive teacher–student relationships are defined by three key elements which are depicted in Figure 11.1: responsiveness, caring, and authoritative guidance. The first element entails being sensitive and responsive to students’ needs (Hamre & Pianta, 2005). For example, a teacher who accurately assesses low social-emotional competence among students may respond by teaching the requisite skills (Connell & Wellborn, 1991; Frey et al., 2005). Support also involves having a caring connection with students that is evidenced in a respect for the students and a positive emotional climate (Birch & Ladd, 1996; Hughes et al., 2008; Noddings, 2005; Wentzel, 1997). Finally, supportive relationships necessitate authoritative communication that provides behavioral guidance and sets high social and academic expectations (Birch & Ladd, 1996; Hughes, 2002; Wentzel, 2002). In contrast, unsupportive teacher–student relationships frequently involve adversarial conflict that is discordant and angry (Birch & Ladd, 1996; Hamre, Pianta, Downer, & Mashburn, 2008; Hughes et al., 2008). When teachers have supportive relationships with their students and engage in low levels of adversarial conflict, students are more accepting of peers, prosocial, and competent in social and academic spheres (Birch & Ladd, 1996; Furrer & Skinner, 2003; Hamre & Pianta, 2005; Hughes et al., 2008; Hughes & Kwok, 2006; Malecki & Demaray, 2003). As shown in Figure 11.1, supportive practices within the dyadic teacher–student context consist of recognizing that bullying has occurred, coaching individual students involved in bullying episodes, and monitoring intervention effectiveness. Within the classroom context, supportive practices to reduce bullying involve supporting prosocial peer interactions (Frey et al., 2005; Frey & Nolen, in press), proactive norm-setting, group instruction on social-emotional skills, as well as increased supervision and monitoring (Olweus, 1993). We propose that positive relationships with teachers motivate internalization of prosocial norms and encourage students to attempt new behaviors. The motivating influence of supportive relationships has been studied extensively with respect to promoting academic gains and prosocial interactions at school (Hughes & Barrois, this volume). However, the quality of the teacher–student relationship has received little attention in the bullying literature on teacher interventions. Research on relational contributions to motivation suggests that existing relational contexts and supportive intervention practices may foster beneficial transformational outcomes for students engaged in bullying.

Transactional Model of Intervention Practices: Two Examples The transactional model provides insight into how interactions between individuals and their environment affect development (Sameroff & MacKenzie, 2003). A tenet of the transactional

222 Jodi Burrus Newman et al. model is that individuals bring particular characteristics (e.g., beliefs, skills, processing biases) to interactions that shape the dynamics of the exchange. Furthermore, the environment in which people are situated shapes the beliefs of those involved (Sameroff, 1995). In our model, the outcome of a teacher’s intervention in a bullying situation is a product of bidirectional influences between the teacher and others in the three relational contexts. A description of a four-step hypothetical sequence helps to illustrate the ways in which the transactional dynamic can unfold for a teacher in a classroom. The individual characteristics of the teacher and students and the quality of their ongoing relationships represent the starting point. Next, the teacher recognizes a bullying situation that is occurring among students. The teacher then responds in some manner. Finally, transformational outcomes are observed. Two contrasting scenarios depicting bi-directional influence will further highlight the nature of transactional dynamics. The first example describes a teacher who does not believe that effective interventions are available, and feels overwhelmed in the face of classroom bullying (Mishna et al., 2005). Next, the teacher hears that one student is repeatedly harassed by the other students but does not view the situation as serious. The teacher responds to the situation dismissively (Mishna et al., 2005) and tells the target to “ignore it” (Kochenderfer-Ladd & Pelletier, 2008). Transformational outcomes might include the target using ineffective strategies such as crying or hitting, which could result in increased harassment from the peer group (Wilton, Craig, & Pepler, 2000). The poor outcome may lower the teacher’s self-efficacy regarding bullying interventions, which could make future interventions less likely (Yoon, 2004). In this case the teacher and social environment interacted to create a negative spiral where the relationships between the target, peers, and teacher deteriorated. A more positive scenario is also plausible. The teacher, instead of being overwhelmed by aggressive students, might feel efficacious and able to respond with several intervention strategies (Yoon, 2004). Next, when hearing about the bullying situation, the teacher investigates and actively intervenes. The teacher might provide assertiveness training to the target (Egan & Perry, 1998), separate the target and bully (Kochenderfer-Ladd & Pelletier, 2008), help the targeted student develop friendships (Boulton, Trueman, Chau, Whitehand, & Amatya, 1999), and endeavor to establish more prosocial classroom norms (Roland & Galloway, 2002). Following the intervention, bullying interactions among students may decrease (Kochenderfer-Ladd & Pelletier, 2008; Roland & Galloway, 2002). Furthermore, targeted students may form positive relationships with other students that buffer them from future bullying episodes (Goldbaum, Craig, Peper, & Connolly, 2003). Finally, students who trust that bullying situations will be handled effectively may be more likely to report incidents (Hunter et al., 2004). In both scenarios the existing characteristics of and relationships among the participants influenced the teachers’ response to bullying and practices employed, and the interventions led to widely divergent transformational outcomes. What are the factors that shape the unfolding dynamic of teacher interventions? We now explore the potential for differing outcomes by examining how individual teacher characteristics might influence bullying interventions. Next, we analyze the contribution of our three relational contexts to intervention practice. Examples from evaluations of the Steps to Respect (Committee for Children, 2001) bullying prevention program are used to illustrate how supportive interactions among teachers, students, classes, and the school community can motivate socially responsible behavior.

Teacher Characteristics The transactional model proposes that individual factors influence the dynamics of social interactions (Sameroff, 1995). We therefore begin our analysis of teacher interventions in

Factors Influencing Teacher Interventions in Bullying Situations 223 student bullying episodes by detailing the characteristics of teachers that might impact intervention practice, including empathy, efficacy, and perceptions of the seriousness of the bullying situation. Empathy. Examinations of pre-service teachers’ responses to bullying scenarios indicate that teacher empathy for the target is positively related to the likelihood of intervention (Bauman & Del Rio, 2006; Craig et al., 2000). These results are corroborated by interview data of practicing teachers (Mishna et al., 2005). Teachers who empathize with targets may be more responsive to students’ needs and provide assertiveness instruction, while less empathic teachers may dismiss bullying as unimportant. Efficacy. Some teachers may want to intervene, but lack confidence that they can handle bullying situations (Nicolaides, Toda, & Smith, 2002). Teachers with high self-efficacy are more likely to take action (Yoon, 2004) and are more confident in the effectiveness of their interventions (Bradshaw et al., 2007). Training provided as part of school-wide bullying prevention programs can bolster teachers’ perceptions that they can intervene effectively (Hirschstein & Frey, 2005; Newman-Carlson & Horne, 2004). Although teachers are more likely to intervene with individual students involved in bullying following training (Hirschstein & Frey, 2005), the evidence is less clear on whether the effectiveness of their actions also increases. Teacher beliefs about effective courses of action in bullying situations are also likely to influence intervention practice. For example, teachers who believe that children can avoid bullying by simply avoiding perpetrators are more likely to change seating in order to separate bullytarget dyads (Kochenderfer-Ladd & Pelletier, 2008). Other examples of problem- or skillfocused strategies (Lazarus & Moskowitz, 2000) are assertiveness training or increasing peer empathy for victimized students. An emotion-focused approach may include providing sympathy or reducing feelings of self-blame for victimization. These approaches appear successful in reducing aggression, victimization, and destructive bystander behavior (e.g., Frey et al., 2005; Frey et al., in press; Hirschstein, et al., 2007). As such, they are likely to be preferred by educators who are familiar with research findings. Avoidant responses (e.g., ignoring the problem) and exclusionary punishments may be adopted because teachers do not know which actions are effective, believe that there is no need for teacher intervention, or believe that supportive teacher interventions are ineffective or counterproductive. Unfortunately, the use of avoidant strategies and punishments such as suspension is related to high levels of problematic student behavior (Kochenderfer-Ladd & Pelletier, 2008; Skiba et al., 2006). Seriousness of Bullying. Teachers are likely to intervene in a bullying episode if they believe that the situation is serious (Holt & Keyes, 2004). Two factors inform a teacher’s definition of seriousness: the particular form of bullying and personal witnessing of the episode (Bauman & Del Rio, 2006; Craig et al., 2000; Mishna et al., 2005; Yoon & Kerber, 2003). There is general agreement among students and teachers that physical attacks and verbal insults involving profanity constitute serious bullying and warrant teacher intervention (Bauman & Del Rio, 2006; Hazler et al., 2001; Naylor, Cowie, Cossin, Bettencourt, & Lemme, 2006; Newman & Murray, 2005; Yoon & Kerber, 2003). Students and teachers do not agree, however, on the severity of other forms of bullying such as relational aggression and meanspirited teasing (Mishna et al., 2005). Relational aggression involves actions such as exclusion and gossip (Crick & Grotpeter, 1996). Mean-spirited teasing is a type of verbal attack that incorporates both humor and aggression (Shapiro, Baumeister, & Kessler, 1991). Many students report that these forms of bullying are as harmful as physical aggression (Crick & Grotpeter, 1996; Crick et al., 2001; Newman & Murray, 2005).

224 Jodi Burrus Newman et al. In contrast to the perceptions of many students, teachers tend to view relational aggression as less serious than physical bullying (Bauman & Del Rio, 2006; Craig et al., 2000; Mishna et al., 2005) and may even find teasing to be humorous (Pexman, Glenwright, Krol, & James, 2005), resulting in fewer interventions (Bauman & Del Rio, 2006; Craig et al., 2000; Yoon & Kerber, 2003). The perceived seriousness of physical attacks may be related to the greater likelihood of teachers directly observing the events. It may be more difficult for teachers to see gossip and ostracism. Teachers must rely on reports from students to learn about these types of events and their impact. Regardless of the form of aggression, teachers rate witnessed events as more serious than reported events (Craig et al., 2000). Summary. The evidence suggests that teachers who are empathic, efficacious, and who witness bullying episodes that they deem to be serious are likely to intervene. However, the research linking teacher characteristics to bullying intervention practice is not without limitations. There is a need for longitudinal research and multi-method assessments because many of the studies are cross-sectional and based on self-report data. Furthermore, the literature has focused primarily on predicting the likelihood that a teacher will intervene. Although this is an important issue, there is very little information on the process by which teachers decide to intervene or select a particular intervention strategy for a given situation. Finally, the actual effectiveness of the interventions employed needs to be evaluated. Despite these limitations, the data warrant a call for increased teacher training on bullying issues. Greater understanding of the harm done to targets by teasing, gossip, and exclusion may engender increased recognition of bullying, empathy for targets, and perceptions of seriousness. Furthermore, better preparing teachers to deal effectively with bullying could also increase the likelihood of intervention and encourage teachers to use more supportive practices.

Teachers in Relation to Students, Classes, and the School Community The Teacher–Student Relational Context Within the dyadic relational context, teachers and students may develop perceptions of one another that influence the likelihood and effectiveness of bullying interventions. Specifically, teachers’ perceptions of students may influence their ability to respond to bullying situations in a supportive manner (Frey, 2005; Frey & Nolen, in press; Mishna et al., 2005; Nesdale & Pickering, 2006). Furthermore, positive outcomes such as reduced emotional distress for targets (Davidson & Demaray, 2007; Malecki & Demaray, 2004) appear to be more likely when students perceive teachers as supportive. Teachers’ Perceptions of Students. Teacher perceptions of students may impact intervention practice (Mishna et al., 2005; Nesdale & Pickering, 2006). Research on the personal characteristics of targets and bullies has confirmed that there is not a single constellation of attributes that uniformly describes each group. Some targets, for example, are withdrawn and suffer from internalizing symptoms. Other targeted students, typified by disruptive and aggressive behavior, report the highest levels of social-emotional dysfunction (Schwartz, Proctor, & Chien, 2001). Teachers who expect that all targets are meek and withdrawn may be unable to recognize bullying when the students involved do not conform to these stereotyped expectations. Teacher judgments about student behavior are also embedded in their evaluation of students as “good” or “bad” children (Nesdale & Pickering, 2006) so that teachers may have a more difficult time identifying popular and cooperative students as perpetrators (Frey &

Factors Influencing Teacher Interventions in Bullying Situations 225 Nolen, in press). Preconceived notions about students, then, can affect teachers’ interpretation of aggression or disruptive behavior as benign or ambiguous, e.g., “That student is a good kid and didn’t mean to do it,” or “I know those kids aren’t a problem” (Frey & Nolen, in press; Mishna et al., 2005). If teachers fail to recognize bullying behavior due to an erroneous belief about the students involved, then opportunities to intervene and provide support may be missed. Students’ Perceptions of Teachers. Perceived teacher support appears to aid students who are involved in bullying situations. Malecki and Demaray (2004) found that the relationship between victimization and academic maladjustment was fully mediated by perceived teacher support. Other research has demonstrated that perceived teacher support moderated the relationship between victimization and emotional well-being among boys. Specifically, frequently targeted boys who experienced low levels of teacher support reported more internalizing problems than frequently targeted boys who experienced higher levels of teacher support. (Davidson & Demaray, 2007). Targets are not the only students to benefit from perceived teacher support. Doll et al. (2004) found that aggressive behavior was reduced when students reported high levels of teacher support. Unfortunately, relationships between teachers and the students involved in bullying are often unsupportive (Hanish, Kochenderfer-Ladd, Fabes, Martin, & Denning, 2004). Perceived teacher support is lowest among students who bully (Demaray & Malecki, 2003). In addition, the more students are involved in bullying, the less likely they are to view teacher interventions in bullying episodes as helpful (Rigby & Bagshaw, 2003; Swearer & Cary, 2003). The negative perceptions of teachers held by students who are highly involved in bullying could have a detrimental impact on future teacher–student interactions (Rigby & Bagshaw, 2003). If students feel that teacher interventions are unhelpful, they may be less likely to seek adult assistance with bullying situations (Hunter et al., 2004). The lack of perceived teacher support among bullies and targets, in conjunction with research indicating that teacher support improves emotional outcomes for students (Birch & Ladd, 1996; Hamre & Pianta, 2008; Malecki & Demaray, 2004), highlights the importance of being responsive to the needs of students who are involved in bullying. When teachers maintain close relationships with these students, they can also monitor the effectiveness of their intervention efforts and make adjustments as necessary to deter future bullying involvement. Steps to Providing Support for Students Involved in Bullying. The coaching model proposed in the Steps to Respect program provides educators with guidelines for brief sessions that provide supportive guidance to targets and bullies separately. The coaching model encourages teachers to: (1) affirm the target’s emotional response; (2) assess the history of the problem and current safety needs; (3) have students generate solutions; (4) practice necessary skills such as assertive responding; and (5) check-in with students to ensure that the plan is working (Hirschstein et al., 2007). Coaching helps teachers respond to the needs of those who are involved in bullying by assessing which skills require development, teaching students those skills, and working with students to ensure that competence is attained. Emotional needs are also met in that teachers are prompted to listen empathically and affirm the student’s emotional reaction. Checking with students to see if the intervention is working can foster a sense of trust with targeted children, and demonstrate teacher commitment to aggressive students who may hope that their teachers have short attention spans. The coaching model allows teachers to intervene following relatively minor incidents, which enables constructive problem-solving approaches, rather than waiting until the behavior is egregious enough to warrant punishments such as detention or suspension (Frey & Newman, 2008). Adversarial conflict is absent from the

226 Jodi Burrus Newman et al. coaching model. Although not incompatible with reprimands, parental notification, or even loss of privileges for repeat offenders, coaching promotes the development of skills such as assertiveness, social problem-solving, and emotion regulation in the context of a supportive teacher–student relationship (Committee for Children, 2001). Research confirms the effectiveness of the coaching model. Teachers who used the Steps to Respect program and reported brief coaching sessions more than once a week saw greater decreases in victimization, aggressive responding, and encouragement of bullying than their colleagues who used the program but coached students less frequently (Hirschstein et al., 2007). Though this study points toward the benefits of coaching, the authors stress that coaching is just one aspect of a school-wide intervention. Interactions at the class and school levels may be critical to the reductions in bullying found in schools that use the Steps to Respect program. Summary. Teacher–student interactions have the potential to attenuate the negative emotional effects of bullying (Davidson & Demaray, 2007; Malecki & Demaray, 2004) and reduce bullying behavior (Doll et al., 2007), provided they take place within the context of a supportive relationship. Teachers may be able to increase student perceptions of teacher support by accurately recognizing bullying situations and monitoring the effectiveness of their interventions. The coaching model used with the Steps to Respect program provides educators with specific guidelines for engaging in supportive one-on-one sessions with bullies and targets. The effectiveness of this approach suggests that more teachers should be trained in the coaching model (Hirschstein et al., 2007). Further research on teacher interventions with individual students is necessary. First, there have been very few studies documenting the effectiveness of specific strategies that teachers use when intervening in bullying. Although we know how general constructs such as responsiveness, caring, and authoritative guidance affect student outcomes, we know very little about how specific teacher intervention practices are perceived by students involved in bullying and the factors that affect student perceptions of teacher support. Second, the effectiveness of the coaching model has only been documented within the context of a bullying prevention program that includes classroom lessons and school-wide rules (Hirschstein et al., 2007). Evaluation study designs that enable program component analyses are rare (for an exception, see Webster-Stratton, Reid, & Hammond, 2004), but necessary in order to establish the effectiveness of the coaching method under other setting conditions. This fact dovetails with a third issue. Dyadic teacher–student interactions, even when they foster social-emotional skills and provide concrete evidence of teacher support, do not address factors related to the larger social ecology. Because bullying reflects power and status concerns within peer groups (Nishina, 2004; Pellegrini, 2002), the classroom provides an important venue for teacher intervention. The Teacher–Class Relational Context Teachers not only have relationships with individual students, but with students as a group. The teacher–class relationship can be considered an element of the classroom climate that reflects the normative quality of support that the teacher enacts across students (Hughes, Zhang, & Hill, 2006). In addition, teacher practices that promote prosocial values, empathy, cooperation, and social responsibility (Rimm-Kaufman, Fan, Chiu, & You, 2007; Solomon, Battistich, Kim, & Watson, 1997) contribute to the social-emotional climate of the classroom and reflect the norms that enhance positive relations among students. These two elements of the teacher–class relational context, teacher connections with the class as a whole and peer relationships, are depicted in our model (see Figure 11.1). It is our contention that a

Factors Influencing Teacher Interventions in Bullying Situations 227 supportive classroom environment characterized by caring teacher and peer relationships, responsiveness to student needs, and authoritative teacher guidance (Hughes, 2002; Wentzel, 2002) will provide a context in which effective anti-bullying interventions can occur. Furthermore, when class-wide interventions incorporate these elements and are implemented within the context of a school-wide bullying prevention program, reductions in bullying are evident (Frey et al., 2005). Peer Relationships. Peers play an active role in assuaging and aggravating bullying. An observational study of elementary students in class indicates that peers are involved, either actively or passively, in 85% of bullying episodes (Atlas & Pepler, 1998). Peers can reject or support targets (Malecki & Demaray, 2004; Salmivalli, Lagerspetz, Bjorkqvist, Osterman, & Kaukialnen, 1996). Although chronic targets are frequently lonely and isolated by their peers (Boivin, Hymel, & Hodges, 2001; Salmivalli et al., 1996), some targets maintain their friends and extract themselves from bullying involvement (Boulton et al., 1999). Furthermore, bullying behavior can be rewarded or rebuffed by peers (Hanish et al., 2004; Pellegrini, 2002; Salmivalli et al., 1996). Socially savvy adolescents often engage in bullying to attain or maintain high peer status (Nishina, 2004; Pellegrini, 2002; Rodkin, 2004). The peer group often assists bullies, inadvertently or consciously, in accruing resources and power. In contrast, younger students often isolate aggressive peers (Hanish et al., 2004), particularly in classes that have established prosocial norms (Henry et al., 2000). Student perceptions of and experiences in peer contexts influence behavioral outcomes associated with bullying. Bullying behavior occurs more often in classes where aggression is accepted as the norm. The prevalence of argumentative behavior is associated with increases in peer bullying (Frey & Nolen, in press). Class-wide endorsements of pro-violence attitudes (e.g., “Sometimes it’s OK to beat people up”) predict high levels of aggressive behavior among adolescents (Bernburg & Thorlindsson, 2005) and preadolescents, even when accounting for prior behavior (Huesmann & Guerra, 1997). In contrast, class-wide student norms against bullying may deter bullying behavior. A scenario-based study of late elementary Finnish students found that high support for group anti-bullying norms predicted peer defense of targets (Salmivalli & Voeten, 2004). Furthermore, student-reported higher levels of peer inclusion predicted lower rates of class-wide aggression (Doll et al., 2004). Finally, students in classes characterized by high levels of prosocial behavior between classmates displayed increased levels of social competence (Hoglund & Leadbeater, 2004). These results suggest that the classroom peer ecology impacts how individual students behave toward each other, for better or for worse. Teacher Influence in the Classroom Context. Teachers appear to deter bullying and promote socially responsible behavior when they promote positive peer relationships (Boulton et al., 1999; Doll et al., 2004), respond to student needs via monitoring and skill instruction (Frey et al., 2005; Olweus, 1993), and provide authoritative guidance (Hughes, 2002; Wentzel, 2002) through norm-setting. Positive peer relationships may be facilitated by arranging group partners and seating to encourage friendships with the target (Doll et al., 2004). When teacher appraisals of students and class participation structures are altered, there is an increased likelihood of inclusion for all students (Cazden, 1993; Cohan & Lotan, 1995; Olweus, 1993). Teachers may also promote positive peer relationships by engaging in caring behavior in their interactions with students class-wide (Hughes et al., 2006). Teachers can be responsive to student needs by monitoring peer interactions and responding immediately to bullying situations (Olweus, 1993). Furthermore, teachers may address skill deficits via class-wide instruction. Regularly scheduled class meetings can provide a forum for building social competence by teaching all students the skills necessary to thwart bullying such as assertiveness and friendship-building (Frey et al., 2005). Some researchers

228 Jodi Burrus Newman et al. have argued that instructing socially savvy bullies in social skills as part of class-wide curriculum may create more effective bullies (Sutton et al., 1999). An alternative perspective is that some students who bully have leadership skills and need to be taught more mature and respectful means of influence. In addition, targets who engage in reactive aggression (Schwartz et al., 2001) may benefit from explicit instruction in emotion regulation (Wilton et al., 2000). Class-wide interventions that provide bystanders and targets with the social-emotional skills to handle bullying situations assertively have been shown to be effective in reducing bullying behavior (Frey et al., 2005; Leadbeater & Hoglund, 2006), when taught in conjunction with school-wide bullying prevention programs. Teachers provide authoritative guidance when they lead students in developing class norms against bullying and for social responsibility. Henry et al. (2000) found that classrooms where both the teacher and students explicitly stated prosocial norms showed the greatest reductions in aggressive behavior. The teacher’s role in establishing these norms and maintaining them is critical. First, positive student-teacher relationships are associated with greater cooperation on the part of students (Wentzel, 1997). Second, teachers must consistently monitor and enforce class norms. When students are targeted despite being in classrooms where most interactions are prosocial, the negative emotional effects may be worse than when students are targeted in classrooms where harassment is normative (Bellmore, Witkow, Graham, & Juvonen, 2004). Steps to Providing Support for the Entire Class. The Steps to Respect program provides educators with guidance and tools for fostering a supportive classroom community by providing a class curriculum and guidelines for teacher-class interactions. Steps to Respect responds to student needs for social-emotional skill competence by teaching friendship-building, assertiveness, emotion regulation, and constructive problem-solving. When social problems arise, teachers are encouraged to draw upon the skills taught in previous lessons and facilitate student use of the concepts in actual bullying situations. The classroom lessons, especially ones on friendship and constructive problem-solving, are designed to promote caring and positive relationships among students. Finally, authoritative guidance is promoted in that prosocial norms for behavior are explicitly defined, discussed, and enforced. Adherence to the Steps to Respect curriculum has been shown to increase teacher ratings of students’ prosocial skills (Hirschstein et al., 2007). Summary. Teachers who support student needs and promote positive peer relationships so that the classroom becomes an inclusive community rather than a status-driven hierarchy are likely to observe positive social, emotional, and academic outcomes (Cohan & Lotan, 1995; Demaray, Malecki, Davidson, Hodgson, & Rebus, 2005; Doll et al., 2004; Hamre & Pianta, 2005; Hughes & Barrois, this volume; Hughes et al., 2006; Juvonen, Le, Kaganoff, Augustine, & Constant, 2004; Patrick, Ryan, & Kaplan, 2007). Furthermore, the skills gained via classwide interventions may serve to deter bullying behavior by teaching targets and bystanders how to assertively end a bullying episode (Frey et al., 2005), thereby transforming the classroom exchanges. Further research is needed to explain the mechanisms underlying how supportive teacherclass interactions, such as norm-setting and friendship-building, influence bullying behavior, especially among middle school students. Young adolescents experience the highest levels of bullying (Nansel et al., 2001) and are also likely to reward bullying behavior by bestowing high status on those who employ bullying tactics (Pellegrini, 2002). Longitudinal research is needed to analyze how individual teacher and student characteristics interact within classroom and school contexts to bolster or attenuate bullying behavior among adolescents. Maintaining a positive relationship with each student may not be sufficient to thwart bullying. Teachers need to be aware of and effectively manage peer relationships within the

Factors Influencing Teacher Interventions in Bullying Situations 229 classroom and ensure that students have the skills to competently rebuke bullying behavior. Although the idea that a supportive classroom context is foundational for academic learning has been put forth and endorsed by master educators (Paley, 1992) and has received empirical support in the research literature (Birch & Ladd, 1996; Juvonen et al., 2004), some teachers fear that intervening will require too much time and detract from academic subjects (Limber, 2004). These concerns can be addressed by providing teachers with accurate information and implementation support. The School Community Relational Context Teachers and students operate within a school community that helps determine the likelihood of intervention and the effectiveness of these efforts. School-wide bullying prevention programs may incorporate the intervention strategies described above such as individual skills training and establishing prosocial classroom norms (e.g., Frey et al., 2005; Olweus, 1993; Leadbeater & Hoglund, 2006). In addition, they tend to include teacher training, adoption of school-wide policies, and agreement among staff members to implement the program consistently. These activities that occur throughout the school are represented in our model as the outermost circle labeled “School Community” (see Figure 11.1). Consistency in expectations across all domains of school life, from the classroom to the playground, is what sets school-wide programs apart from smaller-scale interventions. A child who learns to respond to bullying with a strong and calm voice can expect that most other students from different classes will abide by the same norms when they are together in the lunch room or on the bus. Thus, instead of having widely divergent sets of norms that students must navigate, the entire school community is working together to support students and reduce bullying behavior. For these reasons, researchers endorse the school-wide approach to bullying intervention (Hirschstein et al., 2007; Greenberg et al., 2003; Olweus, 1993). Some educators point out that considerable effort can go into implementing and sustaining a school-wide bullying prevention program (Limber, 2004). Others argue that without a school-wide intervention more effort is required because of the repeated disruptions that detract from academic work. In any case, it can be difficult for educators to maintain their motivation for using a school-wide bullying prevention program if benefits are not immediately visible. An evaluation of the Steps to Respect program found that many of the positive program effects were not apparent until 18 months after the program began (Frey et al., in press). Successful implementation of a school-wide bullying prevention program may require that members of the school community make a long-term commitment to intervention and support one another over time. Supportive School-Wide Interactions. Teachers need the support of their principal and colleagues in order to make bullying prevention a priority and sustain quality implementation over time (Kam, Greenberg, & Walls, 2003). Supportive interactions in the school community context entail responsively meeting teachers’ needs for assistance with the implementation process, fostering positive relationships among school staff, and monitoring progress toward program goals (Smith et al., 2004; Knapp, Swinnerton, Copland, & Monpas-Huber, 2006; Payne, Gottfredson, & Gottfredson, 2006). Some teachers are concerned that they lack the skills to perform interventions effectively (Mishna et al., 2005). These concerns reflect needs for professional development activities that teach intervention skills, raise self-efficacy, and demonstrate how to integrate the program into daily activities. Both teacher training and program support from school administrators have been shown to positively impact the degree to which prevention programs are

230 Jodi Burrus Newman et al. incorporated into existing routines (Payne et al., 2006). Data specifically related to bullying intervention practice indicate that teacher training can help raise self-efficacy (Hirschstein & Frey, 2005; Newman-Carlson & Horne, 2004), and that teachers with high self-efficacy are more likely to take action when confronted with a bullying episode (Bradshaw et al., 2007; Yoon, 2004). Positive relationships among teachers are instrumental in facilitating improved student outcomes and transformative changes in schools (Lee & Smith, 1996; Louis, Marks, & Kruse, 1996). Teachers who work cooperatively to enhance their teaching practice and the educational experience of students create the type of community that is credited with fostering student success (Louis et al., 1996). Communication among teachers and administrators can facilitate program implementation (Payne et al., 2006). Kallestead and Olweus (2003) found that school-wide levels of teacher openness to communication supported the implementation of the Olweus bullying prevention program. Communication regarding the school’s progress toward meeting program goals may be especially critical. A review of bullying prevention program implementation studies found that monitoring outcomes greatly enhanced program effectiveness (Smith et al., 2004). This finding makes sense in light of the extensive research on data-informed decision-making for school reform (Knapp et al., 2006). Reform efforts are more effective when a culture of inquiry is nurtured and data are used to inform decisions regarding teaching practice and program improvement (Copland, 2003). With respect to bullying programs, a 10-step rubric for data-informed decision-making is outlined by Swearer and Espelage (2004). The process includes conducting a multi-informant baseline assessment of bullying, using the data to develop context-specific bullying interventions, sharing the data with key constituencies such as parents and staff, and continually collecting data to refine intervention efforts. Bullying intervention presents a unique challenge because the greatest change is likely to be out of sight in halls and playgrounds and some benefits may take more than a year to detect. This may be frustrating, as educators need verification that their efforts are fruitful. Furthermore, one of the easiest monitoring methods, collecting self-reports of victimization, may be unlikely to detect positive results. Evaluations of the Steps to Respect program found that observed rates of bullying and victimization decreased over the course of 18 months, while self-reports did not change (Frey et al., in press). Therefore, educators should be encouraged to look at alternative outcomes (Leff, Power, & Goldstein, 2004). For example, one school asked students: “What can the adults do to make bullying happen less often?” Student comments (e.g., “If someone tells you that they are being bullied, listen and pay attention! Don’t just tell the bully to stop, make sure they stop, and check-in on the person who was being bullied to make sure it stopped!”) suggested that teachers and administrators were not sufficiently responsive and that there was insufficient follow-up on cases. The school staff then used the student feedback to refine their efforts (Frey & Newman, 2008). Such monitoring strategies may help schools persist through the implementation process and sustain the program over time. Overall, the literature indicates that supportive relationships among educators, which generate a collective commitment to improving school programs and teaching practices, are associated with effective program outcomes. Furthermore, program monitoring may be essential to the success of anti-bullying programs (Smith et al, 2004), and should provide educators with ideas for how to refine their intervention practice (Frey & Newman, 2008). Steps to Supporting Educators. The Steps to Respect bullying prevention program was designed with implementation and sustainability in mind. The program meets teachers’ needs by providing training and a thorough manual. Furthermore, the program comes with evalua-

Factors Influencing Teacher Interventions in Bullying Situations 231 tion tools and examples of effective school-wide procedures. When combined with supportive school leadership, these resources help teachers implement the program with fidelity. Summary. Research demonstrates that bullying behavior can harm targets and perpetrators. It also shows that educators can successfully intervene to support students and prevent bullying, particularly with the use of school-wide interventions (Greenberg et al., 2003). Supportive leadership and program monitoring may help to ensure high-quality implementation. Additional research is needed to determine how teachers can be better supported by the school community in their intervention efforts.

Implications and Ideas for Further Research Bullying is a pernicious problem that, if ignored, can lead to detrimental student outcomes. Teacher interventions have the potential to reduce bullying and assuage its negative effects. This chapter has demonstrated how the individual characteristics and practices of teachers interact with the teacher–student, classroom, and school community relational contexts to affect bullying dynamics and outcomes. Over time, being responsive to the needs of all members of the school community, fostering caring relationships, and providing authoritative guidance may improve intervention efforts. The practices described in this chapter such as coaching, monitoring, skill instruction, and prosocial norm development may promote supportive relationships and deter bullying behavior when implemented in the context of a school-wide program. Throughout this chapter we have emphasized topics that are deserving of further research. In our opinion, a few areas are most pressing. First, mechanisms describing how classroom cultures shift from being bully-tolerant to bully-resistant are clearly needed. The literature points to some factors that influence this shift, but research is still in its infancy. Second, our knowledge of factors related to the effectiveness of specific teacher intervention practices is limited. Specifically, more research is necessary regarding teacher empathy and efficacy. Furthermore, it is time to move beyond assessing the likelihood of teacher intervention toward an understanding of the factors that improve actual intervention practice. Finally, more work is required to identify the needs of teachers as they attempt to support students and reduce bullying at their schools. It is vital that researchers continue to support educators in these efforts because positive relationships among students and teachers within the classroom context, and among educators themselves, serve as the foundation for student learning and well-being.

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12 Development, Evaluation, and Diffusion of a National AntiBullying Program, KiVa Christina Salmivalli and Antti Kärnä, Department of Psychology, University of Turku Elisa Poskiparta, Centre for Learning Research, University of Turku Bullying is a specific subtype of aggressive behavior, which has at least three universally accepted defining characteristics: (1) intent to harm, (2) repetition over time, and (3) a power differential, that is, the victim finds it difficult to defend him- or herself against the perpetrator. One of the key features that differentiate bullying from conflicts, quarrels, or fights, is thus the imbalance of power between the perpetrator and the victim. On average, around 11% of school-aged children are bullied repeatedly (Craig & Harel, 2004), while bullies represent another 11%. Approximately 4% to 6 % of children can be classified as bully-victims who both bully others and are bullied themselves (Haynie et al., 2001; Nansel et al., 2001). Research has shown that victimization has several negative consequences both in the short and in the long term (e.g., Hawker & Boulton, 2000; Isaacs, Hodges, & Salmivalli, 2008; Olweus, 1994; Salmivalli & Isaacs, 2005), and it is widely recognized as a serious problem that needs to be tackled through effective prevention and intervention. After the first large-scale intervention study in the 1980s by Dan Olweus in Norway (Olweus, 1994), an increasing number of intervention programs has been developed and evaluated in different countries. Unfortunately, the results have been much more modest than was hoped for (Smith, Pepler, & Rigby, 2004; Smith, Schneider, Smith, & Ananiadou, 2004). In Finland, the Ministry of Education has been funding the development and evaluation of a national anti-bullying program since 2006. The program, named KiVa (an acronym for Kiusaamista Vastaan, against bullying), has been developed and initially evaluated in the University of Turku, in collaboration between the Department of Psychology and the Centre for Learning Research. The KiVa project is co-led by the first and the third authors, and the evaluation of program effectiveness is the topic of the PhD of the second author. We have just renewed our contract with the Ministry to include also the initial phase of program diffusion. In the present chapter, we describe the theoretical basis and the main contents of KiVa and present our large-scale study to evaluate its effectiveness, along with the plan regarding the diffusion of KiVa for national use.

Background of KiVa The KiVa program is based on our group’s long research tradition on bullying as a group phenomenon. In the early 1990s, we started to examine how children who are neither victims nor bullies behave when witnessing bullying, and how their actions (or lack of them) might

Development of a National Anti-Bullying Program, KiVa 239 influence the maintenance of bullying or the feelings and adjustment of the victim. In the early studies, we identified six different participant roles children may have in bullying situations. To put it shortly, a child might take on the role (or be forced into a role) of a victim, a bully, an assistant of the bully, a reinforcer of the bully, an outsider, or a defender of the victim (Salmivalli, Lagerspetz, Björkqvist, Österman, & Kaukiainen, 2006). Since then, numerous studies have shown that rather than supporting the victim, many children act in ways that encourage and maintain bullying (Andreou & Metallidou, 2004; Camodeca & Goossens, 2005; Goossens, Olthof & Dekker, 2006; Menesini, Codecasa, Benelli, & Cowie, 2003; Salmivalli, Kaukiainen, Kaistaniemi & Lagerspetz, 1999; Salmivalli, Lappalainen & Lagerspetz, 1998; Salmivalli & Voeten, 2004; Schäfer & Korn, 2004; Sutton & Smith, 1999). The behavior of onlookers does matter. The victims who have classmates supporting and defending them are better off than victims without defenders. The defended victims are less depressed and anxious, they have a higher self-esteem, and they are less rejected by peers than victims without defenders (Sainio, Veenstra, Huitsing, & Salmivalli, submitted). Furthermore, it has been shown that in classrooms where reinforcing the bully is normative behavior (i.e., typical of many children), the likelihood of victimization is higher (Kärnä, Salmivalli, & Poskiparta, 2008). In addition, Kärnä, Voeten, Poskiparta, and Salmivalli (2010) have shown that individual-level risk factors, such as anxiety and peer rejection, are more likely to lead to victimization in classrooms where reinforcing is common, whereas a high level of defending in a classroom minimizes the effects of such individual risk factors. An important message from these studies is that to reduce victimization, we do not necessarily need to change the victims and make them “less vulnerable.” The behavior of the aggressive bullies, on the other hand, might be difficult to change directly, if the peer context is ignored. Influencing the behaviors of classmates can reduce the rewards gained by the bullies and consequently, their motivation to bully in the first place. Although individual characteristics of children, such as attitudes, self-efficacy, and empathy towards victims, predict variation in the participant roles they take on (Caravita, DiBlasio, & Salmivalli, 2008; Pöyhönen & Salmivalli, 2008; Salmivalli, Kaukiainen, Kaistaniemi, & Lagerspetz, 1999), classroom context seems to matter as well. Classrooms differ from each other in the levels of reinforcing the bully or defending the victim (Salmivalli & Voeten, 2004). In a recent study, we found that the proportions of variance at the classroom level, so-called intra-class correlations, for reinforcing and defending were as high as .19 and .35, respectively (Kärnä et al., 2010). This means that although two-thirds (65%) of the total variation in defending behavior, for instance, is due to individual differences, the rest (35%) is due to differences between different classrooms and (to a small extent) schools. There is something in individual children, but also something in the classroom context that drives the behavior of children in bullying incidents. In the KiVa program, there is a strong emphasis on influencing the onlookers, who are neither bullies nor victims, to make them show that they are against bullying and to make them support the victim, rather than encourage the bully. Toward this end, we have developed several universal actions, such as student lessons, and an anti-bullying computer game. However, we believe that indicated actions are also needed to tackle the actual cases of bullying that come to light. The indicated actions include individual and small group discussions with the bullies, victims, and prosocial classmates who are challenged to support the victim in the future. These discussions are effectuated by so-called school teams, together with classroom teachers. The schools are provided with support both before and during the implementation of the program.

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Contents of KiVa: Universal Actions Student Lessons The KiVa program has three different developmentally appropriate versions for grade levels 1–3, 4–6, and 7–9. In Finland, children start school at the age of seven, and grades 1–6 represent the primary school years whereas grades 7–9 constitute the secondary school in the basic education. In both primary school versions (grades 1–3 and 4–6), the universal actions of the KiVa program include 20 hours of student lessons (10 double lessons) that are given during one school year. The lessons are carried out by the classroom teachers and they involve discussion, group work, short films about bullying, and role-play exercises. The topics of the lessons proceed from more general topics, such as the importance of respect in relationships and group communication and group pressure, to bullying and its mechanisms and consequences. For instance, in the short films that are viewed and discussed together adults who were bullied as children tell about their schooldays and how their experiences have affected their life even decades later. Several lessons concern the role of the group in either maintaining bullying or putting an end to it. The group exercises involve, among other things, brainstorming different ways to support and help the bullied victims, and practicing these ways. As the lessons proceed, class rules are adopted one by one. Each rule is based on the central topic of the corresponding lesson(s). At the end of the school year the class rules are put together as the KiVa contract, which is signed by everyone. In the secondary school (grades 7–9), the program involves four central themes. The program manual gives recommendations regarding the time to be used for working on each theme, but the schools themselves decide how they organize the school year around the themes (as a sequence of lessons, several theme days, and so on). The central aims of the student lessons (as well as the themes) are: (a) to raise awareness of the role that group plays in maintaining bullying; (b) to increase empathy towards victims; (c) to promote children’s strategies of supporting the victim and thus their self-efficacy to do so; and (d) to increase children’s coping skills when victimized. Essentially, the lessons try to help children resolve the social dilemma they are faced with, that is, doing what they know is right or doing what seems to be normative in the group. The Anti-Bullying Computer Game A unique feature of the KiVa program is the anti-bullying computer game (Figure 12.1) included in the primary school versions of the program. The students play the game during and between the lessons described above. The game involves five levels, each of which is played as soon as particular lessons have been given in the classroom. Additionally, students who have internet access can play the game in their free time at home as well. In the version of the KiVa game created for grades 4–6, each level includes three components that have been named I KNOW, I CAN, and I DO. In the “I Know” component, the students learn new facts about bullying but also examine what they have learnt from the lessons thus far. They are asked questions about the content of the lessons in game-like tasks, and they can test themselves with respect to different characteristics (e.g., how well you can resist group pressure; what kind of classmate you are). In the “I Can” component, the students move around in a virtual school and face different challenging situations in the school corridors, playgrounds, and lunchrooms. They make decisions regarding how to respond in these situations, what to say and what to do, and get

Development of a National Anti-Bullying Program, KiVa 241

Figure 12.1 A Scene from the KiVa Computer Game (“I Can” Component), Version for Grade Levels 4–6.

feedback based on the choices they make. They can also examine the feelings and thoughts of other characters in the game before and after their own actions. The third component, “I Do,” is designed to encourage students to make use of their knowledge and skills in real-life situations. This happens by asking them to report—at each level of the game—which ones of the learned skills they have put into practice—for instance, whether they have treated others with respect, whether they have resisted group pressure, or whether they have supported someone who was victimized. Again, the students get feedback based on their reports. For secondary school students, there is a different virtual learning environment called KiVa Street. It is an internet forum where the students sign in and go around, visiting different places. For instance, they can go to a library and find information about bullying, or they can enter a movie theatre and watch short films about bullying. Similar to the computer game, the KiVa Street aims at providing knowledge, skills, and motivation to change one’s own behavior related to bullying. Involving Both School and Parents To prevent bullying in the schoolyard, we provide vests for teachers supervising the recess, in order to enhance their visibility and to signal that bullying is taken seriously in the school. There are other symbols, such as posters, reminding the students and the school personnel about KiVa. We provide schools with PowerPoint slides they can use when introducing the program for the whole personnel. The parents are also involved in the universal part of the program. A parents’ guide is sent to each home that includes information about bullying and advice concerning ways in which the parents can prevent and reduce the problem.

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The Indicated Actions: Tackling the Acute Cases To effectuate the indicated actions of the KiVa intervention program, there is a team of three teachers (or other school personnel) in each participating school that tackles, together with the classroom teacher, the cases of bullying that come to light. This happens through a set of individual and group discussions that one or two team members have with the victim and with the bullies, and systematic follow-up meetings. In addition to these discussions, the classroom teacher arranges a meeting with 2–4 classmates in order to encourage them to support the victimized child. The teacher manuals and the training provided (see below) include detailed information about how the discussions are carried through. The indicated actions of KiVa are similar in all grade levels. As bullying often occurs when adults are not around and thus remains unnoticed by teachers and parents, it is important to encourage the students to report bullying. We believe that when children are told about the KiVa program, early in the first student lesson, and reminded about it throughout the school year (by the lessons, the computer game, the posters hanging on the school wall, etc.) they come to believe that adults at the school take bullying seriously and the victims are likely to get help. Consequently, both victims as well as onlookers who merely observe bullying will be more likely to report it. Furthermore, both school staff and parents are given information about bullying and advice concerning how to detect it, which hopefully leads to more awareness and skill in identifying the bullying that is going on. The core components of KiVa (both universal and indicated actions) are described in Table 12.1.

Table 12.1 The Core Components (Universal and Indicated Actions) of the KiVa Anti-Bullying Program Universal

Indicated

Target

All children

Individual bully/ies and victim

+ Selected (prosocial, high-status) classmates

Aim

Reducing pro-bullying behaviors Increasing peer support for victims Influencing classroom norms BY – increasing awareness, empathy, and efficacy to intervene immediately

Stopping the ongoing bullying Supporting the victim

Increasing peer support for the victim

BY – making clear that bullying is not tolerated and has to stop

BY – using high-status peers as protective friends and as models for others

Means

Student lessons Anti-bullying computer game Parents’ guide

Individual and small group discussions Follow-up discussions

Small-group discussion

Agent

Classroom teacher

School team member

Classroom teacher

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Training Days and School Network Meetings During the evaluation phase of KiVa, the teachers and school teams from the pilot schools were supported in the implementation in several ways. There were two full days of face-to-face training organized in each province. Furthermore, networks of school teams were created, in which each network consisted of three school teams (i.e., nine teachers or other adults working in the school). The network members met three times during the school year, with one person working in the KiVa project serving as the “key person” and guiding the network. We have found the meetings to be highly beneficial for ourselves (the program developers) as well. We have gained first-hand experience of the successes and possible obstacles in program implementation. The experiences from the training as well as from school network meetings were utilized when preparing the final materials and training model for national diffusion.

Evaluation of KiVa The evaluation study of the KiVa program will provide novel information about the effects of anti-bullying actions. Previous studies of anti-bullying intervention programs (for reviews, see e.g., J. D. Smith, Schneider, Smith, & Ananiadou, 2004; P. K. Smith, Pepler, & Rigby, 2004) have been so-called black box evaluations (Chen, 2005; Rossi, Lipsey, & Freeman, 2004), in that they mainly address the relationship between the intervention and the outcome, ignoring research on underlying processes. Furthermore, the development of individual students’ bullying-related behaviours has not been followed over time. These limitations have left us with contradictory results concerning the effectiveness of anti-bullying interventions. Nevertheless, the overall picture has led Smith and colleagues (2004) to conclude that interventions can be successful, “but not enough is known to indicate exactly how and when.” The KiVa evaluation study overcomes many of the previous limitations by (a) measuring the hypothesized mediators and moderators of program effects and (b) following the development of individual students over time. Theory-based research on the processes underlying program effects (Chen, 2005; Rossi et al., 2004) can provide us with important knowledge about key factors in bullying problems. In addition, it can be assumed that the program effects vary across different students and school classes, and we can learn about these differential effects on the students’ and classes’ trajectories by following them longitudinally. In this way, the design enables to answer three major questions: 1 2 3

Is the program generally effective? For whom is it effective? How does the program produce its effects?

Answering these questions will address several important issues, both applied and theoretical ones. The applied research questions include whether the KiVa program is truly effective and thus ready for broad dissemination (Flay et al., 2005). In addition to knowledge about the program effectiveness, the implementers of the intervention also need to know how to implement the program in order to gain the best results. In the literature this has been referred to as the implementation fidelity-adaptation debate (e.g., Dusenbury, Brannigan, Falco, & Hansen, 2003). The main question in this debate is whether program adaptation increases or decreases program effectiveness. By using detailed measures of implementation and outcomes, the KiVa

244 Christina Salmivalli et al. evaluation study aims at providing an answer to this question for its part. In this way, the school staff can be given advice about the best practices of program implementation. Furthermore, it is being investigated which factors predict the implementation of the KiVa program. Knowledge of the predictors of implementation allows one to estimate what kind of staff members and schools are likely to implement the program to a large extent and are thereby more probable to benefit from it (e.g., Kallestad & Olweus, 2003). Conversely, by identifying schools likely to fall short of implementation, support can be tailored to the needs of the specific schools. This knowledge can then be used to make decisions about introducing the program and allocating resources to support its implementation. The more theoretical research questions of the evaluation also have practical relevance in that the results can be used to pinpoint the key factors in bullying problems, which can then become the targets of intervention measures. If the intervention succeeds in influencing its hypothesized mediators of effects, the associations between these mediators and outcomes can be assessed. This makes it possible to test theories about bullying problems. To put it briefly, it is hypothesized that the implemented intervention actions (at individual and classroom levels) influence social cognitions (at individual and classroom levels) which influence bullies’ and bystanders’ behaviour in bullying situations. Positive changes in these behaviors are expected to decrease victimization and its adverse consequences. Congruent with the view of bullying as a group phenomenon, the emphasis will be on group factors, such as classroom norms and bystanders’ behaviors (e.g., supporting and defending the victim). Thus, investigating the program processes allows many interesting hypotheses to be tested. A further advantage of this kind of theory-driven evaluation is that it increases the internal validity of research by examining whether the program effects unfold in the hypothesized ways (Chen, 2005; Rossi et al., 2004).

Sample All primary and secondary schools in the mainland of Finland were invited to participate in the evaluation of KiVa as either an experimental school or a control school. Nearly 300 schools announced their willingness to participate. Stratified random sampling was used to categorize 234 schools into 117 pilot and 117 control schools, representing all provinces of mainland Finland (Table 12.2). To ensure a good response rate to the questionnaire, schools providing only special education were excluded from the sample. In the first phase (2007–2008) the program was piloted in grades 4–6 in the experimental schools and in the second phase (2008–2009) in grades 1–3 and 7–9. In this manner, all grade levels in the Finnish comprehensive school system are included in the study. As the sample in the first phase consisted of over 7,000 students from over 400 classrooms in 78 schools, the total sample can be estimated to amount to about 1,200 classrooms and more than 20,000 students. In addition, altogether about 1,500 teachers and other participating school staff members will be asked to complete questionnaires concerning the program, themselves, their students, and their schools. Table 12.2 The Study Sample in the Two Phases of Program Evaluation 1 2 Altogether

2007–08 2008–09

4–6 1–3 7–9

39 39 39 117

39 39 39 117

Development of a National Anti-Bullying Program, KiVa 245

Design Two designs are used to evaluate the effects of the KiVa program: a randomized experimental design and a cohort-longitudinal design with adjacent cohorts. In the cohort-longitudinal design, post-test data from students in each cohort are compared with baseline data from same-aged students from the same schools (i.e., in the previous cohort), who have not yet been exposed to the intervention (Olweus & Alsaker, 1991; Salmivalli, Kaukiainen, & Voeten, 2005). There are two reasons for making these different kinds of comparisons. First, the designs complement each other in the sense that, for practical reasons, pre-test/post-test comparison is impossible in grade 1 and cohort-longitudinal design is not being used in grades 3 and 6. Second, the consistency of results from these two designs is an interesting topic in its own right. According to Rossi, Lipsey and Freeman (2004), there are few studies in which results of quasi-experiments are compared with those of randomized experiments. On the basis of the existing studies, it seems that quasi-experiments can yield estimates of program effects that are comparable to those of randomized designs, but they can also produce seriously erroneous results (Aiken, West, Schwalm, Carroll, & Hsiung, 1998; Fraker & Maynard, 1985; Heckman & Hotz, 1989; Heinsman & Shadish, 1996; LaLonde, 1986; Lipsey & Wilson, 1993). The adequacy of the cohort-longitudinal design for evaluating anti-bullying interventions is a particularly interesting issue. This is because the effectiveness evidence for the most widely-known anti-bullying program, the Olweus Bullying Prevention Program, consists mainly of studies using the cohort-longitudinal design (Olweus & Alsaker, 1991; Olweus, 2004; Olweus, 2005).

Measures Most of the data collection is done with internet-based questionnaires. The data includes self-, peer-, and teacher-reports regarding the students’ attributes and behaviors, as well as teachers’ and other school personnel’s reports of their own beliefs and actions. All study participants receive their own personal user ID, which they use as a password for the questionnaires, and so the individual participants can be identified at the various time points while they remain in practice anonymous to the researchers. Students give self- and peer-reports three times during the year. In addition, the bully-victim dyads will be identified by asking children questions such as: “Who do you bully?” or “Who are the ones that bully you?” This allows us to follow the changes that take place in particular bully-victim dyads and whole “bullying networks” of classrooms during the intervention. The follow-up procedure described above is a significant improvement compared with previous anti-bullying intervention studies, which can be demonstrated with an example of students’ outcome measures. The previous bullying intervention studies have often examined the prevalence of bullies and victims as their outcome variables. The designs have, however, not allowed examining whether possible decreases in bullying others are due to some of the bullies stopping bullying, or rather, some of the potential new bullies not starting to bully others in the first place. Similarly, we do not usually know whether decreases in victimization are caused by some of the victims escaping the victim role, or some of the potential victims not ending up as victims at all. Following particular individuals from pre- to post-test measurements will allow differentiation between these trajectories. Examining these alternative processes will provide important knowledge concerning the extent to which the effects of an anti-bullying program are preventive or interceptive.

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Diffusion of KiVa Diffusion has been defined as the process by which members of a social system learn about, decide about, and act on new ideas or practices (Rogers, 1995). The diffusion of the KiVa program for national use has its unique challenges. On the other hand, we have strong backing from both the Ministry of Education and the Finnish National Board of Education (FNBE). The latter organization (working under the auspices of the ministry) is the agency responsible for the development of education in Finland. It draws up national core curricula, which determine the core objectives, contents and guidelines for teaching in Finland. Education providers prepare their own local curricula based on these national documents. We collaborated closely with the FNBE during the development of KiVa, and there is one representative from FNBE in the steering group of the KiVa project. It has been ascertained, for instance, that the topics covered by our student lessons are in accordance with the national core curricula. This allows us to present the KiVa program to teachers and school principals as an effective tool for something they should be doing already, rather than as an extra burden to be carried in addition the to curricula. The Finnish Ministry of Education has provided the University of Turku with funding to initiate large-scale diffusion (i.e., for the first two years of the process), and our aim is to have the program adopted as widely as possible by Finnish comprehensive schools during this period. After that, further funding will be applied for future training, conference days, etc. related to the KiVa program. In the evaluation study, KiVa student lessons were being piloted in each grade level in the experimental schools. When the program is diffused more widely, the students in KiVa schools will have the student lessons three times during their basic education: the first time in grade 1 (program version for grades 1–3), the second time in grade 4 (program version for grades 4–6), and finally, at time of the transition to secondary school in grade 7 (program version for grades 7–9). Following the four-stage process of program diffusion (Rohrbach, D’Onofrio, Backer, & Montgomery, 1996) we will next describe the strategies to promote the (1) dissemination, (2) adoption, (3) implementation, and (4) maintenance of KiVa in Finnish schools (see also Table 12.3). Table 12.3 Diffusion of KiVa: Strategies to Promote the Dissemination, Adoption, Implementation, and Maintenance of the Program Dissemination Press conference Articles in national and local newspapers and in teachers’ journals Newsletters to all schools and municipalities Adoption Evidence of effectiveness Free material and training—low total costs Feasible, user-friendly and attractive materials Implementation Pre-implementation training Program manuals Implementation manual Maintenance Virtual training Internet-based discussion forum Tools to monitor own progress Biannual KiVa conference

Development of a National Anti-Bullying Program, KiVa 247

Dissemination: Raising Awareness of KiVa Among Finnish Schools and Municipalities The diffusion of the program started in the fall of 2008 in the form of a national information campaign. There was a press conference organized by the Ministry of Education, where the program developers presented the first findings regarding the effectiveness of KiVa to national media (at this point the highly promising findings regarding the effects in grades 4–6 are available, see Kärnä, Voelen, Little, Poskiparta, Kalijonen, & Salmivalli, in press). To maximize publicity, this event took place some days prior to the beginning of the school year, when the media is usually very interested in news related to school bullying. In addition, the program developers wrote short articles about KiVa for national and local newspapers as well as teachers’ journals. KiVa was also presented in several national conferences and meetings during the fall. After the press conference and national and local publicising, newsletters were sent to all Finnish schools giving comprehensive education (at that time, 3,226 schools). The newsletter includes a description of KiVa, including program content, the implementation process, and the total costs of implementation. Furthermore, the newsletter provided evidence of the effectiveness of KiVa and user experiences from selected pilot schools. Finally, the newsletter informed the schools about the opportunity to register as KiVa schools by the end of the year and start implementing KiVa next fall, in the beginning of the school year in August 2009. Similar newsletters were sent to the municipalities, which are the local authorities responsible for organising basic education for all children residing within their area. According to Rohrbach et al. (1996), program adoption at the administrative level may be a necessary (although not sufficient) condition for implementation. It was thus important to inform the municipalities about KiVa and encourage them to support registration by the schools in the district as KiVa schools.

Adoption: Encouraging the Schools to Make a Commitment Our aim was to have 600 new KiVa schools starting to implement the program in the fall of 2009 and another 600 schools a year later. Overall, we hoped to have 1,200 schools adopting the KiVa program during the first two years of diffusion. However, as many as 2,300 schools wanted to start implementing KiVa during that period, with 1,400 of them starting in 2009. Evidence of Effectiveness We believe that evidence of the program effectiveness encouraged Finnish schools to adopt KiVa. The Finnish Basic Education Act (since 1999) states that every student has the right to a safe school environment. Education providers have the responsibility of making sure that students do not experience acts of violence or bullying while at school. The legislation concerns all educational levels. It was further amended in 2003, stating that “the education provider shall draw up a plan, in connection with curriculum design, for safeguarding pupils against violence, bullying and harassment, execute the plan and supervise adherence to it and its implementation.” Tools to efficiently tackle bullying had been lacking, however, and, despite the legislative changes of the past decade, bullying and victimization had not decreased in Finland. Thus, efficient tools to fulfill the responsibilities defined by legislation were needed and had long been requested.

248 Christina Salmivalli et al. Free Material and Training—Low Total Costs In order to promote the adoption of KiVa, all materials included in the program as well as the staff training were provided for free for schools registering themselves as KiVa schools during the first two years of the national diffusion. These schools would start implementing KiVa either in the fall of 2009 or in the fall of 2010. Although schools need to take care of the substitute costs and travel costs (created, for instance, by the staff training days), the total costs of the program will be kept as low as possible even after the initial two-year period. Feasible, User-Friendly, and Attractive Materials It is important for program adoption that all materials are user-friendly, attractive, and presented in language and format that are convenient to practitioners. It has been found that in the early stages of decision-making about an innovation, teachers and other school staff might pay even more attention to the practical characteristics of the innovation than to evidence of its effectiveness (Rohrbach et al., 1996). Thus, from the very beginning of developing the KiVa materials, we have tried to ensure high quality and an appearance that is both affirming and appealing.

Supporting Implementation Pre-Implementation Training Program adoption does not guarantee successful implementation. Studies have shown, however, that implementation is a critical factor in the effectiveness of bullying interventions (Eslea & Smith, 1998; Olweus, 2004; Salmivalli, Kaukiainen, & Voeten, 2005). Pre-implementation training tends to increase program implementation (Connell, Turner, & Mason, 1985; Flay et al., 1987). In order to enable schools to implement the KiVa program with fidelity, their staff were provided with two full days of face-to-face training before the implementation. It was noticed during the evaluation phase of KiVa, for instance, that some teachers felt discomfort about the interactive methods such as drama and role-play included in the student lessons. Pre-implementation training helps to reduce this uncertainty. Overall, it is likely to increase the teachers’ feelings of efficacy toward program implementation. The training took place during the spring and early fall of 2009 in several provinces of Finland, based on the locations of the registered schools. With a pool of trained educators among the people working in the KiVa project, we organized 33 separate training days in the spring, and another 33 days in the fall of 2009 across the provimces. The content of the training was similar to what was provided to the pilot schools during the evaluation phase. The first training day was in April, followed by the second day in August, at the beginning of the school year. The first day concentrated on the universal actions of KiVa (e.g., student lessons), while the second focused more on indicated actions (e.g., resolving bullying cases which come to light). Program Manuals KiVa includes comprehensive and detailed program manuals, separately for grades 1–3, 4–6, and 7–9. The manuals describe the contents of the KiVa program (both universal and indicated actions) in such detail that they can be carried out with fidelity by teachers, even without

Development of a National Anti-Bullying Program, KiVa 249 the training. During the development of manuals, several teachers have been consulted regarding their structure and layout, in order to make sure that they are perceived as convenient and user-friendly by the implementers. In addition to the program manuals, all registered schools get an implementation manual which describes the core components of KiVa and helps to understand the program and the process of implementing it as a whole. This manual also gives guidelines for preparing to use the program.

Maintenance: Encouragement to Continue Using KiVa Maintenance of the program by the schools that have adopted it is a challenge of its own. After the initial interest in registered KiVa schools, it is highly likely that implementation will gradually decrease and its fidelity will suffer without special efforts to prevent that. Not only might the schools might gradually lose their interest as time passes since the training, but as a consequence of staff turnover, for instance, the new teachers or other personnel will not necessarily be aware of KiVa and its implementation. Over time, this increases the likelihood of dropping KiVa. Virtual Training We are currently constructing a virtual learning environment at our website (www. kivakoulu.fi) for schools registered as KiVa users. This learning environment will provide KiVa schools with virtual training regarding KiVa and its implementation. The virtual training can be used by new staff in schools already implementing KiVa, as well as the new schools adopting the program. The training contents are similar to those of the face-to-face training organized in the initial phase of diffusion. In addition to the virtual training, the website will also provide access to internet-based questionnaires, a discussion forum, and all curriculum materials along with the KiVa computer game. Furthermore, the website includes a discussion forum for the staff of registered KiVa schools. In this forum they can share experiences and discuss problematic issues. Tools to Monitor Implementation and Outcomes Importantly, KiVa schools have the ability to monitor both their implementation process and the outcomes obtained by using the program. They are provided with an evaluation tool including web-based questionnaires for students and staff. The questionnaires include some of the key measures utilized in the pilot phase of KiVa, now modified to meet the needs of a more applied setting. The implementers get immediate feedback on the prevalence of bullying and victimization in their school, which they can compare with the norm data collected during the evaluation period. They can also follow the implementation process itself, and make alterations to it when needed. The data collected in new KiVa schools will be available to program developers as well. This makes it possible to study the diffusion of KiVa in Finnish schools as well as the outcomes obtained at a national level. KiVa Conference Days Finally, the plan is to organize biannual KiVa conference days in different regions of Finland. Personnel from the registered KiVa schools can take part in these gatherings to hear about the

250 Christina Salmivalli et al. latest research on bullying, to share their experiences and present their obtained outcome, and to get face-to-face training (for instance, for teachers from KiVa schools who never attended the pre-implementation face-to-face-training). The first KiVa days take place in Turku in August 2010.

Conclusions and Future Challenges KiVa is a national anti-bullying program developed at the University of Turku and funded by the Finnish Ministry of Education. KiVa is strongly research-based, and it involves both universal and indicated measures to stop ongoing bullying, to prevent the emergence of new cases, and to ease the plight of the victims. The emphasis of the universal interventions lies in influencing all children’s reactions when witnessing bullying (such as providing safe strategies to support the victimized peer), whereas the aim of the indicated interventions is to stop ongoing bullying that has came to light. KiVa contains an exceptionally large set of concrete tools for educators, including a series of student lessons, a virtual learning environment, and guidelines for effective tackling of the identified cases of bullying. According to Flay et al. (2005), in order to be ready for broad dissemination a program should meet stringent criteria for effectiveness and be supported by relevant provider materials and by evidence that the program can be implemented with fidelity. The nationwide diffusion of KiVa started in a situation when we only had evidence of program effectiveness from three grade levels (grades 4–6, see Kärnä et al., in press). Our evaluation study will continue for quite a while before we have the results ready for each grade level and can make inferences about the mechanisms behind the changes accomplished. Besides utilizing our exceptional data set collected in the KiVa project, another research challenge will be to study the diffusion process itself, as well as the maintenance of KiVa in schools that have adopted it. The forthcoming years will show how the schools will maintain the implementation of KiVa and after the intial stages and whether the program effects will eventually be seen in the national prevalence rates of bullying and victimization in Finland.

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13 Promoting the Well-Being of School Communities A Systemic Approach Chryse Hatzichristou, Konstantina Lykitsakou, Aikaterini Lampropoulou, and Panayiota Dimitropoulou, University of Athens, Greece “I am indebted to my father for living, but to my teacher for living well.” —Alexander the Great, fourth century BC

In recent years, traditional school mental health services have proven to be inadequate to respond to the increased challenges present in the school environment. Several studies in different countries have emphasized that there is a growing number of children with unmet socioemotional needs and that the services provided to them are often inappropriate, insufficient, and poorly coordinated (e.g., health care, educational, juvenile justice and child welfare systems) (Hatzichristou, 2000; Koyanagi, 1995; Oakland & Jimerson, 2007; Pfeiffer & Reddy, 1998; Rog, 1995). As a result, efforts have been made to reform psychological services in order to achieve the optimum fit between children’s developmental needs and the structures, goals and services provided by the school (Baker, Dilly, Aupperlee, & Patil, 2003). The current theoretical models and practices emphasize the need for holistic approaches to service provision in the school context that focus on system interventions and enhance the collaboration among all members of the school community. In particular, recent emphasis has been given to the promotion of children’s well-being. The development and implementation of multi-level prevention programs also reflect these worldwide trends in school mental health. The growing body of research has emphasized a number of contextual and individual characteristics that promote school well-being such as school climate, resilience, social and emotional skills, and academic and psychosocial competencies. However, the field lacks comprehensive models that integrate these parameters within the school setting (Baker et al., 2003). In the following sections, we will: (a) describe the current trends in school mental health related to the promotion of school community well-being; (b) present core theoretical approaches that are prerequisites for the development of effective system interventions for the promotion of school well-being; (c) describe a paradigm of system intervention in the Greek and Cypriot educational and cultural settings; and (d) propose a new and broad approach to school community well-being.

Current Trends in Psychology and School Mental Health Positive Psychology One of the most recent approaches in psychology is positive psychology. Positive psychology is defined as the scientific study of ordinary human strengths and virtues (Sheldon & King,

254 Chryse Hatzichristou et al. 2001). It consists of three domains: positive subjective experiences, positive individual traits, and the institutions that promote these (Seligman & Csikszentmihalyi, 2000). The aim of positive psychology is to “catalyze a change from a preoccupation only with repairing the worst things in life to also building the best qualities in life” (Seligman, 2002, p.3) and to understand and foster the factors that allow individuals, communities and societies to flourish (Fredrickson, 2001). As is evident in both the definition and the goals of positive psychology, its fundamental contribution is the shift from clinical perspectives and illness models of human functioning, that prevailed until recently in psychology, to a mental health model. Traditional psychology emphasized deficits and “fixing” the problem, which was typically identified within the individual. According to illness models, the absence of problems is sufficient for considering an individual to be well-adjusted. Within positive psychology, on the other hand, the mere absence of a mental illness does not necessarily imply well-being. As noted by many positive psychologists, “efforts to improve children’s lives must also focus on developing strengths, facilitating positive responses to adversity and strengthening the important institutions in children’s lives” (Huebner, Suldo, Smith, & McKnight, 2004, p. 81). Therefore, concepts such as resiliency, hope, love, optimism, and forgiveness are important research topics of positive psychology because these represent means for achieving wellness (Snyder & Lopez, 2001). One of the most important concepts in positive psychology is subjective well-being, that is, how and why people experience their lives in positive ways (Gilman & Huebner, 2003). This concept has become an area of increasing interest among researchers who investigate the degree to which positive subjective experiences of individuals lead to a higher sense of wellbeing. Subjective well-being is the basic indicator for evaluating individuals’ wellness and is distinguished from objective indicators such as income level. Subjective well-being is regulated by internal mechanisms and is considered a basic prerequisite for the promotion of people’s mental health (Diener, Lucas, & Oishi, 2002). A number of models and theoretical approaches have been developed to study, explain and understand the structure and correlates of subjective well-being. The most commonly accepted model conceptualizes subjective well-being as a synthesis of two independent components: emotional and cognitive. The emotional component includes the long-term frequency of positive and negative affect while the cognitive component includes the cognitive appraisal of life satisfaction at a cognitive level (Diener & Diener, 1998). According to this perspective, people with a high level of subjective well-being experience positive feelings such as joy or happiness most of the time and evaluate their lives in positive and satisfactory ways. Regardless of the theoretical perspective, research examining subjective well-being initially focused on adults and the study of well-being from an individual-centered perspective. Gradually, positive psychologists also became interested in the subjective well-being of children and adolescents and focused on systems and the interactions that take place within these systems as well as on the individual. This led to further development in the field of well-being. New research efforts considered salient psychological environments as well as aspects of the individual. Schools, families and community were seen as the most important environments that could affect children’s subjective well-being (Marjoribanks, 2004). Most studies of the subjective well-being of children and adolescents examine characteristics of the systems that influence, directly or indirectly, their wellness. However, more recent efforts seek to extend the construct of subjective well-being by proposing more complicated conceptual frameworks or introducing dimensions of well-being such as family well-being, community well-being or school well-being (Bal, Crombez, Van Oost, & Debourdeaudhuij, 2003; Behnke & MacDermid, 2004; Konu, Lintonen, & Autio, 2002).

Promoting the Well-Being of School Communities 255 The aforementioned efforts, despite their differences, have established that ecological orientations and a systems focus are particularly important for the development of effective interventions that promote children’s mental health and well-being. This is particularly true for schools and has important implications for school mental health practice. Systems Interventions Systems interventions are change efforts that make a difference in how human organizations operate and have goals either to improve performance or to enhance the viability and resiliency of the system (Borgelt & Conoley, 1999). A number of possible strategies for developing efficient systems interventions have been proposed (Curtis & Stollar, 1995). However, while systems interventions are easily listed, they are difficult to construct for many reasons (Borgelt & Conoley, 1999). In order to plan systems interventions, a framework is necessary that describes well-functioning systems. Biologically-based systems theory, ecological psychology, family-systems theory, and organizational psychology have provided various conceptual frameworks for understanding the functions of systems. They have also provided important theoretical and practice information that is useful in designing and evaluating systems interventions. Their conceptual principles have been especially useful in designing systems interventions to use within school contexts, since schools are systems that play a significant role in children’s development (Fraser, 1995). Positive psychology perspectives propose approaches to the school system and to systems interventions that have a primary mission of promoting school community well-being. This is particularly important for today’s schools that, in all educational and cultural settings, are expected to serve a wider range of students’ diverse needs than they served in the past (Hatzichristou, Lampropoulou, & Lykitsakou, 2004). Interventions that promote well-being at a school must consider both the common and diverse needs of the school’s members in order to be effective. Culture is a common source of diversity described in the school mental health literature. The development of cultural awareness and understanding requires acknowledgement of similarities and differences between one’s own and other cultures, and it is critical for effective systems intervention (Hatzichristou, Lampropoulou, & Lykitsakou, 2006; Hatzichristou, Karadimas, et al., 2001). This is even more important in positive psychology, since beliefs about “good life” and high levels of subjective well-being might differ among cultures (Diener, Oishi, & Lucas, 2003). Although culture is one of the most prominent dimensions of diversity that has attracted the interest of school mental health researchers and practitioners, several other factors also have critical implications for systemic interventions. These diversity factors have been described in a synthetic approach by Hatzichristou, Lampropoulou, and Lykitsakou (2004). At the individual level, diversity is linked to the characteristics of the students (physical, cognitive, academic, personality, learning style, interests, etc.) and the teachers (personality, age, teaching experience, training, and teaching style). At the family level, differences are related to socioeconomic status, family structure and type, parenting styles, and parental involvement. Diversity regarding the school includes differences in school climate, communication, administrative organization, and community characteristics. Finally, at the level of service delivery, diversity refers to the implementation of different levels of prevention and intervention programs such as universal or selective intervention or the in-service training of school personnel. Through this particular perspective, diversity is viewed broadly and includes a variety of critical features that should be taken into account for developing systems interventions in the school environment.

256 Chryse Hatzichristou et al.

Systems theory System-level intervention

Positive psychology Well-being

SCHOOL COMMUNITY WELL-BEING

Resilience Effective schools

Social and emotional learning

Schools as caring communities

Figure 13.1 A Synthetic Approach to School Community Well-Being.

In the following section, basic theoretical components of the proposed conceptual approach to school community well-being are presented (see Figure 13.1). These theoretical components share common or complementary concepts and elements, even though they have often been considered to be distinct in prior literature. Therefore, this approach synthesizes these elements into a set of guidelines for the development of systems interventions in the school environment.

Prerequisites for Promoting School Well-Being Resilience Promoting school well-being is a difficult task because many critical features must be considered when developing interventions for the school environment. Some basic prerequisites that promote children’s well-being have been identified in the literature. Resilience is one of these prerequisites and refers to the process of positive adaptation under difficult and adverse circumstances (Masten, 2001). Building new strengths in individuals, families and communities has an important protective effect against life contingencies (Masten & Coatsworth, 1998). O’Dougherty Wright and Masten (2005) present a short list of the correlates of resilience that serve a protective function for children at various levels including the community level. Community characteristics that foster children’s resilience include high neighbourhood quality, employment opportunities, good public health care, access to emergency services, and multiple connections of children to caring adult mentors and prosocial peers. Longitudinal studies with high-risk children verify the protective function of supportive systems that counteract environmental adversities (Durlak, 1997; Weissberg & Greenberg, 1998). Schools and classrooms can also function as resilient communities that provide support and guidance for all children (Henderson & Milstein, 1996). Doll, Zucker and Brehm (2004) define resilient classrooms as those having the following characteristics: academic efficacy, academic self-determination, behavioral self-control, caring and authentic teacher–student

Promoting the Well-Being of School Communities 257 relationships, ongoing and rewarding relationships with classroom peers, and strong homeschool collaboration. They note that, since a classroom is a system, neither the child nor the classroom can change without changing the other and argue that “when the changes made by teachers, parents and students complement and support each other, that change can persist and have an enduring impact on the routines and practices of the classroom”(p. 4). Effective Schools The research on resilience also suggests that effective schools are an important protective factor and a prerequisite for promoting children’s well-being. There has been increasing interest in the characteristics of effective schools. Families are not the only ones to “blame” for children’s difficulties (or credit with their successes) and schools’ responsibility is also recognized. This new approach is based on the fundamental assumption that all children are able to benefit from education, including children with severe disabilities (Bickel, 1999; Bickel & Beaujean, 2005). Empirical findings suggest that effective schools have the following characteristics: (a) school climates that promote learning without violence or discipline problems; (b) teachers’ expectations that all students can learn; (c) emphases on basic skills training and more time spent by students on school studying; (d) clear educational goals that make it easier to follow and assess students’ performance; and (e) administrators with leadership skills who pose goals, preserve discipline, visit classrooms frequently, observe classroom processes, and create motives for learning (Bossert, 1985). In addition, the effective schools literature emphasizes understanding the individual needs of students, celebrating diversity as a strength, and creating the appropriate circumstances that promote learning and development (Creemers, Scheerens, & Reynolds, 2000). Schools as Caring Communities Another important factor that influences subjective well-being is the degree to which students and teachers feel their school is a community that cares for its members. The term community refers to a network of relationships connecting individuals and allowing them to form common values and ideals aiming to achieve an important, common goal (Sergiovanni, 1994). A caring community’s members care and support each other, are strongly involved in its activities and decisions, identify themselves with these, have a sense of belonging, and share common norms, goals and values (Goodenow, 1993a; 1993b; McMillan & Chavis, 1986; Solomon, Watson, Battistisch, Schaps, & Delucchi, 1992; Wehlage, Rutter, Smith, Lesko, & Fernandez, 1990). Schools that are considered to be “caring communities” place greater emphasis on relationships among school community members and the promotion of school climate, set concrete educational goals, emphasize skill development in various domains, set goals corresponding to children’s needs, support home-school-community partnerships, and focus on teachers’ helping skills. The importance of this emotional dimension of schools is evident from the fact that characteristics of resilient and effective schools are principally psychological dimensions that relate to emotions and relationships.

Social and Emotional Learning Social-emotional learning is another fundamental prerequisite for the well-being of the school community and is considered important to children’s positive adjustment. It has roots in a

258 Chryse Hatzichristou et al. progressive educational tradition as well as the primary prevention and social competence promotion literatures within psychology, and it is centered on promoting children’s social and emotional well-being (Kress & Elias, 2006). Emotional learning is very significant for children’s well-being. Most of the characteristics that have been used to describe schools that are resilient, effective, or caring communities refer to emotions and relationships among the members of the school community. The emphasis placed on emotional aspects of schooling is also evident in the increasing interest in school climate, school connectedness, and school bonding. The development and implementation of school-based interventions contribute significantly to the promotion of social and emotional learning and school well-being. Therefore, it is critical to develop empirically based interventions that promote children’s mental health and well-being. Recent emphases on resilience, effective schools, and social emotional learning are also reflected in changes in school psychological practice and service delivery in which these shift from the traditional tasks of assessment, classification and counseling to the provision of alternative psychological services (Hatzichristou, 2002). Emerging school mental health practices emphasize strengths, positive responses to adversity, and a focus on systems rather than individual interventions. These provide a new perspective that identifies the purpose of school mental health practice as strengthening the well-being of the whole school community. Resilient, effective schools that are caring communities and promote social and emotional learning are necessary for the promotion of students’ psychological wellness. The proposed model of school community well-being suggests that the aforementioned concepts are integrated in system-level interventions in order to enhance the positive contribution of each domain and provide an advanced synthetic effect on school community wellness. In the following section, a system intervention paradigm is presented that focuses on prevention, resiliency-building and enhancement of the community in the school.

A Systems Intervention Paradigm in the Greek Educational System An Alternative Model for the Provision of School Mental Health Services There is considerable variability among countries in the role and training of school psychologists, the types of school psychological services offered, and the utilization of services (Farrell, Jimerson, & Oakland, 2007; Hatzichristou, 2002; Oakland & Saigh, 1989). In particular, during the past few years, the field of psychology has rapidly expanded in Greece. However, the provision of school psychological services in mainstream public schools is limited despite the progress that has been made (Hatzichristou, Polychroni, & Georgouleas, 2007). This constituted a challenge to develop an alternative service delivery model that would address the growing and unmet needs of different populations of the Greek educational system (Hatzichristou, 1998; Hatzichristou, 2004b). The integrative framework that guided the development of this alternative service delivery model was proposed by Hatzichristou (1998). It synthesizes and expands the following principles: (a) the scientist-practitioner model for school mental health; (b) a systemic (i.e., social, cultural, ethnic, national, ecological) approach to assessment and intervention practices; (c) the evolving roles and functions of school psychologists in research, practice, and training; and (d) a systemic approach to professional development and identity of school psychologists. This integrative conceptual framework led to the development of a data-based model of alternative school mental health services that links theory, research and practice to provide an array of services including assessment, psychological consultation, prevention, intervention,

Promoting the Well-Being of School Communities 259 crisis counseling, research, training, supervision, management and advocacy (Hatzichristou, 2004a). The data-based model of alternative school psychological services was developed in four phases. The three first phases of the model documented the needs of Greek students, teachers and families, as well as their attitudes toward mental health services and professionals. In Phase 1, an empirical data base was developed to describe the profiles of school adjustment and performance of “average” Greek students. In Phase 2, the profiles of at-risk students with unmet needs were described, and in Phase 3, profiles were developed of the particular needs of specific school districts in communities where various intervention programs were being implemented. Throughout the years, each phase was enriched by new research domains and additional goals. In the fourth phase, empirical data derived from the first three phases of the model were integrated into a comprehensive prevention-consultation approach that led to the foundation of the Center for Research and Practice of School Psychology (CRPSP) in the Department of Psychology at the University of Athens. The main goals of the Center are: (a) to promote education, pre-service and in-service training for graduate students, school psychologists, teachers, and parents; (b) to foster University-schools-community service partnerships and collaboration; (c) to conduct scientific research and publish results; and (d) to develop, implement and evaluate multi-level interventions in the school community. One of the major goals of the CRPSP is the development, implementation and evaluation of universal and selective evidence-based interventions in the Greek schools. These interventions include: (a) programs for the promotion of mental health and learning (Hatzichristou, 2004c, 2004d); (b) inter-cultural programs for the support of immigrant and remigrant students (Giavrimis, Konstantinou, & Hatzichristou, 2003; Hatzichristou, Gari, et al., 2001); (c) programs for sex and health education; (d) programs for the inclusion of children with special educational needs in mainstream schools (Hatzichristou & Polychroni, 2007); and (e) crisis intervention programs (Hatzichristou, 2000). In addition, a cross-cultural program was implemented during the Olympic Games of 2004 in Athens, titled The Olympic Spirit Through Children’s Voice, with the purpose of capturing children’s and adolescents’ awareness concerning the Olympic values and ideals through art and literature works (Hatzichristou, 2004e). One of the main prevention programs that was developed by the scientific team of the CRPSP and implemented in public mainstream schools of Greece and Cyprus for many years was the Program for the Promotion of Mental Health and Learning: Social and Emotional Learning in Schools. In the following section, this program will be described in detail. Program for the Promotion of Mental Health and Learning (PPMHL) The Program for the Promotion of Mental Health and Learning (PPMHL) integrates empirical data with recent theoretical approaches in school mental health. The program’s particular characteristics include linking current relevant theory and research with practice, adjusting to the needs of students in the Greek cultural and educational setting, emphasizing “typical” instead of atypical behavior, and using multi-dimensional and multi-method assessment (evidence-based intervention). The program’s purpose is to promote children’s mental health and learning, and to create a positive climate in the school environment (Hatzichristou, 2004b, 2004c, 2004d). It consists of 10 thematic units: (i) communication skills; (ii) identification, expression, and dealing with feelings; (iii) self-concept and self-esteem; (iv) coping strategies; (v) conflict resolution; (vi) diversity in culture; (vii) diversity in individual, family and social characteristics; (viii) learning/study skills; (ix) social skills; (x) crisis intervention in the school community. For each thematic unit a specialized educational curriculum includes a literature

260 Chryse Hatzichristou et al. review, practical guidelines for implementing the program, and classroom activities with specific goals and methods for preschool, primary and secondary students. PPMHL Development: Levels of Intervention First Level. The first implementation of PPMHL used different models of intervention that corresponded to schools’ resources, the various needs of the students, and feedback from the evaluation of previous implementations of PPMHL. Initially, it was implemented by school psychologists who were members of the scientific team of the CRPSP. Then, during the following years, graduate students of school psychology and teachers were trained to implement PPMHL under the supervision of the CRPSP scientific team. The graduate students attended university courses on primary and secondary prevention, models of consultation, and promotion of children’s mental health in school environments. In addition, they received specialized training in the implementation of PPMHL in the classrooms and regular supervision from the CRPSP scientific team throughout the implementation. In its latest implementation in Greek and Cypriot elementary schools, the program was implemented by teachers who received specialized training by members of the CRPSP scientific team. In addition, a network of the participating schools was developed to facilitate sharing of ideas and experience and to disseminate the program to other teachers in the school community. At the end of the academic year, parents, students, and teachers participated in a closing ceremony in which PPMHL activities were described (Dimitropoulou, Lykitsakou, & Hatzichristou, 2005). Second Level. This last model for PPMHL implementation produced a greater impact than when it was implemented by graduate students or CRPSP researchers (Hatzichristou, et al., 2002), in part because the intervention became a “school matter.” In response, we searched for a new system-wide approach that would further maximize the effects of the program, entitled Program for the Promotion of School Community Well-being (PPSCW). PPSCW included two axes of intervention: (a) the implementation of PPMHL by teachers in their classrooms; and (b) the promotion of resilience of the school community and the creation of broader school networks. Elements of systems theory, schools as caring communities, resilience, and well-being provided the theoretical background of this project. To promote resilience in the schools, teachers were trained to use the problem-solving model that was presented at the Invitational Conference on the Future of School Psychology (Hatzichristou & Lampropoulou, 2004) in order to develop action plans for enhancing the sense of community in the schools. Teachers of each school evaluated critical domains of resilience (Henderson & Milstein, 1996), set priorities and goals, and planned to take specific actions in response to the particular needs of their school. Coordinators (educators and administrators) from each school participated in regular meetings with the scientific team of the CRPSP to share ideas about problems that emerged in the process, as well as possible solutions. To implement PPMHL in their classrooms: (a) a limited number of schools were selected to participate in the program; (b) all teachers and administrators in those schools received awareness-building workshops and special training on the promotion of school well-being (instead of selecting a limited number of participants from more schools); (c) teachers implemented the PPMHL program in the classrooms; (d) the PPMHL program was disseminated to the parents and teachers of other schools; (e) materials related to PPMHL were uploaded to the program internet site; and (f) various group projects were presented by students and teachers in a closing ceremony at the end of the program.

Promoting the Well-Being of School Communities 261 An important component of program design is program evaluation. The need for evidencebased interventions has been particularly emphasized during the last decade. Researchers have defined concepts, terms, and issues that are meaningful and realistic criteria for judging the efficacy of intervention and prevention programs (Kratochwill & Stoiber, 2002). The CRPSP developed a multi-level assessment model including process and outcome evaluation; evaluation by teachers and students; pre-, post-, and during the program assessment; and use of a control group. Data are collected by different instruments and methods (questionnaires, diaries and logs, personal reports from teachers) during PPMHL supervision, activities and work projects. These data constitute valuable feedback for program improvement and refinement. PPMHL Evaluation In the following section, results of the evaluation of two prevention programs implemented in Athens, Greece, and Nicosia, Cyprus, are presented. Both projects included intervention programs to promote mental health and learning through the enhancement of children’s social and emotional skills within a network of caring community schools. The target groups, goals, and content of the interventions varied in response to the particular needs of the participants and the goals of the program (see also Hatzichristou, Lampropoulou, & Lykitsakou, 2006). The Athens program was implemented in schools with a high percentage of migrant and immigrant students and particularly emphasized the promotion of intercultural understanding and communication. Therefore, the hypotheses tested in these evaluations were that prepost assessment comparisons would reveal significant benefits for students and teachers in domains corresponding to the specific aims of each intervention program. Evaluation designs differed in the two programs. In the Cypriot project, certain schools were participating in the program for the second consecutive year, whereas in the Athens program, the evaluation design included use of a student control group. Thus, program efficacy was also examined by comparing first-to-second years of intervention and experimental-to-control groups. Method Cyprus Participants. In the Cyprus program, matched data were collected for 66 teachers and 706 students of grades 4–6 in 18 mainstream elementary schools. Five hundred and twenty students (73.6%) participated in the program for the first time and 186 continued the intervention from the previous academic year. Athens Participants. In the Athens program, participants were 85 elementary and kindergarten teachers and 1,486 students of grades K–6. Because of the large number of participating students, a smaller sample of fourth- to sixth-graders from randomly selected schools and classrooms were included in the evaluation process. Paired data were recorded for 319 students of the experimental group and 197 students of the control group. Measures Student Instruments. All students completed the PPMHL student questionnaire before and after the intervention, and an additional final report of process evaluation. The PPMHL questionnaire was constructed by the CRPSP team and includes open-ended and Likert scale items that assess knowledge and attitudes about the topics discussed in the classroom during PPMHL. The Cypriot and Greek projects had slightly different content and goals, and

262 Chryse Hatzichristou et al. therefore the content of the questionnaires differed as well to match the respective topics. In addition, Cypriot students completed the Social Skills Rating System—Student Form for grades 3–6 (SSRS; Gresham & Elliott, 1990) to provide estimates of cooperation, self-control, assertion and empathy skills. A smaller sample of 252 of these students (n = 252) completed a sociometric survey to assess peer acceptance using statements of preference in questions such as “name 3 classmates that you would prefer to play with during recess/to sit next to in the classroom.” Also for these students, content analysis was conducted for the coding of qualitative data. The Athens questionnaire included additional questions to assess changes in attitudes and social interaction among students of different cultural groups. In this chapter, selected qualitative and quantitative results of program evaluations will be presented. Teacher Instruments. At the beginning and end of their training, teachers completed a PPMHL teacher questionnaire that was constructed by the CRPSP scientific team, including various qualitative and quantitative items assessing the PPMHL program and implementation procedures. In addition, teachers’ personal and professional efficacy, beliefs about the school climate, and educational goals and values were also assessed. Only results of Cypriot teachers’ pre- and post-assessment of school climate will be presented in this chapter. For the assessment of school climate the “School as a Caring Community Profile” (SCCP-II; Lickona & Davidson, 2001) was used. The SCCP-II evaluates the sense of community in the school along five Likert-type subscales: perceptions of student respect, perceptions of student friendship and belonging, perceptions of students’ shaping of the environment, perceptions of support and care by and for faculty/staff, and perceptions of support and care by and for parents. In addition, estimates of teachers’ efficacy and their perceptions of the PPMHL implementation procedure will be presented. Implementation and Assessment Procedure For both projects program planning begun with goal-setting, selection of participants (schools, teachers, and students) and decisions about the content of and timetables for the intervention. Next, teachers’ awareness workshops and training were conducted, including an initial two-day general awareness seminar for most teachers and school administrators, and a two-day special seminar for a selected group of teachers who would implement the program in the schools. After this initial training, teachers began the implementation of PPMHL with their students. During the intervention, four additional seminars were conducted for teachers (one session per month). All participants (teachers and students) received and elaborated the PPMHL educational material designed by the CRPSP scientific team (Hatzichristou et al., 2004). The Cypriot program included five thematic units: (a) communication skills; (b) identification, expression and dealing with feelings; (c) self-concept and self-esteem; (d) coping strategies; and (e) conflict resolution. The first three units were also included in the Athens project along with a fourth unit on issues of diversity and culture. Classroom intervention took place once a week on a designated day and during a designated academic period and included classroom discussions and other activities (e.g., group or individual activities, roleplaying, and games). In the first session, children discussed the program rules and signed a classroom “contract.” Then, three or four sessions were dedicated to each theme and a final session was spent on the program closing. The first thematic unit was Communication skills. The topics discussed in the class were: meaning, elements, means and types of communication, verbal and non-verbal communication, identification of non-verbal messages, and realization of others’ perspective (empathy).

Promoting the Well-Being of School Communities 263 The second thematic unit was Identification, expression and management of emotions. The goals of this unit are to help children enrich their emotional vocabulary, distinguish between pleasant and unpleasant feelings (not good or bad, positive or negative), realize that it is acceptable to have and express all of them, identify their own feelings as well as feelings of others through non-verbal elements, and realize and accept the diversity of own or others’ emotions in a certain situation. The goals of the third thematic unit, Self-concept and self-esteem, were to enhance self-image and self-esteem, to help children realize the dimensions of self-concept (personal, social, ideal) and the factors that influence it, value more important inner qualities, identify and accept their strengths and weaknesses, and feel more competent talking about or presenting themselves to a group of people. The goals of the Diversity and Culture Unit were to help students realize the various levels and positive aspects of diversity, understand the nature and consequences of stereotypes, familiarize themselves with other cultures, discover differences and similarities with their own, and interact better with their classmates from different cultural backgrounds. In addition to training and implementing the school program, teachers in Cyprus were assigned some additional tasks: (a) presenting the program to parents; (b) uploading material about the program to the PPMHL website; and (c) preparing a short multimedia presentation of PPMHL, as implemented in their schools, for other participating teachers, program coordinators, and education officials and administrators. The Athens teachers also made a multimedia presentation at a closing ceremony. The Athens event was open to a wider audience (teachers, parents, students, program coordinators, and other education stakeholders) who attended students’ and teachers’ creative projects inspired by the implementation of the program (e.g., PowerPoint presentations, theatrical play, songs, storytelling, pantomime, etc.). Teachers completed pre-PPMHL measures at the beginning of the initial seminar while students completed pre-program measures before the beginning of the intervention in the classroom. Respectively, post-PPMHL teacher measures were collected at the end of the training and the post-PPMHL student measures were collected at the end of the intervention in the schools. Results Student Reports. Content analysis, conducted for students’ responses to open questions on the PPMHL student questionnaire, identified a limited number of response categories for each question. Frequencies were then estimated for every category and pre-post changes in responses were tested with the McNemar test (suitable for two related dichotomous variables; detects changes using the chi-square distribution). For quantitative data pre-post comparisons of means were conducted using the paired samples t-test. For the assessment of the Communication Skills Unit a representative question was: How do we communicate with other people? As shown in Table 13.1, pre-post comparisons of children’s responses revealed significant changes consistent with the intervention goals: After the intervention, a significant increase was found for response categories “Verbally” [χ2(1) = 4.05, p = .000], “With technical means” [χ2(1) = 7.89, p = .000], and “Non-verbally” [χ2(1) = 17.82, p = .000]. A representative item for the unit describing the identification, expression, and management of emotions required that children write as many feelings words as they could think of. Two major categories of the responses were pleasant and unpleasant emotions and scores for

264 Chryse Hatzichristou et al. Table 13.1 Frequencies of Children’s Pre–Post Responses to Indicative Open Questions of the Thematic Units “Communication Skills” and “Self-Concept”

Response categoriesc

“How do we communicate with other people?” Pre Post χ2 % % (df = 1)

p

Verbally Technical means Relating, hanging out with others Good manners, positive feelings Non-verbally

12.3 45.6 16.7 12.7 5.6

.000 .000 .229 .212 .000

External appearance Interests, hobbies School performance General estimation of self Personality/character traits

24.2 64.7 12.7 16.7 20.2

4.05 7.89 3.46 8.28 17.82

“When I describe myself, I say that I am . . .” Pre Post χ2 % % (df = 1)

p

34.5 7.1 12.7 25.8 46.8

.000 .135 .169 .000 .000

48.8 11.5 17.1 41.7 73.0

11.04 0.67 7.76 14.23 17.82

c = Cypriot sample.

this question were the number of emotions that were mentioned. As shown in Table 13.2, there was a significant increase in the number of emotions mentioned after the intervention for both pleasant [t(251) = –3.11, p = .002] and unpleasant emotions [t(251) = –7.07, p = .000] for the Athens experimental group but not for the Athens control group. Similar findings were noted for the Cypriot sample. A significantly higher percentage of the students mentioned both pleasant [χ2(1) = 13.86, p = .003] and unpleasant emotions [χ2(1) = 11.36, p = .000] after the intervention. An interesting finding was that a significantly greater percentage of children with low peer acceptance (0–3 preferences from classmates) mentioned unpleasant emotions after the intervention, whereas no such change was noted for pleasant emotions. Similarly, for the Athens sample, immigrant students mentioned significantly more unpleasant emotions at the end of the intervention. Table 13.2 also presents the results of the pre-post comparisons for the Self-Concept and Self-Esteem question When I describe myself, I say that I am … and shows significant changes consistent with the intervention goals. Students reported significantly more traits of external appearance (tall, blond, blue eyes etc) [χ2(1) = 11.04, p = .000], descriptions of self [χ2(1) = 14.23, p = .000], and character [χ2(1) = 17.82, p = .000] after the intervention. For the last two thematic units of the Cypriot intervention program (Coping strategies and Conflict resolution) the analysis revealed non-significant trends towards positive change. As shown in Table 13.3, results were quite satisfactory for the items of the Diversity and Culture Unit in the Athens intercultural program, After the intervention, children reported that they were more willing to accept invitations of classmates from other countries to their homes [t(306) = –2.55, p = .011], to spend more time with them outside school [t(306) = –2.41, p = .016], to play together in the neighborhood [t(307) = –3.06, p = .002], and to help each other with homework [t(307) = –3.61, p = .000]. No significant changes were detected for the control group. Scale reliability for this set of items was α = .89.

Promoting the Well-Being of School Communities 265 Table 13.2 Pre–Post Comparisons of Students’ Responses to an Open Question of the Unit “Identification, Expression and Management of Emotions” for the Experimental and Control Group, for Migrant and Native Students (Athens Sample), and for Students with Low and High Peer Acceptance (Cypriot Sample) “Name as many feelings as you can think of” Experimental Groupg Control Groupg Pre

Post t

Response categories Pleasant Unpleasant

Ma 2.56 2.74

M 3.33 3.77

–5.12*** –5.44***

313 304

Pleasant Unpleasant

2.46 2.69

Post

–1.63 –2.07*

77 73

Post

2.49 2.28

Pre

Post

M

M

2.59 2.76

Low peer acceptancec (0–3 preferences from peers) Pre

M

df

M 2.90 3.35

M

t

df

0.00 –0.67

173 169

t

df

–4.87 –4.98

234 229

Native studentsg t

Ma

Post

2.49 2.18

Migrant studentsg Pre

Pre df

3.47 3.90

High peer acceptancec (4–7 preferences from peers)

χ2

Pre

Post

χ2

%

%

(df = 1)

34.8 21.4

47.3 47.3

12.49 8.94

p Pleasant Unpleasant

%

%

39.2 25.5

49.0 51.0

p

(df = 1) 1.59 2.32

.383 .007

.005 .000

*p < .05. ***p < .001. c = Cypriot sample. g = Greek sample. a = Means of number of mentioned emotions.

Table 13.3 Pre–Post Comparisons for Items of the Thematic Unit “Diversity and Culture” Experimental Groupg

Control Groupg

With classmates from other countries I would like to . . .

Pre M

Post M

t

df

Pre M

Post M

t

df

Play together at recess Sit at the same desk Invite them to my home Be invited to their home Spend time outside school Work together in school activities Play together in the neighborhood Help each other with homework

3.80 3.25 3.34 3.27 3.55 3.82

3.82 3.38 3.43 3.48 3.78 3.87

–0.37 –1.44 –1.19 –2.55* –2.41* –0.56

317 316 316 306 306 305

3.68 3.14 3.23 3.14 3.46 3.74

3.81 3.27 3.34 3.25 3.62 3.79

–1.29 –1.18 –1.03 –1.10 –1.18 –0.39

193 192 192 189 187 191

3.51

3.77

–3.06**

307

3.51

3.69

–1.49

191

3.68

3.96

–3.61***

307

3.82

3.70

1.09

193

Note Scale from 1 = not at all to 5 = very much. g = Greek sample. *p<.05. **p<.01. ***p<.001.

In addition to this assessment of changes related to the content of each thematic unit, a general measure of social skills (SSRS) was administered at the end of the intervention to students from Cyprus. Table 13.4 describes results of an independent samples t-test that was used to

266 Chryse Hatzichristou et al. Table 13.4 Comparison of Social Skills of Students After One and Two Years of Participation in the Program

SSRS subscalesc

1st year in intervention M SD

2nd year in intervention M SD

t

df

p

Cooperation Self-control Assertion Empathy

1.36 1.37 1.50 1.50

2.64 2.22 2.28 2.67

–41.54 –29.49 –24.46 –39.88

474 474 474 474

.000 .000 .000 .000

0.32 0.30 0.32 0.33

0.33 0.31 0.37 0.28

Note Scale 1 = never, 2 = sometimes, 3 = very often. c = Cypriot sample.

compare fifth- and sixth-graders who completed their second year of PPMHL intervention to those who participated in the program for the first time. (Since all fourth-graders in Cyprus were completing the program for the first time, fourth-graders were excluded from this analysis.) Results clearly indicated higher acquisition of social skills for second-year participants along all four subscales of the SSRS. Scale reliability index alpha was quite satisfactory (α = .90). Teacher Reports. As mentioned previously, participating teachers were assigned additional tasks to enhance collaboration among all members of the school community, thus promoting positive climate and sense of community in the schools. Results of this effort were tested by pre- and post-comparisons of Cypriot teachers’ reports on the SCCP-II (total scale alpha = .94). As shown in Table 13.5, there was a significant increase in the SCCP-II dimensions related to teachers’ perceptions of student characteristics: student respect [t(66) = –6.84, p = .000], student friendship and belonging [t(66) = –6.29, p = .000], and students’ shaping of their environment [t(66) = –5.67, p = .000]. Teachers were also asked to estimate their own personal and professional competence related to communication and management of emotions. The alpha index for this set of items was .64. Results are reported in Table 13.6, and show that after the intervention, teachers reported that they coped more efficiently with their difficult emotions [t(64) = –2.88, p = .005] and that students asked for their help with personal matters more often [t(65) = –4.86, p = .000]. Finally, in their general evaluation of the program, teachers were asked to describe benefits of PPMHL for their students and for themselves. According to teachers, the program helped students communicate and interact better with others; improve their cooperation skills; better understand others’ perspectives, needs, and feelings; express their own feelings; and Table 13.5 Pre–Post Comparisons of Teachers’ Evaluation of Sense of Community SCCP-II subscales c

Pre-assessment M SD

Post-assessment M SD

t

df

p

Student respect Student friendship and belonging Students’ shaping of their environment Support and care by and for faculty/staff Support and care by and for parents

2.84 2.82 2.45 3.96 3.43

3.33 3.37 3.00 4.06 3.51

–6.84 –6.29 –5.67 –1.34 –1.28

66 66 66 66 66

.000 .000 .000 .185 .204

0.37 0.55 0.56 0.58 0.55

Note Scale from 1 = almost never to 5 = almost always. c = Cypriot sample.

0.51 0.57 0.72 0.61 0.59

Promoting the Well-Being of School Communities 267 Table 13.6 Pre–Post Comparisons of Teachers’ Evaluation of Competence Itemsc Students ask for my help for personal matters I feel comfortable when students talk to me about their feelings With some students I find it hard to communicate Generally, I express my feelings to others Generally, I cope well with difficult feelings

Pre-assessment M SD

Post-assessment M SD

t

df

p

2.71

0.92

3.38

0.97

–4.86

65

.000

4.36

0.76

4.29

0.80

0.60

65

.551

2.50

0.95

2.30

0.91

1.50

65

.140

3.41

1.12

3.56

1.11

–1.24

63

.221

3.38

0.93

3.71

0.86

–2.88

64

.005

Note Scale from 1 = not at all to 5 = very much. c = Cypriot sample.

improve their behavior and self-discipline. As for their own benefits, teachers reported that the program helped them improve both personally and professionally; understand, communicate and get along better with their students or with others; reflect on and realize aspects of their personal and professional identity; acquire new knowledge; and feel more competent dealing with challenging situations in the school. Discussion of Results Results of the PPMHL evaluation presented above provide evidence that the described intervention programs account for significant positive effects and benefits for both teachers and students. In particular, most children seemed to benefit from the program (students reported that the program helped them express their feelings, improve their communication and their relationship with their classmates, realize and accept their strengths and weaknesses, and improve their academic performance). Furthermore, there is evidence that children responded to and benefited from the presented material selectively—for example, less popular children (low peer acceptance) or those from different cultural groups reported significant benefits in the emotional domain, particularly in the acknowledgement and expression of unpleasant feelings (see also Hatzichristou et al., 2004). Results also indicate that intervention at a system level has the potential to produce extended and dynamic effects on school climate and sense of community. These results provide encouraging feedback, particularly when interpreted relative to the limited duration of the Athens and Cypriot programs (six–seven months). Results from the comparison between the first and second year of intervention suggest that stronger, concrete indications of change will be evident after a longer period of program implementation (Holtzman, 1992). Moreover, the evaluation data provide valuable feedback for detecting strengths and weaknesses of the program and for making necessary adjustments to maximize the program’s efficacy. Another important source of feedback is the “live” experience gained in the process: Visiting the schools, meeting the students, interacting with teachers during the training, and sharing important experiences with them during the workshops can provide different, unique perspectives of intervention effects that may be more accurate and useful.

268 Chryse Hatzichristou et al.

School Community Well-Being: A Challenge for Different Educational Settings Recent trends in school mental health have contributed to an increased understanding of positive development and adjustment in the school environment. Nevertheless, the relevant literature lacks guiding theories that can be applied to school-based practices (Baker et al., 2003). In this chapter, an effort has been made (a) to provide such a guiding conceptual model, (b) to describe a practical application of this model in the Greek educational system, and (c) to present empirical evidence of its efficacy. The proposed approach reflects a shift from deficit-based clinical models to strengthsbased mental health models of school mental health services. Within the positive psychology perspective, the mental health model emphasizes strengths, focuses on problem-solving processes, enhances social and emotional skills, promotes resilience at both individual and systems levels, and encourages contextual changes that contribute to schools’ functioning as psychologically healthy environments. Moreover, the shift from individual to system intervention incorporates concepts such as school climate, sense of community, resilient classrooms and systems. A new model, the school community well-being model, was described that provides a broader approach to supporting well-being and guidelines for the promotion of well-being within the school community. In this model, the well-being of the whole school community is a primary target for outcome of the intervention programs, and provides the context for the promotion of all other significant correlates of well-being (e.g., students’ academic and psychosocial development and adjustment). Core concepts of school mental health related to this model include resilience, effective schools, schools as caring communities, social and emotional learning, and evidence-based interventions. These are prerequisites for the promotion of school well-being and are integrated into the model in an operational synthesis that provides practical guidelines for the development and implementation of system-level interventions in the school environment. The intervention program that is described in this chapter constitutes a paradigm of how this model is put into practice. More specifically, the PPMHL included the implementation of a program for the promotion of social and emotional learning in the classroom and the enhancement of individual (teacher, student) and classroom resilience. In addition, the resiliency wheel was applied for the development of action plans and interventions at a system level (school community). These interventions also aimed at the promotion of positive climate and sense of community in the school through the involvement of students, teachers, parents and other school personnel and administrators. Taking into consideration the implications of the effective schools literature and research, as well as the principles of evidence-based practices, these multi-level interventions were adjusted to the particular needs and priorities of each context (participating schools) and their effectiveness was evaluated using various methods, sources and instruments. Furthermore, it is suggested that the described procedures can also be adjusted and applied in different cultural contexts (Hatzichristou, Lampropoulou, & Lykitsakou, 2006). Deriving from this theoretical approach, the described evidence-based intervention paradigm is the outcome of a long-term dynamic process that links theory with practice, needs assessment, integration and evaluation of new theoretical and methodological elements, feedback and empirically based re-adjustments. This approach can produce a wide positive impact on important psychological dimensions of the school community. As we broaden the focus from individuals to systems, we expect to identify similar parameters of positive development and psychological functioning among different educational

Promoting the Well-Being of School Communities 269 settings. Thus, we believe that the basic principles and theoretical components of the proposed approach can be applied to various contexts (e.g., cultures, social and educational systems, types of intervention), taking into account their particular needs and characteristics, and making all necessary adjustments in program development, design, and implementation. With regard to culture, which is a basic source of diversity affecting school-based interventions, we suggest that it is only one of the many kinds of diversity that should be considered. Our specific approach employs a “meta-cultural” perspective that focuses on similarities of cultures and individuals (common needs and adversities) while considering the positive potential, competencies, and strengths as a means of promoting the well-being of systems and, therefore, the well-being of individuals.

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14 Promoting Student Resilience Strong Kids Social and Emotional Learning Curricula Oanh K. Tran, California State University, East Bay Kenneth W. Merrell, University of Oregon The consequences of social and emotional problems of young people have received considerable attention over the past several years as a result of the Surgeon General’s warning that our nation is facing a public crisis in mental health care for children and adolescents (Sturm, Ringel, & Andreyeva, 2003; US Public Health Service, 2002). Estimates suggest that between 15% and 22% of U.S. children and youths have social and emotional difficulties (e.g., aggression, anxiety, depression, social withdrawal). Specifically 7.5 million children suffer from one or more mental disorders, yet 75% to 80% of these youths do not receive appropriate interventions (Greenberg, Domitrovich, & Bumbarger, 2001; Greenberg et al., 2003). Within any given year, an estimated 20% of school-age children experience social and emotional problems (Coie, Miller-Johnson, & Bagwell, 2000).

Current School-Based Mental Health Interventions School-based mental health intervention efforts have been ineffective, fragmented, unstructured, and often target only specific problems (Elias, Zins, Crazyk, & Weissberg, 2003; Greenberg et al., 2003). Comprehensive interventions are significantly lacking and with limited follow-up for students who do receive services. As well, schools often use reactionary and punitive approaches (e.g., school suspension or expulsions) that are unsuccessful in addressing the core of the problem (Lewis, Sugai, & Colvin, 1998) and simply move the problem elsewhere, such as the home or community environments (Sprague & Horner, 2006). Reactionary methods are quick fixes that remove the student from the situation or school context, but do not effectively address the needs of the student. Because these methods punish students for “bad behaviors without teaching them expected behaviors, the students’ socioemotional problems are likely to recur with greater intensity and frequency. Research has indicated that less extreme but chronic behavior problems also put children at risk for more severe antisocial behaviors and other later-life difficulties (Sprague & Horner, 2006; Walker, Colvin, & Ramsey, 1995). Specifically, failure to acquire adequate social and emotional skills is associated with many negative outcomes, including higher-than-average rates of mental illness, incarceration, family strife, and unemployment or underemployment (Asher & Coie, 1990; Rudolph & Asher, 2000). Educational systems have only recently pushed for school reforms that better support students’ mental health needs. There is now much more advocacy for systematic prevention and early intervention efforts that address school problems and promote students social and emotional well-being (Coie et al., 2000; Lewis et al., 1998; Sheridan & Gutkin, 2000). Students’ overall success is jeopardized if schools do not participate in such efforts to prevent and intervene in social and emotional problems. Consequently, schools need proactive measures to

274 Oanh K. Tran and Kenneth W. Merrell prevent and reduce social and emotional problems, while enhancing student functioning and academic success for all students.

Social and Emotional Learning: A Framework for Student Success Schools are expected to not only educate a diverse array of students, but to also effectively and efficiently address the social and emotional challenges of their diverse student population. Children who are hurting and hurting others cannot learn effectively, and their presence in school requires attention when their problems become so severe that these drain energy, focus, and potential from the learning environment. As well, indirect social and emotional factors also exist within the school and classroom environments that influence learning, including student-teacher interactions, social interactions, classroom climate, peer group, and school safety (National Center for Educational Statistics, 2002; Wang, Haertel, & Walberg, 1997). These indirect factors have the potential to increase or decrease students’ social and emotional problems and achievement difficulties. Social and emotional learning (SEL) is an important and emerging focus that links education and children’s mental health and that has the aim of preventing student problems and promoting students’ healthy social and emotional development. In 1994, the Fetzer group conceptualized social and emotional learning as a framework to address the social and emotional difficulties confronted by students and the ineffective responses by schools (Greenberg et al., 2003). Students’ social and emotional problems may be prevented when they acquire knowledge and skills to recognize and manage emotions, understand others’ perspectives, develop positive goals, make responsible decisions, and cope with interpersonal difficulties. Focusing solely on academic instruction and behavior management may not be sufficient for helping students attain academic success, nor create the ideal learning environment (Zins, 2001; Zins, Weissberg, Wang, & Walberg, 2004). SEL should be combined with instruction and behavioral management into integrated and cohesive programs that reinforce one another and promote ideal outcomes and academic success for all students (Zins et al., 2004). Social and emotional learning is a proactive and educative approach to decreasing problem behaviors, while enhancing essential life skills and resilience to be successful in school and in life. SEL uses a broad range of tools and systematic techniques to teach social, emotional, and life skills and promote resilience. Resilience is defined as “successfully coping with or overcoming risk and adversity or the development of competence in the face of severe stress and hardship” (Doll & Lyon, 1998, p. 349). Importantly, SEL aims to prevent negative life outcomes by integrating effective curricular programming into a school program (Ragozzino, Resnik, O’Brien, & Weissberg, 2003; Zins et al., 2004). Similar to academic skills, students should be taught SEL skills and given the opportunity to practice skills in and out of the classroom. The opportunity to practice such skills in activities should be reinforced while addressing the complex situations children face in academic and social and emotional development (Zins & Elias, 2006). The increase in student social and emotional problems and the effectiveness of SEL as prevention and early intervention efforts have been well documented (Greenberg et al., 2001; Greenberg et al., 2003; Ragozzino et al., 2003). Studies of SEL programs have found that they result in an increase in attendance and decrease in dropout rates (Ragozzino et al., 2003), as well as enhanced student connections to school (Greenberg et al., 2003). Other studies have documented the usefulness of SEL programs in improving student attitudes, behaviors, and academic performance (see Greenberg et al., 2003). By incorporating evidence-based social and emotional learning curricula, positive environments are created that enhance and teach

Promoting Student Resilience 275 healthy adaptive functioning to increase opportunities for school and life success. Sprague and Horner (2006) suggests that positive environments that are safe and predictable will foster emotional development and social interactions which, in turn, will increase student engagement and behavior. Although preventative interventions through SEL strategies are not limited to school settings, we believe that schools are ideal venues for SEL. Not only are schools the one setting in our society where virtually all children and adolescents participate (thus allowing access to almost all children), but most schools have at least the basic framework of organization and staff in place that is necessary for delivering preventative interventions. Teachers and other school personnel have specific training in delivering instruction to diverse learners, and the practice of providing services at various ranges of a continuum—from universal or school-wide to individualized intensive efforts—is fairly ubiquitous. Figure 14.1 illustrates the applicability of prevention science in school mental health. The figure illustrates exposure to the root causes of problems, onset or protection from development of particular disorders, and the culmination of eventual negative outcomes or impairments. Finally, it explains how a continuum of services is necessary to address the progression of mental illness.

The National and International Movement in Social and Emotional Learning Federal and state government agencies and the public have only begun to realize the extent of social and emotional problems in today’s schools, from kindergarten to college. Social and emotional learning is supported by several national organizations, such as Learning First Alliance, National Collaboration for Youth, Nation’s League of Cities, and the Collaborative for Academic, Social, and Emotional Learning (CASEL). CASEL is one of the leading promoters and advocates of prevention and early intervention efforts through social and emotional learning. The mission of CASEL (2007) is to “enhance children’s success in school and life by promoting evidence-based social, emotional, and academic learning as an essential part of education, from preschool through high school.” CASEL collaborates, publishes, and disseminates critical information relating to children’s mental health and promotion efforts. Its primary goal is to bridge the gap between research and practice by fusing science and theory in

Prevention science research cycle

Causes

Cure, impairment, relapse

Onset, protection

Exposure

Disorder

Outcome

Primary prevention (Universal)

Secondary prevention (Targeted)

Tertiary prevention (Indicated)

Figure 14.1 Prevention of Mental Illness Through Continuum of Supports.

276 Oanh K. Tran and Kenneth W. Merrell real-world settings. The CASEL organization has increasingly worked to provide practitioners and school administrators with the guidelines, tools, informational resources, policies, training, and supports needed to improve and expand SEL programming efforts (see http://www.casel.org/). In 2004, following the tragic death by suicide of United States Senator Gordon Smith’s son, the US Congress passed the Garrett Lee Smith Memorial Act (see http://gsmith.senate.gov), which enables states, Indian tribes, colleges and universities to develop suicide prevention and intervention programs. Further, the act acknowledges the significant toll of social and emotional problems on youths’ ability to succeed in college. The act provides program grants for community and school-based efforts, such as State-Sponsored Suicide Youth Prevention, Campus Suicide Prevention, and Mental Health Services for Adolescents at Risk for Suicide. At the state level, the state of Illinois mandated the use of social and emotional learning programs in its public schools due to the high rates of social and emotional problems in children and adolescents in the state. The state estimated that one out of 10 children in Illinois suffer from social and emotional problems serious enough to cause impairment. The Illinois Children’s Mental Health Act (2003) allocated $5 million to enhance screening, assessment, and support services (SAS) for children and adolescents to address the mental health crisis. The Illinois Mental Health Partnership was formed and developed a blueprint that outlined comprehensive, coordinated efforts to address the mental health needs in the state. The policy addressed teaching and assessing social and emotional skills and protocols for responding to children with social, emotional, or social and emotional problems, that impact learning ability. The Illinois Learning Standards (ILS) were created, defining what all students in all Illinois public schools should know and be able to do in the seven core social and emotional domains as a result of their elementary and secondary schooling. More recently, the state of New York in 2006 legislated comprehensive, coordinated social and emotional development standards for every school district. Additionally, in 2007, New York introduced a law that required the completion of a social and emotional development curriculum as part of teacher certification. The SEL movement has spread internationally as well. In Europe, Education through Characters with emotional-Intelligence and Role-playing Capabilities that Understand Social interactions (E-CIRCUS) was developed due to the growing need of educational supports in the realm of social and emotional learning in the classroom. E-CIRCUS aims to develop conceptual models and innovative technology to support social and emotional learning through role-play and affective engagement for complex social situations. Specifically, a sophisticated technology system provides interactive learning on anti-bullying and intercultural empathy. The goal of E-CIRCUS is to improve quality and innovation in social and emotional learning through technology. UNICEF and partners in Nigeria in 2001 created school environments friendly for children, focused on quality curriculum along with early childhood care and development, reviewed teaching curriculum for gender sensitivity, and raised national awareness on education for girls. Other international efforts include Singapore, Australia, United Kingdom, and Israel. There is widespread recognition and support for the SEL framework in schools and communities to promote student resilience through teaching social and emotional skills and embracing a positive culture for student learning and development. To continue to do “business as usual” while ignoring students’ social and emotional problems provides no benefit to the child, family, community, or society.

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Fostering Social and Emotional Resilience Through SEL: The Strong Kids Programs One example of a structured SEL preventative intervention is the Strong Kids programs, which we developed with our colleagues, premised on the notion that resilience skills can be learned and taught in schools. Using effective instructional design principles such as activating prior knowledge and providing opportunities for practice, Strong Kids incorporates the specific curricular concepts that are defined in Cowen’s (1994) five proposed pathways to psychological wellness. These pathways include: • • • • •

promoting early attachments building competence with developmentally appropriate skills being in a setting that promotes wellness feeling empowered about the future coping with stress using relevant skills

These Strong Kids programs are semi-scripted SEL curricula, focusing on prevention and early intervention of internalizing problems that have the goals of promoting students’ social and emotional competence and teaching them skills to increase their resilience to life stressors. Strategies and techniques found in affective education, cognitive therapy, and behavioral intervention approaches are integrated throughout the lessons. There are five Strong Kids programs, each one for a specific developmental level. Strong Start includes separate curricula for preschool-age children, ages 3–5 (Merrell, Whitcomb, & Parisi, in press) and children in grades K–2 (Merrell, Parisi, & Whitcomb, 2007). Strong Kids (Merrell, Carrizales, Feuerborn, Gueldner, & Tran, 2007a, 2007b) includes separate curricula for students in grades 3–5 and grades 6–8. Strong Teens (Merrell, Carrizales, Feuerborn, Gueldner, & Tran, 2007c) is designed for students at the high school level, grades 9–12. These curricula are practical, easy-to-use, and brief (10–12 lessons of 30–50 minutes each, with optional booster lessons for use two or three months later), adaptable across a range of students and settings, and designed to be taught in small groups or with entire classes by educators or support service professionals. A minimal level of training is needed to teach these curricula, and the manuals for each program include suggested practices for becoming proficient in delivering the curricula. Additional information on these preventative intervention programs, including free downloadable assessment and progress monitoring tools, is available on the Strong Kids website (http://strongkids.uoregon.edu). Strong Start: SEL For Young Children. The Strong Start programs (for pre-K and grades K–2) follow an instructional approach that differs from the two Strong Kids curricula and the Strong Teens program, because most younger students do not have the cognitive maturity to grapple with abstract concepts such as thinking about emotions, and because younger children require a shorter and more activity-based intervention sessions. In addition, most pre-K and many K–2 students do not have the reading ability required to complete the worksheets and homework assignments that are part of the Strong Kids and Strong Teens curricula. Thus, the Strong Start versions of the programs are briefer, more activity-focused, do not require reading ability, and require less cognitive maturity than the counterpart programs for older students. The two Strong Start programs have an identical organization and format. The differences between the two programs are primarily related to length of activities and lessons, language used by the group leader, and developmental focus of the activities. The lesson titles and content focus of the 10 Strong Start lessons are as follows:

278 Oanh K. Tran and Kenneth W. Merrell 1 2 3 4 5 6 7 8 9 10

The Feelings Exercise Group (introducing children to the Strong Start curriculum) Understanding Your Feelings 1 (teaching children to name basic emotions or feelings) Understanding Your Feelings 2 (teaching appropriate ways to express various feelings) When You’re Angry (managing anger, helpful ways of expressing anger) When You’re Happy (teach students how it feels to be happy and to use developmentally appropriate positive thinking strategies) When You’re Worried (managing anxiety, worry, and fear) Understanding Other People’s Feelings (teaching students the basic elements of understanding how other people might feel in various circumstances) Being a Good Friend (basic communication and friendship-making skills) Solving People Problems (basic strategies to solve common problems of getting along with other children) Finishing UP! (reviewing major concepts of Strong Start program; closure activities)

Like the Strong Kids and Strong Teens programs, the Strong Start programs include optional booster-session activities that are designed to maintain the gains children have made through participating in the program. One aspect of Strong Start that differs from the programs for older children and adolescents is that there is a strong emphasis on having teachers or group leaders use children’s books as a way to teach and reinforce the concepts that are covered in the lessons. This has a corollary effect of promoting early literacy readiness. For example Audrey Penn’s The Kissing Hand, a book about Chester Raccoon and his mother and how love works to reassure when we are frightened, is listed among the recommended titles from which teachers or group leaders may select to enhance Lesson 6, When You’re Worried. Likewise, Arnold Lobel’s Frog and Toad Are Friends, a classic children’s book on the topic of friendship and understanding others, is among the recommended selections to be used in support of Lesson 8, Being a Good Friend. Strong Kids and Strong Teens: SEL in the Classroom: Our SEL curricula for older children and adolescents include Strong Kids for grades 3–5, Strong Kids for grades 6–8, and Strong Teens for grades 9–12. These programs are age-level developmental adaptations of the same curriculum concepts. They are similar in design features and content, and differ only with respect to the examples and language used, so that they are more age-appropriate to the specific developmental periods targeted. The lesson titles and content focus of the 12 basic lessons in Strong Kids and Strong Teens are: 1 About Strong Kids/Teens (pre-testing, curriculum overview, rules, ice-breaker activities) 2 Understanding Your Emotions, Part 1 (increasing emotional vocabulary, defining emotions) 3 Understanding Your Emotions, Part 2 (appropriate expression of emotions) 4 Dealing with Anger (understanding anger, cognitive-behavioral anger management training) 5 Understanding Other People’s Feelings (empathy training, taking perspective of others) 6 Clear Thinking, Part 1 (identifying thinking errors and maladaptive beliefs) 7 Clear Thinking, Part 2 (actively changing maladaptive beliefs and thinking errors) 8 The Power of Positive Thinking (learned optimism training) 9 Solving People Problems (interpersonal conflict resolution skills and practice) 10 Letting Go of Stress (practice in cognitive and behavioral methods of relaxation) 11 Achieving Your Goals (goal-setting, behavior education, behavior-affect connection) 12 Finishing UP! (cumulative review of major concepts, planning for future, post-testing)

Promoting Student Resilience 279 Using a psychoeducational teaching approach, the Strong Kids and Strong Teens programs incorporate the strategies from affective education, cognitive therapy, and behavior change techniques to help students increase their knowledge of healthy social-emotional behavior, enhance their resilience, and fortify their coping and problem-solving skills. Each lesson includes opportunities for review of prior concepts, instruction and practice of new skills, corrective feedback, activities to promote maintenance and generalization (“transfer training” tips), and student handouts/worksheets. The lessons are scripted with suggested text for teachers and group leaders, but they are easily adaptable for specific circumstances, and the manuals include suggestions for adapting the curricula for specific social, cultural, geographic, or other circumstances. For example, a lesson in Strong Kids, Dealing with Anger, begins by defining and distinguishing between pertinent terms related to anger and emotions. Students are confronted with the idea of acceptable feelings and emotions in relevant situations. They are asked to share personal examples or situations when they have felt angry or been aggressive. Students then learn of a strategy that they can use to identify their cognitions, emotions, and behaviors in similar situations that may lead to anger or aggression. Additionally, students learn about strategies to control their anger, and use alternative solutions to prevent inappropriate and unacceptable behaviors. Several scenarios are provided for illustration, small and large group discussions, application, and implementation of these prosocial strategies for managing their anger. Supplements, handouts, overhead transparencies with concepts, scenarios, and skills are used to teach, model, and practice prosocial behaviors and skills. Conversations between students, peers, and teachers are positively supported throughout the lesson. The lesson is easily described with semi-scripts that a teacher or interventionist can implement with limited preparation.

Implementing SEL in Schools: Lessons Learned Our experience in developing and implementing the Strong Kids programs, bolstered by the feedback we have received from our field-test partners in school and clinic settings, has helped us understand some of the nuances in preparing and delivering instruction for optimal success. Given the lessons we have gleaned through this process, we encourage users of Strong Kids (and similar SEL programs) to consider the following practices for best results: •

•

Provide an agenda to students (using a blackboard, overhead transparency, or flip chart) at the start of each lesson, so that a visual reference is established for the focus of that lesson. We have found that this technique not only helps students—in true “advance organizer” fashion—understand what to anticipate, but also helps teachers consider the key issues that need to be covered during the brief time they have, and to use their time well. We have observed the Strong Kids programs being implemented in many, many classrooms. Although there clearly are individual differences related to teachers’ skill level and instructional style, those teachers who include a clear agenda statement or visual at the start of the lesson are more likely to cover the most salient points of the lesson. Additionally, when students can anticipate the lesson’s activities beforehand, they are better prepared to learn and be actively engaged in the instruction. Prior to starting the first lesson, clarify the behaviors that are expected of students, and reinforce these expectations as needed throughout the instruction. A few simple and positivelystated rules for behavior go a long way towards establishing a positive climate for SEL instruction. Again, we have observed many differences—sometimes dramatic

280 Oanh K. Tran and Kenneth W. Merrell

•

•

•

•

differences—across classroom environments with respect to teachers’ classroom and group management skills and effectiveness. Those teachers who follow these guidelines almost always seem to have fewer behavior management problems during implementation of Strong Kids than those who do not establish these routines. The “simple” and “positively-stated” aspects of rules and behavioral expectations are key ingredients to successful classroom management that are often overlooked. For example, a teacher may indicate and consistently reinforce the following rules: Respect others, Come prepared, and Personal things stay in the group. Plan for smooth transitions into the lessons, as well as between components of the lessons. Have all materials prepared and organized, make sure that any equipment to be used is working properly, and precorrect any potential behavioral difficulties. In a study on the effectiveness of teacher behaviors during implementation of Strong Kids, Levitt (2008) found a recurring theme in the qualitative social validity interviews conducted with teachers at the conclusion of the curriculum: Teachers believe that having all materials prepared is an important element in a successful lesson. Make sure that all students have a clear view of the instructor/group leader. For smaller groups we have found that a “horseshoe” placement of chairs or desks works well. For class-wide implementation, it is important that all desks or tables are situated so that there is a clear view of the instructor and any visuals to be used. Most of our implementation research and consultation has been with classroom teachers who are using one of the Strong Kids programs as a universal preventative intervention with all students in their classroom. We have noticed that class size is one of the ingredients that can pose a challenge. We have observed lessons being taught in classrooms with more than 40 students. Situations like this one require extra planning on the part of the teacher in order to ensure that all students are in a physical location that is conducive to effective participation. Adapt the scripts and examples provided in the intervention manuals, to make them optimally engaging and effective for local situations and needs. Ideally, these adaptations should keep the basic premises of the lessons intact, but modify the language and examples so that they are most relevant for the students. As we have indicated in the front of each of the Strong Kids manuals, we wrote the scripted instructions and suggested examples in colloquial American English, using general examples that might be understandable across geographic and cultural contexts within the United States. Although necessary and useful, such general scripting may not be the best choice in all situations. For example, regional and ethnic differences in English dialect may create situations where minor adaptations in teachers’ instructional language promotes optimum engagement by students. It’s also worth considering that the type of examples that are used to illustrate social and emotional concepts will sometimes need to be tailored to the specific circumstances of the group. For example, if the classroom teacher observed a group of students having difficulties sharing ideas for a group play, the teacher then might consider using that specific situation in discussing some problem-solving strategies. Don’t limit the Strong Kids instruction to the formal instructional delivery period wherein the lessons are taught. Look for opportunities during the instructional day and throughout the week to reinforce and re-teach concepts that were previously introduced. Although all of the suggestions we have derived from our implementation and consultation experiences are important, this particular issue may be the most critically important of all. The most effective teachers—those who seem to have achieved the most impact in teaching the Strong Kids lessons—find opportunities to reinforce, model, re-teach, and comment on the key concepts throughout the week. For example, some teachers have posted the Strong

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•

Kids list of six common thinking errors (“Clear Thinking”) in a prominent spot in the classroom, and then discuss specific thinking errors that occur with students. On many occasions, we have been pleasantly surprised to observe students independently referring to these concepts at various times of the school day. These observations suggest that the Strong Kids appear to be easily generalizable in students’ daily lives. Related to the previous suggestion, we have found that careful planning for transfer training will facilitate the transfer of new skills across time and situations. The use of the supplementary materials and homework assignments (included in the curriculum manuals) greatly aids in this regard. Each Strong Kids lesson includes suggestions for teachers for activities and assignments that will promote maintenance and generalization of the knowledge and skills that are learned. Anecdotally, we have observed that those teachers who emphasize the transfer training tips in their presentation of SEL lessons are most likely to have students who use the skills across situations and retain the knowledge gains over time. More research is needed to confirm this impression, but the initial findings are promising.

Effectiveness of Strong Kids: Research Findings and Issues Several studies have been conducted that support the effectiveness of the Strong Kids programs (see Merrell, 2010 for an overview). One of the important issues to consider in designing and implementing effectiveness and efficacy trials for preventative interventions is what outcomes or evidence are appropriate to demonstrate that the intervention is achieving the desired aims. In this case, our aims were relatively modest, given that the Strong Kids programs were designed to be low-cost, low-intensity, time-limited preventative interventions. We never anticipated that a brief SEL program like Strong Kids would produce the same types of robust gains that might be expected from a more intensive intervention that required a much more extensive outlay of personnel, time, materials, and other resources. On the other hand, interventions that are expensive, time-consuming, and difficult to implement are much less likely to actually be used in the real world of schools, where staff have many competing demands placed on them and little support or time for such efforts (Merrell & Buchanan, 2006). The notion of what type of intervention intensity is best to produce meaningful gains is an empirical question that has yet to be fully answered. Interventionists and intervention researchers hope for large gains or strong outcomes, but it is unclear in most instances where the point of optimum return is with respect to the time and resource investment needed for intervention gains. Presumably, a more intense and more extensive intervention is more likely to produce the most gain, but in a world of limited time and resources (i.e., the world of schools and classrooms), more is not always best. We presume that had we designed Strong Kids to include 36 lessons (one for each week of the school year), extensive interventionist training requirements, and a longer instructional period that was spread across each day of the week, the program would produce stronger potential gains. On the other hand, we have serious doubts that the programs would be as likely to be adopted under such conditions. If that assumption is correct, then the use of brief and low-cost preventative interventions such as Strong Kids may actually produce a greater net gain in the lives of children and adolescents than intensive programs whose prohibitive costs make them less likely to ever be used in the first place. Not coincidentally, research examining the social validity and user satisfaction of Strong Kids has consistently shown that the brief, easy-to-use, and time-limited aspect of Strong Kids is one of its most appealing features in the view of teachers and group leaders (e.g., Harlacher, 2008; Isava, 2006; Levitt, 2008; Gueldner & Merrell, 2007; Tran, 2007).

282 Oanh K. Tran and Kenneth W. Merrell In many ways, our aim in explicitly designing the Strong Kids programs to be brief, low-tech, and low-cost was to develop programs that had a high degree of social validity and would be used with high fidelity, notwithstanding that they may not produce such strong gains as more intensive programs. However, the question of “how much gain is enough” is still an open issue, and we have continually maintained that even the most powerful intervention will produce no gains if it is too difficult and costly to actually use in real-world settings. Our first set of implementation studies of Strong Kids (Merrell, Juskellis, Tran, & Buchanan, 2008) were basic pre- and post-test design intervention trials within groups, in both general and special education settings. The results of these studies were encouraging, and indicated that participation in the Strong Kids programs was associated with significant gains in students’ knowledge of healthy social-emotional behavior, including effective strategies to cope with adversity and respond to stress in ways that enhance resilience. These studies also demonstrated that, in some cases, students who participated in Strong Kids implementations evidenced meaningful reductions in problem symptoms associated with internalizing problems. At the same time that we were conducting these initial within-group studies, our research partners from other institutions (Brown, 2006; Faust & Larson, 2007) conducted similar studies that produced similar results: significant student gains in knowledge of healthy social-emotional behavior and, in some cases, meaningful reductions in internalizing problem symptoms. Building upon these initial within-group studies, our next studies were between-group randomized treatment-control prevention trials, so that any gains from participation in the Strong Kids programs would not be attributed solely to maturation or other within-group factors as a function of time. We were also interested in evaluating the social validity of the programs in this next set of studies, from the perspective of both the students and teachers or group leaders. These studies resulted in similar findings to the initial pilot studies with respect to significant knowledge gains, including studies of implementations in residential treatment schools (Berry-Krazmien & Torres-Fernandez, 2007), residential treatment centers (Isava, 2006), and regular education settings (Gueldner & Merrell, 2007; Harlacher, 2008; Levitt, 2008; Tran, 2007). Each of these studies demonstrated high levels of user satisfaction and social validity. As well, these studies evaluated treatment integrity or fidelity, and found very high levels of teacher and group leader compliance (typically, 80% to 90% or higher) with the structure and goals of the curriculum. In view of the fact that treatment fidelity is often difficult to achieve, it is interesting to consider what components of these interventions led to higher fidelity. We cannot yet definitely answer this question, but we believe strongly (based on some results, including extensive feedback from teachers) that the design of the intervention curriculum is key in this regard. The scripted or semi-scripted aspect of the Strong Kids curricula—in addition to the clearly delineated steps and lesson organization—is an important element in promoting treatment fidelity or intervention adherence. In addition, at least one of these studies (Levitt, 2008) has indicated in a fairly convincing manner that the use of structured consultation with teachers who are implementing Strong Kids seems to be associated with higher levels of fidelity. The Tran (2007) study also evaluated issues related to timing or pacing of instruction, and showed that the Strong Kids program could be delivered with equal effectiveness using high pacing (two lessons per week over six weeks) or typical pacing (one lesson per week over 12 weeks), although many students and teachers preferred the pace of “one lesson per week.” In addition, one of the second wave of studies demonstrated convincingly that the Strong Teens program could be successfully adapted into Spanish language and for use with recent Latino immigrants, while still producing meaningful treatment gains and high levels of user satisfaction and social validity (Castro-Olivo, 2006).

Promoting Student Resilience 283 Currently, members of our research group are conducting or preparing to conduct research to evaluate longer-term follow-up of treatment gains, feasibility of emotion knowledge measures for measuring treatment gains with young children, utility of the Strong Teens program as an embedded part of a high school health curriculum, feasibility of using Strong Start in daycare settings, and uses of these programs with specific populations that have unique social-emotional needs (i.e., students with Asperger’s Disorder of high functioning Autism). Our plans for additional research in the future also include the impact of the Strong Kids programs on students’ social-emotional assets and resilience, in contrast to negative symptom reduction, and the reliability and validity of experimental new strength-based social-emotional self-report assessments, the Social and Emotional Assets and Resilience Scales. We anticipate that future research topics will emerge as we move from one phase to the next.

Summary and Concluding Comments The demands on professionals in today’s schools are challenging given the high needs of our student population. If schools do not address the needs of students immediately and proactively, schools will continue to require greater resources to meet those needs and the future may be dim for our students. The negative effects are not only implicated in school settings, but can affect society as a whole. It cannot be argued that student success in academics and social and emotional functioning are aspirational outcomes. Without one or the other, students will not be able to succeed in school and life and develop to their full potential. Social and emotional learning (SEL), a vital, proactive framework to address the increasing challenges and needs in schools, is an ideal and promising option to effectively address students’ mental health concerns. SEL benefits both students and teachers through an inclusive and systemic way of embracing a positive learning environment that encompasses core skills for academic and life success. The integration of SEL into the classroom curricula is more likely to reinforce students’ academic learning and social and emotional behaviors. SEL has become recognizably important for academic success and integral as part of the classroom curriculum. SEL’s framework and utility is easily adaptable for class-wide and school-wide approaches in the realm of prevention and early intervention. SEL produces a benefit for all students by fostering positive learning environments. The evidence-based Strong Kids programs are innovative, socially valid, and effective SEL programs to address children’s mental health needs in schools. The Strong Kids programs aim to promote resilience skills to support and foster students’ healthy development by teaching social and emotional concepts in order to successfully cope with inevitable life stressors. Importantly, the concepts and skills from Strong Kids are intended to meaningfully decrease students’ internalizing symptoms and increase social and emotional knowledge of skills. Measuring the accumulation of resilience skills and the application of these skills in real-life situations should be continued efforts in order to better understand prevention and intervention curricula. The empirical support for Strong Kids programs is positive for both students and users, and we believe the programs will continue to demonstrate positive results. Social and emotional concepts and resilience skills taught in Strong Kids are essential for all students to learn. Strong Kids promotes resilient behaviors through proactive education on how students can spring back from and successfully adapt to adversity. The end benefits of Strong Kids decrease the time, energy, and resources that may possibly be allotted in the future in dealing with students’ social and emotional problems by teachers, school psychologists, school counselors, and

284 Oanh K. Tran and Kenneth W. Merrell administrators. Strong Kids evidently increases students’ engagement in learning. Ultimately, learning results in success for educational systems and society.

References Asher, S. R., & Coie. J. D. (Eds.). (1990). Peer rejection in childhood. Cambridge, UK: Cambridge University Press. Berry-Krazmien, C., & Torres-Fernandez, I. (2007). Implementation of the Strong Kids curriculum in a residential facility. Poster presentation presented at the annual meeting of the National Association of School Psychologists, New York. Brown, M. H. (2006). Effects of the Strong Kids curriculum on students at-risk for internalizing behaviors. Unpublished master’s thesis, Brigham Young University, Provo, Utah. Castro Olivo, S. (2006). The effects of a culturally-adapted social-emotional learning curriculum on socialemotional and academic outcomes of Latino immigrant high school students. Unpublished doctoral dissertation, University of Oregon, Eugene. Children’s Mental Health Act (2003). Children’s Mental Health Act of 2003. Retrieved March 24, 2007, from http://www.isbe.net/spec-ed/pdfs/cmh_act_fact_sheet.pdf Coie, J. D., Miller-Johnson, S., & Bagwell, C. (2000). Prevention science. In A. J. Sameroff, M. Lewis, & S. M. Miller (Eds.), Handbook of developmental psychopathology (2nd ed.) (pp. 93–112). New York: Kluwer/Plenum. Collaborative for Academic and Social and Emotional Learning (CASEL), (2007). CASEL’S mission and goals. Retrieved August 12, 2007, from http://www.casel.org/ Cowen. E. L. (1994). The enhancement of psychological wellness: Challenges and opportunities. American Journal of Community Psychology, 22, 149–179. Doll, B., & Lyon, M. (1998). Risk and resilience: Implications for the delivery of educational and mental health services in schools. School Psychology Review, 27, 348–363. Elias, M. J., Zins, J. E., Graczyk, P. A., & Weissberg, R. P. (2003). Implementation, sustainability, and scaling up of social-emotional and academic innovations in public schools. School Psychology Review, 32, 303–319. Faust, J. J., & Larson, J. (2007). Preventing anxiety and depression: The effects of a social-emotional curriculum. Poster presentation at the annual meeting of the National Association of School Psychologists Conference, New York. Garrett Lee Smith Memorial Act (2004). Garrett Lee Smith Memorial Act Passes House and Senate. Retrieved April 2, 2007, from http://www.apa.org/ppo/issues/eglsupdt904.html Greenberg, M. T., Domitrovich, C., & Bumbarger, B. (2001). The prevention of mental disorders in school-aged children: Current state of the field. Prevention & Treatment, 4, 1–63. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnick, H., et al. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474. Gueldner, B. A., & Merrell, K. W. (2007). Evaluation of a social-emotional learning intervention using performance feedback to teachers in a structured consultation model. Manuscript submitted for publication. Harlacher, J. E. (2008). Social and emotional learning as a universal level of support: Evaluating the follow-up effect of Strong Kids. Unpublished doctoral dissertation, University of Oregon, Eugene. Isava, D. M. (2006). An investigation of the impact of a social-emotional learning curriculum on problem symptoms and knowledge gains among adolescents in a residential treatment center. Unpublished doctoral dissertation, University of Oregon, Eugene. Levitt, V. H. (2008). Promoting social-emotional competency through quality teaching practices: The impact of consultation on a multidimensional treatment integrity model of the Strong Kids program. Unpublished doctoral dissertation, University of Oregon, Eugene.

Promoting Student Resilience 285 Lewis, T. J., Sugai, G., & Colvin, G. (1998). Reducing problem behavior through a school-wide system of effective behavioral support: Investigation of a school-wide social skills training program and contextual interventions. School Psychology Review, 27, 446–459. Merrell, K. W. (2010) Linking prevention science and social emotional learning: The Oregon Resiliency Project. Psychology in the Schools, 47(1), 55–70. Merrell, K. W., & Buchanan, R. (2006). Intervention selection in school-based practice: Using public health models to enhance systems capacity of schools. School Psychology Review, 35, 167–180. Merrell, K. W., Carrizales, D. C., Feuerborn, L., Gueldner, B. A., & Tran, O. K. (2007a). Strong Kids— Grades 3–5: A social-emotional learning curriculum. Baltimore: Paul H. Brookes Publishing. Merrell, K. W., Carrizales, D. C., Feuerborn, L., Gueldner, B. A., & Tran, O. K. (2007b). Strong Kids—Grades 6–8: A social-emotional learning curriculum. Baltimore: Paul H. Brookes Publishing. Merrell, K. W., Carrizales, D. C., Feuerborn, L., Gueldner, B. A., & Tran, O. K. (2007c). Strong Teens— Grades 9–12: A social-emotional learning curriculum. Baltimore: Paul H. Brookes Publishing. Merrell, K. W., Juskelis, M. P., Tran, O. K., & Buchanan, R. (2008). Social and emotional learning in the classroom: Evaluation of Strong Kids and Strong Teens on students’ social-emotional knowledge and symptoms. Journal of Applied School Psychology, 24, 209–224. Merrell, K. W., Parisi, D., & Whitcomb, S. A. (2007). Strong Start—Grades K– 2: A social-emotional learning curriculum. Baltimore: Paul H. Brookes Publishing. Merrell, K. W., Whitcomb, S. A., & Parisi, D. M. (in press). Strong Start—Pre-K: A social-emotional learning curriculum. Baltimore: Paul H. Brookes Publishing. National Center for Educational Statistics (2002). Retrieved Sept 08, 2007 from http://nces.ed.gov/surveys/els2002/ Ragozzino, K., Resnik, H., O’Brien, M. U., & Weissberg, R. (2003). Promoting academic achievement through social and emotional learning. Educational Horizons, 81, 169–171. Rudolph, K. D., & Asher, S. R. (2000). Adaptation and maladaptation in the peer system. Developmental processes and outcomes. In A. J. Sameroff, M. Lewis, & S. Z. Miller (Eds.), Handbook of developmental psychopathology (pp. 157–175). New York: Kluwer. Sheridan, S. M., & Gutkin, T. B. (2000). The ecology of school psychology: Examining and changing our paradigm for the 21st century. School Psychology Reviews, 29, 485–502. Sprague, J. R., & Horner, R. H. (2006). School wide positive behavioral supports. In S. R. Jimerson & M. J. Furlong (Eds.), The handbook of school violence and school safety from research to practice. Mahwah, NJ: Lawrence Erlbaum. Strong Kids (2008). The official Strong Kids website http://strongkids.uoregon.edu/ Sturm, R., Ringel, J. S., & Andreyeva, T. (2003). Geographic disparities in children’s mental health care. Retrieved Sept 21, 2007, from http://pediatrics.aappublications.org/cgi/reprint/112/4/e308 Tran, O. K. (2007). Promoting social and emotional learning in schools: An investigation of massed versus distributed practice schedules and social validity of the Strong Kids curriculum in late elementary aged students. Unpublished doctoral dissertation, University of Oregon, Eugene. U.S. Public Health Service (2002). Report of the Surgeon General’s Conference on Children’s Mental Health: a national action agenda. Washington, DC: Department of Health and Human Services; 2000. Retrieved March 18, 2007, from http://www.surgeongeneral.gov/topics/cmh/childreport.htm. Walker, H. M., Colvin, G., & Ramsey, E. (1995). Antisocial behavior in schools: Strategies and best practices. Pacific Grove, CA; Brooks/Cole. Wang, M. C., Haertel, G. D., & Walberg, H. J. (1997). Toward a knowledge base for school learning. Review of Educational Research, 63, 249–294. Zins, J. E. (2001). Examining opportunities and challenges of school-based prevention and promotion: Social and emotional learning as an exemplar. Journal of Primary Prevention, 21, 441–446. Zins, J. E., & Elias, M. E. (2006). Social and emotional learning. In G. G. Bear & K. M. Minke (Eds.), Children’s needs III (pp. 1–13). Bethesda, MD: National Association of School Psychologists. Zins, J., Weissberg, R., Wang, M., & Walberg, H. J. (Eds.). (2004). Building academic success on social-emotional learning: What does the research say? New York: Teacher’s College Press.

15 Stimulating Positive Social Interaction What Can We Learn from TIGER (Kanjertraining)? Lilian Vliek, Institute of Kanjertraining, Almere, The Netherlands Bram Orobio de Castro, Utrecht University, Utrecht, The Netherlands Social interactions are important from birth on. People long for social contact and support. It is one of the first necessities of life. When people have difficulties in social interactions, they can tend to show roughly two forms of behavior. They can show depressive, socially anxiouslike symptoms and social withdrawal (internalizing behavior) and they can show aggressive, overactive or impulsive behavior (externalizing behavior). These behaviors can cause conflicts with others and cause low social acceptance by others. This in turn increases the problematic behavior. Five to 10% of primary school children have problems being socially accepted by peers and hence show developmental problems (Boivin, 2005). In adolescents, this percentage seems to be even higher, between 10% and 20% (Scholte & Engels, 2005). Many studies have shown that children who show problem behavior early in life have a high chance of maintaining these problems during puberty and young adulthood (e.g., Van Lier, 2002). This makes it important that children learn at a young age how to interact socially in neutral and stressful situations. Indeed, many studies prove that preventive intervention at a young age, when problem behaviors are not yet present, is effective in breaking through this developmental process (see for a review Nation et al., 2003). T.I.G.E.R (Training I Go for Emotional well-being and Respect; “Kanjertraining” in the Netherlands) is used in the Netherlands as a preventive intervention in primary and secondary schools to stimulate constructive social interaction, well-being, and a positive climate in the classroom. The training is also used in child mental health care centers with children who have problems in social interaction, have depressive feelings, are too aggressive and/or have a low self-esteem. Many of the starting-points of TIGER are theory-driven, but some of the starting-points have their origin in practice. The founder of TIGER, Gerard Weide, based the training on both scientific knowledge and many years’ experience with children in primary and secondary school classes. Insights and methods from TIGER may be a good starting-point for future studies on effective ways of stimulating positive social interaction. At the beginning of this chapter, scientific knowledge about factors influencing social behavior will be discussed. These will be reformulated in recommendations for startingpoints for preventive interventions in Box 15.1. Thereafter, the main theoretical insights from TIGER will be discussed. This will be followed by a description of TIGER method. At the end of this chapter, results of the first study on the effectiveness of TIGER will be discussed. These may give an indication of the fruitfulness of the theoretical visions and the methods of TIGER.

Stimulating Positive Social Interaction 287

Risk and Protective Factors Children and their environment are in a continuous interaction with each other. Child behavior will influence reactions of the environment, which in turn will trigger certain child reactions. This may result in a vicious circle of positive or negative behavior by child and environment (see Rutter, 2006). Thus, the child can create an environment that increases the problem behavior of the child, and the environment contributes to the development of a child who makes the environment more problematic, while both the child and the environment do not intend to do so. Although there is this complex contribution of interacting factors, for clarity, in the following, these factors are discussed separately. While reading, keep in mind that these factors do not have to be causal factors. Both the factors and the behavior of the child can be the result of a third (maybe unknown) factor. Moreover, each factor that will be discussed has very little predictive value by itself. Only the combined action of these factors indicates an increased risk. Since problems in social interaction can imply socially anxious, depressed behavior but also aggressive, overactive behavior, research on both internalizing and externalizing behavior is combined. This list of factors is certainly not complete, but gives an overview of the main influences on the child’s behavior. Child Intelligence, gender and temperament have been found to be risk factors in the development of problem behavior. High intelligence is found to be a protective factor in the development of problem behavior (Farrington, 1995). Gender is found to be correlated to the direction of the problem behavior. Boys tend to show more direct physical aggression, while girls show more relational aggression (e.g., gossiping). During childhood, boys and girls have the same chance to get depressed, while in adolescence, girls feel depressed twice as often as boys do (Birmaher, Ryan, Williamson, Brent & Kaufman, 1996). Temperament is also found to influence behavior problems. A difficult temperament is associated with aggression, while behavioral inhibition is associated with internalizing problems. Behavioral inhibition is assumed to be a biologically-determined temperament factor that is characterized by the tendency to react with shyness, anxiety and withdrawal in social and non-social situations that are new or unknown (see Muris, 2008). Additionally, sensitivity to stress is a risk factor that can make children vulnerable to developing behavioral problems (see Rutter, 2006). Environment Parents. When parents show emotional involvement, affection and support, children show more prosocial behavior and have higher self-esteem in social contacts (Rudolph & Asher, 2000). A secure attachment has an important role in this, since this lays the basis for the development of relationships further on in life. Sensitive and responsive rearing have a large influence on the optimal functioning of the child (Propper & Moore, 2006). The more parents respond properly to the temperament of the child, the more positive the emotional regulation of the child. Besides these protective factors, some risk factors have been identified that are associated with externalizing or internalizing behavior of the child. Precursors for aggressive behavior are strong and physical punishment, inconsistent use of rules, and the absence of monitoring (see Rutter, 2006). Reactive aggression (impulsive reactions to perceived threat, with high emotional arousal) is specifically associated with neglectful rearing. Proactive aggression

288 Lilian Vliek and Bram Orobio de Castro (well-considered and well-directed aggressive behavior) is associated with aggressive role models in the family who use aggression as a way of achieving their personal goals (see Rutter, 2006). Anxious behavior of the child is associated with anxious rearing, parental control and rejection. Thus anxious children often have parents who warn them of all possible dangers, give the child little autonomy and show a negative rejecting attitude toward the child (Muris, Meesters, & Van Brakel, 2003). Since there is a continuous interaction between parents and children, these child-rearing styles are developed in interaction with characteristics of the child. Parents and children do not show these behaviors randomly. For example, when a child is very active or cries a lot, this requires more energy from these parents than less demanding children might require. These parents become more vulnerable to stress and inconsequent reactions. This, in turn, may contribute to aberrant behavior of the child. In such transactions, it is often not possible to distinguish between the cause and the consequences. The main family characteristics that increase the risk for behavioral problems in children are conflicts between parents (especially in front of the children), divorce (except when this causes a decrease of conflicts), low income and low education, psychosocial problems of parents, delinquency of parents and a small social network around the family (see Rutter, 2006). Friends. Friends have a big impact on children’s behavior and even more impact on adolescents’ behavior. For children with many risk factors, having friends turns out to be a protective factor against showing aggressive behavior and being aggressed against. The quality of the friendship is important in this. Friendship is not a protective factor when friends have problems in social interactions among themselves (which is often the case) (Hodges, Boivin, Vitaro, & Bukowski, 1999). A friend who is caring, warm and intimate forms a protective factor against bullying when many risk factors are present (Bollmer, Milich, Harris, & Maras, 2005). In the development of aggressive behavior, two peer relationship processes occur at the same time: being expelled by socially competent children and being attracted to peers with similar problems. This results in two separate groups of children (see Rutter, 2006). In adolescence, when peer groups and conformation to the group become more important, “deviancy training” is a common phenomenon. Deviancy training holds that in a group of adolescents with aberrant norms and ideas, others in the group adopt these ideas and will show an increase in problem behavior, strengthened by others in the group (Deater-Deckard, 2001). Teacher. A risk factor in school for aggression is the social climate in the classroom. This includes bullying, negative relationships with teachers and a negative climate (see Rutter, 2006). The teacher is mainly responsible for the climate in the classroom. Because teachers are models for their students, those who model positive traits contribute to positive behaviors of children in class. Teachers who are willing to question themselves and who are straightforward, honest, skilled and knowledgeable are modeling the best behaviors for children to imitate (Goldstein, 1995). Teachers’ expectations have been proven to affect children’s level of achievement. Lowered expectations result in lowered interest and effort by the teacher and therefore negatively impact the child’s behavior and performance (Good & Brophy, 1978). Teachers who are effective in managing the classroom express their expectations concerning behavior, establish clear rules, and enforce those rules systematically and consistently (Emmer, Evertson, & Anderson, 1980). Teachers rated positively by students generally are reported to possess more positive views of others and to be less critical or attacking. They are perceived as being friendly and helpful and as handling problems in a democratic fashion

Stimulating Positive Social Interaction 289 (Sabatino, 1983). These characteristics will improve the relationship of the teacher with the students, which will have a positive impact on child behavior.

Mechanisms in the Relationship Between Risk Factors and Behavior Which mechanisms function as mediators between environmental factors and the behavior of the child? Mechanisms that will be discussed are social learning, social information processing, regulation of emotions, self-esteem and respect for others, and social skills. Social Learning Social learning perspectives suggest that people learn behavior by watching other people (e.g., Bandura, 1986). For example, when many people in the child’s environment show aggressive behavior, the child will easily imitate this behavior. This phenomenon is called modeling. Besides imitation, people also learn by experiencing the consequences of their behavior: in general, when behavior is followed by a reward, this behavior will be repeated. When behavior is followed by punishment, people will tend to reduce this behavior. This is called operant conditioning. These mechanisms explain part of the relationship between environmental factors and the behavior of the child. When a child is less sensitive to punishment or rewards, this relationship will be weaker. Social Information Processing According to theories of social information processing (Crick & Dodge, 1994), behavior of people in similar situations differs because people process social information differently. People can pay attention to different information than others do and can interpret this selective information in a different way. This causes emotions and reactions to differ between people. According to Crick and Dodge (1994), social information processing contains six steps: encoding of information, interpretation of this information, development of an emotion, generation of a reaction or several reactions, selection of the reaction with the highest expected benefit, and execution of the reaction. Problem behavior develops, according to Dodge, when one or more steps are performed atypically. In many studies, it is indeed found that certain types of social information processing are related to specific forms of aggressive behavior (see Dodge, 2006; Orobio de Castro, Merk, Koops, Veerman, & Bosch, 2005). Aggressive children are often found to perceive information as threatening and to interpret reactions of others as hostile. Moreover, they believe that an aggressive reaction would be beneficial. Children who feel shy and depressed have also been found to interpret reactions of others in a hostile way. However, these children do not generate aggressive reactions but instead act out withdrawal reactions (Quiggle, Garber, Panak, & Dodge, 1992). As part of the fifth step (response evaluation), self-efficacy is thought to play an important role. Self-efficacy refers to one’s own judgment of being able to perform the behavior (Bandura, 1994). It has been found that aggressive and shy children think that they are not able to perform the more socially adequate behavior. They realize that aggression and shyness are not the best reactions, but they expect only to be able to show aggressive or shy behavior. Vicious Circles, Social Skills In general, problem behavior strengthens itself. This makes it difficult to change one’s behavior. For example, children who are socially anxious, often try to avoid social situations. This,

290 Lilian Vliek and Bram Orobio de Castro in turn, causes more anxiety since children do not have the opportunity to learn social skills. Children who have depressed feelings often show angry, jealous and shy behavior. This causes others to reject these children, which in turn increases their depressed feelings and the corresponding behavior (Stark & Smith, 1995). Aggression is intensified by group forming and deviancy training, as discussed earlier. These vicious circles of problem behavior are often driven by a deficiency of social skills and can be broken by teaching social problem-solving skills to these children, and by promoting positive social interaction with more socially skilled peers. Children reduced their aggressive and rebellious behavior when they learned other socially accepted behaviors through which to reach their goals. Teaching effective coping strategies also contributed to a decline in anxious behavior in children (Kazdin, 2003). Emotion Regulation Emotion regulation influences many competencies that allow children to modulate and cope with strong emotional states. Relevant competencies are internalized coping mechanisms (e.g., calming self-talk, cognitive strategies to reframe upsetting events), attentional control (e.g., shifting attention from provocative stimuli), and instrumental behavioral strategies (e.g., behaviors that alter emotion-provoking situations). Research has consistently shown that deficits in emotion regulation are predictive of reactive aggression, rejection, exclusion and bullying by peers (Eisenberg, Fabes, Murphy, Maszk, Smith, & Karbon, 1995; Pope & Bierman, 1999; Shields & Cicchetti, 1998). Self-Esteem and Respect for Others For a long time, the relationship between self-esteem and behavior was unclear. Many researchers assumed that aggressive people had low self-esteem, but a long history of research and theories does not support that notion. Salmivalli (2001) discusses the relationship between self-esteem and behavior and concludes that “high” or “low” self-esteem is not enough to describe this relationship. Instead, the “narcissistic” or “defensive” self view is found to be associated with problem behavior. Narcissism is a personality trait in which people strive to feel superior over others. (It does not refer to the personality disorder.) A defensive self-view refers to “not being open for criticism.” Salmivalli and colleagues (1999) showed that adolescents could be divided into three groups. The group with high self-esteem in combination with high narcissism often bullied; the group with low self-esteem and low narcissism was often bullied; and the group with high self-esteem and low narcissism showed mostly prosocial behavior (Salmivalli, Kaukiainen, Kaistaniemi, & Lagerspetz, 1999). Similar findings were found for children between 10 and 13 years old. Thomaes (2007) studied the reaction of narcissistic and not-narcissistic children in shameful situations. The results showed that most aggression was shown by narcissistic children with high self-esteem. A low self-esteem proved to be a protective factor for the development of aggression in narcissistic children. Kernis (2003) wrote an interesting article about optimal self-esteem. She gives a definition of optimal self-esteem in which making choices by the authentic self contributes to an optimal self-esteem. She states: “Optimal self-esteem involves favorable feelings of self-worth that arise naturally from successfully dealing with life challenges; the operation of one’s core, true authentic self as a source of input to behavioral choices; and relationships in which one is valued for who one is and not for what one achieves” (p. 13). This theory was recently supported by Thomaes, Reijntjes, Orobio de Castro and Bushman (under review). Children with

Stimulating Positive Social Interaction 291 realistic self-views were least vulnerable to social rejection, whereas children with overly positive or overly negative self-views suffered most emotional distress in response to social rejection. Thus, optimal self-worth appears to be a combination of knowing and accepting one’s strengths and weaknesses.

Box 15.1 Recommendable Factors to Incorporate in Preventive Interventions to Stimulate Positive Social Interaction Focus on risk and protective factors 1. Train parents in their way of interacting with the child. It seems useful to promote emotional involvement, affection, support, consistent use of rules and to discourage physical and strong punishment, aggressive behavior of the parent, anxious rearing, control and rejection. 2. Involve peers in the training. A classroom would be a good environment to do this, since children spend a lot of time here, and subgroups will be formed easily. In this way, both the socially competent children, the shy children and the more aggressive children can help to ameliorate the behavior of the group. 3. Teach parents and teachers to set a good example (modeling) and to praise positive behavior and to neglect or restrict negative behavior (operant conditioning). 4. Stimulate positive relationships with teachers and a positive climate in the classroom. Focus on mechanisms 5. 6. 7. 8. 9.

Train children in interpreting social information Practice with social skills Strengthen self-efficacy Train children in emotion recognition and regulation Stimulate realistic self-esteem and encourage respect for others

Focus on the future (recommended and used by TIGER) 10. Remind children of their intent to be a “good” person. Ask them how they want to behave and use this as a guideline in stimulating the child to behave like this. 11. Make children responsible for their behavior, and teach them that they can choose how to behave (at a developmentally appropriate level).

In the upper part of Box 15.1, recommended factors for incorporating into an intervention on positive social interaction are summarized. TIGER uses most of these factors in its intervention and has additional theoretical assumptions about behavior. This makes it useful to take a closer look at TIGER.

Training I Go for Emotional well-being and Respect (T.I.G.E.R) TIGER (“Kanjertraining” in the Netherlands) is one of the most frequently used training methods for social emotional development in the Netherlands and was first developed in 1996. Currently, TIGER is used in three settings:

292 Lilian Vliek and Bram Orobio de Castro 1 2 3

As a preventive intervention in class delivered by trained teachers in primary and secondary schools. When high aggression and anxiety are present in the classroom and the teacher has lost control; the training is delivered by psychologists in the classroom as a crisis intervention. In child mental health care centers, the training is delivered by psychologists to children who experience problems in social interaction, show aggression or depressive symptoms, and/or have low self-esteem.

The Dutch word “Kanjer” (in Kanjertraining) does not have a translation in English, but means something like “well done, you are like a tiger, you are a champion/hero!”. People feel proud to be a “Kanjer.” In the training a “Kanjer” is somebody who is authentic, reliable, socially competent and respectful to others and him/herself. A “Kanjer” has a constructive coping strategy: he/she searches for respectful solutions on the basis of equality. The name of the training is translated into TIGER (Training I Go for Emotional well-being and Respect) in order to grasp the broad concept of “Kanjer.” The goal of TIGER is to stimulate authentic and respectful social behavior and well-being. In schools, an additional goal is to improve the climate in the classroom, which is defined by positive relationships between classmates and between the teacher and children. Ageappropriate manuals have been developed for each age group between four and 16 years old. In each age group, approximately 10 lessons of one and a half hours are given every other week. Each lesson starts with the interactive reading of a story, followed by role plays (with four caps) and the practice of social skills. Thereafter, social dilemmas are discussed in a Socratic way. Five principles hang in front of the classroom on a poster and are behavioral guidelines that are discussed each lesson. A lesson always ends with a physical exercise to increase trust in the group. In each lesson, exercises of former lessons are repeated. Examples of themes of the lessons are presenting oneself, giving compliments, feelings, conflict situations, showing interest, trust, critics, friendship, is it ok that you exist? and the diploma ceremony. The caps and the principles are easy to use in daily situations, so that generalization is straightforward. Theoretical Basis of TIGER TIGER has an extensive theoretical basis that is described in a book for teachers and parents in the Netherlands (Weide & Vliek, 2007). For the purpose of this chapter, only the most important and most distinctive assumptions are discussed. These are summarized in the lowest part of Box 15.1. Authenticity: To Live to One’s Desire In TIGER, problem behavior (internalizing and externalizing behavior) is seen as notauthentic behavior. To live authentically is defined as: to live according to one’s desire, to do what fits you. When people feel authentic, they are in balance with their emotions, thoughts and traditions, physical sensations, and desires. This means, for example, that a boy who discovers that he feels attracted to other boys may come into conflict with his physical sensations, desires and traditions (e.g., in his family, being gay is not accepted). As a consequence, this boy may show shy, depressed behavior when he is with his family. To be able to live authentically, he has to talk with his family to come into balance with his feelings, traditions and desires.

Stimulating Positive Social Interaction 293 TIGER sees authentic behavior as crucial to the development of the self, and thereby to the development of well-being and self-esteem. When a person manages to live to his or her desire, personal goals will be reached, which will make the person happy. Moreover, making authentic choices in one’s life will increase the feeling of being a unique person, independent of the desires of others, which will improve self-esteem. What does TIGER mean by living according to one’s desire? People can long for desires such as a big house, a nice car, status and fame. When these are fulfilled, this will most likely not contribute to an optimal (authentic) self-esteem. By “desire,” TIGER means a more fundamental desire. Irrespective of origin, culture, religion and experiences, most people have the universal desire to be a good mother or father, a good student, a good friend, a good son or daughter. Children have this desire too. They want to feel accepted as a good son or daughter, a good friend and a good student. They want to be seen, heard and understood. Unfortunately, this is often not the case. Many children feel bad, stupid or mean. Some children react to this by withdrawal, shyness and anxiety; others show aggression; and others give up, become careless or indifferent, and do not take themselves or others seriously. In all these cases, children do not live to their desire: they do not really want to behave like this. This desire to behave authentically as a “good” person is used as a starting-point in the training. Responsibility Another theoretical basis of the training lies in taking responsibility. TIGER is of the opinion that people are responsible for their behavior. This also holds for children (at a developmental appropriate level). TIGER assumes that children can choose how they behave. Children are not the product of their environment, but have control over their lives and can make autonomous choices. In a practical sense this means that parents cannot come up with an alibi like: “My child misbehaves in school, but this is because my child is dyslectic, has ADHD or has a father in jail.” These factors may indeed have effects on the child, but may not be used as an alibi for rule-breaking behavior. This means that it is not so important to know how many problems a child has, how bad the child’s environment is, or which stressful life-events have been taking place. For children, what they want to do about the situation is more important. How does the child want to deal with difficulties in life? The training assumes that stress and problems are part of life. People must not expect a Disneyland in their life. They have to learn to deal with the challenges in life. They should realize that the challenges are the things that teach them the most. A clarifying metaphor is used: “When I sail in nice weather, I do not really know whether I can sail well or not. Only the storm will teach me how to sail very well.” The same holds for challenges in life. Remarkably, in the developmental psychology literature, the roles of the desire of the child and responsibility of the child are underexamined. Theoretically, Piaget and Vygotsky have already emphasized the active constructive role of children in their own development. Still, most theoretical models are conducted as if children do not have choices in their behavior, as if children’s behavior is the sum of external factors that determine how they behave. The authentic preferences and choices of the child are understated. In interventions, these elements are therefore also peripheral. Only two studies have recently measured the influence of focusing youth on their personal values. The first study was published in Science (Cohen, Garcia, Apfel, & Master, 2006). African American seventh-graders completed a 15-minute assignment to reaffirm their sense of self-integrity: seeing oneself as good, virtuous, and efficacious. Students indicated their two or three most important values and wrote a paragraph about why these values were important to them. The goal of this

294 Lilian Vliek and Bram Orobio de Castro intervention was to examine whether self-affirmation would enhance academic performance of negatively stereotyped minority students. Results were positive: adolescents who wrote the values paragraph had higher grades in the next periods than controls. The main question remains: Does this assignment, in which students focus on their personal values, also influence behavior? Yes, this seems to be the case. Recently, the same results were found for aggression. In narcissistic adolescents who completed the same 15-minute assignment, aggression was reduced for a one-week period (Thomaes, Bushman, Orobio de Castro, & Cohen, under review). These results demonstrate that making adolescents aware of their own values (their real desires) can improve school performance and can even reduce aggression. Self-Esteem and Respect for Others According to TIGER (Weide & Vliek, 2007), self-esteem in combination with respect for others is important in the development of social behavior. Since social behavior is about the interaction between the self and the other, it is important that the child hold respect for both parties. TIGER assumes that a low self-esteem in combination with high respect for others will lead to feelings of inferiority and will contribute to internalizing behavior. On the other hand, a high self-esteem (or sometimes an inflated ego) in combination with low respect for others will often lead to feelings of superiority and power and can easily result in aggressive behavior towards others (who are “worthless”). Children who have a balance in self-esteem and respect for others will tend to show respectful social behavior in which both parties (self and other) are respected for who they are. These assumptions are in accordance with the studies of Salmivalli and colleagues (1999) and Thomaes (2007), wherein an imbalance between regard for self and regard for others is highly problematic. Parents, School Policy and Teacher TIGER acknowledges that parents, teachers, and school policy have an important impact on the child’s behavior and development. These people around the child can stimulate positive social interaction in two ways: by functioning as role models, and by supporting the child’s adequate social behavior through giving feedback, behaving authoritatively, and setting and maintaining limits. Parents and teachers have to learn to fulfill this “authentic” role. In school, the teacher and the parents have a shared responsibility for the behavior of the child in class. This implies that parents should be invited to school when a child misbehaves or has the intention of doing so. It is very important that the school makes clear policy about rules and procedures in school. In fact, the school has to fulfill its authentic role too: make policy so that children can learn in a safe climate.

Method of TIGER The TIGER intervention takes into account most of the influencing factors and mechanisms that are summarized in Box 15.1: parents, teachers and peers, interpretation of social information, social skills, emotion recognition, self-esteem and respect for others. In addition to these influences on the child’s behavior, TIGER uses two future-focused elements (described in the lowest part of Box 1.15). The training strategies are discussed below.

Stimulating Positive Social Interaction 295 How to Stimulate Authentic Behavior? TIGER states that for showing authentic socially competent behavior it is important that the child is acquainted with this behavior, is skilled enough to perform it, wants to show this behavior (desire) and chooses to show it in everyday life (responsibility). Practicing Skills In order for a child to learn socially competent behavior, the child has to be acquainted with this behavior. When children grow up in a very hostile environment where they are often shouted at, they may not know that there is a more positive form of social interaction. Therefore, children have to see the socially competent behavior of others to realize that there are alternative ways of responding. Only knowing or observing this behavior is not enough to demonstrate it oneself. Children have to learn these behavioral skills. Practicing in training is a first step, practicing in everyday life is the second. The environment plays an important role in forming the behavior of a child. To make it easier for the child to show the new behavior, the environment (parents, peers, teachers) must also be trained. In addition to being skilled in behavior, the child must also learn new ways of thinking. Since hostile attributions of children are correlated with aggression and anxiety, it is important that the child learns to attribute social information in a friendly way. The main method that is used to foster children’s understanding and skill in social behavior is demonstrating four types of behaviors or coping strategies. Four colored caps are used to illustrate these behaviors. Children learn to recognize, become conscious of, and become skilled in these four types of behavior. The black cap (called the Bully-bird) stands for aggressive and dominating behavior; the yellow cap (called the Rabbit) stands for shy, anxious and depressed behavior; the red cap (called the Monkey) stands for funny-making, careless and hanger-on behavior; and the white cap (called the Tiger) stands for authentic social behavior with respect for the self and the other. The last one is called Tiger behavior and includes constructive socially competent behavior, daring to give one’s opinion in a respectful way, sharing one’s feelings, helping and being trustworthy. A key point is that children behave like a cap but are not identified as a cap. Social (Tiger) skills (e.g., presenting oneself, talking about feelings, giving and receiving feedback) are practiced and are repeated during each lesson to automate these skills. Difficult situations are played in role-plays of, for example, bully situations that have taken place in reality. These caps can also be used outside the training sessions: children, teachers and parents can ask children “Which cap are you wearing?” to make children conscious of their behavior. Subsequently, they can ask the child whether he/she wants to put on the white cap. Children learn that the four types of behaviors often go together with thoughts about the self and the other. These thoughts and other themes are discussed (like “What is friendship?”, “Whom do you want to belong to?”) so that children learn to change their cognitions about social interactions, which is assumed to change their way of processing social information. Besides the caps, five TIGER principles are used as guidelines for behavior and are posted in front of the classroom on a poster. These are: We trust each other; We help each other; Nobody plays the boss; Nobody laughs at another; and Nobody acts pitiful. Live to One’s Desire, Authenticity As mentioned before, TIGER uses its view of people’s universal desire (I want to be a good child, son, student) as a starting-point in the training. Since it is assumed that most children

296 Lilian Vliek and Bram Orobio de Castro do not want to behave shy, anxious or aggressive, but prefer to behave authentically and prosocially, this intrinsic motivation of children is used. For example, when children show aggressive behavior, the trainer or teacher asks: “Is it your intention to hurt that child?” “When it is not your intention, that is fine, then you will stop behaving like this.” “When this is your intention, I decide that you have to stop this now, because this is not allowed in school.” The trainer or teacher shows his or her authority (but only after making the child conscious and responsible for his decision and behavior). While using the caps, the child is never forced to put on the white cap (figuratively). The consequences of this behavior are shown in role-plays so that children experience that the Tiger behavior has the highest benefit. The advantage of this approach is that the behavior of the child is intrinsically motivated. In the first lesson, children are asked whether they want to be trusted. Since not wanting to be trusted is a predictor for aberrant behavior, this is an important starting-point for the training. When children state that they do not want to be trusted, this is a reason to invite their parents to school, and to discuss this with the teacher and the head teacher of the school. Responsibility Children learn to give up their feelings of being a victim. Many children who are bullied, shy and anxious have these feelings of helplessness. They think: “I don’t have influence, this always happens to me.” Remarkably, many aggressive children also have these feelings: “Why do they always blame me? I am a victim of the rules.” Both groups of children have to learn to take responsibility for their behavior. The lessons are sequenced so that children gradually learn that they can choose their own behavior. This is illustrated by the use of the caps: one can choose to wear another cap; a child is not a cap, but behaves like a cap. Moreover, the trainer or teacher always let children choose whether they want to participate in an exercise, so that children develop feelings of control in these situations. The last Tiger principle, We are not helpless, means that despite bad circumstances, people can always choose how to deal with situations. An example: Some children have the feeling that nobody wants to play with them and that they are victims. In a TIGER workbook, this type of child is exemplified by a boy named Julian. He believes that everybody must get along with each other. He likes some “cool” children in his class and wants to be friends with them. These children just don’t like him. Julian chooses to go to the movies at the same time as the “cool” children do. They say to him that they don’t like him to be with them, but Julian places himself next to them. The boys get angry and Julian is hit. Julian goes home and complains to his mother. He and his mother think he is a poor victim. The next day, the “cool” boys go swimming. Julian wants to play with them, so he goes to the same swimming pool. He is shouted at and beaten again by the same boys, and history repeats itself. This story teaches children that apart from the “cool” group, Julian also has something to learn. He has to learn that not everybody likes him and that this is fine. Julian should not stalk children who do not like him. He has to look for other children in class to play with. Therefore, he has influence over his situation and is not a victim. Self-Esteem and Respect for Others Positive self-esteem is achieved when a person receives enough social support and feels competent in the fields that are important for the person (Harter, 1999). In TIGER, children become skilled in social interaction by practicing skills, and the environment around the child

Stimulating Positive Social Interaction 297 is involved in the training and is stimulated to see, hear and understand the child and to give more social support to the child. One of the lessons concerns the theme “Is it ok that you exist?” Children learn that it is ok that they exist, not because of their achievements, but because they are loved by people around them. This is comparable to the view of Kernis (2003): “Optimal self-esteem involves [. . .] relationships in which one is valued for who one is and not for what one achieves” (p. 13). Moreover, TIGER stimulates children to make authentic choices: to live according to their desire. This will increase their feeling of being unique people, independent of the desires of others, which will increase their self-esteem. This is comparable to the view of Kernis (2003): “Optimal self-esteem involves [. . .] the operation of one’s core, true authentic self as a source of input to behavioral choices (p. 13). In addition, children talk with each other in the training and share opinions to stimulate respect for others and for other views. Physical exercises are completed to increase trust in the group and to learn to touch each other in respectful ways instead of hitting and kicking each other. Parents, School Policy and Teacher TIGER acknowledges that parents and teachers have an important impact on the child’s behavior and development. Teachers and parents learn in TIGER to expect that children have the intention to be a good son/daughter, a good student and a good friend. Children do not always behave in accordance with their desire. Therefore, teachers and parents learn to help children to live to their desires. In both settings where TIGER is given (schools and child mental health centers) parents are actively involved in the training. In child mental health centers, parents are trained in parallel parent groups. This is obligatory: it is not possible to complete the training without parents participating. In schools, a parent evening is held before the training starts. If the training is delivered by a psychologist in a troublesome class, parental involvement is a crucial element of the training. In addition to the lessons that are given to the children in class, the school, the teacher and the psychologist make clear rules about which behaviors are tolerated in school and which behaviors are not. Parents are informed that if their child misbehaves, or has the intention to do so, parents are obliged to come to school. Sanctions must be clear: if the child does not behave according to the rules of the school, the child has to leave the school. This confrontational and clear approach has big impact on the behavior of the children in class. Most of the time, it is not necessary to expel children. The goal is to protect the children who show normal positive behavior and to set clear rules for aggressive children. TIGER encourages head teachers to make such clear policy. Teachers follow a three-day course to learn to deliver the training in class. In this course, much attention is paid to the behavior of the teachers themselves. Teachers become conscious of their own behavior and of the influence this has on the children (by modeling). The teacher also learns that he/she has to fulfill his/her authentic role as a teacher: dare to behave as an authority, make clear which behavior is expected, make clear rules and maintain these. In general teachers and parents learn to pay more attention to children who behave in a positive way than to children who show aggression or are overactive.

Effectiveness of TIGER The effectiveness of TIGER was examined in troublesome school classes when the training was delivered by an experienced psychologist. The goal of this study was to establish whether

298 Lilian Vliek and Bram Orobio de Castro the climate in the classroom improved and whether children developed more respectful social behavior, had a decline in depressed thoughts and aggressive behavior, and developed higher well-being and self-esteem. Method Eleven classes (third- to sixth-grade) in 11 primary schools in the Netherlands were trained (n = 237, mean age 9.9 years, 49% boys). In each school, a control class was selected (n = 254, mean age 10.4 years, 54% boys), that was not trained but completed the same questionnaires at the same time as the training class did. The ethnicity of the training group was 86% Dutch, 4% western foreigner and 10% non-western foreigner. A child was rated as foreigner when at least one of the parents was born in a foreign country. The ethnicity of the control group was respectively 88%, 4% and 8%. The control group and the training group did not significantly differ in age, gender or ethnicity. Before and after the training children completed part of the school questionnaire (Smits & Vorst, 1990) and the TIGER questionnaire. The Emotional Well-Being subscale of the School questionnaire was used including the Relationship With the Teacher subscale and the Perceived Social Acceptance by Classmates. Validity of these subscales was established in earlier research (Smits & Vorst, 1990) and the reliability of the subtests was respectively .91, .83 and .90 (Cronbach’s alpha). The TIGER questionnaire was developed for this study and measures self-esteem (Cronbach’s alpha: .79), positive social interaction (alpha: .76), depressed thoughts (alpha: .81) and aggressive behavior (alpha: .78). Each subscale consists of approximately 10 statements for which children chose “totally not true,” “not really true,” “a little true,” or “totally true” using a four-point Likert scale. The validity of the self-esteem scale was demonstrated by correlation (r = .67) with the global self-esteem subscale of the Self Perception Profile for Children (Harter, 1988). Children filled in the questionnaires in class before and after the training. The training had an average of 15 training hours. These were given in class in 10 two-weekly sessions of one and a half hours each, or in three full schooldays spread over three months. Analyses Data were analyzed with regression analyses. The scores on the post-test were entered as dependent variable. Pre-test scores and age were entered in the first step of each analysis to correct for age and pre-test differences between the control and training group. To test the differences in post-test scores between the training and control group, the variable control versus training group was entered in the second step. Results of this second step are reported. An important question in intervention studies is: for whom does the training work? Since a preventive intervention, such as TIGER, is given to a variety of children with or without problematic behavior, not all children will profit in the same way. When the intervention is effective for children who had mean or high scores at pre-test, but not for children who had low scores (and needed the training the most), it is not a very effective training in a practical sense. The climate in the classroom can already improve when only the children who score the lowest show progress. Therefore, the size of the effects was calculated separately for four quartile groups (lowest 25%, highest 25%, and the two groups in-between). It was hypothesized that children with the lowest pre-scores in the training group would show the largest effect size. Effect sizes were calculated by subtracting the mean post-test score of the control group from the mean post-test score of the training group and dividing this by the pooled standard deviation. The post-test scores were first corrected for pre-test differences. The pooled standard

Stimulating Positive Social Interaction 299 deviation was calculated as SD pooled = √ [ (Nt * SDt2) + (Nc * SDc2) / Nt + Nc – 2] (t stands for training group; c for control group; N for number of subjects and SD for standard deviation). According to Cohen (1988), effect sizes (d) are defined as small if d is between .20 and .50, medium if d is between .50 and .80, and large if d is larger than .80. Results Mean scores before and after the training are shown in Table 15.1 and in Figures 15.1–15.7. All scores are standardized. All differences between the training and control group were significant in the regression analysis after controlling for pre-test differences, p’s < .01. All were in the expected direction. Effect-sizes are shown in graphs 15.8–15.14. Total effect-sizes can be seen on the left. Additionally, for each quartile group the effect-sizes are plotted. The 0–25 group is always the group with most problems at the beginning of the training. The effect-size of these lowest scorers is the most important since these children needed the training the most and their improvement can cause an improvement in the climate of the whole class. Effect-sizes of this group varied between .33 and .78. The training had the largest effect on depressed thoughts, perceived social acceptance by classmates, self-esteem and positive social interaction. Effect-sizes of these scales were all above .5, which is a medium effect size. This suggests that these findings are not only theoretically meaningful, but also have practical significance: the lowest-scoring children improved in a meaningful way. The training had less, but still positive, impact on aggressive behavior, relationship with the teacher and well-being. Effect sizes were small (between .3 and .5), suggesting that children who need the training the most experienced an increase in well-being, an improvement in the relationship with the teacher and a reduction in aggression. Remarkably, the children who scored just below Table 15.1 Descriptive Statistics for Outcome Measures of Training and Control Group Before

After

Scale

Group

N

M

SD

Positive social interaction

Training Control

224 253

–,03 –,01

1,02 1,03

Emotional well–being

Training Control

236 244

–,02 ,24

Depressed thoughts

Training Control

224 253

Aggressive behavior

Training Control

Perceived social acceptance

M

Difference SD

MD

Sign.

,35 ,10

,96 1,01

–,38 –,11

**

,97 ,85

,18 ,08

,91 ,99

–,20 ,16

**

–,12 –,27

1,08 ,97

–,66 –,43

,92 1,05

,54 ,14

**

223 253

–,08 –,04

,99 ,90

–,20 ,01

1,05 1,05

,12 –,05

*

Training Control

237 244

–,04 ,14

,96 ,87

,17 ,12

,85 ,91

–,21 ,02

*

Relationship with the teacher

Training Control

238 244

,11 ,21

,85 ,89

,19 –,01

,93 1,20

,09 ,22

**

Self–esteem

Training Control

224 253

–,06 ,15

1,13 ,97

,50 ,24

,89 1,08

–,56 –,09

**

Notes M = mean. MD = mean difference (pre-test-post-test). N = number of participants. SD = standard deviation. Sign. = significance level of training effect: *p<.01. **p<.001, indicating that the training group improves more than the control group.

300 Lilian Vliek and Bram Orobio de Castro

Positive social interaction

0.5 0.4 0.3 Training Control

0.2 0.1 0 –0.1

Before

After

Figure 15.1 Positive Social Interaction 0.5

Self-esteem

0.4 0.3 Training Control

0.2 0.1 0 –0.1

Before

After

Figure 15.2 Self-Esteem 0.05 Aggressive behavior

0 –0.05 Training Control

–0.1 –0.15 –0.2 –0.25

Before

After

Figure 15.3 Aggressive Behavior

Depressive thoughts

0 –0.1 –0.2 –0.3

Training Control

–0.4 –0.5 –0.6 –0.7

Before

Figure 15.4 Depressive Thoughts

After

Stimulating Positive Social Interaction 301

Perceived social acceptance

0.25 0.2 0.15 0.1

Training Control

0.05 0 –0.05

Before

After

Relationship with the teacher

Figure 15.5 Perceived Social Acceptance

0.25 0.2 0.15 Training Control

0.1 0.05 0 –0.05

Before

After

Figure 15.6 Relationship with the Teacher

Emotional well-being

0.25 0.2 0.15 Training Control

0.1 0.05 0 –0.05

Before

After

Figure 15.7 Emotional Well-Being Positive social interaction

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large

medium small none Total

0–25

Figure 15.8 Positive Social Interaction

25–50

50–75

75–100

302 Lilian Vliek and Bram Orobio de Castro Self-esteem

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large medium small none Total

0–25

25–50

50–75

75–100

Figure 15.9 Self-Esteem Aggressive behavior

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large

medium

small

none Total

0–25

25–50

50–75

75–100

Figure 15.10 Aggressive Behavior Depressive thoughts

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large medium small none Total

0–25

25–50

50–75

75–100

Figure 15.11 Depressive Thoughts Perceived social acceptance

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large medium small none Total

0–25

25–50

50–75

Figure 15.12 Perceived Social Acceptance by Classmates

75–100

Stimulating Positive Social Interaction 303 Relationship with the teacher

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large medium small none Total

0–25

25–50

50–75

75–100

Figure 15.13 Relationship with the Teacher Emotional well-being

Effectsize

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

large medium small none Total

0–25

25–50

50–75

75–100

Figure 15.14 Emotional Well-Being

the median of the group (group 25–50) on self-esteem and well-being improved more: their effect-sizes were large, >.8.

Discussion In this chapter, insights from research and practice have been combined to give an overview of knowledge on stimulating positive social interaction in youth. Research has identified many factors that are associated with positive social interaction and problem behavior in children and adolescents. TIGER adds to these factors two fruitful starting points for interventions: the desire of children to be a “good” child, friend or student and the feeling of responsibility for their behavior. TIGER has incorporated these factors into its intervention. The effectiveness of TIGER was studied recently. Results were positive and suggest that reminding and focusing children on their fundamental desires in combination with making them responsible for their behavior is effective in stimulating positive social interaction and self-esteem, and in decreasing depressive symptoms. However, these tentative conclusions need further testing, since it is not possible in the used design to study the exclusive influence of these factors: the effects could also be due to other methods that are used, such as the involvement of parents, the teacher and the head teacher of the school. Even so, other evidence for the importance of making youth conscious of their desires and values has been found in two remarkable studies (Cohen, Garcia, Apfel, & Master, 2006; Thomaes, Bushman, Orobio de Castro, & Cohen, under review). In both studies, an assignment of only 15 minutes had a big impact on

304 Lilian Vliek and Bram Orobio de Castro adolescents. The students had to identify and write about their two or three most important values. This improved their grades (Cohen et al.) and decreased aggression (Thomaes et al.). These results suggest that the desire of children and adolescents is an important factor in the forming of their behavior, and hence is recommendable to incorporate in an intervention for stimulating positive social interaction. The current study meets most of the criteria of evidence of effectiveness of Flay et al. (2005) that are described by Hughes and Barrios (this volume). In the current study a detailed description of the intervention with goals and population, etc., was available. The study measured those behaviors and outcomes that the training intended to alter and did this with reliable measures. Validity of all measures was established either with correlations with other measures or on the base of face validity. The data were collected by individuals who did not deliver the training. In this study, psychologists delivered the training and teachers collected the data that are filled in by children. The study sample is described, so the “Generalizability of Findings” standard was met. It is necessary to adjust for multiple comparisons when multiple outcomes were assessed. In the current study, seven outcomes were assessed and all were significant at a level of p < 0.01. This assures that these results cannot be attributed to chance. This study also meets the “Statistically Significant Effects” standard, since on each measured outcome a positive effect was demonstrated, which is far more than the minimum of 50% of the findings in the expected direction. Follow-up measures, half a year after finishing the training, are collected at the time of writing. Results will follow. Two criteria were not met. The “Clarity of Causal Inference” standard and the standard for “Statistical Analysis.” Both result from doing research in a real-world setting. The control groups were not matched with the training groups because all classes with problematic scores were trained as quickly as possible. It was not ethical to wait to give the training to a problematic class, for the purpose of creating a matched control class. The disadvantage of using these unmatched groups was minimized by correcting for pre-test differences in the analyses. Moreover, in the graphs the uncorrected means are shown and they show that, in most of the subtests, the training group started worse but ended better than the control group (the lines cross). This suggests that the training caused large improvements that are probably not due to pre-test differences. In order to meet the standard for statistical analysis, it is necessary to use multi-level analyses to account for the clustering of students into classroom groups. This analysis was not possible because of the limited sample size. Flay et al. (2005) also describe criteria for broad dissemination. Although the study on the effectiveness of TIGER has not met all criteria, the training meets all additional criteria for broad dissemination. The comprehensive manual and books are complete and user-friendly. This creates a high chance of implementation fidelity, although this has not been proven yet. The program can easily go to scale. In fact, in the Netherlands this training is already used on a large scale. A network of school psychologists and organizations spread over the country educate teachers and deliver the training in mental health care centers. Cost information is available too. Monitoring and evaluation tools are widely used in the form of web-based questionnaires for children and teachers. When evidence on the effectiveness of TIGER is extended with studies meeting all criteria, the training can easily be distributed on a large scale. With this chapter, we hope to give a new emphasis to researchers studying factors influencing social behavior, to include two additional factors: the desire of children to behave prosocially and their feeling of responsibility. Since intervention studies are a good method to test causal relationships, researchers and practitioners should find each other and make a combined project on this topic. We hope that future studies on the effectiveness of TIGER will contribute to this knowledge.

Stimulating Positive Social Interaction 305

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Acknowledgement The authors gratefully thank Ewoud Roede, Lisa Jonkman, Nick Broers and Wouter Meijer for their enthusiastic support during the beginning of the research project. We also thank all children who filled in the questionnaires and the teachers, schools and trainers for their participation in the study.

16 A Hybrid Framework for Intervention Development Social Justice for Bullying in Low-Resource Schools Samuel Y. Song, Seattle University Wakako Sogo, University of North Carolina at Chapel Hill The human suffering from Hurricane Katrina . . . provoked new debates about tough public policy decisions, the nation’s troubled racial history and the racial and economic barriers that still separate Americans. . . . Black members of Congress expressed anger Friday at what they said was a slow federal response to Hurricane Katrina. . . . members of the Congressional Black Caucus, along with members of the Black Leadership Forum, National Urban League and the NAACP, held a news conference and charged that the response was slow because those most affected are poor. Secretary of State Condoleezza Rice, the most prominent black person in the Bush administration, downplayed the criticism. . . . “The African-American community has obviously been very heavily affected. But people are doing what they can for Americans. Nobody wants to see any American suffer.” CBS News, September 3, 2005

These excerpts from the national news highlight the complex—yet real-world—concerns of caring for all the communities of Louisiana after the tragedy of Hurricane Katrina. Race, poverty, and the concern for social justice are familiar parts of the United States’ landscape, and their importance is heightened when serving low-resource communities effectively, particularly during times of disasters. Another US tragedy is the well-documented disparity between the mental health needs of children and youth and the resources available to meet those needs including qualified personnel and effective interventions. As in the situation of Hurricane Katrina, this tragedy is magnified for children and youth living in low-resource communities such as those found in urban centers and rural regions of the country. The longstanding disparity between mental health needs and the availability of effective services for all children and youth has contributed to the growing interest in evidence-based interventions (EBIs) among mental health researchers such as prevention scientists (Atkins, Graczyk, Frazier, & Adil, 2003; Doll & Cummings, 2007; Weissberg & Greenberg, 1998; Collins, Murphy, & Bierman, 2004). Even though intervention research first began in the early 1900s, the EBI movement has emphasized rigorous tests of interventions’ efficacy and the translation of research findings into effective practices (Song & Stoiber, 2008). The EBI movement has penetrated virtually all scientific disciplines that aim to intervene on measurable outcomes. Regrettably, the effectiveness of EBIs in real-world settings has been the “Achilles’ heel” of the movement. The high-quality interventions that result from EBI research do not necessarily work in actual practice for a variety of reasons related to the interventions themselves and the contexts in which the interventions are implemented. Contextualizing these problematic findings within the national dilemma of effective mental health services, the disturbing

308 Samuel Y. Song and Wakako Sogo consequence is that children and youth suffer as the unintentional victims of services that are too limited and frequently ineffective. The problem of translating EBIs into real-world solutions is even worse in low-resource communities because much of EBI research has been conducted in suburban districts with a majority of Caucasian students (e.g., Frey et al., 2005). Low-resource communities include urban and rural settings that are typically wracked with numerous social disparities and harmful environmental factors. School settings, the focus of this chapter, are illustrative of the problems of the wider low-resource community. For example, compared to suburban schools, many urban schools are found in impoverished communities with fewer economic resources for education, more community problems (e.g., violence, drugs), and fewer qualified school professionals to do the job (Atkins et al., 2003; Cappella, Frazier, Atkins, Schoenwald, & Glisson, in press). Rural schools are in similar and sometimes worse conditions. Poverty is greater in rural America than in urban areas as approximately 83% of the persistently poorest counties in the United States are in rural areas with child poverty rates that often exceed 35% (Save the Children, 2002). In addition, nearly three out of 10 public school students attend rural schools and over 40% of public schools in the United States are in non-metropolitan areas (Institute for Education Sciences, 2003; National Center for Education Statistics, 2000). There are a number of problems that rural schools must address. These include educating a proportionally higher number of students from low-income families with limited resources with which to educate them. For example, there are limited resources to purchase school supplies, provide professional development activities, and hire teachers and support school personnel who have appropriate training and credentials (National Education Association, 1998; Save the Children, 2002). Finally, these challenging ecological factors found within rural school communities limit the development of rural school youth who, at the same time, have access to fewer intervention programs that work (Dulmus, Theriot, Sowers, & Blackburn, 2004; Duncan & Brooks-Gunn, 1997; Farrell, Valois, Meyer, & Tidwell, 2003). To summarize, translating EBIs into low-resource communities is much more complex and challenging, which has made EBI research difficult in low resource communities. Consequently, we know the least about how to help children and youth in low-resource communities yet they are the most in need of services. This deplorable situation constitutes a problem of social justice, as these communities—and their children and youth—systematically do not have adequate access to effective mental health services (Atkins et al., 2003; Shriberg, Bonner, Sarr, Walker, Hyland, & Chester, 2008). While such a critique may appear scathing, the issue of translating EBIs into the real-world settings of low-resource communities has been consistently raised by a number of leading EBI scholars (Atkins et al., in press; Atkins et al., 2003; Cappella et al., in press; Song & Stoiber, 2008). How do we efficiently develop EBIs that are effective in the real-world setting of low-resource schools? The purpose of this chapter is to contribute to the scholarly discussion on the EBI researchpractice gap by asserting that prevention science needs to move towards innovative hybrid models of prevention in schools, especially in low-resource communities. First, we will describe the EBI research-practice gap within the context of school-based prevention and lowresource communities. Second, we propose a solution. Third, we illustrate the solution with the problem of school bullying prevention.

The Problem: Tension Between Efficacy vs. Effectiveness The traditional approach to EBI prevention research occurs across several sequential phases: efficacy, effectiveness, sustainability, and scalability. Benefits of this traditional EBI approach

A Hybrid Framework for Intervention Development 309 are both scientific and practical. From a scientific perspective, the EBI movement has created standards for evaluating interventions across a number of disciplines, contributed to the development of sophisticated research design and analytical techniques, and contributed to higher-quality intervention research. From a practical perspective, the EBI movement has contributed to the development of high-quality interventions that may be used in the real world for a variety of problems. Still, this type of research paradigm has two significant obstacles: (a) the age-old problem that efficacious interventions are not widely disseminated into practice; and (b) the ethical dilemma that each phase of research may potentially last decades before an intervention’s realworld effectiveness is established, delaying the intervention’s implementation with real children. These result in a research-to-practice gap and an inherent tension between efficacy and effectiveness. The problems associated with the research-practice gap are varied. The traditional model’s emphasis on efficacy and internal control first with homogenous samples and highly trained therapists requires substantial research resources without demonstrating whether the efficacious intervention works in practice. Despite having identified a number of EBIs for youth problems, we know that real-world settings often do not use them well or at all; and the complexity of real-world settings limits the quality and relevance of EBI knowledge coming from rigorous research studies (Atkins, Frazier, & Cappella, 2006; Higa & Chorpita, 2008). This area of research is called EBI dissemination research, which examines the adoption and implementation of EBIs in the real world. Adoption barriers to effective dissemination of EBIs have been categorized by Higa and Chorpita (2008) as knowledge and attitudes. Knowledge barriers consist of awareness of the EBI and skills in implementing the EBI or “the know-how.” Attitude barriers include negative and positive beliefs about and feelings towards an EBI and its use in practice. Higa and Chorpita also identify practice or contextual characteristics of the setting in which individuals work as strong determinants of using EBIs (2008). These include few incentives to start using EBIs and many costs for using EBIs such as extra time to learn new interventions and money for training. Once a decision to use an EBI has been made, the next step is to actually implement the EBI in practice as it was designed. The quality of an intervention’s implementation is critical to the effectiveness and durability of an intervention, as each intervention must be delivered (or put into action) to have an effect (Durlak, 1998). Therefore, it is no surprise that intervention researchers from various disciplines have underscored its importance and called for further research in this area (Durlak, 1998; Graczyk, Domitrovich, & Zins, 2003). Achieving highquality intervention implementation in schools is a complex process that relies on social processes between the intervention developers and the school professionals and staff, community members, and youth in schools who either deliver or receive the intervention. Therefore, implementation is strongly influenced by a variety of contextual factors (Ozer, 2006). To address the dissemination of EBIs, various models have been proposed that differ along the traditional lines of those valuing research (or science, theory, internal validity, or expert model of intervention delivery) and those valuing practice (or clinical service, practicality, external validity, or collaborative model of intervention delivery). Understandably, there has been a strong tension between these two divergent world views and their competing values and priorities. Dissemination is often the center of the research-practice debate; specifically, how to achieve high-quality intervention implementation. A key factor related to high-quality intervention implementation is ecological fit (or sometimes referred to as ecological validity),

310 Samuel Y. Song and Wakako Sogo which is the degree to which an intervention integrates well with the local school context (e.g., a school’s unique strengths and needs). To achieve ecological fit, many models emphasizing real-world practice propose that certain aspects of the intervention must be adapted or modified to fit the local school context. Interestingly, this process of adaptation has been increasingly recognized as a naturally occurring phenomenon in schools. For example, in a four-year study of 293 local school-based educational change projects, the researchers concluded that successful implementation in schools always involved a process of mutual adaptation of the intervention to the existing local school context (McLaughlin, 1990). Indeed, many schoolbased intervention researchers have claimed that it is virtually impossible to implement an intervention as originally designed in a school (e.g., Graczyk et al., 2003). One way to achieve ecological fit is by collaborating or partnering with school stakeholders to tailor programs to match the unique needs of their students, teachers, schools, parents, and communities. Various scholars have developed explicit methods of partnering with local schools to collaboratively develop all phases of intervention research including development, implementation process, and evaluation (e.g., Atkins et al., 2006; Nastasi et al., 2000). Additional benefits of such collaboration include promoting “buy-in” of the intervention by the school community; increasing acceptability (degree to which consumers find the intervention procedures and outcomes acceptable in their daily lives); enhancing feasibility (degree to which intervention components can be implemented in naturalistic contexts); and enhancing sustainability (extent to which the intervention can be maintained without support from external agents). For example, teacher acceptance and commitment to a program or intervention strategy, as well as the presence of a site-based facilitator to support the implementation, are among the most potent determiners of high-quality and sustained use of peer coaching and other evidence-based practices (Gersten, Chard, & Baker, 2000). The outcome of attending to these contextual factors is to improve the likelihood for high-quality implementation, resulting in more effective and durable effects for the target community (Nastasi et al., 2000; Stoiber & Waas, 2002). In contrast, collaboration with stakeholders to adapt the intervention has been criticized as lacking scientific rigor and relying too much on local wisdom or consensus without capitalizing on the intervention research base for guidance (Bierman, 2003). For example, collaborative approaches may allow for selection of an intervention component that is not based on empirical evidence, and certain modifications in intervention programs may reduce its impact. How can we relieve this tension between scientific rigor and real-world practice? Clearly, tension exists between the two goals of rigor and engaging school context, or research versus practice. In response to this tension, scholars have suggested a hybrid model that connects these competing goals and the strengths of their approaches, resulting in intervention research that is both rigorous and contextual, efficient and effective (Hohmann & Shear, 2002; Weissberg & Greenberg, 1998). Hybrid Model Solution At the most common and general level, a hybrid model of prevention and intervention disseminates existing EBIs into diverse schools with an emphasis on real-world practice. For example, the work of Mark Atkins and colleagues emphasizes sustainability of EBIs in low-resource urban schools in Chicago (Atkins et al., in press; Atkins et al., 2003). Using a number of innovative theoretical frameworks and other practical innovations, their research seeks to disseminate EBIs effectively first through collaborative adaptation of intervention

A Hybrid Framework for Intervention Development 311 implementation protocols to ensure that these high-needs and low-resource schools are getting as much help as possible (i.e., enhancing sustainability). For example, social diffusion theory posits that innovations become disseminated throughout a social network through the persuasion of key opinion leaders (Rogers, 1995). To produce effective and sustainable change in schools, Atkins and colleagues have developed a model based on social diffusion theory in which key teachers are selected and trained as intervention deliverers who in turn train other teachers in the intervention to improve sustainability (Atkins et al., in press; Atkins et al., 2003). Other scholars have criticized this type of approach as lacking standardization and intervention fidelity (Bierman, 2003). These scholars have proposed “adaptive interventions” that are more researcher-controlled and scientifically studied (Collins et al., 2004). Adaptive interventions attempt to account for varying individual needs by assigning different dosages of certain program components based on predetermined decision rules, thereby accommodating to real-world practice while maintaining scientific replicability and control (Collins et al., 2004). While improving interventions by allowing for clinical flexibility, this approach is still reminiscent of the traditional model of intervention development that favors science over real-world practice, is a rather lengthy process as decision rules need to be studied extensively, and may not be appropriate for low-resource schools in high need (Atkins et al., 2006). A hybrid model has also been applied to the development of interventions. The Deployment-Focused Development Model addresses the research-practice gap and slow rate of dissemination of EBIs into clinical service by speeding up the process (Weisz, 2000; Wiesz, Jensen, & McLeod, 2005). In six steps, this model quickly moves from small, pilot efficacy tests to trials in actual service settings with an emphasis on the real-world setting in which the intervention will be employed. The objective of the first step is to produce a manualized treatment protocol based on theoretical and empirical research as well as input from clinicians who actually work with the population and disorder. The goal is an intervention manual that is well grounded in practice and clearly explains intervention components using real-life illustrations and concepts whenever appropriate. Step 2 involves an initial efficacy trial under controlled conditions to assess whether the treatment can produce beneficial effects. Step 3 consists of a series of single-case pilot tests in real clinic settings to further adapt the protocol to fit the clinic setting while staying faithful to the core principles and the model of change guiding the treatment protocol. These case studies would include clinic-referred clients treated in clinical settings by research-affiliated therapists who know the protocol well under the supervision of a research team member and an experienced member. Step 4 examines the newly adapted treatment protocol with a series of sequential group-design studies. Each study would focus on a key factor of representative clinical practice (e.g., using non-research therapists) with the goal of positioning the protocol for a full test of effectiveness and dissemination feasibility studies in Step 5. This step is a series of group-design clinical trials comparing the protocol in realworld practice against a usual care condition. Finally, Step 6 examines the treatment program in relation to the actual clinical settings in which it is used. Due to the degree of collaborative input by real-world clinicians in this model, the treatment program effects should be enhanced compared to other models of intervention development. Weersing and colleagues have suggested that while this model may not be the best idea in completely novel areas of treatment research, it is a good framework for treatment adaptation; and they have used it to develop an efficient treatment protocol for youth anxiety and depression using cognitive behavioral therapy strategies (Weersing, Gonzalez, Campo, & Lucas, in press). Common threads of hybrid models include a balanced valuing of science and real-world practice in dissemination or development of interventions compared to the traditional model.

312 Samuel Y. Song and Wakako Sogo One area to which a hybrid model has not been applied is the conceptualization of the problem targeted by interventions. Typically, for example, problems are targeted at the organism or system that needs intervention such as the individual or classroom, while practical issues of intervention fidelity are considered separately during the intervention evaluation. In contrast, it is also possible to simultaneously consider intervention fidelity and evaluation as one complex problem. Doing so will “prime the pump” for successful deployment and will expedite the intervention development described by Step 1 of Weisz et al.’s (2005) Deployment Focused Development Model. Recasting traditionally-defined problems onto a hybrid framework may prompt more effective interventions that are sustainable in lowresource schools. In the remainder of the chapter, we discuss a hybrid framework for intervention development that incorporates the conceptualization of the problem into existing hybrid models. To promote a deeper understanding of the utility of the hybrid framework, we use the problem of school bullying to illustrate the model for intervention development.

Hybrid Framework of School Bullying Prevention The hybrid intervention development model proposed here adds a preliminary procedure before following a process of development, refinement, pilot testing, and manualizing of the intervention (adapted from the Deployment Focused Development Model’s Step 1; Weersing et al., in press; Wiesz et al., 2005). As portrayed in Figure 16.1, the process of developing the intervention manual consists of four distinct steps. First, the traditional problem must be recast into a hybrid problem that includes dissemination challenges. Second, based on this reconceptualization of the problem into a hybrid problem, the critical processes that are likely to address the hybrid problem must be determined. Third, empirically supported interventions that influence these critical processes are identified and/or developed using procedures that are empirically supported. These interventions are written in a manual. Fourth, the intervention manual is adapted based on feedback from relevant school personnel who have implemented interventions for the hybrid problem. Then, this refined version of the manualized intervention is ready to be evaluated in the next phase (Step 2 of Deployment Model). Finally, it is recommended that the intervention manual be further refined to promote the ecological fit of the intervention to a particular school prior to its evaluation. Step 1: Reconceptualizing School Bullying as a Hybrid Problem It is clear that the problem of school bullying is broader than simply the negative outcomes associated with it. Indeed, school bullying is a hybrid problem because it faces two central

Step 1

Step 2

Step 3

Step 4

Step 5

Conceptualize hybrid problem

Conceptualize the solution to the hybrid bullying problem

Develop intervention manual

Adapt intervention manual based on relevant school personnel feedback

Adapt intervention to promote ecological fit

Figure 16.1 Hybrid Intervention Manual Development.

A Hybrid Framework for Intervention Development 313 obstacles that make school bullying intervention challenging: (a) difficulties addressing the various ecological factors that contribute to bullying and (b) the limited real-world feasibility of comprehensive bullying intervention programs in schools. Song and Stoiber (2008) have critically reviewed the research regarding the complexity of bullying and bullying interventions and cogently argued for an expanded view of the bullying problem that they called the real bullying problem. This section presents their argument and extends it to a hybrid framework. Ecological Phenomenon. School bullying involves much more than the two participants of bully and victim. Bullying involves the whole school and is best viewed as an ecological phenomenon that emerges from social, physical, institutional, and community environments as well as the individual characteristics of the youth involved (Swearer & Doll, 2001). Within this ecological framework, the disposition in the individual (e.g., limited social competencies, negative attributions, poor emotion regulation skills) interacts with the social environment (e.g., peer relationships, class rules, teacher behaviors, school culture) to foster bullying behavior (Bronfenbrenner, 1979; Pianta & Walsh, 1996). Various ecological factors can have a powerful influence on school bullying by either encouraging or maintaining its occurrence (Hirschstrein, Edstrom, Frey, Snell, & MacKenzie, 2007; Orpinas & Horne, 2006; Doll, Song, & Siemers, 2004). Important ecological factors to consider include teachers’ behaviors and beliefs surrounding bullying, and peer and victim responses to bullying. Teachers are essential for effective school bullying intervention. However, they often do not effectively intervene on students’ behalf for several reasons. First, teachers and school personnel sometimes contribute to bullying through their inaction (Boulton & Underwood, 1992; Hoover, Oliver, & Hazler, 1992; Olweus, 1991; Twemlow et al., 2001). Studies have shown that school adults intervene at low rates despite teacher reports of higher intervention (Craig & Pepler, 1997; Leff, Kupersmidt, Patterson, & Power, 1999). Teacher inaction during bullying episodes may be due to their pro-bullying beliefs and limited knowledge about bullying. Over 90% of teachers acknowledged that bullying occurred in the classroom but downplayed it, while 25% believed it was helpful to ignore bullying (Stephenson & Smith, 1989). Second, even when teachers do intervene, they often do not use effective strategies. For example, it has been found that didactic methods do not likely lead to a student appropriately asserting him- or herself during a conflict (Hirschstein et al., 2007) while the degree to which teachers incorporate interactive instructional techniques such as role playing and peer coaching has been associated with reduced aggression among students (Conduct Problems Prevention Research Group, 1999). Alarmingly, however, teachers who implement bullying prevention programs report much lower use of role-plays and class meetings than of didactic methods (Kallestad & Olweus, 2003). Peers also tend not to intervene during bullying episodes despite being present 85% of the time (Craig & Pepler, 1997; Naylor & Cowie, 1999). Some peers have actively joined in the bullying, while others have encouraged bullying by watching, laughing, or remaining silent (Craig & Pepler 1997; Naylor & Cowie 1999; Salmivalli, Lagerspetz, Bjorkvist, Osterman, & Kaukianen, 1996). Finally, within this bullying-encouraging context, it is not surprising that victims also do not seek help from others. Children who are bullied may also encourage bullying by not telling teachers or adults about their bullying difficulties (Pepler, Craig, Ziegler, & Charach, 1994). This may be due to fear of retaliation from the bully or because they perceived adults as inept, uncaring, or unable to protect them (Pepler et al., 1994). Lack of Evidence-Based Interventions. School districts’ adoption of anti-bullying policies and their establishment of bullying prevention and intervention strategies are increasing across the country (Limber & Small, 2003). Although intervention research in general has

314 Samuel Y. Song and Wakako Sogo been evident since the early 1900s, the interest in translating research findings into effective instructional and intervention practices has been particularly invigorated with the EBI movement (Kratochwill, 2006; Stoiber & Kratochwill, 2002a, 2002b; Shavelson & Towne, 2002; Stoiber & Kratochwill, 2000). Thus, the interest in developing EBIs for bullying is based on the goal of developing interventions that can be effectively applied in real school settings. Despite a well-established literature on the definition of bullying and its consequences, there is limited empirical evidence of the effectiveness of bullying intervention programs. In particular, there is a severe gap in peer-reviewed research on these programs. Without welldesigned research, we cannot move forward in determining what bullying interventions work, and what conditions are necessary for optimal outcomes to be achieved. Hence, there is a clear need for high-quality intervention studies that examine essential dimensions and components of anti-bullying intervention programs. A number of bullying interventions have been developed to alter the frequency and degree of bullying behavior, with the aim of improving student safety and academic, health, and mental health functioning (Flannery et al., 2004; Hirschstein et al., 2007). Most of these bullying intervention approaches are comprehensive, utilizing multiple components to target a broad range of bully, victim, and bystander decisions and behaviors throughout the “whole school.” Such comprehensive, whole-school bullying intervention programs are often viewed as most efficacious, with one program reporting a significant reduction of bullying by 50% (Olweus & Limber, 1999). However, recent meta-analyses of whole-school bullying intervention research have found that many replication studies show no beneficial effect, and surprisingly some have a negative effect on bullying (Smith et al., 2004). The primary reason for these mixed results was the poor quality of intervention implementation. Indeed, in a recent comprehensive examination of education intervention studies, treatment integrity was measured less than 15% of the time (Gresham, MacMillan, BeebeFrankenberger, & Bocian, 2000; Snyder et al., 2002; Wolery & Garfinkle, 2002). Interestingly, in the study conducted by Smith and colleagues (2004), a greater reduction in studentreported victimization was shown in the bullying intervention studies where intervention fidelity was addressed. The importance of addressing intervention fidelity in intervention research cannot be understated. Stoiber and Kratochwill (2002a) conclude that perhaps the greatest obstacles to establishing EBIs pertains to the interrelated issues of acceptance, feasibility, and sustainability. School administrators typically assess the cost-benefit ratio to make sure that the intervention is both cost-effective and practical to implement. For schools and staff to “buy in” to bullying intervention programs, costs in materials, time, and training must be balanced against the expected “pay-offs” of the intervention. Since teachers and administrators often “miss” incidences of bullying they may also underestimate the degree to which bullying is viewed as a problem “big enough” to require a comprehensive intervention approach. Conclusion. Considering school bullying broadly as a hybrid problem of research and practice has revealed the true complexity and challenge of effectively intervening. It is clear that effective interventions for school bullying must address the various ecological factors that contribute to bullying and the real-world feasibility of comprehensive bullying intervention programs in schools. The next step is to determine what solutions might best address the hybrid problem.

A Hybrid Framework for Intervention Development 315 Step 2: Reconceptualizing the Solution to the Hybrid Bullying Problem: The Protective Peer Ecology Conceptualizing bullying as a hybrid problem allows us to conceptualize bullying intervention in a new way by developing hybrid solutions. Based on extant theory and research, this section discusses the rationale for an innovative conceptual framework for effective school bullying prevention focused on the critical processes that reduce bullying—the protective peer ecology, which has been previously detailed by Song and colleagues (Song, 2006; Song et al., 2005, 2009; Song & Stoiber, 2008). First, the peer ecology is introduced and the protective peer ecology is discussed with its associated rationale for its importance. Then, the integration of the protective peer ecology framework within existing school practices is proposed with an illustration of it. Peer Ecology and Effective Bullying Prevention. From an ecological framework, peers are part of the microsystem, which is the immediate, proximal setting in which behavior unfolds and is an essential context for development. “The peer ecology is that part of a children’s microsystem that involves children interacting with, influencing, and socializing one another. Peer ecologies do not include adults, but can affect and be affected by them” (Rodkin & Hodges, 2003, p. 384). The peer ecology may be the most influential context for bullying prevention (Song, 2006; Song et al., 2005, 2009; Song & Stoiber, 2008) and as such may be the best target for bullying prevention. Peers can help correct the inherent power imbalance between bullies and victims and can address the pro-bullying school environments effectively (i.e., inaction of school personnel). For example, peers can protect one another from bullying more effectively than adult school personnel because peers are typically present during the majority of bullying interactions, and they can detect even the covert occurrences of bullying (e.g., Craig & Pepler, 1997). Victims of bullying may also be more likely to come to a peer for help and support instead of school personnel. Finally, peers are a naturally existing school resource that may substantially reduce intervention implementation burdens for schools, thereby addressing the real-world challenges for effective bullying prevention. This idea may be the most compelling reason to focus on peers. Indeed, experts in evidence-based interventions have advocated for more research focused on the identification of the critical mechanisms that lead to improved outcomes (Hoagwood & Olin, 2002). Once identified, innovative interventions can be developed that target the critical mechanisms producing streamlined—yet powerful—interventions (Kazdin, 2001; Schoenwald & Hoagwood, 2001; Weersing & Weisz, 2002; Weisz, 2000). These streamlined programs are likely to be more feasible to implement and thus more effective because peers are a critical mechanism for bullying prevention. Further research on school bullying prevention needs to focus on the peer ecology and the identification of the critical peer processes that lead to reduced bullying. Protective Peer Ecology and Effective Bullying Prevention. To guide the investigation of critical processes within the peer ecology, the idea of a protective peer ecology is helpful (Song, 2006; Song et al., 2005, 2009). A protective peer ecology refers to the aspects of children’s interactions with one another that serve as a shield against internal or external sources of stress. Rodkin and Hodges (2003) explained that peer ecologies include social structures that organize children’s behavior horizontally and vertically. Horizontal structure includes numerous and diverse social relationships, which provide multiple paths for children to enjoy social support. Examples include dyadic friendships, peer groups, and enemy relationships. Vertical structure of peer ecologies refers to social power, social status, and the consequent degree of

316 Samuel Y. Song and Wakako Sogo influence. Some children have more power in the peer group and are more valued than other children. In support of the protective peer ecology, research has convincingly demonstrated that positive peer affiliations (i.e., having a friend, number of friendships, peer acceptance) are significantly related to being bullied less (Hodges, Malone, & Perry, 1997; Pellegrini, Bartini, & Brooks, 1999). It has also been demonstrated that complex peer group dynamics had a strong influence on peer behaviors toward bullying (Cairns, Leung, Buchanan, & Cairns, 1995; Farmer, Estell, Bishop, O’Neal, & Cairns, 2003). Other researchers have shown that having friends who are strong and aggressive was more important in being bullied less than having friends who lacked these characteristics (Hodges & Perry, 1999). Additional research has directly supported the importance of protection. These researchers found that peer protection mediated the relation between having a best friend and being bullied less over the period of one year (Hodges, Boivin, Vitaro, & Bukowski, 1999). Finally, many bullying prevention researchers believe that peers who protect others are critical for bullying prevention and have advocated for schools to create an “anti-bullying” culture among their students (Frey et al., 2005; Olweus & Limber, 1999). Theoretically, peers may be the best suited to address the complex ecological factors that encourage and maintain bullying in schools. On a practical level, mobilizing peers as interveners may ease implementation burdens on schools because peers are “already there.” Finally, there is strong evidence supporting the importance of peers in reducing bullying, including general peer relation factors such as positive peer relationships and direct peer action to protect others from bullying, both of which are important in understanding a protective peer ecology against bullying. Improved Implementation and Integration with Existing School Practices. Protective peer ecological interventions may be more acceptable to school personnel and may be implemented more readily because they are more manageable compared to multi-component bullying programs (Song & Stoiber, 2008). Better implementation should lead to increased sustainability of the intervention over time in schools. Thus, achieving high implementation and sustainability will lead to better outcomes for children who are exposed to bullying. Focusing on the protective peer ecology for bullying prevention will also produce less redundancy with other school intervention programs, which likely results in fewer implementation challenges in schools (Song & Stoiber, 2008). For example, to support children’s social and academic development, numerous schools have turned to prevention models that follow a three-tiered approach, such as school-wide Positive Behavior Supports (PBS; Sugai & Horner, 2006). Implementing another school-wide intervention program in addition to PBS, such as one of the comprehensive bullying prevention programs, may very well conflict directly with existing school resources. However, the protective peer ecology framework can be integrated into existing school-wide intervention programs without being redundant and adding unnecessary intervention components. Interventions based on the protective peer ecology framework may be integrated into all three tiers of school-wide intervention programs, which is a unique advantage of the protective peer ecology framework (Figure 16.2). Step 3: Intervention Manual Development Up to this point, we have conceptualized school bullying within a hybrid framework—from the beginning—resulting in a focused target from which to build an intervention. Specifically, the protective peer ecology has been identified because of its value in effectively combating school bullying (critical mechanism of change) and its potential to be easily implemented and

A Hybrid Framework for Intervention Development 317

Intensive protective peer ecology interventions

Targeted protective peer ecology interventions

Universal protective peer ecology interventions

Existing school wide programs

Figure 16.2 Protective Peer Ecology Framework in Schools.

sustained in schools. Step 3 of the hybrid model is the initial development of the intervention manual itself. First, intervention components need to be evaluated and selected for inclusion in the initial manual. These intervention components need to meet two criteria: First, the intervention must be empirically supported; and, second, the intervention must be theoretically consistent with the protective peer ecology framework. Though we are in the process of completing this step, we will describe our current work in this area. Criterion 1: Empirically Supported Bullying Interventions. Despite the urgent need to establish an evidence base in the area of bullying interventions, school bullying intervention research has not matured to meet the rigorous scientific standards of EBIs. Evaluations of the evidence or scientific base of an intervention typically focus on whether reliable and valid methods were applied in conducting the intervention research study (Song & Stoiber, 2008). The following criteria have been considered pertinent in determining effectiveness of instructional interventions: (a) used an experimental and/or quasi-experimental design; (b) occurred for a reasonable length of time; (c) incorporated multiple outcome measures; (d) collected post-intervention as well as follow-up data after a specified period of time subsequent to program implementation; (e) assessed treatment integrity (i.e., degree to which intervention was implemented as intended); (f) employed appropriate statistical methods; (g) had a sufficient sample size to measure effects; and (h) showed significant positive effects on appropriate outcomes, such as improved school climate or school safety (Stoiber & Waas, 2002). Recently, three independent systematic reviews of school bullying intervention evaluations concluded that the findings were mixed regarding the popular comprehensive “whole-school” approaches, and did not recommend any specific program as “more effective” (Baldry & Farrington, 2007; Merrell, Gueldner, Ross, & Isava, 2008; Vreeman & Carroll, 2007). Indeed, highlighting this limitation in the bullying intervention research, Song and Stoiber referred to

318 Samuel Y. Song and Wakako Sogo it as “the uneasy state of the science” (2008, p. 239). As a consequence, instead of EBIs, we examined programs that had “empirical support” for use in schools because studies had demonstrated that the programs resulted in positive school bullying outcomes. Criterion 2: Consistent with Protective Peer Ecology. Bullying programs with empirical support also needed to be consistent with a protective peer ecology framework. To meet this criterion, bullying programs needed to explicitly identify a protective peer ecology intervention goal and include an intervention strategy to influence it. Virtually all school bullying programs propose that the development of an “anti-bullying culture or climate” in which students protect and support one another is important; however, most of these programs do not explicitly address it as an intervention goal or provide an intervention strategy to promote it. We defined a protective peer ecology intervention goal as increasing the proportion of peers or students in a class, grade, or school who: (a) intervene against bullying interactions or on behalf of victims of bullying (e.g., stand up to bullies); and (b) support victims of bullying (e.g., social or emotional support). Intervention strategies explicitly targeted to achieve the goal could be direct or indirect and consist of any of these: (a) Teaching social competence to students (e.g., empathy, social skills); and (b) Implementing environmental strategies (classroom management, behavioral strategies, peer group strategies, collaborative strategies). Identified Program Components. Based on the two criteria, we identified several promising programs. The programs were varied yet some components were similar conceptually. Therefore, we present the components that could guide the development of our intervention manual: 1

2

Teaching social competence to all children, specifically bystanders, through a teacherdelivered curriculum. We were not surprised to discover this component, as most bullying intervention programs seek to build social competence of all students. However, fewer bullying programs have specific goals and curriculum focused specifically on bystanders. Social competence curricula include teaching awareness of bullying and skills to use in bullying situations. Though there were several programs here, Steps to Respect (Committee for Children, 2001) is the model program. This program is a social competence curriculum with a specific target of bystanders and peers who encourage bullying. Delivered by teachers, the program expects to influence social norms regarding bullying behavior transactionally by changing individual students. Creating protective peer groups is the intentional formation of peer groups to either intervene during bullying situations and/or provide support for students. There were three variations of this intervention component found. First, Olweus’ school bullying program (1993) is recognized as the standard comprehensive school bullying intervention program and as more effective than other programs (Baldry & Farrington, 2007; Vreeman & Carroll, 2007). It uses a whole-school approach that is based on the ecological perspective (school-level, classroom-level, individual-level, and community-level components). While this program is much broader than the protective peer ecology, it does focus specifically on obtaining help from “neutral students” in the classroom to

A Hybrid Framework for Intervention Development 319 provide support and protection for the victims. The teacher enlists help of “neutral” or well-adjusted students to alleviate the situation of the victims. The teacher establishes informal cooperation with some friendly and resourceful students in the class who are not involved in bullying. These “key students” display active disapproval of bullying by protecting the victim and neutralizing the bully (Olweus, 1993, pp. 105 & 117). Second, Salmivalli and colleagues used a peer-led intervention campaign (Salmivalli, Kärnä, & Poskiparta, this volume; Salmivalli et al., 2005). Instead of teachers leading the anti-bullying campaign, model peers were trained and implemented it while also providing social support to students in need. Third, Cowie and colleagues’ peer-support model was used as part of a whole-school intervention which focuses on providing social support for students in need (Cowie & Smith, this volume; Menesini, Codescasa, Belenni, & Cowie, 2003). Step 4: Adapt Intervention Manual Based on School Personnel Input The purpose of this step is to obtain feedback from experienced school practitioners who have implemented school bullying programs before, such as school psychologists, school counselors, school social workers, teachers, and administrators. The feedback will be used to modify the interventions, and adapt the procedures, language, and supporting materials to ensure ecological fit. From using these procedures in an iterative process, the core components of school bullying interventions are selected to be included and developed in a manual. Step 5: Adapt Manual to Promote Ecological Fit and Prepare for Intervention Evaluation While the focus of this chapter is on the novel development of the intervention manual rather than evaluation, it is important to highlight the first step in the Intervention Evaluation Phase. This step is a precursor to the initial implementation and evaluation of the intervention manual in a local school. Its purpose is to further refine the manual to the local school ecology in which the intervention will be implemented. Three primary goals will be ensuring that the procedures and language will work for the school, conducting resource mapping for each school; and securing “buy-in” by each school’s key stakeholders. Resource mapping will occur for each school that focuses on school resources directly related to bullying (e.g., school bullying policies, other violence interventions). Following the procedures outlined by Adelman and Taylor (1998), resource mapping seeks to organize existing school resources so that new interventions can be integrated with them. The degree to which school resources are not organized and integrated is an important setting factor that negatively influences intervention implementation. Sustainability can also be addressed by integrating new interventions with existing school resources (Adelman & Taylor, 1998). At this point, the intervention manual will be finalized for the particular school and is ready to be used and evaluated. Preparing for Evaluation. A crucial step in preparing for the intervention’s evaluation is considering the availability of assessment tools that can detect the intervention’s impact on key constructs related to the program goals. For example, while there are a number of instruments to assess bullying, there are few that assess critical factors of the protective peer ecology and the failure to assess this aspect of schools’ anti-bullying climate has been a serious shortcoming in prior evaluations of anti-bullying curricula. The first author has developed an instrument that appears to be promising as a tool to be used in an evaluation study of the protective peer ecology intervention framework. The middle school version of the Protective Peer Ecology Scale (Song, 2005) was developed to measure four critical variables of the protective peer ecology: peer protection, peer protector, peer encouragement of bullying, and peer encourager of

320 Samuel Y. Song and Wakako Sogo bullying. It was an extension of the original elementary school version of the Protective Peer Ecology Scale (Song, 2004, 2006), which was developed from a comprehensive review of developmental research, focus group interviews with school personnel and children, and expert review. The elementary Protective Peer Ecology Scale demonstrated good psychometric properties in previous studies (Song, 2006; Song & Siegel, 2006a; Song & Siegel, 2009; Song et al., 2009); for example, all items loaded strongly on their respective factors and separately from the other, accounting for a cumulative total of 50.5% of the variance explained by the factors; internal consistency using coefficient alpha was .86 indicating adequate reliability; and the sub-scales related both significantly and in the expected directions with known correlates, i.e., positive relations with positive peer relationship variables and negative relations with being bullied variables. The middle school version was developed through a series of focus group interviews consisting of relevant school personnel, i.e., school administrator, teachers, school counselors, and school psychologists. The focus group critiqued the elementary version of the scale and several new items that were developed to assess peer encouragement of bullying. The focus group made suggestions regarding language, appropriateness, and format of the scale and its items. Based on the findings, 26 items were chosen for the final scale to assess four factors: peer protection, peer encouragement of bullying, peer protector, and peer encourager of bullying. Peer protection and peer encouragement of bullying subscales measure perceptions of the peer context regarding classmates’ protection from bullying and classmates’ encouragement of bullying. Ratings are obtained through students’ responses on a five-point scale (never to always) to the prompt, “If I’m being bullied . . .” Peer protection from bullying is a sub-scale comprising eight items (e.g., my peers would try to stop the bullying) that measures students’ perceptions that peers would intervene if they were bullied. The peer encouragement of bullying subscale is a five-item subscale that includes items such as “my peers would laugh.” The third subscale peer protector includes eight items designed to assess a student’s inclination to protect others from bullying (e.g., I would try to make the others stop bullying) that students rate on a five-point scale (never to always) in response to the prompt, “If I know that someone in my school is being bullied . . .” Finally, the peer encourager of bullying subscale is five items with the same prompt and format, but the items assess a student’s inclination to encourage bullying in the peer context (e.g., I would laugh). All four subscales of the first draft of the Protective Peer Ecology Scale—Middle School Version had adequate dimensionality and reliability. Preliminary analyses of the dimensionality of the scale have provided strong support for the four factors based on exploratory factor analysis using principal axis factoring. Using a sample of 428 sixth- through eighth-graders from the Northeastern US, all items loaded strongly on their respective factors (ranged between .50 and .90) and independently, explaining a total of 67.4% of the variance. Values for coefficient alpha were all strong indicating adequate reliability: peer protection (.94), peer protector (.92), peer encouragement (.87), and peer encourager (.86). To examine the concurrent validity of the scale, bivariate correlations were examined with commonly accepted measures of known correlates: peer inclusion (Peer Relationships scale of the ClassMaps survey; Doll, Zucker, & Brehm, 2004); social support (Peer Social Support subscale of the Classroom Life Measure; Johnson, Johnson, Buckman, & Richards, 1985); and bullying/being bullied (the University of Illinois Victim Scale; Espelage & Holt, 2001). The findings were consistent with theory and supported the validity of this scale in a concurrent analysis: (1) the correlations between the Protective Peer Ecology Scale’s factors were in the expected directions and significant (p < .01), as peer protection was correlated positively with peer protector yet negatively related with peer encouragement and peer encourager; peer protector was

A Hybrid Framework for Intervention Development 321 correlated negatively with peer encouragement and peer encourager; and peer encouragement and peer encourager were related positively; and (2) the correlations between the Protective Peer Ecology Scale and the known correlates were also in the expected directions and significant (p < .01), as peer protection and peer protector related positively with positive peer relation variables yet negatively with being bullied; peer encouragement was related negatively with positive peer relation variables yet positively with bullying and being bullied; and peer encourager related positively with bullying. Particularly important for the development of a protective peer anti-bullying intervention, the findings were consistent with theoretical relations between peer protection, peer relationships, and bullying. Being protected by classmates and protecting others were correlated positively, but correlated negatively with having a peer context that encourages bullying and encouraging bullying. Students who had peer protection from bullying also had positive peer relationships, and were bullying and bullied less. Students who protected others from bullying also had positive peer relationships, and were bullying less. In contrast, students who had peers that encouraged bullying also had less positive peer relationships, and were bullying and being bullied more. Finally, students who encouraged bullying also were bullying more. The availability of the Protective Peer Ecology Scale will make it possible to explicitly examine the changes in peer protection that might occur in response to the intervention. The next step in this line of research will be to evaluate the intervention manual under controlled conditions to describe the effects of the treatment. While this is not the focus of this chapter, evaluating the intervention is a phase that would continue to improve the intervention program based on a series of studies (Wiesz et al., 2004; Atkins et al., 2003; Song & Stoiber, 2008).

Summary Prevention science needs to move towards innovative hybrid models of intervention in schools to meet the urgent needs of low-resource communities for effective mental health interventions. The traditional model of intervention development is rigorous—and may be too rigorous to be effective and efficient in real-world practice, as dissemination of the intervention into real-world situations is not emphasized until efficacy is established in researchercontrolled settings. Hybrid models of intervention development have been proposed by leading prevention science researchers as a balance between rigor and real-world practice. One area where a hybrid model has not been applied is the conceptualization of the problem on which to intervene. We proposed a model of how to re-conceptualize a traditional problem as a hybrid problem by integrating the challenges of dissemination into the problem definition from the beginning. Doing so focuses attention on solutions that address dissemination in addition to alleviating the original problem. To illustrate this hybrid framework, we discussed the problem of school bullying. School bullying is problematic not only because it is harmful to children and schools who experience it (traditional problem), it is also difficult to implement the interventions in schools to demonstrate positive outcomes; therefore, the hybrid problem of school bullying. The protective peer ecology is the best solution because it addresses the complexity of the hybrid problem. To address the traditional problem of bullying prevention programs, the protective peer ecology is advantageous because peers are a critical mechanism in bullying, as they intervene effectively and are privy to more bullying than adults. To address the implementation issues, focusing on the protective peer ecology is advantageous because it leads to sleek yet strong interventions; peer interventions may integrate easier within existing whole-school

322 Samuel Y. Song and Wakako Sogo interventions and reduce competition for resources in schools; and peers are a free and naturally existing resource in schools reducing implementation costs for schools. While the hybrid approach to intervention development here is applicable to all schools, it is most critical to schools in low-resource communities. A hybrid framework allows researchers to help all communities quickly and sensitively, so that no children have to wait for help like many waited after Hurricane Katrina. Indeed, time is of the essence.

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17 Check & Connect Enhancing School Completion Through Student Engagement Sandra L. Christenson, University of Minnesota Amy L. Reschly, University of Georgia

“The dropout rates are astronomical because humans are not machines into which you can input data. They require emotion to process information. You take kids who didn’t benefit from stable, nurturing parental care and who have not learned how to form human attachments, and you stick them in a school that functions like a factory for information transmission, and the results are going to be terrible.” —David Brooks, Star Tribune, July 7, 2006

Relationships. Rigor. Relevance. Ambitious, realistic teacher and parent expectations for student performance in schools—what can be viewed as instructional rigor and motivational home support for learning (Bempechat, 1998)—are associated with varied, positive indicators of student performance (Brophy, 2004; Ysseldyke & Christenson, 2002). Similarly, the explicit connection between the relevance of the high school curriculum and the world of work and post-secondary options is integral to higher graduation rates and academic achievement for many students (National Research Council and Institute of Medicine, 2004). Only more recently has both the role of relationships (Hughes & Kwok, 2006; Pianta, 1999) and school connectedness (Blum & Libbey, 2004) been underscored for students’ school success, especially for students placed at risk of educational failure. Scientific findings from resiliency research have demonstrated repeatedly the power of a caring adult in the life of youth who face adversities. Masten and Coatsworth (1998) suggested that a strong relationship with a caring adult, high expectations and standards, and opportunities for meaningful participation contribute to youth “beating the odds.” Furthermore, these ingredients of supportive communities can be supplied by formal programs, families, neighbors, and significant adults such as educators, coaches, clergy, and youth workers. Described as “ordinary magic” (Masten, 2001), resilience refers to the natural consistency and continuity of everyday interactions between adults and youth that are critical for positive outcomes. Unfortunately, not all youth have a supportive relationship with a caring adult, whether a parent, teacher, relative, friend, or neighbor. Thus, critical questions include: Where are the resources to assist youth to build relationships of trust with caring, responsible adults? To whom are youth connected? In this chapter, procedures for implementing Check & Connect, an evidence-based model of student engagement that stems from relationships, is described (www.checkandconnect. org). An important premise of Check & Connect is the shift in focus from preventing negative outcomes, such as dropout, to promoting student competence, school success, and school completion. The model is a service delivery mechanism for meeting the needs of students placed at risk of educational failure; therefore, the referral concern for Check & Connect can be varied (e.g., attendance, reading, behavioral, or emotional difficulties). Finally,

328 Sandra L. Christenson and Amy L. Reschly although Check & Connect is an individualized intervention at the targeted or indicated (i.e., intensive) level, we speculate that engaging all students at school and with learning necessitates thinking of school-wide (universal) interventions as well as individualized interventions, and accordingly we provide suggestions for universal supports to this targeted intervention.

New Orientation for Intervention A non-traditional conceptualization of educational risk and the focus on engagement as a way to differentiate a dropout versus a school completion goal undergird the Check & Connect philosophical and theoretical orientation. First, students at risk for educational failure are typically described in terms of status variables (e.g., income, ethnic/cultural background) rather than alterable variables (e.g., attendance, academic performance, behavior). Pianta and Walsh (1996) cautioned professionals to conceptualize risk as a statistical relation between one index, for example, poor academic skills, and the likelihood of attaining a given outcome of interest, such as dropping out of school, given specified conditions or factors. Therefore, risk emphasizes probabilistic relations between specific factors and identifiable outcomes; assigning risk status to an individual simply connotes that he/she shares characteristics similar to a group in which there is known probability of attaining a certain outcome that is greater than the probability in the general population. They stated that the notion of risk is “a useful construct with which to think about and design ways to interrupt cycles of failure” (p. 20); however, they aptly noted: “research describes what is, given existing circumstances. It says little about what can be, given different circumstances” (p. 30). Furthermore, students at risk for educational failure are those for whom there is little to no interface of systems or support for learning. According to Pianta and Walsh, “School failure is at its core caused by an inability or unwillingness to communicate—a relationship problem” (p. 24). Similarly, support defined by Garbarino (1982) emphasized connections that occur whenever individuals (e.g., parents, teachers) or systems (schools, churches, families) have ongoing contact with each other that is organized around concern for the needs and progress of youth. For a significant number of students, however, discontinuity between home and school is a risk factor, particularly with respect to expectations, value placed on learning, and communication patterns (Pianta & Walsh, 1996). Second, it is our contention that the distinction between dropout prevention and school completion is relevant for intervention design for students who show signs of disengaging from learning and, as a result, are at risk over time of not completing high school. This distinction is critical to interrupting cycles of failure that begin early in a child’s schooling (Barrington & Hendricks, 1989; Finn, 1989). Furthermore, the differentiation of these two concepts illustrates well the focus of prevention science on reducing risk and enhancing protective factors. Dropout prevention efforts, albeit discussed most often in the literature, are characterized by creating barriers to prevent the occurrence of risky behavior (e.g., tardiness or absenteeism). For example, students may be transported to school to improve attendance and keep them in school; they also may be provided much assistance to complete school work on which they receive mostly Ds to earn credits for high school graduation. Such strategies, however, do not ensure that students will learn or have a successful school experience—one that leads to making a personal investment in one’s schooling and life-long learning needed for future success, especially for the availability of post-secondary enrollment options. In contrast, the goal of school completion interventions is for students to graduate from high school with skill proficiency and a personal investment in learning.

Check & Connect: Enhancing School Completion 329 When the goal of intervention shifts to promote school completion, supportive strategies to help students acquire skills to meet both academic and social standards—the demands of the school environment—are used. Additionally, relationships with students are built for the express purpose of fostering and supporting their self-perceived competence and relatedness with peers and teachers. To promote school completion, attention is given to standards and support (Lee & Smith, 1999), specifically providing support that helps students meet ongoing academic and behavioral standards specified by the school. The desired goal of interventions is to have students graduate with academic and social competence, and be academically resilient and consistent learners (Finn & Rock, 1997), rather than to have accumulated the required amount of seat time. Thus, school completion interventions aim to promote a “good” outcome, not simply prevent a “bad” outcome for students and society (Christenson, Sinclair, Lehr, & Godber, 2001). A significant feature of the shift in focus from preventing dropout to promoting school completion lies in the description of youth; with a school completion lens, youth are not referred to as “at-risk,” but as “placed at risk” because of contextual circumstances. The language of dropout tends to saddle the burden of change solely on the youth and to equate the problem with the student (Dorn, 1996). From this perspective, expectations may be lowered and interventions are developed with the intent of “fixing deficiencies” within the child. The language of school completion, however, emphasizes the importance of the personenvironment fit to meet expectations and the distribution of responsibility for change across the school, family, community as well as the student to provide necessary supports (Christenson & Anderson, 2002; Wentzel, 1998).

What is Check & Connect? Check & Connect is a targeted or indicated intervention intended to complement universal intervention initiatives of schools and districts (see www.ici.edu/checkandconnect). It is a comprehensive model designed to enhance students’ engagement at school and with learning—the “bottom line” of school completion programs (Christenson et al., 2001; Christenson et al., 2008). Check & Connect promotes student engagement through relationshipbuilding and systematic monitoring of students’ school performance—all done with persistence with marginalized students. It consists of four important parts: (1) a mentor who works with students and families over an extended period of time (a minimum of two years including summer); (2) regularly checking on students’ school adjustment, behavior, and educational progress; (3) intervening in a timely manner to re-establish and maintain students’ connection to school and learning and participation with constructive learning activities to enhance students’ social and academic competence; and (4) partnering (i.e., establishing a connection) with students’ families when possible. The “check” component is designed to facilitate the continuous assessment of student levels of engagement with learning in three categories: attendance, academic performance, and behavior; students’ functional behavior in these areas is used to guide intervention. The “connect” component includes two levels of student-focused interventions: all students receive basic intervention, whereas individual students’ indicators of disengagement are used to supplement basic intervention and to guide the delivery of more intensive interventions. Partnering with families, an essential component of these interventions, is based on a solution-oriented and family-centered approach (McWilliam, Tocci, & Harbin, 1998). Seven core intervention elements, presented in Table 17.1, serve to guide actions of mentors and evaluate treatment integrity (Sinclair, Christenson, Lehr, & Anderson, 2003); three of

330 Sandra L. Christenson and Amy L. Reschly Table 17.1 Core Elements of Check & Connect Model of Student Engagement Elements

Description

Relationship Building

Mutual trust and open communication, nurtured through a long-term commitment that is focused on student’s educational success. Cognitive-behavioral approach to promote the acquisition of skills to resolve conflict constructively, encourage the search for solutions rather than a source of blame, and foster productive coping skills. Support that is tailored to individual student needs, based on level of engagement with school, associated influences of home and school, and the leveraging of local resources. Student access to and active participation in school-related activities and events. A persistent source of academic motivation, a continuity of familiarity with the youth and family, and a consistency in the message that “education is important for your future.” Systematic check of warning signs of withdrawal (attendance, academic performance, behavior) that are readily available to school personnel and that can be altered through intervention. Following highly mobile youth and families from school to school and program to program.

• Problem-Solving • Individualized and Timely Intervention • Affiliation with School and Learning • Persistence Plus

Routine Monitoring of Alterable Indicators Following Students and Families

these elements—relationship-building, systematic monitoring, and following students and families—are theorized to be integral to the success of Check & Connect. Relationship-Building. A central tenet of the model, the power of relationships, is supported by a strong correlation between the presence of a caring adult and positive school and postschool outcomes for youth placed at high risk for failure (Masten & Reed, 2002). It is important to note that some students on a mentor’s caseload may already be connected with an adult in the school building- a special education case manager, hall monitor, custodian, or coach. The mentor’s role is not to replace established relationships, but to work with that adult in the process of supporting the student’s engagement at school and with learning. Trust is enhanced by the program’s long-term commitment to a student, preferably at a minimum of two years. Problem-Solving. Mentors model and coach the use of a problem-solving approach, intended to promote the acquisition of conflict resolution skills, the use of productive coping skills and successful habits of learning, and the capacity to seek solutions rather than a source of blame. A goal of problem-solving is capacity-building and minimizing the likelihood of creating student and family dependency on the mentor. For problem-solving, students are guided through real and/or hypothetical problems using a five step strategy: (1) “Stop. Think about the problem.” (2) “What are the choices?” (3) “Choose one.” (4) “Do it.” and (5) “How did it work?” Repetitive use of problem-solving with students to address the demands of the school environment and the issues facing individual students reinforces the sense of caring for the student by the mentor (Anderson, Christenson, Sinclair, & Lehr, 2004). Individualized and Timely Intervention. The premise of the connect component of the model is an individualized approach, one that involves direct teaching, guidance, support, delivered in a timely manner. Two levels of student-focused interventions maximize the use of finite resources and responsiveness of the mentor: basic intervention, which is the same for and delivered to all students, and intensive interventions, which supplement basic interventions and address the specific indicator of student disengagement noted during monitoring (i.e., check). Basic intervention is a deliberate conversation with each

Check & Connect: Enhancing School Completion 331 student—at least monthly for secondary students and weekly for elementary students. This conversation institutionalizes the continual exchange of information about students’ progress in school, the relationship between school completion or educational progress and the check indicators of engagement, the importance of staying in school, and review of problem-solving steps used to resolve conflict and cope with life’s challenges. A unique feature of Check & Connect is that interventions are delivered by someone—a mentor–who is in a position to commit resources to a student and their parents for an extended period of time (across school years). Affiliation with School and Learning. Mentors facilitate student access to and active participation in school-related activities and events (before, during, after school) as well as constructive learning activities during the summer. Research has shown that student participation in extra-curricular activities is associated with reduced dropout rates (Feldman & Matjasko, 2005; Gilman, Meyers, & Perez, 2004; Rumberger, 1995). Helping youth gain access has been the most common barrier to surmount across Check & Connect sites. Mentors’ efforts have included informing students about options, reviewing their schedule for potential conflicts, addressing transportation challenges, waiving enrollment fees, filling out registration forms and obtaining parent permission, walking students to the first meeting, and checking with program staff and students for feedback on their experiences. Persistence-Plus. To persist in the face of challenges, marginalized youth and their families need a sense of optimism and hopefulness for their children’s learning (Floyd, 1997). To accomplish this task, mentors apply persistence-plus concepts: Persistence means there is someone who is not going to give up on the students’ ability to learn or allow the student to be distracted from the importance of school and learning how to behave and improve academically. Continuity means there is someone who knows the students’ educational history, is familiar with the students’ family background, and is available throughout the school year, summer, and into the next year. Consistency means mentors reinforce the same message—a caring adult believes school and learning are important, and students can succeed, do the work, express frustration constructively, and learn—education is important for the students’ future. Mentors believe in students’ ability to alter their thoughts, feelings, and behavior. Students are told, “You can succeed, be on time, attend classes, complete the work, express frustration in a constructive manner, and stay in school.” With repetition from as many individuals as possible they learn that caring adults mean what they say. Persistence, continuity, and consistency are provided in tandem and collectively represent a persistent source of academic motivation for the student placed at risk for educational failure. Routine Monitoring of Alterable Indicators. The monitoring of alterable indicators of student engagement—those within the power of parents, teachers, and students to change—characterizes the check component of the model. Monitoring is essential for students at-risk of disengaging as a learner for two reasons. It provides both a systematic and an efficient way to connect students with immediate interventions and an essential link to students’ educational progress and performance. The alterable indicators for behavioral engagement include such variables as tardy to school, skipping classes, absenteeism, out-of-school suspension, disciplinary consequences (e.g., behavior referrals, detention, in-school suspension), classroom participation; those for academic engagement include course failures, objectives passed, earned credits, work completion, status on graduation requirements. Mentors check levels of engagement for each student on their caseload daily or weekly, striving to maintain near-daily awareness of the levels. Attendance information and the other indicators of participation are obtained primarily from school records, attendance clerks, teachers, and assistant principals. These individuals are consulted to verify contradictory information, as are the student or

332 Sandra L. Christenson and Amy L. Reschly family members. Students’ progress is summarized at monthly intervals, documented and reviewed with the youth, and used to guide the need for more intensive intervention. Following Students and Families. Across years and in different applications of Check & Connect, a conscious decision was made to shift from a school-based intervention to one in which the mentor, and consequently intervention, follows the student and family, aligning staff and student caseloads with service capacity focused at the county or district level (Lehr, Sinclair, & Christenson, 2004; Sinclair, Christenson, & Thurlow, 2005). This decision rested on our belief that to make a substantive impact on the population of youth who drop out of school, issues of mobility, a significant covariate of school failure for the population in question, had to be addressed (Christenson et al., 2001; Rumberger & Larson, 1998). In our research, up to a third of targeted students attended multiple schools during a single year within a given service area (Sinclair et al., 2003). We surmised that lack of stability seriously undermined the potential for youth to develop a sense of belonging and school connectedness. Even if schools offered a continuum of services to address the needs of disenfranchised students, the potential benefit could be lost if youth do not remain in the building long enough or trust someone enough to participate.

Theoretical Underpinnings of Check & Connect Although the core elements of Check & Connect have been described, a critical analysis of the merits of the model requires a depiction of why these parts were integrated and the degree to which there is a sound theoretical base for this model of engagement, especially as it relates to school dropout. Check & Connect was informed to a great extent by an analysis of critical engagement variables, systems theory for home–school–community collaboration, and the literature on resiliency, cognitive-behavioral interventions and motivation to increase the holding power of schools (Reschly & Christenson, 2006a, 2006b). Coleman’s (1987) notion of social capital (i.e., referring to the amount of adult-student interaction focused on students’ academic and personal matters as well as the support networks available to the family) is crucial to the mentor’s role, as are McPartland’s (1994) components to increase the holding power of schools (i.e., provide opportunities for success in schoolwork, communicate the relevance of education to future endeavors, create a caring and supportive environment, and help students with personal problems). Mentors strive to support students’ educational progress in the context of the family and the school and to increase social capital where it does not naturally occur by brokering existing resources. The model is designed to both reduce risks and enhance protective factors in students’ lives. Student engagement with school is considered the primary theoretical model for reducing dropout prevention and enhancing school completion (Christenson et al., 2008). As suggested by Rumberger (1987), dropping out of school is not an instantaneous event; rather, it is better likened to a long process of withdrawal and disengagement. Relatedly, Finn’s (1989, 1993) Participation-Identification model suggests that student participation in school activities over time is essential for successful performance outcomes to be realized and subsequently for students to identify with school. From this view, students will remain engaged and complete school if they feel like they belong to and share common values with school. Accordingly, the majority of students who drop out are expressing an extreme sense of alienation, which most likely was preceded by several behavioral indicators of withdrawal and unsuccessful school experiences. Finn and Rock (1997) have shown that participation differentiates at-risk secondary students who are academically successful and their less successful counterparts. School completers with academic success (resilient students) engaged significantly more often

Check & Connect: Enhancing School Completion 333 in a distinct set of school behaviors related directly and clearly to learning than school completers with poor academic performance (non-resilient completers) or dropouts. School behaviors included going to school and class on time, being prepared for and participating in class assignments, expending necessary effort to complete class assignments and homework, and avoiding being disruptive in class. Consistent with these findings, Floyd (1997) stressed the importance of supportive home and teaching environments, the development of perseverance (willingness to work hard in the face of barriers), and optimism (belief that academic efforts would pay off) for economically disadvantaged African American secondary school students. As the national concerns about school dropout rates heightened in the late 1980s, Finn (1989) proposed categorizing the large and somewhat overwhelming list of variables associated with students’ exit status (see Rosenthal, 1998) into two main groups: status predictor variables that educators have little ability to change, such as socioeconomic status of the student body and community, and behavioral or alterable predictor variables that are more readily influenced by educators, families, and students, such as out-of-school suspensions and course failure. The helpfulness of the distinction between status and alterable predictors lies within a suggested course of action for educators—to focus efforts on those predictors of dropout amenable to change (Christenson et al., 2001). Another level of differentiation is between indicators of engagement and facilitators of engagement found within the large set of alterable predictors of dropout. Indicators convey a student’s degree or level of connection with school and learning, such as attendance patterns, accrual of credits, and problem behavior. Facilitators of engagement are those contextual factors that influence strength of the connection, such as school discipline practices; parental supervision of homework completion and motivational messages about learning and schooling; and peer attitudes toward academic accomplishment. Facilitators of engagement have implications for intervention practice and policies, while indicators can be used to guide identification procedures—initiating referrals at the first signs of withdrawal—as well as to direct the progress monitoring of individual students and programs (Sinclair et al., 2003). In Check & Connect, engagement is not conceptualized as an attribute of the student, but rather as an alterable state of being that is highly influenced by the capacity of school, family, and community to provide consistent support for learning. Furthermore, student engagement requires psychological connections within the academic environment (e.g., positive adult– student and peer relationships) and active student behavior (e.g., attendance, participation, effort, prosocial behavior). Finally, based on a review of the literature, Christenson and Havsy (2004) drew three conclusions about the role of context for student engagement. First, school policies and practices such as tracking, retention, suspension, and rigid rule structures negatively affect student engagement, whereas other practices and policies such as smaller schools, opportunity for creativity and student choice, and highlighting the relevance of curricula to personal life goals, enhance levels of engagement. Schools with a committed faculty, positive teacher– student relationships, an orderly environment, and a school emphasis on academic pursuits were associated with lower rates of absenteeism and dropping out (Bryk & Thum, 1989). Second, relationships and networks between students play a key role in belonging. Having friends at school supports involvement in school-related activities (Berndt & Keefe, 1995). Goodenow (1993) found that middle school students who were more socially integrated had a significantly greater sense of belonging than those with less peer acceptance, and those with support from friends found the transition to and during ninth grade to be smoother (Isakson & Jarvis, 1999). The influence of peers for belonging may be particularly important during adolescence. Peers highly influence students’ day-to-day behavior in school, such as time spent on homework and enjoyment of school (Steinberg, Dornbusch, & Brown, 1992). There

334 Sandra L. Christenson and Amy L. Reschly is evidence that students who eventually drop out associate with like-minded students, those who do not feel part of the social world of school or value educational success (Goodenow & Grady, 1993; Hymel et al., 1996). Third, family support and involvement are associated with student engagement. Statistically significant home correlates of school completion include the presence of study aids, high educational expectations and aspirations, and parental monitoring and participation (Rumberger, 1995). Students who perceived greater parental support during and after the transition to middle school had significantly higher belonging (Isakson & Jarvis, 1999). Engagement is a positive, moderate correlate of motivational home support for learning, specifically structuring the home environment and emphasizing children’s efforts to succeed (Bempechat, Graham, & Jimenez, 1999). In closing, the theoretical underpinnings of Check & Connect underscore reducing risk and enhancing protective factors. In this way, they are congruent with Pittman and Irby’s (1996) contention that problem-free is not fully prepared. In fact, Check & Connect is highly consistent with their answers to what it means to be fully prepared and fully engaged: •

• •

•

“Academic competence, while critical, is not enough. The outcomes and indicators push beyond academic knowledge to include cognitive, social, physical, and behavioral competence. Competence itself, while important, is not enough. Skills and knowledge are important, but competence is not the only goal. Services, in and of themselves, are not enough. Young people need basic services—from health care to housing to recreational facilities. But they also need supports and opportunities. Programs, in and of themselves, are not enough. Development is an ongoing process. Programs are ways to structure specific services, supports, and opportunities to achieve a specific goal. Adults have to work with young people to help them access and use the programs and opportunities they need to create pathways to success.” (Pittman, Irby, Tolman, Yohalem, & Ferber, 2001, p. 8)

How It Works The key to Check & Connect is the mentor, or monitor, as this person has been referred to in some of our implementation projects. Regardless of the label used, the mentor is a cross between a monitor, advocate, and service coordinator (Christenson et al., 1997) and serves as a coordinator, intervener, and resource for teachers, the student’s family, and the student. The primary goal of the mentor is to work closely with individual students, their families, and teachers to help the student stay connected to school and to keep the student from slipping through the cracks. Mentors create positive relationships with youth and between family and school; promote regular school participation in academic, social, and emotional learning; and keep school progress a salient issue for students, parents, and teachers. They are often the person in a student’s life who keeps education salient, provides social support for families (e.g., navigates school system and requirements; assists with communication), and serves as an anchor point for students, families, and teachers. Importantly, Check & Connect adopts a “no blame” approach to working with youth and families, recognizing that all parties have strengths that can be amplified to ensure success. Relationships between students and mentors are built over time and based on trust and familiarity. Mentors make regular, systematic, formal connections with students that involve check (e.g., checking grades and attendance) and connect (e.g., develop individualized

Check & Connect: Enhancing School Completion 335 intervention strategies; promote access to services for students/families; assist students and families in navigating secondary school system) components and informal connections that involve showing interest in the student’s broader life—and they do these persistently. The use of the aforementioned five-step problem-solving process enables the mentor to be supportive and non-judgmental as the student learns new behaviors. Mentors believe in students’ ability to be personally responsible for their learning and school adjustment regardless of their living arrangements. They continually work with students to acquire the skills and confidence to handle likes/dislikes for school by using the five-step problem-solving strategy and goal-setting, as well as acknowledging each improvement and step toward success. They explicitly reinforce their expectation for students to be self-motivated learners—one way or another. While past efforts and mishaps are not overlooked, the tendency to blame and shame students or their families is redirected toward generating a new action plan and moving forward. The centrality of relationships for students’ learning is evident in the literature; however, the translation of theory and research into practice has been sparse. Check & Connect is one exception. The Check Component. Student levels of engagement are checked regularly and used to guide the mentors’ efforts to increase and maintain students’ connection with school. Systematic checking by the mentor of student attendance (absences, tardiness, skips), social/behavior performance (suspensions, behavioral referrals, detentions), and academic performance (course grades, credits earned) keeps interventions focused on the student’s educational progress—it helps to identify early warning signs of withdrawal—and necessary supports and opportunities for the student to be engaged. The Connect Component. Mentors respond to the student’s educational needs according to his/her type and level of risk for disengagement. Two levels of interventions are used to enhance protective factors for students, help students develop habits of successful learning, and maximize the use of finite resources in schools. All targeted students receive basic interventions because it is not sufficient to simply monitor student performance. Basic interventions consist of: (a) sharing monitoring data; (b) discussing the relevance of school for students’ goals; (c) practicing the five-step problem-solving strategy to enhance students’ adaptation to schooling demands; and (d) fostering opportunities for participation. Students showing high-risk behaviors (i.e., not meeting the predetermined criteria for successful performance) receive additional intensive, individualized interventions such as academic support, direct teaching of coping strategies, use of increased goal-setting strategies, and family–school problem-solving supportive meetings. The delivery of intensive interventions is based on indicators of the student’s disengagement as evidenced in the check data. Individual needs of the student and family dictate the specific intervention strategy used. In sum, Check & Connect is data-driven and designed to maximize personal contact and opportunities to build trusting relationships. Trust and familiarity are developed over time through persistent outreach to the student and family. Efforts include regularly checking on student attendance and academic performance, providing ongoing feedback about student progress, modeling the use of problem-solving skills, frequently communicating with families about both good and bad news, and being available to the youth to listen about personal concerns. The mentors’ interactions with students, parents, educators, and others are guided by the check and connect components of the model.

Implementation Procedures for Check & Connect Knowing how to implement Check & Connect is as important for intervention success as knowing the elements of the model of student engagement. These eight critical steps for

336 Sandra L. Christenson and Amy L. Reschly beginning this targeted or indicated intervention provide the necessary initial information for school professionals to begin: (1) determine indicators of disengagement—what will be monitored or checked systematically; (2) identify who will be served; (3) select mentors; (4) determine the need for basic and/or intensive intervention; (5) organize existing resources for intervention; (6) begin systematic monitoring and provide timely interventions; (7) meet regularly to ensure treatment integrity; and (8) evaluate program effects. Step 1: Determine Indicators of Disengagement. Common indicators that convey a student’s degree of connection with school and learning are attendance patterns, accrual of credits, and problem behavior. Indicators of risk for disengagement—those that will be checked regularly—must be identified by school personnel. Alterable predictors of disengagement are those that interventionists can modify; hence they are useful in prevention/intervention efforts. In Check & Connect, mentors check functional risk (early signs of disengagement), not demographic risk (status characteristics that do predict poor outcomes) (Doll & Cummings, 2008). However, it is necessary to note demographic risk when identifying students as status variables (e.g., race/ethnicity, socioeconomic status (SES), disability status, or having a sibling or parent that dropped out of school) place students at risk for dropout (Reschly & Christenson, 2006a). Facilitators of engagement are those contextual factors that influence strength of the connection. Both the indicators and facilitators may be classified by whether it is a protective or risk factor for disengagement or dropout. Although not an exhaustive list, several examples of alterable variables associated with dropout, the final form of disengagement from school, may be found in Table 17.2. Table 17.2 Alterable Variables Associated with School Dropout Protective

Risk

Students

• • • • •

Complete homework Come to class prepared High locus of control Good self-concept Expectations for school completion

• • • • •

High rate of absences Behavior problems Poor academic performance Grade retention Working

Families

•

Academic support (e.g., help with homework) and motivational support (e.g., high expectations, talk to children about school) for learning Parental monitoring

• • • •

Low educational expectations Mobility Permissive parenting styles Few educational resources

Orderly school environments Committed, caring teachers Fair discipline policies

• • • •

Weak adult authority Large school size (>1,000 students) High pupil-teacher ratios Few caring relationships between staff and students Poor or uninteresting curricula Low expectations and high rates of truancy

• Schools

• • •

• •

References: Byrk & Thum, 1989; Ekstrom, Goertz, Pollack, & Rock, 1986; Hess & D’Amato, 1996; Rumberger, 1995; Wehlage & Rutter, 1986. Copyright 2006 by the National Association of School Psychologists. Bethesda, MD. Reprinted with permission of the publisher. www.nasponline.org.

Check & Connect: Enhancing School Completion 337 As is illustrated in Table 17.2, there are a variety of reasons why students disengage from school and drop out; no one reason has sole predictive power (Dynarski & Gleason, 2002). However, Hammond, Linton, Smink, and Drew (2007) have concluded that the combination of four variables—attendance, grades, retention, and SES—provide a highly accurate prediction of dropout. In general, however, students disengage because of lack of connection or personal support for learning. To implement Check & Connect, interventionists will want to consider alterable indicators of disengagement in the categories of: Attendance/Truancy (e.g., tardiness, skipping, absences), Behavior Problems (e.g., discipline referrals, bus referrals, poor social skills, suspensions), and Academic Performance (failing classes/behind in credits; literacy skills). Because these indicators will be checked regularly and used to determine the level or intensity of intervention, it is necessary to define them in observable, specific ways. Step 2: Identify Check & Connect Students. To identify those students who are showing early signs of disengaging and/or are below school averages for engagement indicators, it is relevant to use population-based screening. School professionals will want to determine school averages on their chosen indicators. It is most efficient to define the target population by using readily accessible and reliable sources of data (attendance, behavioral referrals, credits accrued, GPA, etc.). Check & Connect was designed as a targeted (secondary prevention) (Lehr et al., 2004) or indicated (intensive or tertiary prevention) intervention (Sinclair et al., 2005). Not all students need a Check & Connect mentor. Check & Connect has been implemented with students who are showing early signs of disengaging and/or students who have many risk factors, have been diagnosed with a disability, or are often truant. A final consideration in the selection of students is the mentor-student ratio. Typically mentors carry a caseload of students based on one student for one hour per week contact; therefore, a mentor who works 20 hours per week has a caseload of 20 students. If students require less intensive intervention, mentors serve 25 students for half time employment. Step 3: Select Mentors. Mentors in our research applications have been graduate students or community professionals; all mentors have completed an undergraduate degree (Christenson et al., 1997). From our experience, certain qualities of the mentor are crucial. Desirable characteristics for mentors include the willingness to persist with students, despite their behavior and decision-making; personal belief that all students, particularly those living in at risk circumstances, have abilities and strengths; a willingness to cooperate and collaborate with families and school staff; advocacy skills, including the ability to negotiate, compromise, and confront conflict; an adequate level of organization (case management, documentation of intervention efforts); and ability to work well independently in a variety of settings. Mentors work outside of the defined workday in order to reach youth and families during weekend and evening hours and throughout a full 12 months to ensure continuity and sustained contact over the summer. Step 4: Use “Check” Data to Determine the Level of Intervention Delivery. Mentors use the student’s “check” data to make decisions about whether to deliver basic or intensive interventions. Mentors monitor student levels of engagement (i.e., risk factors) at least weekly. Increased risk leads to interventions to reconnect students by implementing intensive intervention support. Risk is defined by the percentage or number of incidents per month for each category (attendance, academics, behavior). Risk criteria used in previous applications of Check & Connect may be found in Table 17.3 (www.checkandconnect.org). Step 5: Organize Existing Resources for Intervention. Noting the local capacity of schools and communities to support students’ engagement is as important for the effectiveness of Check & Connect as is identifying target students and determining their intervention levels.

338 Sandra L. Christenson and Amy L. Reschly Table 17.3 Guidelines for Establishing Criteria for Intensive Interventions For secondary students: ƒ Skipping—ⱖ15% incidents per month (e.g., # classes skipped/# classes times days enrolled), defined as missing selected class periods within a day without an excused reason. ƒ Absenteeism—ⱖ15% days per month (e.g., # days absent/# days enrolled), defined as three-quarters to a full day’s absence for excused or unexcused reasons. Days absent for out-ofschool suspensions are also included here. ƒ Out-of-School Suspension—ⱖ2 days suspended, for which the student is not allowed on school property for a defined number of days. ƒ Other Behavioral Incidents—ⱖ4 incidents per month, defined as consequences for in appropriate behavior including detention, in-school suspension, referrals to the office. ƒ Failing Classes—ⱖ1 course failure (F) or no credit (NC) per grading period. ƒ Behind in Credits—earning less than 80% of possible credits per grading period and thereby not earning enough credits to graduate in at least five years. For elementary students: ƒ Tardiness—ⱖ15% incidents per month, defined as arriving late for school. ƒ Absenteeism—ⱖ15% incidents per month, defined as three-quarters to a full day’s absence for excused or unexcused reasons. Days absent for out-of-school suspensions are also included here. ƒ Out-of-School Suspension—ⱖ2 days suspended, defined as a consequence for inappropriate behavior, for which the student spends a defined number of school days at home—not allowed on school property for the suspension period. ƒ Academic Difficulties—performing below satisfactory levels in reading and math. ƒ Bus Incidents, Behavior Referrals, Detentions—ⱖ3 incidents per month, defined as problem behaviors that require the attention of staff outside of the classroom. Note: Risk is determined by a percentage of time or the number of incidents per month.

Schools vary in resource allocation. The connect interventions must be realistic and feasible to implement. Resource mapping (Adelman & Taylor, 2006) is used by program supervisors to organize existing school and community resources for intervention. A helpful question to guide the organizing of services is: What services/programs are available to mentors and school personnel to support students’ engagement at school? Mentors are service coordinators; they “broker available services” while advocating for supplemental services. We have speculated that a unique feature of the Check & Connect procedure is not the specific interventions per se, but the fact that interventions are facilitated by a person, the mentor, who is trusted and known by the student and who has demonstrated his or her concern for the school performance of the youth persistently and consistently over time. School personnel often rely on the information known to the mentor about the student and his/her home life and school performance. Step 6: Systematic Monitoring and Timely Intervention. A monitoring sheet is used routinely to record student engagement data and describe interventions. To identify patterns of student engagement, it is beneficial for mentors to reflect on both daily performance within the week and a monthly check to summarize student engagement in the areas of attendance, academics, and behavior over time. Further, mentors must establish an efficient system to access school/district student information (e.g., attendance, suspensions, credits earned). Indication of moving to another school or program is important to acknowledge so that the smoothest transition can be made for the student. Interventions can be categorized as student-connect (i.e., basic and intensive) and parentconnect. Basic interventions are comprised of four parts: (1) general information about the monitoring system and the mentor’s role; (2) problem-solving around different topics related

Check & Connect: Enhancing School Completion 339 to the importance of staying in school (e.g., economics of staying in school, how to ask for help, how to handle frustration) and the value of learning; (3) problem-solving with students about indicators of risk; and (4) ongoing feedback. The use of a five-step problem-solving strategy helps to empower students to take control of their behavior. Also, problem-solving is the basis for teaching students such productive coping skills as seeking social support, focusing on solving the problem, working hard and seeking to belong and participate. Mentors help students integrate their thoughts, feelings, and behaviors to meet the demands of the school environment. As school situations are discussed, they do not “side with” the student; however, they strive to understand the student’s personal characteristics, task demands, and difficulty as perceived by the student in a specific situation. Intensive interventions, which supplement basic interventions, generally fall in the broad categories of problem-solving (e.g., increase number and frequency of problem-solving sessions, hold parent problem-solving meetings, use individualized behavioral contracts, and work with administrators to provide alternatives for out-of-school suspensions); academic support (e.g., tutoring via the mentor or others, use of individualized academic contract); and recreation/community service (e.g., access to after-school activities; summer employment, service learning). Examples of intensive interventions, some categorized according to McPartland’s (1994) typology for engaging at-risk students, are presented in Table 17.4. Interventions are not prescriptive, meaning that there is no cookbook or one-to-one correspondence between student performance and the intervention coordinated by the mentor. Rather, interventions are individualized based on student need and indicators of disengagement and risk while considering the elements of the model and resources available to support the student’s connection with school. For example, if skipping classes is the primary area in Table 17.4 Illustrative Examples of Student Connect Intensive Interventions Providing opportunities for success in schoolwork: • Increase homework and work completion rates by ensuring an appropriate instructional match and/or encouraging home support for learning • Teach students time management and organizational skills (e.g., tracking assignments) • Problem-solve with students around taking responsibility, e.g., getting to class on time Communicating the relevance of education to future endeavors: • Setting personal goals that are ambitious and realistic • Illustrating the importance of attending school to achieve personal goals • Discussing wages as a function of schooling, linking discussions to budget planning Creating a caring and supportive environment by emphasizing student role: • How to behave appropriately in class; teach anger management • How to make friends/maintain friendships • How to respond to teasing; receiving/giving constructive criticism • How to ask for help • Learning to accept limits Creating a caring and supportive environment by emphasizing teacher role: • Encouraging teacher to foster positive relationships through discussion and student interests • Encourage teachers to provide clear, informed feedback • Encourage teachers to deliver the Check & Connect message about staying in school Helping students with personal problems: • Coping with family changes (e.g., moves, divorce, new boyfriends) • Problem-solving around substance abuse issues • How to talk with parents to get support

340 Sandra L. Christenson and Amy L. Reschly which a student demonstrates increased risk, a mentor may determine which classes the student skips, looking for a pattern and antecedents or events that perpetuate the pattern, in order to design and implement the best intervention and plan for the student. Alternately, if a pattern of suspensions (in-school and out-or-school) emerges as the area of increased risk, the mentor may talk with the student about the reason for suspension and problem-solve regarding what could be done differently next time; or, if the student has an increase in failing classes or is behind in credits, the mentor may determine whether families need suggestions, resources, or support for helping with learning at home and, if so, provide it. Partnering with families is essential. A family-centered approach (McWilliam et al., 1998) is used to guide parent-connect interventions. The mentor’s role includes engaging caregivers in the student’s schooling and learning, facilitating contacts with other resources as requested by the family, or minimizing barriers identified by the family. In general, the two foci for parent connections are participating at school (conferences, school events, building positive relationships with teachers, using problem-solving strategies,) and making education a priority in the home (increase out-of-school learning time, information about promoting successful learners, goal-setting, enhancing parent-youth communication, increasing motivational support for learning, navigating school rules). Partnering with families is strengthened by keeping the focus of family–school connections on improving a student’s educational performance. Mentors serve as a liaison between home and school, enhancing communication with families by providing information about school policies and practices and maintaining a positive, solution-oriented, and problem-solving approach. Varied personalized home-school communication strategies have been used, including calling parents on a regular basis, not just when there are problems; writing accessible notes (i.e., in the family’s first language) to parents about what is going on in school; making home visits regarding educational progress and/or at least once a year for a positive reason; finding out whether parents need suggestions, resources, or support to help with a student at home; directly inviting parents to be partners; attending routine parent–teacher conferences; and working with school staff and community supports to offer parent education classes or workshops that families identify as being interesting or important. Home visits are an integral element of outreach efforts to engage students in learning. The ultimate goal of the home visit, which is just one of several ways to communicate with students and their caregivers, is to establish a dialogue that is sometimes not possible over the telephone, through email, homework hotlines, letters and memos. Persistent and respectful communication is dependent upon using all means of contact, which often fall outside of the defined workday—in the evenings, over the weekend. This approach shifts the majority of outreach efforts from attempts at contact to actual interactions and exchange of information. Furthermore, the actual parent-connect strategies have varied on our different applications of Check & Connect; in some projects we have employed parent aides in the classroom, encouraged parents to serve as participants in parent–teacher action research, helped parents earn a GED, while in others we have negotiated with parents how they want to be involved in the monitoring system. In all cases, mentors are responsive to parent needs and strive to provide parents with options for engagement and work to strengthen classroom and building-level policies and practices that welcome and encourage family engagement, especially for disengaged, marginalized parents. Mentors are vigilant about what needs to occur to strengthen a cooperative family–school relationship. Step 7: Meet Regularly as a Check & Connect Team. A project supervisor is responsible for day-to-day direction of the intervention and has typically been staffed by a licensed professional, either employed by the project or employed in the school district, such as a

Check & Connect: Enhancing School Completion 341 special education coordinator or school psychologist. A critical role of the supervisor is to keep the intervention grounded in the theoretical framework and efforts focused on improving the student-environment fit that will allow youth to meet the demands of school. The position of the supervisor has been anchored at the district level or county level rather than the building level, in large part out of response to comorbidity of high mobility and dropout rates. The project supervisor provides the essential link to the schools, networks mentors with critical school and community resources, provides vital expertise regarding appropriate intervention and procedures, and lends legitimacy to the program among school staff. Timely technical assistance from the supervisor is essential, as is routine professional development. The program supervisor facilitates weekly to semi-monthly staff meetings. The meetings are used to review appropriate procedures and practices, exchange information about useful resources, provide case consultation, clarify roles in relation to other professionals, discuss strategies for communicating with other professionals and families, and maintain treatment integrity. Step 8: Evaluate Effects of Program Implementation. A key feature of Check & Connect is the use of data to guide intervention and program improvement. All schools maintain student records in some format that include basic indicators of engagement, such as grade-level reading assessments, tardies, attendance, suspensions, credits earned, and course grades. Check & Connect was designed to utilize existing data. To create a feasible evaluation system, school personnel determine what alterable indicators and other referral information are readily available, reasonably reliable, and most meaningful to collect routinely. Intervention fidelity is important. For example, the project supervisor routinely checks with program staff to verify whether student progress is recorded with consistency and accuracy, such that everyone is using the same operational definitions (e.g., differentiating excused and unexcused absences, in- and out-of-school suspensions). Ideally a randomized design can be employed; if not, data can be reported for matched comparison groups or changes in school data relative to school district data. Finally, we have found social validity data to be relevant to parents and teachers and beneficial to ongoing implementation. In sum, merits of Check & Connect include persistent outreach—consistent long-term support that is student- and family-focused, relationship-building—following students and families from school to school, prevention—monitoring alterable predictors of disengagement and dropping out, efficiency—using existing resources and two levels of intervention to maximize finite resources, and capacity-building—promoting acquisition of skills and confidence. Furthermore, partnering with families, systematically identifying students for intervention, using data to guide intervention and improvement, maintaining a focus on students’ educational progress, and making a sustained and long-term commitment describe aspects of successful implementation. Many elements of Check & Connect are highly consistent with the conditions of engagement delineated by the National Research Council and the Institute of Medicine (2004): opportunities for adolescents to experience supportive adult relationships, to feel a sense of belonging, to develop positive social values and norms, and to develop skills and a sense of personal self-efficacy; personalized instruction; ongoing assessment of student skills; high expectations paired with supports; meaningful connection with students’ culture and lives outside of school; and coordination with community resources. Finally, despite the careful planning for implementation and the sound theoretical basis of Check & Connect, there have been challenges, many of which have not been overcome. Family mobility, alienating school policies and practices, fragmented/strained home-school communication, mismatch between family and school goals, and limited social capital or parent and teacher “connectedness” have characterized challenges for Check & Connect mentors.

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Empirical Base of Check & Connect Check & Connect has been implemented in urban and suburban communities; in elementary, middle, and high school settings; and with youth with and without disabilities. Analysis of program effectiveness data consistently yields positive results: reduced rates of truancy, tardiness, suspensions, course failures, and dropout, and increased rates of attendance (Sinclair et al., 2003). In particular, treatment-control differences in critical student engagement variables such as participation (attendance), behavior (social skills ratings), academics (credits earned), and ultimately graduation rates have been demonstrated for middle and high school students with disabilities (Sinclair, Christenson, Hurley, & Evelo, 1998; Sinclair et al., 2005). Also, students receiving Check & Connect were more likely to access relevant educational services (e.g., alternative programs) and to be involved in individual transition planning. Two experimental studies of Check & Connect (Sinclair et al., 1998; Sinclair et al., 2005) have recently met the evidence standards of the U.S. Department of Education’s What Works Clearinghouse (WWC, 2006; www.whatworks.ed.gov). Statements of evidence from these two studies as reported by the WWC include: •

•

Students enrolled in Check & Connect were significantly less likely than control group students to have dropped out of school at the end of the first follow-up year (corresponding to the end of the freshman year; 9% versus 30%) and earned significantly more credits toward high school completion during ninth grade than controls. Check & Connect students were significantly less likely than control group students to have dropped out of school at the end of the fourth follow-up year (corresponding to the senior year for students who make normal progress; 39% versus 58%).

Overall, the WWC found Check & Connect to have positive effects on staying in school and potentially positive effects on progressing in school. Non-experimental studies suggest that Check & Connect actively engages students and families with school and the student’s learning. For example, in one study (Lehr et al., 2004), 87% of parents of Check & Connect students in grades K–8 were rated by teachers as having become more supportive of the child’s education (as evidenced by parental follow-through, communication with school, and homework completion). Teachers’ perceptions of student behavior became more positive as well—90% indicated that students in grades K–8 were showing improvement in homework completion, interest in school, and attendance. Especially encouraging were teachers’ assessments of students who received sustained intervention (at least two years): teachers rated these students as significantly more likely to be eager to learn, follow rules, think ahead about consequences, get along with others, show respect for others, and persist when challenged by difficult tasks—all critical competencies for school success and school completion. Nearly two-thirds of the students showed improved attendance and 15% had stabilized their level of engagement. One-third of the students were receiving all passing marks.

Future Efforts and the Relevance of Engagement for Intervention Planning This chapter has described Check & Connect as designed and implemented across the past 15 years. Two areas of further research are necessary to fully engage students placed at risk of educational failure and promote their successful school completion.

Check & Connect: Enhancing School Completion 343 Student Engagement—a Multi-Dimensional Construct Recently, we have postulated that effective interventions to promote school completion must address student engagement comprehensively (Christenson et al., in press), accounting for more than attendance and academic credits (primary dependent variables in Check & Connect studies to date) by attending to students’ commitment to learning, perceptions of academic and social competence, and sense of belonging. Corroborated by the perspective of the National Research Council and Institute of Medicine (2004), interventions to engage students fully must attend to the student’s belief of “I can” (perceptions of competence and control), “I want to” (personal values and goals), and “I belong” (social connectedness to peers and teachers). This expanded framework suggests that paying attention to students’ emotional and intellectual feelings about school is essential for understanding their schooling experiences and academic outcomes. Very importantly, as we implemented Check & Connect in different sites and with varied students, two factors stood out. First, engagement for students at high risk of educational failure was much more than academic (e.g., time on task) or behavioral (e.g., attendance, participation) engagement. Data collected from intervention staff and students suggested the critical importance of students’ sense of belonging and perceptions of the relevance of school work to students’ future endeavors. In response to these observations, Christenson (cited in Sinclair et al., 2003) theorized that student engagement is a multi-dimensional construct comprised of four subtypes: academic, behavioral, cognitive, and psychological (now referred to as affective) with multiple indicators for each subtype. For example, academic engagement consists of variables such as time on task, credits earned toward graduation, and homework completion; while attendance, suspensions, voluntary classroom participation, and extra-curricular participation are indicators of behavioral engagement. Cognitive and psychological engagement includes less observable, more internal indicators, such as self-regulation, perceived relevance of schoolwork to future endeavors, value of learning, personal goals and autonomy (for cognitive engagement), feelings of identification or belonging, and relationships with teachers and peers (for psychological engagement). Student self-report data are necessary to accurately capture students’ cognitive (e.g., motivation-to-learn) and psychological (e.g., connection to school and learning) engagement. In this taxonomy, the indicators underlying each subtype are theorized to be consistent with important contexts (e.g., relationships with adults at school, support from family members, peer support). This conceptual framework is based upon three psychological needs of students: autonomy, relatedness (belonging), and competence (Baker, 1998; Osterman, 2000). The degree to which Check & Connect influences students’ cognitive or psychological (now referred to as affective) engagement is untested. Appleton, Christenson, Kim and Reschly (2006) have developed a scale, the Student Engagement Instrument, to measure these two subtypes of engagement. The results of exploratory and confirmatory factor analyses for urban students in ninth grade yielded a six-factor structure, with three factors aligned with cognitive engagement: Control and Relevance of Schoolwork, Future Aspirations and Goals, and Extrinsic Motivation; and three with psychological engagement: Teacher–Student Relationships, Peer Support for Learning, and Family Support for Learning. The factor structure has been replicated with a suburban/rural sample of students in grades 6–12. Our future applications of Check & Connect will assess the degree to which a broader view of engagement, accounting for students’ sense of belonging and relevance of school work—what Brophy (2004) has referred to as explicitly programming for students who are apathetic (i.e., do not see the value of schoolwork) and discouraged (i.e., lack confidence to persist)—results in higher rates of school completion.

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A Universal Application of Check & Connect Yet another area of inquiry would be adapting what has been learned from the targeted and indicated intervention of Check & Connect to a universal or school-wide implementation strategy. Such an approach would be more cost-effective and allow schools to focus on engagement for all students. In keeping with the growing research base, we know that both universal and individual interventions are necessary for fostering engagement (Christenson et al., 2008; National Research Council & Institute of Medicine, 2004). A school-wide application of Check & Connect, based in an advisor system, could involve: •

Systematic monitoring of student performance in broad areas of attendance, academic achievement, and behavior. This is the “check” piece. Schools could set specific criteria for these indicators of engagement. It is necessary to consider: ƒ

ƒ ƒ

ƒ

•

•

• •

The demands of checking will need to be deconstructed. For example, a home room advisor could check on four–five students per week, reaching the assigned 20–25 students per month. Use of a simple monitoring sheet may expedite the task as well as link student performance to student and parental feedback. Some students (those in the middle and tip of the triangle—20%) may need more frequent checking; they would be referred to a Check & Connect mentor. All students (even those doing extremely well) can be checked, as this serves as an avenue to make feedback very specific to level of student performance. An advisor could use the Graduation Achievement Rate (GAR), which was developed by a school psychologist, to motivate students and family support for the student to finish a course. The GAR is expressed as a percentage, determined by dividing the total number of credits earned to date by the total number of credits possible. For example, if a GAR of 79% is necessary to graduate on time at one school, a student must earn 66 out of a total possible 84 credits (Hansen, Cumming, & Christenson, 2006). The advantage of the GAR is that it is educationally relevant, easily observed, and a clear definition of “risk” and meaningful measure for students and families. Task accuracy, not only task completion, should be monitored during credit acquisition to ensure fostering of academic competence.

Maintain a focus on basic intervention. Basic interventions use minimal resources in an effort to keep education a salient issue, particularly after a working relationship has been established between the mentor, student, parents, and school staff. Home room advisors could provide the basic intervention, emphasizing systematic feedback about students’ overall progress in school; the importance of participating/staying in school, relevance of school for future endeavors, and the value of learning; and problem-solving with students about indicators of risk for disengaging from school and learning and/or embellishing student knowledge about opportunities for involvement, etc. Maintain contact with parents. Establish a communication system using email or phone calls. Regular systematic contact to parents could mean once per quarter for universal students, but once per month for higher risk students (perhaps the Check & Connect mentors working at the selected or indicated level communicate on behalf of the teachers). Consider crafting a school-wide message about why school is important. The persistence plus message used in Check & Connect could be adapted to fit the school context. Establish a referral system to counselors, social workers, school psychologists and others serving as Check & Connect mentors for the targeted and indicated students. The second

Check & Connect: Enhancing School Completion 345 tier (targeted) could receive Check & Connect monitoring/mentoring in small group format, while students in the third tier would connect with their mentor individually—as Check & Connect was originally designed.

Check & Connect and Principles of Health Promotion and Prevention Check & Connect is consistent with the principles and program characteristics for efficacious prevention programs described by Nation et al. (2003). It is a comprehensive service delivery system that allows for varied and individualized interventions while addressing the needs of school, home, and the student. Community resources are coordinated with schools to enhance resource allocation. It actively engages students and families in problem-solving to foster students’ academic and social skills. Through the two levels of service delivery, basic and intensive, Check & Connect attends to “dosage” and uses resources efficiently; some students and families need more intense intervention of longer duration. Finally, the theoretical base of Check & Connect is strong, drawing on research in developmental resilience, cognitive behavioral interventions, and family–school partnerships. For the aforementioned reasons, the label of Check & Connect must be reserved for those applications that implement the engagement model as characterized by the seven elements. Due to the number of inquiries we receive about Check & Connect, we surmise that there is tremendous interest in implementing a version of our model. We believe that any effective application of an intervention must fit the specific context. However, for those interested in drawing on only parts of the model (e.g., a person at the school who checks and connects regularly with the student), we encourage that this intervention modification make reference to check in and check out. Check in and check out may have several iterations, such as Homework Check In and Check Out or Engagement Check In and Check Out. The difference between a reference to check in and check out and Check & Connect is the persistent support provided by a strong relationship based in problem-solving to attain academic and social standards across school years and contexts. In closing, the early identification of risks, systematic monitoring of alterable engagement variables and a focus on both risk reduction and enhancement of protective factors to foster student competence and engagement at school and with learning is evident throughout the theory and implementation procedures of Check & Connect. Mentors help students address specific concerns related to disengaging from schoolwork and help students prepare to succeed in meeting standards. Our data indicate that students can be successful “despite the odds;” they can beat the odds if provided with the persistent support from an individual—in our case the Check & Connect mentor. The hallmark of the model is relationships. In the Star Tribune, Nick Coleman (July 6, 2007) stated: There doesn’t seem to be the interest or the resources to help young people build relationships of trust with caring, responsible adults. If we don’t help kids connect to people who care about them, why would you expect anything different than what we’re getting? Without relationships and a focus on both risk reduction and enhancement of protective factors to foster student competence and engagement at school and with learning—it may be impossible.

Author’s Note The authors wish to acknowledge the contributions of many individuals who were instrumental in developing and implementing Check & Connect, which originated from grant

346 Sandra L. Christenson and Amy L. Reschly funding from the US Department of Education, Office of Special Education Programs, beginning in 1990. Thanks are extended to: Mary Sinclair, Cammy Lehr, Martha Thurlow, Christine Hurley, David Evelo, Colleen Kaibel as well as many Research and Community Program Assistants who served as Check & Connect mentors.

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348 Sandra L. Christenson and Amy L. Reschly Nation, M., Crusto, C., Wandersman, A., Kumpfer, K. L., Seybolt, D., Morrissey-Kane, E., & Davino, K. (2003). What works in prevention: Principles of effective programs. American Psychologist, 58, 449–456. Osterman, K. F. (2000). Students’ need for belonging in the school community. Review of Educational Research, 70(3), 323–367. Pianta, R. C. (1999). Enhancing relationships between children and teachers. Washington, DC: American Psychological Association. Pianta, R., & Walsh, D. B. (1996). High-risk children in schools: Constructing sustaining relationships. New York: Routledge. Pittman, K. J., & Irby, M. (1996). Preventing problems or promoting development. Baltimore: IYF-US, International Youth Foundation. Pittman, K. J., Irby, M., Tolman, J., Yohalem, N., & Ferber, T. (2001). Preventing problems, promoting development, encouraging engagement: Competing priorities or inseparable goals? (working draft). Baltimore: IYF-US, International Youth Foundation. Reschly, A., & Christenson, S. L. (2006a). Promoting school completion. In G. Bear & K. Minke (Ed.). Children’s Needs III: Understanding and addressing the developmental needs of children (pp. 103–113). Bethesda, MD: National Association of School Psychologists. Reschly, A., & Christenson, S. L. (2006b). Prediction of dropout among students with mild disabilities: A case for the inclusion of student engagement variables. Remedial and Special Education, 27, 276–292. Rosenthal, B. S. (1998). Non-school correlates of dropout: An integrative review of the literature. Children & Youth Services Review, 20(5), 413–433. Rumberger, R. W. (1987). High school dropouts: A review of issues and evidence. Review of Educational Research, 57(2), 101–121. Rumberger, R. W. (1995). Dropping out of middle school: A multilevel analysis of students and schools. American Educational Research Journal, 32(3), 583–625. Rumberger, R. W., & Larson, K. A. (1998). Student mobility and the increased risk of high school dropout. American Journal of Education, 107, 1–35. Sinclair, M. F., Christenson, S. L., Hurley, C., & Evelo, D. (1998). Dropout prevention for high-risk youth with disabilities: Efficacy of a sustained school engagement procedure. Exceptional Children, 65, 7–21. Sinclair, M. F., Christenson, S. L., Lehr, C. A., & Anderson, A. R. (2003). Facilitating student engagement: Lessons learned from Check & Connect Longitudinal studies. The California School Psychologist, 8, 29–42. Sinclair, M. F., Christenson, S. L., & Thurlow, M. L. (2005). Promoting school completion of urban secondary youth with emotional or behavioral disabilities. Exceptional Children, 71(4), 465–482. Steinberg, L., Dornbusch, S. M., & Brown, B. B. (1992). Ethnic differences in adolescent achievement: An ecological perspective. American Psychologist, 47, 723–729. Wentzel, K. R. (1998). Social relationships and motivation in middle school: The role of parents, teachers, and peers. Journal of Educational Psychology, 90(2), 202–209. What Works Clearinghouse. (2006). Intervention: Check & Connect. Washington, DC: U.S. Department of Education, Institute of Education Sciences, Author. Retrieved January 4, 2008, from http://ies. ed.gov/ncee/wwc/reports/dropout/check_conn/ Ysseldyke, J. E. & Christenson, S. L. (2002). FAAB: Functional Assessment of Academic Behavior. Longmont, CO: Sopris West.

18 Prevention and Early Intervention for Preschool Children at Risk for Learning and Behavior Problems Maribeth Gettinger, Carrie Ball, Laura Mulford, and Alicia Hoffman University of Wisconsin–Madison Introduction Many of America’s young children face significant challenges as they enter kindergarten because they lack essential readiness skills and behaviors for school success. It is estimated that more than 25% of children in the United States, especially those from low-income families, are at risk for significant social-emotional, behavioral, and academic problems (Chambers, Cheung, & Slavin, 2006). Unfortunately, without early intervention, children who get off to a poor start in school rarely catch up. The majority of children who are poor readers at the end of first grade continue to be poor readers through elementary and secondary school (Torgesen, 2000). Similarly, there is a high continuity between challenging behaviors in preschool and emotional disturbance during adolescence and adulthood (Campbell, 2002). In recent years, attention to the needs of young children who are at risk for academic and/or behavioral challenges has increased. Prevention and early intervention programs for children are expanding throughout the United States, in part due to recent federal initiatives, such as Good Start, Grow Smart, aimed at promoting the use of scientifically-based strategies to enhance young children’s social-emotional and literacy development. Such early childhood initiatives are based on a growing body of developmental research that highlights the importance of children’s early experiences for their future learning and adjustment. The preschool years represent a critical period in children’s lives, a time during which early intervention can substantially enhance long-term development. Indeed, there is evidence that early intervention programs for preschoolers can be successful in preventing negative outcomes (Guralnick, 1997). According to the National Research Council, for example, implementing evidencebased emergent literacy strategies in preschool can prevent reading problems in elementary school and ultimately reduce the need for special education (Snow, Burns, & Griffin, 1998). The purpose of this chapter is to synthesize key findings and conclusions stemming from early childhood prevention and intervention research. Although early intervention encompasses a broad range of different types of service delivery, including medical interventions and parenting programs, the scope of this chapter is limited to prevention and early intervention in the context of preschool and early education settings. Specifically, this chapter focuses on center-based early childhood programs which emphasize school readiness as a goal for children between two and five years. Current thinking about early intervention is that preschool experiences determine long-term adjustment, and that investment in high-quality early education will be reflected in greater success in school (Brooks-Gun, Fuligni, & Berlin, 2003). In sum, intervening with preschool children is seen as a primary means of preventing school failure. This chapter begins with a brief history and overview of the importance of early intervention and prevention programs for preschool children. Next, the chapter delineates factors that

350 Maribeth Gettinger et al. have been shown to place young children at risk for school failure, as well as critical elements of effective early intervention and prevention programs designed to address these risk factors. Finally, the chapter describes two recent evidence-based prevention programs for promoting the development of emergent literacy skills and positive behaviors in high-risk preschoolers.

History and Rationale for Early Intervention Discussions about the importance of early intervention and how to design effective programs have appeared in the professional literature for over 100 years. Research in early intervention has had a notably shorter history than practice. Only since the 1960s has there been movement toward establishing an evidence base to guide early childhood education and intervention. Brief History of Early Intervention Historically, research and development in early childhood experienced a significant period of growth during the 1960s. Several factors contributed to the increased attention to early intervention. First, the 1960s witnessed a resurgence of cognitive-developmental theory as an alternative to psychodynamic and behavioral approaches (Ershler, 1992). Cognitivedevelopmental theory emphasizes the importance of children’s preschool experiences in promoting cognitive growth and predicting eventual success in school, especially responsive adult–child interactions and language-stimulating activities (Demetriou & Raftopoulos, 2004). A cognitive-developmental approach in early childhood education is characterized by planned activities that are implemented flexibly in response to the needs and interests of children. Second, spearheaded by President Johnson’s “War on Poverty,” there was a growing societal commitment during the 1960s toward improving the lives of children from economicallydisadvantaged backgrounds. In 1965, as part of Johnson’s domestic agenda, Head Start was established to provide comprehensive services, including early education, to low-income children and their families. More recent federal initiatives, such as Good Start, Grow Smart (President Bush’s early childhood initiative), hold states accountable for providing children with evidence-based practices and experiences to prepare them for elementary school. The aim is to ensure that all children enter kindergarten with the necessary behaviors, skills and competencies to make adequate progress toward key benchmarks and, ultimately, experience success in school. The educational goals of these early intervention initiatives are achieved by providing optimal learning environments for low-income children to enable them to acquire language and cognitive skills that are typically obtained by their more affluent peers prior to kindergarten entry (Zigler & Styfoco, 2004). Not surprisingly, a major focus of early intervention research during the 1960s and 1970s was to evaluate the effectiveness of Head Start and similar compensatory early childhood programs. Many studies demonstrated short- and long-term benefits of participation in highquality programs in comparison to no preschool experience (e.g., see Chambers et al., 2006). In particular, several studies were conducted in community-based programs with children (often minority) living in poverty to document the ameliorative effects of early intervention. In 1975, for example, 11 early education research groups in the Consortium for Longitudinal Studies (CLS, 1983) conducted a pooled, longitudinal post-test of the effects of their respective programs over a 10-year period. The CLS found that, compared to children who had not participated in early intervention programs, those who attended programs were less likely to be retained a grade in school or receive special education. Moreover, participating children

Prevention and Early Intervention for Preschool Children 351 expressed higher achievement motivation and school-related self-esteem. Despite initial gains (through grade 1) made by children who participated in early intervention programs, however, intelligence and achievement test scores did not differ between participatory and comparison children. More recent evaluations of Head Start have documented similar initial gains among children, with performance on standardized tests of language and literacy approaching national averages. Fewer gains, however, have been found to be sustained over time (Ludwig & Phillips, 2007). Despite methodological limitations (most notably, the lack of randomized trials), preliminary evaluations of large-scale early childhood programs, such as Head Start, suggested that early intervention initiatives had considerable promise for effectiveness (Ramey & Ramey, 1998). This potential for positive outcomes has sustained continued interest among researchers, practitioners, and policymakers in developing and evaluating early intervention and prevention programs for high-risk preschoolers. Importance of Early Intervention and Prevention for Preschool Children The underlying rationale for early intervention and prevention for young children stems from an understanding of the importance of the preschool years, as well as research documenting the incidence of children at risk for behavior and learning problems and associated long-term negative outcomes. The preschool period is critical in preventing the development of behavior and learning problems and, in turn, minimizing the risk for school failure among children. Most theories of children’s development recognize the tremendous growth that occurs during the early childhood years and acknowledge that experiences in preschool often determine the course of social, behavioral, and academic performance through elementary and secondary school. Many skills that are important for children’s long-term adjustment, such as language and self-regulation, begin to develop during the preschool period. Developmentalists believe that strengthening these skills in young children will help to establish an essential foundation for learning and contribute to later behavioral competence (Keenan, Shaw, Delliquadri, Giovannelli, & Walsh, 1999). Despite the importance of the early childhood years, the number of young children in this country who may be at risk for learning and behavioral challenges is increasing. It is estimated that as high as 25% of preschool children may exhibit challenging behaviors, such as aggression, non-compliance, or disruptiveness, that interfere with their learning and development (Qi & Kaiser, 2003). In one study, kindergarten teachers reported that aggressive behaviors, such as kicking or threatening others, occurred once a day for 40% of their students, and there were six or more instances of such behavior for 10% of students on a daily basis (Willoughby, Kupersmidt & Bryant, 2001). Fewer than 15% of young children who show signs of problem behavior, however, may receive intervention (Kauffman, 1999). Without adequate early intervention, children with challenging behaviors are at high risk for persistent social-emotional and adjustment problems. There is a well-documented continuity between challenging behaviors in preschool (in particular, disruptive and aggressive behaviors) and adjustment difficulties in adolescence. In a review of longitudinal studies of “hard-to-manage” preschool children, Campbell (2002) found that 50% of children with moderate to severe “acting out” problems in preschool exhibited some degree of emotional disturbance in middle school. If left untreated, children with problem behaviors may continue to struggle in many areas of their lives. They are often rejected by peers, have restricted opportunities for learning appropriate behavior, receive limited positive feedback from teachers, and are likely to experience social-emotional problems

352 Maribeth Gettinger et al. and academic difficulties that require special education. In fact, children who have not acquired adaptive behavior patterns by the end of third grade are at risk for school dropout, juvenile delinquency, and substance abuse (Kamps & Tankersley, 1998). Beyond behavioral challenges, young children may also face significant challenges in learning to read and write because they lack essential early literacy skills when they begin school. Children who are poor readers at the end of elementary school are most often those who fail to develop early literacy skills during preschool and kindergarten (Torgesen, 2000). Early reading skill deficits at the beginning of kindergarten tend to remain, or even increase, through elementary school, creating a continuously widening gap between children who have good literacy skills and those who do not (Scarborough, 2001; Snow et al., 1998). Unfortunately, children who enter kindergarten with limited literacy and language skills are at high risk of eventually being referred for special education services (Whitehurst & Lonigan, 2001). A child who completes second grade without being able to read, for example, has only a 25% chance of reading at grade level by the end of elementary school (Snow et al., 1998). Furthermore, the majority of children with reading difficulties in grade 4 will continue to have reading problems at the end of high school, and they have a higher probability of dropping out of school (Scarborough, 2001). Moreover, research has shown that, as age increases, children from poor families score progressively lower scores on standardized measures of intelligence and achievement (Whitehurst & Lonigan, 2001). Thus, intervening when children are in preschool and kindergarten is necessary to prevent long-term negative behavioral as well as academic outcomes.

Risk Factors and Protective Mechanisms in Early Childhood Development Effective prevention programs are based on the premise that early response to learning and behavioral problems can lead to better school outcomes for children. The concepts of risk and protective mechanisms are central to understanding early intervention for preschool children. Risk refers to the increased likelihood that children will experience the negative behavioral and cognitive outcomes delineated in the previous section. According to Rutter (2006), protective mechanisms operate to ameliorate or minimize the negative outcomes associated with risk. The overarching goal of early intervention and prevention is to avoid or minimize the negative impact of factors that place children at risk through protective mechanisms, i.e., by strengthening the development of competencies and behaviors that promote success. As such, early intervention for preschoolers is intended not only to prevent the development of future problems, but also to promote resilience and strengthen the conditions necessary for children’s optimal development in academic and behavioral domains. Research has identified risk factors that make certain groups of children particularly vulnerable to difficulties in acquiring behavioral competence and proficiency in early literacy skills. Risk status is multi-faceted and determined by multiple variables (Rutter, 2006). Child characteristics, family background, and environmental variables all contribute to the prevalence of risk associated with children’s learning and behavior problems. Moreover, risk factors tend to be additive in nature, such that an accumulation of multiple factors increases the likelihood of poor outcomes for children (Pianta & Nimetz, 1992). Although the majority of adolescents with learning and behavior difficulties have a history of challenging behavior or learning problems in preschool, not all preschoolers with challenges go on to experience problems in elementary and secondary school. Thus, an understanding of both risk factors and positive behaviors that promote the development of children’s behavioral and literacy competence is necessary for developing and implementing effective preschool intervention and

Prevention and Early Intervention for Preschool Children 353 prevention programs (Shonkoff & Meisels, 2000). Four factors, in particular, have dominated the research on variables that place preschool children at risk for school failure: (a) delayed language development; (b) lack of self-regulation; (c) family structure and parenting practices; and (d) low-income environment. Language Development Delayed or limited language development among young children is a strong predictor of negative outcomes in school (Vernon-Feagans, Hammer, Miccio, & Manlove, 2001). Up to 75% of preschool children with identified language delays experience subsequent language impairments and difficulties with learning to read and write in elementary school (Snow et al., 1998; Vernon-Feagans et al., 2001). Furthermore, because language is the primary means by which adults affect children’s behavior, language delays may also impede development of social competence and are associated with behavior problems and delinquency in adolescence (Stattin & Klackenberg-Larsson, 1993). The development of children’s language is heavily influenced by family background and socioeconomic variables. Children living in poverty are at elevated risk for experiencing language delays (Hart & Risley, 1995). Limited language skills are a major factor in explaining the academic difficulties often experienced by low-income and bilingual children (VernonFeagans et al., 2001). Compared to children from middle-income families, children from economically-disadvantaged families experience significant difficulties learning to read and write because they enter school with lower knowledge of letters and less familiarity with words. Children from low-income families and with limited English proficiency are often reared in homes that fail to provide sufficient early literacy experiences and materials to promote printrelated skills. Their families typically do not support the acquisition of literacy skills to the same degree that parents of higher socioeconomic status do (Lonigan, Bloomfield, Anthony, Bacon, Phillips, & Samwel, 1999). For example, middle-income children begin school having had as many as 6,000 books read to them, whereas children from low-income families may start school without ever having been read to at home (Moustafa, 1997). Research by Hart and Risley (1995) provided an in-depth examination of the quality of verbal interactions between parents and children of professional versus welfare families. They found that parents in professional households were more likely to converse with their children for an extended period of time, and to use a greater quantity, type, and variety of word choices compared to parents from welfare households. Furthermore, children from welfare families were verbally reprimanded more often, heard more negative statements from their parents, and were exposed to a more restricted vocabulary compared to the children from professional families. Lack of Self-Regulatory Behaviors Self-regulation is related to children’s behavioral competence and learning; it is ranked by kindergarten teachers as the most important characteristic necessary for school success (RimmKaufman, Pianta, & Cox, 2000). There is evidence that self-regulation has a stronger association with school readiness than other factors, including age, background knowledge, or mastery of pre-academic skills such as counting and letter recognition (Bodrova & Leong, 2006). Moreover, recent research has documented links between self-regulation in preschool children and their functioning in school far beyond kindergarten and first grade (Bronson, 2000; Raver & Knitzer, 2002; Schonkoff & Phillips, 2000). Across multiple studies, self-regulation during children’s

354 Maribeth Gettinger et al. preschool years has been shown to be highly correlated with cognitive, social-emotional, and academic competence during adolescence. Lack of social-emotional or cognitive self-regulation among preschoolers is characterized by high levels of aggression, limited social skills, inattention, and emotional outbursts. Young children who lack self-regulation are at high risk for disciplinary problems because they are less capable of cooperating with peers and resolving conflicts. Furthermore, children with low self-regulation are often disengaged during classroom learning activities, do not follow classroom routines and rules, and fail to benefit from learning in various social contexts (e.g., large and small groups). Research demonstrates that a lack of self-regulation can also stand in the way of a child’s ability to develop positive teacher–child interactions in kindergarten and first grade, which, in turn, predict both poor academic performance and behavior problems in later years (Raver & Knitzer, 2002). Although there is agreement among researchers and practitioners that self-regulation is important for successful functioning in school, there are contrasting perspectives on the underlying nature of self-regulation in young children. One perspective views self-regulation as a personality characteristic, or temperament (Hart, Atkins, & Fegley, 2003). Within this perspective, preschoolers are described as being “under-regulated” or having a “difficult temperament,” characterized by a predominance of negative moods such as anger or difficulty in controlling their behaviors and emotions. With the advent of sophisticated methods of brain research, an alternate perspective has emerged in recent years, specifically that self-regulation is a function of brain development. Within this perspective, low levels of self-regulation in preschoolers are associated with under-utilization of certain areas of the brain located in the prefrontal cortex which are responsible for planning and reflection (Bronson, 2000). Regardless of the perspective one adopts, research suggests that lack of self-regulation in early childhood, evidenced in anger outbursts, inattention, and inability to control emotions, may be a risk factor for later behavior problems and limited academic success. Family Risk Factors Research has documented the effects of parenting styles and discipline practices on children’s development (Darling & Steinberg, 1993). Parenting practices can either prevent or exacerbate learning and behavior problems in children. The nature of parent-child interactions, in particular, can be a direct antecedent for subsequent child behavior and developmental outcomes. Specifically, early displays of problem behavior often stem from parent-child interactions that are insensitive, inconsistent, and demanding (Herrenkohl, Herrenkohl, Rupert, Egolf, & Lutz, 1995). Conversely, parent-child interactions that are positive and supportive, yet authoritative, contribute to children’s behavioral and academic competence (Baumrind, 1971). Research has further demonstrated that family size and structure may also be familial risk factors for preschool children. Children reared in two-parent households have been shown to function better socially and academically than those who are raised in single-parent households (Seltzer, 2000). One explanation for this finding is that a reduced household income, which often occurs in single-parent families, contributes to negative child outcomes. There are also clear indications that children are more susceptible to poor developmental outcomes when they are members of a large family, which is often characteristic of low-income families (Blake, 1989). Sameroff, Seifer, Barocas, Zax, and Greenspan (1987) examined the relationship between multiple risk factors and intelligence in a sample of 215 four-year-old children. Among the risk factors examined in their study, a family size of four or more children was

Prevention and Early Intervention for Preschool Children 355 identified as a high-risk variable. Sameroff et al. suggested that, similar to single-parent families, the negative influence of large family size may be associated with limited resources. When resources (e.g., money, time) are limited in families, there are negative consequences in terms of children’s development of positive behavior and readiness skills. Families that are continuously faced with stressors and conflicts are also more likely to model for their children unproductive and unhealthy ways of coping and problem-solving (Amato, 2006). Collectively, research on family factors points to the impact of low socioeconomic status (discussed below) as another developmental risk factor. Poverty Educators have long recognized that young children from low-income families are at a disadvantage when they enter school in terms of both academic and behavioral functioning. In fact, the largest number of children in the United States who are at risk for poor school outcomes are those living in poverty (Hart & Risley, 1995; Vernon-Feagans et al., 2001). Low-income children perform 11% to 25% lower on standardized measures of achievement than do their middle-income peers (Vernon-Feagans et al., 2001). According to the Children’s Defense Fund (2007), the number of children living in poverty was approximately 13 million in 2006. It is estimated that one in four children in the United States will live in poverty at some time during their preschool years (before age five). Moreover, a disproportionate number of poor children are from African American and Hispanic families. In 2004, 34% of African American children and 30% of Hispanic children were living in poverty, compared to 10% of white children (Ryan, Fauth, & Brooks-Gunn, 2006). Young children are particularly vulnerable to the effects of deprivation and poverty, and the impact can last through young adulthood. Research over the last 20 years has linked childhood poverty with negative cognitive, verbal, and behavior outcomes that appear as early as age two (Ryan et al., 2006). These early deficits persist when children enter elementary school, as evidenced by lower achievement, and the gap continues through middle and high school with higher rates of special education, grade retention, and school dropout (Duncan & BrooksGunn, 1997). The negative effects of poverty on children’s academic development have been well documented and are most often linked to limited exposure to language and lack of cognitive stimulation among low-income families (Hart & Risley, 1995; Reynolds, Ou, & Topitzes, 2004; Vernon-Feagans et al., 2001). Specifically, low-income families are not able to provide books and other language and literacy resources that middle-income families provide for their children. In one study, researchers found that 90% of middle-income families visited the local library at least once a month compared to only 40% of low-income families (Campbell & Ramey, 1994). Although the links between poverty and children’s social-emotional development are not as well-researched, there still exists some evidence that children growing up in poverty develop more emotional and behavior problems than do children who live above the poverty line (Duncan & Brooks-Gunn, 1997). In sum, there is an abundance of research indicating that poverty experienced during the first five years of life can seriously hinder child development in cognitive, social-emotional, and behavioral domains. Beyond their exposure to impoverished family environments, preschool children living in poverty are also likely to receive low-quality child care and early education (Frede, 1998; National Institute of Child and Human Development [NICHD] Early Child Care Research Network, 2003). The quality of child care and early education has a direct impact on children’s overall functioning (Barnett, 1995). The NICHD Study of Early Child Care (2003), for

356 Maribeth Gettinger et al. example, found that low-quality care is associated with negative outcomes related to school readiness, expressive and receptive language, and social-emotional functioning and behaviors. Whereas children who receive services from low-quality programs are at risk for displaying learning delays, the developmental advances that result from attending high-quality early childhood programs are strong predictors of future personal and academic achievement (Barnett, 2001; Dickinson & Sprague, 2001). In a review of the literature on preschool education for economically disadvantaged children, Barnett (2001) identified funding as a necessary ingredient for producing high-quality early education programs, including funding to provide adequate professional development and to hire a sufficient number of certified teachers to keep class size and teacher–student ratios low. Consistent with Barnett’s conclusions, the Cost, Quality, and Child Outcomes Study Team (1995) analyzed the quality of services provided by 400 early childhood centers in four states. The lowest quality of early childhood care and education was found in programs that provided services to families and children living in predominantly low-income neighborhoods. Compared to early childhood programs in middle-income communities, programs in low-income communities had significantly fewer financial resources and less external funding. According to Barnett (2001), financial support is critical for improving the overall quality of early childhood programs for low-income children to better prepare them for school.

Resilience and Protective Behaviors A key to the success of preschool programs is targeting behaviors or skills for change that have maximum benefit for children’s long-term success. Sometimes referred to as “keystone variables,” these behaviors represent the foundation skills necessary for subsequent adaptation to natural learning environments (Barnett, 2002). As such, keystone variables hold significant potential for improving maintenance and generalization of behavior change. Emerging evidence underscores the importance of two particular aspects of children’s preschool experience for promoting long-term success. Specifically, high-quality early education should be designed to support the development of children’s (a) capacity for self-regulation, social competence, and positive learning behaviors, and (b) early language and literacy skills (Shonkoff & Meisels, 2000). In fact, the Committee on Integrating the Science of Early Childhood Development (Shonkoff & Phillips, 2000) points to promoting early literacy, language, and learning processes as well as developing children’s capacity for emotional and attentional selfregulation as important targets for effective early intervention and prevention efforts with high-risk preschoolers. Early intervention and prevention programs offer one way to facilitate favorable outcomes among high-risk preschoolers. Early intervention in preschool targets high-risk children before they enter elementary school. Theory supporting early intervention suggests that such programs mitigate the risk factors that some children face by providing opportunities for learning and developing keystone competencies necessary for later success. Although information is somewhat limited on which specific ingredients are key to a successful early intervention and prevention program, research does highlight the importance of certain elements, which are summarized in the following section.

Critical Elements of Early Intervention and Prevention Programs Prevention and early intervention programs for preschool children are expanding throughout the United States. Research demonstrates the success of programs that identify and strengthen

Prevention and Early Intervention for Preschool Children 357 resilience in at-risk children before they develop problems that require intensive treatment. For this reason, practitioners and researchers increasingly focus on preventive approaches to promote the development of resilience in children. Reynolds (2005) conducted a comprehensive analysis of early childhood development programs and concluded that several key features typically characterize the most effective programs. The following section describes these features. Family Involvement. Reynold’s (2005) analysis identified the involvement of parents and families in all phases of early intervention programs (i.e., assessment, planning, and implementation) as a key to their success. In that early intervention is intended to promote optimal development within children’s natural learning and social environments, an emphasis on the contributions of families is understandable. Frede (1998) examined the commonalities and differences among model early intervention programs and found that programs with longterm effectiveness consistently included a family involvement component. Family involvement has been shown to contribute to positive outcomes for both children and family members. For children, the likelihood of their achieving success in school is maximized when families are involved (White, Taylor, & Voss, 1992). When parents are positively and actively involved in early intervention, they develop more positive attitudes towards themselves, their child, and early childhood professionals; greater life satisfaction; and, better communication with their children (White et al., 1992). Intentional Strategies for Promoting Skills. Among the factors that are consistently associated with both cognitive and behavioral outcomes for preschoolers is the intentional enhancement of development. According to Pianta and Nimetz (1992), through intentional enhancement, preschools operate as protective mechanisms to moderate the link between risk conditions and negative outcomes. In intentional early childhood models, the teacher follows a structured program with expectations for children’s behavior and consistent ways of responding to children. Although there is an emphasis on intentionally promoting children’s cognitive and social development, early childhood educators function as facilitators, not directors, of children’s learning. There is typically a balance of teacher-initiated and child-selected learning activities which are determined based on teachers’ knowledge of “key experiences.” Rather than representing behavioral objectives (characteristic of direct instruction approaches), key experiences function as guideposts for teachers to use in planning activities and interacting with children. When compared to maturational approaches, in which children learn primarily through unstructured free-play activities without teacher direction, intentional programs generally produce better cognitive and social-emotional outcomes (Chambers et al., 2006). Ongoing Professional Development and Teacher Support. Another factor that contributes to effective early intervention programs is the level of professional development and support that teachers receive to implement new strategies. Surprisingly few studies have explored training methods that effectively promote teachers’ development of early intervention skills and knowledge. The extant research does point to two important conclusions about professional development linked to successful early intervention (Klein & Gilkerson, 2000). First, professional development should align early educators’ learning opportunities with their real classroom experiences. That is, professional development must provide teachers with a way to directly apply what they learn in training sessions to their own early childhood settings. Second, professional development should be ongoing with extended opportunities for mentoring, feedback, and refinement of practices. According to researchers, professional development experiences that incorporate these elements are likely to have a positive influence on the outcomes of early intervention (Zaslow & Martinez-Beck, 2006).

358 Maribeth Gettinger et al. Multi-Tiered Intervention. Successful approaches to early intervention are also characterized by incorporating a multi-tiered intervention hierarchy (VanDerHeyden & Snyder, 2006). Inherent in a multi-tiered model is the practice of providing more intensive intervention and supplemental individualized support to children based on their needs. The application of multi-tiered models in early intervention rests on the assumption that when children are at risk of performing below expectations (cognitively or behaviorally), teachers and professionals respond in ways to prevent serious difficulties and promote children’s success, such as making adjustments in the environment or providing additional assistance. Despite the alignment between multi-tiered approaches and the goals of early intervention, the systematic implementation and evaluation of multi-tiered interventions in early childhood contexts is limited (Vanderheyden & Snyder, 2006). Only a few multi-tiered intervention models have been described in the early intervention literature. Sandall and Schwartz (2002), for example, developed a model of teaching young children with special needs in preschools that incorporates a tiered intervention hierarchy which includes making curriculum modifications, embedding a focus on objectives for individual children within typical classroom activities, and providing individualized instruction aimed at achieving IEP goals. Collectively, these findings present a portrait of the optimal context for early intervention and prevention for preschool children. Specifically, positive outcomes are most likely to occur for young children who participate in early intervention programs that (a) actively involve families, (b) are directed toward strengthening or enhancing children’s competencies, (c) include evidence-based approaches to professional development for teachers and staff, and (d) incorporate a multi-tiered intervention hierarchy. The following sections describe the conceptual framework and implementation of two early intervention programs that were designed with these critical elements in mind, the first focusing on emergent literacy skill development and the second on the development of behavioral competence in high-risk preschoolers.

Prevention and Early Intervention to Promote Emergent Literacy Skills Building an effective early intervention program to promote development of emergent literacy skills depends on a foundation of appropriate learning goals. Over the last decade, several comprehensive reports have synthesized the converging evidence about emergent literacy and identified skills that are highly predictive of later success in learning to read (e.g., Snow et al., 1998; Whitehurst & Lonigan, 2001). These skills include phonological awareness (e.g., hearing and manipulating smaller sounds in words), alphabet knowledge (e.g., identifying and naming letters), print awareness (e.g., noticing print and following words on a page), and oral language and vocabulary (e.g., describing events and telling stories). There is evidence that children who have acquired these skills profit more from formal reading instruction than do children without them. Moreover, reading success requires coherent, intentional instruction in these skills prior to kindergarten entry. That is, children need continuous exposure to print, frequent oral and written language interactions with adults, and explicit instruction to develop skills. In addition, early childhood environments play a key role in developing children’s language and emergent literacy (Dickinson & Sprague, 2001). As such, effective early intervention for emergent literacy skills is multi-dimensional and includes a combined focus on the literacy environment, teacher interactions during book reading, and letters and sounds. Torgesen (2000) summarized the outcomes from five prevention and early intervention programs that incorporated these components and found that all resulted in a significant reduction in reading difficulties among young children in first grade.

Prevention and Early Intervention for Preschool Children 359 High-Quality, Literacy-Rich Environments Children’s literacy skills and behaviors are strongly influenced by features of the learning environment. Preschool contexts that support literacy development do so through two primary mechanisms: (a) provision of print-rich environments, and (b) engagement in facilitative teacher–child interactions. Print-rich environments surround children with accessible, highquality books, as well as diverse print and writing materials. Importantly, young children need models of language and print (e.g., signs, labels, displays of children’s writing), posted at eye level and in all areas of the classroom. Posted print increases children’s exposure to written language and strengthens their ability to associate sounds with letters and words. According to Makin (2003), there are several ways to create print-rich environments. For example, teachers can label objects in the classroom, post children’s names with pictures at their cubbies, write out a daily schedule, or create a poster of classroom rules. As often as possible, teachers should also model the use of print (e.g., reading the schedule) as a routine part of daily life. Within print-rich environments, teachers themselves function as active facilitators of language development through their interactions with children. Teacher–child interactions have the potential to foster language development, particularly among children living in lowincome families who may have minimal exposure to good language models. According to Massey (2004), about one-third of teachers’ verbal interactions with preschool children should be focused on strengthening literacy and language skills. Teachers may encourage children’s language development in multiple ways. They can work to establish nurturing relationships, encourage children to express themselves, model reading and writing behaviors, talk about environmental print in the classroom and community, embed new vocabulary into conversations with children, and promote oral language and vocabulary in social and play contexts. Taken together, these two features of classroom environments (exposure to print and teacher–child interactions) are protective mechanisms that support the development of emergent literacy skills, particularly for children at high risk for reading failure due to poverty or other risk factors.

Shared Book Reading Interactive, adult–child shared book reading is an approach to book reading that enjoys strong research support in terms of its influence on children’s emergent literacy development. Ezell and Justice (2005) estimate that the quantity of shared book reading children experience in preschool accounts for about 10% of the variance in primary-grade reading achievement, even without accounting for quality. Shared book reading involves a dynamic, interactive exchange between adults and children as they focus on reading together. When done well, shared book reading can be used to help children develop oral language, phonological awareness, alphabet knowledge, and concepts of print (Strickland & Schickedanz, 2004). Therefore, it represents an excellent classroom activity to help children make progress toward each emergent literacy goal. The key component of shared book reading involves the use of language. Through listening to and talking about stories, children have opportunities to practice using language to talk about events and express ideas beyond the “here and now” (Ezell & Justice, 2005). Interactions that foster language use include summarizing or retelling the story, making predictions about what will happen, and talking about favorite events in the story. Teachers can also increase vocabulary by talking about words in the book that may be unfamiliar to preschoolers (Dickinson, 2001). Two additional evidence-based practices are recommended for use during

360 Maribeth Gettinger et al. daily shared book reading. The first is a procedure called dialogic reading, which includes asking open-ended questions, following children’s answers with additional questions, repeating and expanding what children say, and following children’s leads and interests (Whitehurst, Arnold, Epstein, Angell, Smith, & Fisceht, 1999). The second strategy is print referencing, which includes talking about print (e.g., “Where is the letter A on this page?”) and pointing to print during reading (Ezell & Justice, 2005). Alphabet Knowledge and Phonological Awareness As an important predictor of reading development, experts recommend that alphabet knowledge should be developed intentionally within an early intervention approach. Whereas many emergent literacy skills can be taught in meaning-focused contexts and do not necessarily require a high level of systematic instruction, letter knowledge does seem to be increased by an explicit instructional approach (Connor, Morrison, & Slominski, 2006). That is, each letter should be introduced and specifically targeted in some systematic way. For example, the class can have a “letter of the week.” Activities might include identifying the upper- and lower-case letter, learning the letter sound, talking about words that start or end with the letter (including reading alliterative books that use the letter), tracing and writing the letter, and writing words or drawing pictures of things that start with the letter. Although researchers have not identified a specific order in which letters of the alphabet should be introduced, it is clear that children learn the letters in their first names more readily than other letters (Justice, Pence, Bowles, & Wiggins, 2006). In addition, children’s first names are often the first words they learn to identify and read. Beyond alphabet knowledge, it is recommended that activities to help develop phonological awareness should be incorporated into daily literacy instruction as well. In general, the types of activities to promote phonological awareness include developing children’s awareness of syllables, beginnings and endings of words, and individual sounds. As with alphabet instruction, using children’s names to engage in word-play activities is likely to increase engagement. Syllable awareness typically involves clapping the syllables in words and talking about how many “claps” are in various words. A focus on word beginnings (alliteration) and endings (rhyming) can involve reading alliterative or rhyming books and pointing out how words sound alike or different. Early on, children may simply repeat the teacher’s words and listen to the sounds. This requires extensive teacher modeling and talking about how words sound the same. As sound awareness develops, children will be able to identify whether or not a word begins or ends with a certain sound. They may also begin to discriminate whether two words begin or end with similar sounds. At this stage, silly songs involving word-play may be introduced. Using picture cards to play matching games can also be an engaging method to practice with sounds. In addition, talking about which students’ names start with similar sounds or dismissing students by the first sound in their names are ideas for incorporating names into word-play activities (Kirk & Clark, 2005). Finally, children will be able to generate alliterative and rhyming words, at which point they will be able to engage fully in silly songs and guessing games that require them to think creatively about words and sounds.

Description of an Early Intervention Program: Focus on Emergent Literacy The Exemplary Model of Early Reading Growth and Excellence (EMERGE) is an example of a comprehensive, early intervention program designed to help children from low-income families acquire emergent literacy skills to prepare them for later success in school (Gettinger &

Prevention and Early Intervention for Preschool Children 361 Stoiber, 2007). Based on research findings concerning literacy development, the EMERGE program targets three inter-related goals. Specifically, through the implementation of a multitiered instructional model (explained below), EMERGE strives to: (a) create high-quality, literacy-rich learning environments in early childhood programs that provide services for low-income children and families (e.g., Head Start); (b) increase the amount of time children are engaged in interactive shared book reading, both at home and school; and (c) maximize the use of research-based practices to support children’s development of alphabet knowledge and phonological awareness. Key components of the EMERGE program are explained in the following sections. Three-Tiered Intervention Hierarchy. The use of a three-tiered intervention hierarchy in EMERGE ensures that all children have access to high-quality early literacy practices. As shown in Figure 18.1, tier 1 includes a literacy-rich environment, shared book reading, and activities that support children’s development of phonological awareness and alphabet knowledge. In tier 1, the focus is on optimizing the quality of the environment and classroom practices to promote early literacy development among all children. Tier 2 includes daily, teacher-directed, small-group activities that provide greater exposure to language and print, additional practice with literacy skills, and/or activity adaptations for groups of three–four children based on their individual needs. Finally, tier 3 includes intensive, individualized tutoring whereby children receive explicit and highly focused training in early literacy skills from specialized early literacy tutors. Tier 3 instruction is provided for children identified to be at high risk for developing reading difficulties (i.e., approximately 20% of the lowest children on progress-monitoring measures). Although each tier in the EMERGE hierarchy is distinguished based on the intensity and individualization of early literacy instruction provided, there are common features that are similar across all tiers. Specifically, the activities and strategies provided within each tier are designed to achieve the same core literacy goals and incorporate scientifically-based practices for emergent literacy development. There is a common emphasis across all three tiers on strengthening fundamental skills that have been shown to predict optimal reading

Tier 3: Individual tutoring

Tier 2: Daily small-group instruction

Tier 1: Shared book reading Literacy-rich environment Alphabet knowledge and Phonological awareness activities

Figure 18.1 EMERGE Multi-Tier Intervention Hierarchy

362 Maribeth Gettinger et al. development in elementary school. In effect, each EMERGE tier represents an increasingly stronger focus on, and greater assistance with, acquiring these skills. An important feature of the application of a multi-tiered approach for preschoolers, such as EMERGE, is that movement between tiers is flexible and based on multiple indicators of children’s responsiveness to universal tier 1 intervention, rather than strict, test-based decision rules. Development of young children’s emergent literacy skills is characteristically sporadic and variable over time. Thus, determining the need for tier 2 adapted instruction or tier 3 individualized tutoring often relies on teachers’ ongoing observations of children’s progress and performance during everyday learning situations, in addition to monthly progressmonitoring data (see below). Progress-Monitoring. In recent years, significant progress has been made toward developing measures that are quick, easy to administer, sensitive to growth over short periods of time, and useful for monitoring progress in early literacy and language development among preschool children (Missall, Reschly, Betts, McConnell, Heistad, Pickart, Sheran, & Marston, 2007). In EMERGE, four progress-monitoring measures are administered individually to children by classroom teachers or aides at one-month intervals. These include picture naming, rhyming (identifying word pictures that rhyme with a stimulus word), alliteration (identifying word pictures that start with the same sound as a stimulus word), and alphabet knowledge (naming letters in random order). Progress-monitoring occurs during times of the day that are routinely scheduled for learning centers. Specifically, during morning or afternoon center time, children rotate individually through the progress-monitoring center. EMERGE teachers receive training and on-site coaching in how to administer and use the information obtained from progress-monitoring procedures. All teachers prepare a summary of individual child data. In addition, monthly progress-monitoring data are aggregated by classroom (average scores) and graphed over time. In this way, progress-monitoring data allow teachers to see which children are above or below average in their classrooms and to ascertain which children are making slower progress than others toward acquiring literacy skills. A literacy coach reviews progress-monitoring data (classroom and individual) every month with classroom teachers. In collaboration with the literacy coach, teachers use this information in two ways: (a) to make decisions about group formation and literacy activities during small-group periods for children who are below the class average (tier 2); and/or (b) to make adjustments, as needed, in their tier 1 literacy instruction and classroom environments for all children. Professional Development. Professional development comprises a major component of the EMERGE program. Teachers’ knowledge and use of scientifically-based classroom practices are central to the implementation of EMERGE procedures. The professional development component in EMERGE is designed to improve teachers’ understanding of language and literacy, as well as their application of evidence-based practices. This is achieved through two types of professional development activities. First, teachers participate in monthly, three-hour professionaldevelopment training sessions to acquire skills and resources necessary for (a) implementing tier 1 intervention; (b) conducting monthly progress-monitoring; (c) using information about children’s early literacy performance to alter tier 1 instruction and/or to plan tier 2 small-group instruction for selected children; and (d) designing high-quality literacy environments. The second type of professional development involves on-site early literacy coaching/mentoring and collaborative planning with a certified literacy coach for two hours every week. The objectives of literacy coaching are to model strategies, work one-on-one with teachers and children, monitor implementation integrity, and provide scaffolded, individualized support for teachers. All teachers have weekly contact with a literacy coach who provides consultation, observation, feedback, and support for teachers as they implement new techniques in the classroom.

Prevention and Early Intervention for Preschool Children 363 Family Involvement. A final component of EMERGE is family involvement, which is encouraged through multiple activities. First, a family literacy center operates one day every week at each site. The center is staffed by literacy specialists who offer suggestions and coaching to families about home-based activities, model interactive shared book reading strategies, and allow parents and children to sign out developmentally-appropriate books to read together at home. In addition, take-home books and nursery rhymes are read multiple times with children in classrooms and distributed to families on a weekly basis. Finally, families receive a monthly newsletter that offers suggestions and describes the literacy and language activities to complete with children at home. Preliminary Evaluation of EMERGE Overall, children participating in EMERGE classrooms make notable progress in attaining important early literacy skills during their pre-kindergarten years. This conclusion is supported by the results from a preliminary evaluation of the effectiveness of EMERGE for promoting children’s development of early literacy and language competencies (Gettinger & Stoiber, 2007). Specifically, comparisons of children’s performance on baseline and end-ofyear progress-monitoring measures in 15 EMERGE versus 10 randomly-designated control classrooms suggest that the multi-tiered, scientifically-based instruction of EMERGE is associated with higher performance across multiple indicators of emergent literacy development. Figure 18.2 displays the September and May performance of children in EMERGE and control Alliteration

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Figure 18.2 EMERGE and Control Children’s Performance on Emergent Literacy Measures

364 Maribeth Gettinger et al. classrooms on four measures. It is important to note that the performance data represented in the graphs in Figure 18.2 are aggregated across three age groups, including five-year-old children who entered kindergarten the following fall, as well as four- and four-year-old children with one or two more years of preschool experience before starting kindergarten. The proportion of three- and four-year-old children was similar across EMERGE and control classrooms. As shown in Figure 18.2, across all ages and on each early literacy and language indicator, children in EMERGE classrooms made greater gains compared to children in control classrooms. Summary As detailed above, a large body of literature exists concerning appropriate goals and evidencebased procedures for preventive emergent literacy programs at the preschool level. EMERGE was described as an example of a program designed to provide early intervention to low-income children to ensure they begin kindergarten with the fundamental skills necessary for learning to read. Conceptualized within a multi-tiered approach, EMERGE relies on monthly progressmonitoring data and ongoing observations by teachers to provide whole-group instruction, small-group support, and individualized tutoring for children to promote their early literacy and language development. Through EMERGE, early identification of children who do not respond to tier 1 intervention, combined with the provision of literacy-rich environments and evidencebased instruction across all intervention tiers, contributed to significant gains and higher performance on emergent literacy indicators compared to children in comparable classrooms.

Prevention and Early Intervention to Promote Development of Behavioral Competence As is true for preventing difficulties in learning to read, the preschool years are critical for establishing a foundation to prevent the development of disruptive behavior. Challenging behaviors, particularly aggression and poor peer relations, are the most common referral issues to mental health services for preschool children (Keenan & Wakschlag, 2000). Behaviors that place children at risk for delinquency in adolescence, such as aggression, inattention, and inappropriate attention-seeking, may be evident as young as two years of age (Keenan et al., 1999). For all children, especially those at greatest risk, the development of social-emotional and behavioral competence cannot be left to chance. Even in preschool, children with challenging behaviors are viewed by teachers as being “hard to teach”; they typically receive less instruction and positive feedback from teachers than do other children. Preschool teachers and daycare providers report that managing difficult behaviors is their single greatest challenge (RimmKaufman et al., 2000). At the same time, however, many teachers of young children do not have adequate knowledge or skills to address the needs of children with challenging behaviors (Gettinger, Stoiber, Goetz, & Caspe, 1999). Teachers indicate they spend much of their time responding to the disruptive behaviors of a few children. Moreover, they often report using negative approaches, such as punishment, to minimize behavior problems. Reactionary and punishment-oriented approaches, however, are neither effective nor preventive; thus, children’s behavior may continue to decline (Maag, 2001). Experts agree that proactive interventions designed to both promote positive behavior and prevent the occurrence of challenging behavior should be implemented as early as possible (Denham & Burton, 1999). Thus, there is a strong need for early intervention and prevention programs targeted at the social-emotional and behavioral needs of preschoolers.

Prevention and Early Intervention for Preschool Children 365 Much has been written about what early childhood professionals can do to promote behavioral competence in young children. The most effective strategies for early intervention and prevention of behavior problems in preschool children represent competence-enhancement approaches (Pianta & Nimetz, 1992). Competence-enhancement strategies are designed to help children develop skills and behaviors that enable them to deal effectively with the social, behavioral, and learning demands of school. The intervention model focuses on the promotion of growth (competence enhancement) rather than on remediation of problems. The goal of these strategies is to protect children against future dysfunction by building positive behaviors. The results of early intervention programs that use competence-enhancement strategies provide support for the conclusion that young children can acquire skills and behaviors to prevent the occurrence of long-term behavior challenges (Ryan et al., 2006). Within competence-enhancement approaches, the specific behaviors targeted for early intervention are determined based on an understanding of the nature of children’s competence and adaptation in problem situations. In their descriptions of preschool children who exhibit social-emotional and behavioral competence, early childhood teachers described such children as being able to (a) sustain attention and remain engaged, enthusiastic, and curious during learning activities; (b) inhibit impulsivity and follow classroom rules, routines, and directions; and (c) take turns and be responsive to other children’s feelings (Lewit & Baker, 1995). Although the preschool years are seen as the primary period for developing these competencies in children, there is evidence that many high-risk preschoolers are not acquiring such behavioral competence, especially self-regulatory behaviors, prior to school entry (Bronson, 2000). Experts agree that the success of early intervention and prevention programs rests on their effectiveness in terms of intentionally enhancing and strengthening positive characteristics and behaviors in children. Indeed, the potential benefits of early intervention programs that incorporate competence-enhancement strategies are promising (Denham & Burton, 2003). Nonetheless, according to Barnett (2002), universal application of effective prevention programs in preschools requires at least three additional features: (a) behavior support strategies to create positive learning environments and develop nurturing teacher–child relationships so as to minimize problem behaviors and promote development of children’s competence; (b) functional assessment to identify the underlying reasons or circumstances that trigger problem behaviors; and (c) collaborative problem-solving among all teachers and parents to establish goals and develop a comprehensive intervention plan.

Positive Behavior Support Consistent with Barnett’s recommendations, research has demonstrated that positive behavior support and functional assessment result in the prevention of challenging behaviors and promotion of behavioral competence among young children, and that collaborative support teams facilitate consistent and accurate implementation of interventions. Positive behavior support (PBS) relies on proactive, rather than reactive, strategies and is based on an understanding of environmental variables that contribute to the occurrence of children’s behavior. PBS focuses on developing positive behavior and preventing challenging behavior through environmental change and proactive management strategies (Stormont, Lewis, & Beckner, 2005). A recent meta-analysis revealed large effect sizes for PBS across multiple behaviors and diverse participants (Marquis et al., 2000). An increasing number of case studies have appeared in recent years demonstrating the effectiveness of PBS applications specifically with young children (e.g., Kamps, Ellis, Mancina, Wyble, Greene, & Harvey, 1999).

366 Maribeth Gettinger et al. In general, comprehensive PBS interventions for young children include three primary elements: (a) implementing changes in the preschool or daycare environment to support positive behavior (e.g., offering choices; eliminating crowded spaces; providing explicit directions; using predictable schedules and routines; foreshadowing transitions and activity changes; clearly communicating rules and expectations); (b) providing children with positive attention and support for appropriate classroom behaviors (rather than reactive attention for misbehavior); and (c) when necessary, teaching and encouraging children to engage in positive replacement behaviors or activities (e.g., reading a book instead of throwing puzzle pieces) (Stormont et al., 2005). Studies evaluating the benefits of using these three elements of preventive classroom management report success in improving the social and behavioral performance of preschool children (Kamps et al., 1999; Neilsen, Olive, Donovan & McEvoy, 2000). Furthermore, when teachers implement these management strategies, disruptions are minimized and the total amount of time that children are immersed in learning throughout the day is significantly increased (Pianta, LaParo, Layne, Cox, & Bradley, 2002). Functional Assessment The development of PBS for prevention and early intervention programs begins with an understanding of children’s challenging behavior derived through functional assessment (Crone & Horner, 2003). Functional assessment is a process that identifies environmental events, circumstances, and interactions that trigger problem behavior. Information obtained through functional assessment maximizes the effectiveness and efficiency of behavior support because it leads to the design of interventions that directly target the function of children’s behavior (DuPaul & Ervin, 1996). Interventions derived from functional assessment focus specifically on prevention by replacing challenging behaviors with functionally equivalent social and communication skills (Chandler & Dahlquist, 2002). Studies evaluating the use of functional assessment with young children report success in improving the social and behavioral performance of preschool and kindergarten children identified as having significant behavior risks (Kamps et al., 1999; Neilsen & McEvoy, 2004). Collaboration Research has linked success with PBS and functional assessment to collaboration and teaming among early childhood professionals. For interventions to be implemented with consistency and success in resolving challenging behaviors, professionals must collaborate as members of a team throughout both the assessment and intervention process (Fox, Dunlap, & Cushing, 2002). Thus, establishing a collaborative problem-solving team that includes teachers as well as parents is viewed as a critical component of PBS for early intervention. Within a collaborative approach, team members share responsibility for developing, implementing, and monitoring comprehensive behavior support plans. Approaches that emphasize team-based decision-making have proven to be effective in conducting functional assessment and providing positive behavior support (Chandler, Dahlquist, Repp, & Feltz, 1999).

Description of an Early Intervention Program: Focus on Behavioral Competence Recently, an experimental early childhood prevention program, entitled Functional Assessment, Collaboration, and Evidence-Based Practice (FACET), has been developed with

Prevention and Early Intervention for Preschool Children 367 these individual components of effective prevention in mind (Gettinger & Stoiber, 2006). Specifically, FACET is designed to support early childhood educators in accommodating the needs of young children with challenging behaviors. The program was developed based on four key principles derived from the literature on early childhood prevention of behavior problems. First, young children’s challenging behaviors are linked to multiple factors, including features of the preschool environment; thus, effective interventions should address environmental as well as child variables. Second, evidence-based interventions should be designed to promote the development of learning environments that support positive behaviors, rather than punish inappropriate behaviors. Third, a collaborative, team-based approach maximizes the effectiveness of assessment and intervention procedures. And, finally, interventions are most effective when they are applied early and implemented as planned. In the FACET program, functional assessment data are collected and used to develop a comprehensive behavior support plan that may be implemented by teachers with whole classrooms, small groups of children, or individual target children. As such, FACET is well-aligned with a multi-tier prevention approach and can be implemented with varying degrees of intensity and focus on specific goals across multiple intervention tiers. When implemented as a universal prevention approach at the classroom level, the FACET program is intended to achieve two primary objectives. The first is to improve classroom practices, self-efficacy beliefs, and competence among early childhood educators through professional development, ongoing consultation, and collaboration. A preliminary evaluation of FACET implementation in 25 early childhood classrooms revealed that teams of teachers, parents, and other professionals were able to independently and successfully implement the FACET procedures with fidelity, even without ongoing support of expert consultants (Gettinger & Stoiber, 2006). Second, FACET is designed to bring about positive behavior change in young children and promote the development of social-emotional and behavior competence through implementation of a Comprehensive Behavior Support Plan (CBSP). CBSPs are designed to incorporate three integrated intervention components: (a) strategies for teaching children new skills or behaviors; (b) changes and adaptations in features of the classroom environment; and (c) positive-consequence strategies to support appropriate behaviors. The preliminary evaluation of FACET revealed that comprehensive interventions combining teaching, environmental, as well as consequence strategies were associated with significant and sustained gains, i.e., more positive behaviors and fewer instances of negative, disruptive behaviors (Gettinger & Stoiber, 2006). The FACET program incorporates a five-step procedure that is implemented through collaboration among members of a team that includes teachers, parents, and other early childhood professionals. Figure 18.3 provides an overview of the FACET steps, with a description of the objectives or outcomes for each step. An important component of the FACET approach is professional development and support for early educators. Multiple features of the professional-development model were designed to maximize the extent to which the steps in Figure 18.3 are implemented with accuracy and consistency. First, professional development relies on a balance of didactic training to increase knowledge and skills related to prevention practices with practice-oriented and mentoring components to promote accurate and sustained implementation of procedures. Second, continuous support and training are provided over a period of four months, during which time teachers focus on one prioritized concern or target child(ren). The ongoing nature of professional development allows sufficient time for teachers to practice applying skills, evaluate their implementation and effectiveness, and receive feedback and guidance from the consultants. Third, the information presented during the training sessions focuses on naturalistic early intervention contexts. Teachers are presented with explicit examples of intervention strategies for each component of a CBSP, as well as

368 Maribeth Gettinger et al.

Activities or outcomes

FACET steps

Step #1: Conduct functional assessment.

1. 2. 3. 4. 5. 6. 7. 8.

Identify primary behavioral concern. Describe context for the behavior of concern. Indicate conditions related to the behavior (slow/fast triggers). Identify functions (pay-off) of the behavior. Describe previous strategies and effectiveness. Identify student assets and school/home resources. Identify alternative positive behavior to strengthen. Write summary statement integrating assessment information.

Step #2: Establish goals and benchmarks.

1. 2. 3. 4. 5.

Establish a target date for goal attainment. Describe what child is expected to do. Describe the context for performance of goal behavior. Define benchmarks for goal behavior on a 7-point scale. Collect baseline of goal behavior performance.

Step #3: Develop a Comprehensive Behavior Support Plan (CBSP).

1. 2. 3. 4. 5.

Determine Learning Environment Strategies. Determine appropriate Response Strategies. Determine appropriate Teaching/Self-Control Strategies. Delineate team member roles and responsibilities. Evaluate the intervention plan prior to implementation.

Step #4: Implement the CBSP and monitor progress.

1. 2. 3. 4.

Implement the CBSP, as planned. Collect goal attainment scaling data to monitor progress. Meet monthly as a team to evaluate the CBSP and progress. Revise the CBSP, as needed, and document revisions.

Step #5: Summarize and evaluate outcomes.

1. Team summarizes progress and determines what components of the CBSP facilitated progress and what was not effective. 2. Make consensus decision about continuation of CBSP or revision of goal/benchmarks; record date and decision. 3. Team summarizes revisions in CBSP.

Figure 18.3 FACET Steps and Outcomes

general evidence-based strategies to prevent challenging behaviors and promote positive behaviors. All team members learn to identify triggers and the function of appropriate and challenging behaviors, and to apply interventions based on functional assessment data. Finally, professional development focuses on simple changes in preschool environments to reduce the frequency of challenging behaviors, such as offering choices to children, creating well-organized learning centers, limiting the number of children in potentially crowded spaces, and providing children with leadership opportunities or responsibility in the classrooms. These aspects of professional development contributed to high implementation fidelity ratings (over 60% of the steps in Figure 18.2) when teachers implemented strategies on their own. Studies evaluating FACET have used multiple performance measures to assess outcomes related to teacher behaviors and skills (observations, competency ratings, and measures of self-efficacy), child behaviors (observations and ratings by teachers and parents), and

Prevention and Early Intervention for Preschool Children 369 classroom environments (observations). Using an experimental-control group design with over 50 preschool-age children and their teachers (with random assignment to no-treatment control conditions), positive results have been found to support FACET as an effective intervention approach (Gettinger & Stoiber, 2006). Results indicate, first, that early childhood educators demonstrate more effective classroom practices and higher ratings of skills/knowledge and self-efficacy beliefs at post-intervention, compared to their baseline measures and compared to similar educators in no-treatment control classrooms. Second, children in FACET classrooms make significant behavioral gains from baseline to post-intervention. They exhibit a differentially higher occurrence of positive behaviors and fewer negative behaviors from baseline to end-of-year assessment, compared to children in control classrooms. They also receive higher ratings of positive behaviors and lower ratings of negative behaviors from both teachers and parents. Finally, FACET team members implement the approach with high integrity. Moreover, the extent to which the FACET procedures are implemented is significantly correlated with overall child gains (Gettinger & Stoiber, 2006; Stoiber, Gettinger, & Fitts, 2007). To date, studies evaluating the long-term benefits of FACET are limited; however, an initial analysis revealed that intervention gains are maintained at least one year following implementation (Stoiber & Gettinger, 2007). An evaluation of FACET provides support for the effectiveness of an intervention program that incorporates functional assessment, collaboration, and evidence-based treatment (positive behavior support) with young children who exhibit challenging classroom behaviors. In sum, early intervention and prevention programs for children with challenging behaviors that are guided by problem-solving, functional assessment, intervention design principles including positive behavior support, and progress-monitoring have yielded positive results.

Conclusion and Future Directions Findings from several studies conclude that preschoolers with learning and behavior challenges, especially those living in impoverished environments, are at risk for adjustment problems that may last into elementary school and beyond. Thus, the need for early intervention and prevention programs for preschool children is justified. Indeed, developmental research supports the notion that early childhood is a time during which children’s long-term cognitive and social-emotional outcomes can be significantly affected. The provision of preschool prevention and early intervention services has been an area of considerable growth over the last 10 years. Whereas many programs affect the lives of young children, this chapter has focused specifically on center-based, early childhood programs for children between two and five years of age (prior to kindergarten entry). Through systematic implementation of early intervention approaches, such as the two programs described in this chapter, challenging behaviors and academic problems can be prevented and positive outcomes can be achieved for high-risk preschoolers. Despite the rapid development of early intervention, a number of areas for continued research remain. Of critical importance is the need for more rigorous program evaluations and long-term follow-up studies. From a research perspective, the existence of methodological problems has posed significant challenges to our ability to establish unequivocal statements regarding the effectiveness of early intervention. For example, in small-scale, tightlycontrolled evaluations, early intervention and prevention approaches are likely to be implemented as designed. In large-scale investigations, however, what actually gets implemented in preschools can vary widely. Therefore, research should expand beyond small-scale, controlled studies to naturalistic implementation in randomly designated experimental classrooms.

370 Maribeth Gettinger et al. Another inherent problem in the evaluation of early intervention for preschoolers is that the impact of programs may not be apparent until elementary or middle school, and, conversely, short-term benefits may not be indicative of long-term gains. Thus, longitudinal studies such as the Chicago Longitudinal Study (Reynolds et al., 2004) that follow children through elementary and middle school are essential in evaluating the effectiveness of prevention programs. Finally, it is important to continue research regarding the impact of different types of professional development to strengthen the skills and practices of preschool teachers and the link to child outcomes. Currently, there is a lack of solid research regarding the effects of specific professional development activities on teacher, caregiver, and classroom variables that promote children’s social-emotional competence and emergent literacy (Zaslow & MartinezBeck, 2006). Experimental studies that indicate the degree to which professional development interventions succeed in changing caregiver/teacher behavior and promoting positive child outcomes are necessary for continued development and implementation of early intervention programs. Finally, although it is clear that early intervention has both short- and long-term benefits, the optimal length of participation, particularly in compensatory early education programs like Head Start, is not clear, nor is the nature and duration of follow-up into elementary school that is necessary for long-term benefits. Most evaluation studies demonstrate that some short-term and long-term effects of early intervention and prevention are possible for high-risk preschool children. Nonetheless, it is unrealistic to expect a preschool intervention to have a lasting impact on children’s academic and behavioral functioning unless intervention is supplemented and extended through elementary school. Evidencebased prevention programs are needed at every age level to maximize the benefits of early intervention programs.

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19 Partnering to Achieve School Success A Collaborative Care Model of Early Intervention for Attention and Behavior Problems in Urban Contexts Thomas J. Power, Heather Jones Lavin, Jennifer A. Mautone, and Nathan J. Blum, The Children’s Hospital of Philadelphia, University of Pennsylvania School of Medicine Attention and behavior problems are highly prevalent. The incidence of attentiondeficit/hyperactivity disorder (ADHD) has been estimated to be between 5% and 10% (American Psychiatric Association, 2000; Brown et al., 2001), but the prevalence of attention and behavior problems is much higher and may range up to 30%. These problems pose a significant public health concern; they place children at enormous risk for academic failure, which can result in school disengagement, dropout, and deleterious health and mental health outcomes in adolescence and adulthood (DuPaul et al., 2004; Fergusson & Horwood, 1995).

Importance of Family Environment Success in elementary school is critical for healthy child development. Developmental research has affirmed that early school success depends in large measure upon the quality of the home environment (Christenson & Sheridan, 2001; Rimm-Kaufman & Pianta, 2003). A family environment in which there is a strong attachment between parent (or caregiver) and child prepares the child to engage in effective relationships with adults and peers upon entering school. In turn, successful teacher–student relationships contribute to strong academic achievement and effective peer interactions in school (Pianta, 1997). Further, a family environment in which the parents are effective in helping their children regulate their behavior and emotions facilitates the development of motivation and task engagement needed for educational success. Family involvement in education is another means by which the family can contribute to school success. Researchers have identified two fundamental ways that families can promote children’s educational success: (a) involvement in the home, such as setting aside time for literacy activities, assisting with homework, and limiting television viewing; and (b) collaboration between family and school, such as conferencing with teachers to resolve issues that are interfering with the child’s education (Fantuzzo, Tighe, & Childs, 2000).

Risk of Children with Attention and Behavior Problems Children with attention and behavior problems, including those with ADHD, are clearly at risk for early school failure (DuPaul & Stoner, 2003), and the negative outcomes associated with it. Although attention and behavior problems contribute directly to difficulties acquiring academic skills and relating effectively with peers in school, risk factors in the

376 Thomas J. Power et al. family environment further contribute to the child’s lack of preparedness to perform competently in school. Interactions with their parents are often conflictual, making it difficult for these children to maintain strong attachments with their parents (Barkley, 2006). In turn, failure to establish strong attachments with caregivers may contribute to self-regulation deficits and relationship problems in school, which can lead to educational and social impairments. Families of children with attention and behavior problems also may have more difficulty supporting their children’s education than other families. Structuring the home environment so that it promotes their children’s education may be difficult for these families, due to conflictual parent-child relationships and non-compliant child behavior (Power, Karustis, & Habboushe, 2001). Also, parent–teacher relationships may be strained as a result of frequent complaints by educators to parents that their children are disruptive.

Health Disparities: Risk of Children in Low Income, Urban Settings Children with attention and behavior problems living in low-income, urban settings face an additional set of risks. Poverty confers upon these children and their families enormous risk, including stress related to increased rates of violence, parental psychopathology, single parenting, and health problems. These factors, in turn, are associated with decreased cognitive stimulation in the home, disrupted parent-child attachments, harsh parenting, and ineffective parenting practices (McLoyd, 1998; Wahler & Dumas, 1989). Further, children with attention and behavior problems from urban communities often attend schools that are underresourced and stressed, and community-school relationships in these settings may be strained due to the incongruence between the culture of the school and that of the community (Christenson & Sheridan, 2001). A high percentage of children with behavioral health disorders, including ADHD, who live in low-income settings do not receive any services. Children from low-income communities who belong to racial/ethnic minority groups are especially at risk for not receiving care (Kataoka, Zhang & Wells, 2002). In addition, young children and those who are female often fail to have their mental health needs addressed.

Challenges with Service Delivery The major sites for service delivery for children with attention and behavior problems are schools and primary care practices. Schools have numerous advantages for delivering services to children with these difficulties, such as (a) a naturalistic context for providing early intervention and prevention programs to improve academic and social functioning; (b) the availability of professionals to address the health (school nurse) and mental health (school counselor, school psychologist) aspects of child development; and (c) a mechanism for organizing interdisciplinary teams to resolve students’ academic, behavioral, and social problems. However, school-based models of service delivery pose significant challenges, including variations in the ability of school professionals to partner effectively with families, variable use of evidence-based interventions, limited ability to address behavioral difficulties in the home environment, and a lack of resources to address the needs of students with significant learning and behavior problems. Primary care practices also have many assets for delivering services. Primary care providers (PCPs) often have longstanding relationships with children and families, affording them a helpful perspective on child development and opportunities to build a trusting relationship.

Partnering to Achieve School Success 377 In addition, although PCPs devote much of their time to treating sick children, they have a strong orientation to prevent illness and to intervene when signs of risk emerge (Brown, 2004). However, PCPs are limited with regard to their influence on school performance. PCPs may believe that medication for children with attention and behavior problems is their only treatment option, even though they recognize that psychosocial interventions are critical. Also, PCPs face challenges in using medications to treat school behavior, because of problems getting feedback from schools (Power, Mautone, Manz, Frye, & Blum, 2008). The service delivery systems nested within schools and primary care are complementary in many ways. Forging a partnership between these two systems is an optimal approach to intervention, but collaborative care is far from the norm. These systems typically are disconnected, placing families in the untenable position of having to coordinate systems of care (Guevara et al., 2005). For low-income families from urban settings, it is often impossible for them to coordinate systems of care for their children who need assistance.

Need for a Multi-Systemic, Collaborative Approach to Care Promoting school success for children with attention and behavior problems requires an approach that focuses on development in the family and school systems and that promotes strong relationships with providers in primary care. Also, effective approaches to care require that the family, school, and health systems be closely connected to foster productive partnerships. Existing approaches to intervention focus on change in a single system of care. For example, many family intervention programs have been developed to address the needs of children with disruptive behavior problems (e.g., Barkley et al., 2001). Virtually all of these programs focus on change in the family system; none of them places emphasis on change in both the family and school systems. As another example, school intervention programs have been developed to improve the educational functioning of children with ADHD (Kern et al., 2007). However, the principal focus of these programs is on change in school and not in the family. A well-developed model for providing an integrated approach to early intervention that cuts across the family and school systems is Conjoint Behavioral Consultation (CBC; Sheridan & Kratochwill, 2008). This model addresses target behaviors at home and school using strategies developed through functional behavioral assessment. The critical mechanism for change is the development of a problem-solving partnership between parents and teachers. The CBC model provides an important foundation for the Family–School Success program for children with attention and behavior problems, which is currently being developed and evaluated through the ADHD Center at The Children’s Hospital of Philadelphia (Power, Soffer, Clarke, & Mautone, 2006). Although CBC was developed principally to foster problem-solving between the family and school, the model is highly applicable for collaboration that cuts across the family, school, and primary care health systems (Power, DuPaul, Shapiro, & Kazak, 2003). Figure 19.1 provides a simplified depiction of our model for collaborative care that connects the family, school, and primary care systems through a partnership process to foster the successful development of children. This model provides a mechanism for promoting collaboration among systems to develop, implement, and evaluate multi-modal interventions for children with attention and behavior problems. Need to Engage Families in the Process of Change Although a collaborative model of care is critical for successful early intervention and prevention programming, it is not sufficient. Families of children with attention and behavior

378 Thomas J. Power et al.

Family

School

Child

Primary care

Figure 19.1 Effective Intervention for a Child with ADHD Typically Involves a Partnership Involving the Family, School, and Primary Care Practice.

problems, particularly those from low-income urban settings, often have difficulty getting engaged and staying engaged in intervention. Research (see Eiraldi, Mazzuca, Clarke, & Power, 2006) suggests that lack of engagement in intervention is due to both access factors (e.g., insurance and financial status, availability of transportation, and scheduling flexibility) and beliefs about mental health conditions, their treatment, and the professionals who provide services (e.g., stigmatizing effects of mental health labels, beliefs in one’s ability to be successful in accessing systems for the family, and trust in health providers). A problem with service delivery for children with behavioral health problems, including ADHD, is that it often does not account for a family’s readiness, willingness, and ability to engage in treatment (Dishion & Stormshak, 2007). Families may be provided with services that are not consistent with their beliefs about appropriate care for their children or that fail to consider their readiness for intervention. In these cases, families may fail to initiate treatment or drop out prematurely, placing limits on intervention effectiveness and the sustainability of change. An effective approach to care involves providing evidence-based interventions in response to a family’s readiness, willingness, and ability to engage in treatment. Such an approach requires a focus on: (a) forming and sustaining strong partnerships with families; (b) developing care plans in a manner that is highly responsive to the values and priorities of families; (c) implementing care plans in a sequence that is responsive to a family’s readiness to commit to the treatment; and (d) reviewing progress with families and modifying care plans as needed (Dishion & Stormshak, 2007). Key Components of a Collaborative Care Program for Urban Families Research has demonstrated that effective intervention requires the use of evidence-based strategies and careful attention to process variables (e.g., family engagement in intervention, readiness for change). The following is a description of the evidence-based strategies that are important for a family-mediated, educational intervention to be effective. Subsequently, we describe adaptations that are necessary for intervention to be effective in low-income, urban settings.

Evidence-Based Intervention Strategies Four core intervention strategies are important to support families in promoting their children’s educational success. Also, there are additional strategies that may be useful for those

Partnering to Achieve School Success 379 children who have ADHD and comorbid conditions. The following is a description of each strategy. Strengthening Parent–Child Attachments. Effective strategies have been developed for strengthening parent–child relationships. For example, the Child’s Game (McMahon & Forehand, 2003), Child-Directed Interaction training (Eyberg, Schuhmann, & Rey, 1998), and child-centered play strategies (Webster-Stratton & Reid, 2003) are highly useful and effective approaches that have been incorporated into many family behavior therapy programs. All of these approaches provide guidance to parents in how to interact with their children in an attentive, responsive, non-directive manner. Improving Children’s Self-Regulation Skills. The application of social learning theory primarily through the use of positive reinforcement strategies, such as differential attention, praise for adaptive behavior, and token economy systems, has been demonstrated to be effective in regulating the behavior of children. Social learning theory has guided the development of strategies such as the Parent’s Game (McMahon & Forehand, 2003), Parent-Directed Interaction training (Eyberg et al., 1998), and parent-directed strategies (Webster-Stratton & Reid, 2003), all of which are highly effective in helping children control their behavior and emotions. The importance of parent training among underserved families has been highlighted by research demonstrating the mediating effects of parenting behavior on the relationship between poverty and the social-emotional functioning of children (McLoyd, 1998). Strengthening Family–School Partnerships for Problem-Solving. Strengthening parent-teacher relationships and guiding both parties in the use of needs assessment and problem-solving strategies to address children’s educational problems is a fruitful strategy that can yield highly successful outcomes (Sheridan & Kratochwill, 2008). Conjoint behavioral consultation (CBC) is a structured process of asset-building and problem-solving in which parents and teachers work collaboratively through the four stages of behavioral consultation: (a) needs (problem) identification, (b) needs (problem) analysis, (c) plan implementation, and (d) plan evaluation. A critical component of this model is the formation of a partnership between parents and teachers that facilitates competence promotion and problem-solving. This model has been applied with success among children who have a wide range of behavioral and educational problems, including youngsters with ADHD (Sheridan, Eagle, Cowan, & Mickelson, 2001). Families and schools often engage in adversarial relationships that are not conducive to the formation of problem-solving partnerships. These types of relationships are particularly prevalent with children who have attention and behavior problems. In these situations it is important to prepare the family for collaboration with the school and to prepare educators for collaboration with families to promote effective, productive partnerships (Power et al., 2003). Strengthening Partnerships Between Families and PCPs. Promoting effective partnerships between families and PCPs is an important strategy in fostering adaptive child development at home and school, particularly for children with health concerns such as ADHD. In our work, the CBC model has been adapted to establish problem-solving partnerships between families and PCPs to address concerns arising in the family and school. Also, these partnerships are vital in planning and implementing medication trials, if indicated. When it is not feasible to include school professionals in collaborative care meetings with PCPs and families, it is important that a school consultant be available to represent the concerns and perspectives of school professionals at these meetings. Similarly, the school consultant can serve as a representative for PCPs at family–school meetings. Additional Components of Comprehensive Care. For children with attention and behavior problems, several additional strategies may be needed. First, children with ADHD often benefit from medication. It is important to provide pharmacological intervention when it is

380 Thomas J. Power et al. clinically indicated and the family is receptive to this treatment. Second, some children require an in-depth diagnostic evaluation when there are questions about comorbid conditions. It is important to provide this type of evaluation when the clinical team sees a need and the family is receptive to the service. Third, many children with attention and behavior problems encounter crises that disrupt family and school activities. During these periods, it is important to provide crisis intervention services.

Necessary Adaptations in Urban Contexts Although numerous evidence-based intervention programs have been developed for children with attention and behavior problems, most of these approaches were not designed specifically for children from low-income, urban settings. As a result, existing approaches may not be responsive to the unique needs of these children and their families, which may compromise the effectiveness of intervention efforts (Tucker & Herman, 2002). Children from low-income, urban settings with attention and behavior problems often do not receive any intervention throughout childhood and adolescence (Kataoka et al., 2002). Families often must overcome significant barriers to obtain effective care for their children. Studies have found that when low-income children engage in treatment, they are more likely to drop out than their peers in more affluent settings (Kazdin & Wassell, 1998; McMahon, Forehand, & Griest, 1981) and may have poorer treatment outcomes (e.g., Dumas & Wahler, 1983; Handen, Janosky, McAuliff, & Breaux, 1994; Rieppi et al., 2002; Webster-Stratton, 1985; Webster-Stratton & Hammond, 1990). Therefore, it is imperative that researchers and clinicians address this disparity by assisting these families in overcoming barriers to service utilization (Power, Eiraldi, Clarke, Mazzuca, & Krain, 2005). Towards this end, several adaptations to current evidence-based interventions may be necessary. Forming Partnerships with Families. As indicated, families often have difficulty becoming engaged in intervention. Establishing a trusting partnership with families at the outset is critical for success (Cunningham & Henggeler, 1999; Miller & Rollnick, 2002). Clinicians may be at a disadvantage in forming these partnerships due to cultural or socioeconomic differences with families. The use of a participatory intervention model (PIM; Nastasi, Moore, & Varjas, 2004) is helpful in establishing a strong, trusting relationship that can lead to positive outcomes. PIM is a partnership-based approach that promotes the formation and continual development of collaborative relationships between clinicians and families. This approach fully acknowledges the expertise of families and clinicians in their respective domains. Families are experts in their culture and family traditions, which influence their intervention goals and preferred methods of intervention. Clinicians are experts in evidence-based intervention strategies and methods of evaluating outcome. Through collaboration families and clinicians have the opportunity to design approaches to care that are acceptable and feasible to families and have the potential to produce substantial change. Sustaining Partnerships with Families. Even when clinicians are successful in forming partnerships with families at the outset, it can be difficult to sustain these relationships to achieve positive outcomes. Team members must continually reach out to families to maintain their engagement and to assist them in overcoming barriers to care (Dishion & Stormshak, 2007). During periods when the child is adjusting reasonably well, it is important to continue checking in to maintain gains and prevent relapse. Programming to prevent relapse is particularly critical for children with chronic disorders such as ADHD who are likely to experience impairments throughout the course of child development.

Partnering to Achieve School Success 381 It is imperative for clinicians to stay connected to families through regular check-ins. A family often may not be able to come to the office for treatment, but a clinician may be able to check in with a parent over the telephone for 10 minutes or so. An ongoing problem is that families sometimes do not return phone calls from clinicians. In typical practice, a clinician may try twice to contact a family, and if no response is received, the child’s file may be closed. Despite the efficiency of such procedures, it may not be appropriate for low-income families who are multiply stressed. Often, these families experience negative life events, such as losing a car or job, that take precedence over addressing a child’s behavior problems at a particular point in time. Further, with the advent of disposable cell phones, some families are difficult to reach because their phone numbers change constantly. We maintain that it is necessary for clinicians to persist in their efforts to sustain partnerships with families. Promoting Readiness for Change. Interventions are likely to be effective only when families are willing, ready, and able to use them (Miller & Rollnick, 2002). To increase the likelihood that an intervention will be effective, it is important to resolve barriers to care and assist the family in getting ready for treatment (Power, Eiraldi, et al., 2005). It is important to recognize that family readiness for change may vary greatly across a range of service options. For example, a family may be ready for family intervention but not ready to work effectively with the school. Alternatively, a family may be ready for collaboration with the school, but not ready for a trial of stimulant medication for ADHD. Therefore, it is essential for clinicians to assess family readiness for a variety of options and then implement only those components to which the family is ready to commit. Dishion and Stormshak (2007) have outlined several strategies for promoting the change process. Empathy for the family is critical for building a trusting partnership and promoting change (Miller & Rollnick, 2002). When working with low-income families, it is especially important for the clinician to acknowledge and empathically understand the multiple stressors that can have an effect on children and families. Further, a useful strategy is for clinicians to acknowledge the successful help-seeking steps already taken by families, instead of highlighting the problems with adherence and follow through they have demonstrated. In addition, applying a partnership model (i.e., PIM) in collaborating with families can promote a sense of empowerment and lead to greater investment in treatment (Dishion et al., 2007; Nastasi et al., 2004). Promoting Teacher Investment in Intervention. For family–school collaboration to be effective, it is essential for both parents and teachers to be invested in the process. Just as parents vary greatly with regard to their readiness to become engaged in targeted intervention, teachers also differ markedly with regard to their willingness to form partnerships with parents and invest in mutual problem-solving (Power et al., 2009). Given that family–school relationships are often strained when coping with ADHD, it is important for clinicians to prepare the school for conjoint behavioral consultation (Power et al., 2003). This may be done using strategies that are similar to those used in promoting family readiness for change, such as empathizing with teachers about how challenging it is to work with the child, acknowledging the multiple ways that teachers have tried to instruct the student and manage his or her behavior, and developing an intervention plan through a partnership process with teachers (Power, BlomHoffman et al., 2005).

The Partnering to Achieve School Success Project Partnering to Achieve School Success (PASS) is an early intervention service based in primary care, which is designed to promote family-centered and culturally effective care for children and families residing in underserved urban communities who are coping with attention and behavior problems. This program can be conceptualized as an indicated prevention program

382 Thomas J. Power et al. in that it targets children with clear signs of risk in an effort to intervene before serious problems emerge (Institute of Medicine, 1994). The program focuses on building relationships within and between systems of care (i.e., families, schools, primary care, community mental health; Power et al., 2003) and improving parent–child relationships during early childhood to prevent learning and conduct disorders. In addition, one of the overarching goals of PASS is to foster increased independence and self-empowerment for all families enrolled in the program (Tucker & Herman, 2002). A psychology clinician (a psychologist or advanced trainee working under supervision) and PCP serve critical roles in establishing and sustaining a partnership with each family and linking the major systems of care. The components of PASS were designed to incorporate the evidence-based intervention strategies and necessary adaptations for urban families described earlier in this chapter (see Table 19.1). The major components of PASS are: (a) Engagement and Progress Monitoring, (b) Brief Family Intervention, (c) Family–School Consultation, (d) Medication Management, (e) Psychological Evaluation, and (f) Crisis Intervention. As many urban families struggle to attend sessions or drop out of treatment prematurely, the Engagement and Progress Monitoring component of PASS is particularly important to foster engagement throughout intervention. Engagement and Progress Monitoring. All families enrolled in PASS receive the Engagement and Progress Monitoring component of the service; the focus of this component is on forming a collaborative partnership with families to reduce barriers to care. Motivational interviewing strategies are utilized to promote each family’s readiness, willingness, and ability to become actively engaged in treatment (Dishion & Strormshak, 2007; Miller & Rollnick, 2002). During Table 19.1 Components of PASS Intervention Component

Description

Engagement & Progress Monitoring • • • Brief Family Intervention • • Family–School Consultation

• • • •

Medication Management

• • • •

Psychological Evaluation Crisis Intervention

• • • • • •

Identify current concerns Discuss and problem-solve barriers to care Promote readiness for change Foster positive parent–child relationship Teach positive reinforcement strategies, giving commands, using punishment strategically Discuss parent concerns about school Discuss teacher concerns about school Direct observation of child behavior during school day Provide family–school consultation to determine mutual areas of concern and develop appropriate intervention strategies Educate family about empirical support for pharmacological interventions for ADHD Monitor progress on different doses/types of medication and report feedback to PCP Attend medication consultation appointments Monitor progress on different doses/types of medication and report feedback to PCP Attend medication consultation appointments Assess child for comorbid internalizing conditions Conduct academic and cognitive ability screening Identify areas of concern Develop immediate plan to address issues Determine whether additional resources are necessary

Partnering to Achieve School Success 383 the initial family session, the psychology clinician works to form a partnership with the family, identify presenting concerns, assess child development in the family and school systems, evaluate the family’s help-seeking history, and begin to design a collaborative care plan. Following the initial session, the clinician confers with the PCP to further develop the care plan. The components of intervention included in the collaborative care plan depend upon: (a) whether the professionals on the team believe the treatment is clinically indicated and could be helpful; and (b) whether the family is ready, willing, and able to receive the service. A brief description of each intervention component that may be included in the collaborative care plan is described below. Throughout the course of intervention, the clinician contacts the family by telephone at least once per month to promote the ongoing engagement of the family in treatment and monitor progress related to target behaviors and treatment goals. Brief Family Intervention. The Brief Family Intervention module is included for families who present with concerns about home behavior and may be responsive to brief family behavior therapy. Family-based sessions focus on fostering a positive parent–child relationship, teaching strategies for providing positive reinforcement, giving commands, and using punishment effectively. The number of sessions varies depending on the amount of time needed to accomplish treatment goals and the commitment of the family to treatment. Family–School Consultation. For cases in which there are school-based behavioral and academic concerns, families and school staff participate in the Family–School Consultation module to foster a collaborative family–school partnership to support the child. The Family– School Consultation component is based on the Conjoint Behavioral Consultation model developed by Sheridan and Kratochwill (2008). The clinician first schedules individual meetings with the family and teacher to prepare each party for the conjoint consultation process. The family meeting includes a discussion of the family’s goals for the child at school, the family’s current relationship with the teacher and other school staff, and strategies for developing collaborative relationships with teachers. The school meeting includes a direct observation of the child in the classroom to assess child behavior and the classroom environment and a meeting with the teacher to obtain information about the child’s school performance and the teacher’s perception of the family–school relationship. Subsequently, a family–school consultation session is conducted to foster the development of the family–school partnership, identify mutual areas of concern, and engage in a problem-solving process to plan an intervention for the child. After this meeting, the clinician maintains contact with the family and teacher by telephone and monitors child progress regularly. Additional face-to-face sessions are scheduled as needed. Medication Management. If the family and PCP determine that medication treatment is warranted and acceptable, the PASS care plan includes the Medication Management module. This component includes education for the family provided by the PCP and psychology clinician related to medication treatment for ADHD. Also, the clinician collects parent and teacher rating scales throughout the medication trial to monitor response to treatment and identify the optimal medication and dosing schedule. Information about the child’s response to treatment is regularly provided to the PCP. In our program, the use of an electronic medical record system facilitates communication between the clinician and PCP. In addition, the clinician regularly attends medication consultation appointments to support the development of a collaborative partnership between the family and PCP related to the treatment of ADHD and related difficulties. Psychological Evaluation. A psychological evaluation is provided when there are concerns about comorbid internalizing conditions. In these cases, the clinician conducts a structured diagnostic interview, administers parent- and teacher-report measures of emotional and

384 Thomas J. Power et al. behavioral functioning, and administers child self-report measures of anxiety and depression. Also, the clinician screens for cognitive and academic functioning using standardized tests. Children who require psychoeducational evaluations to determine eligibility for special education services typically are referred to the school, and this recommendation is discussed in the context of the Family–School Consultation described above. Crisis Intervention. Because many low-income, urban families frequently struggle with unforeseen stressors related to family issues as well as child behavior (e.g., homelessness, serious illness and limited access to medical care, frequent suspension from school), a Crisis Intervention module is included to respond to crises that may arise during the course of treatment. The clinician identifies areas of concern and factors contributing to the crisis and collaborates with the family to develop an intervention plan to address the issues. If it is determined that additional resources are needed for the family to cope, the clinician confers with a social worker assigned to the primary care practice to assist the family in obtaining needed services. Follow-up support and guidance are offered by the clinician and social worker, as needed.

Preliminary Findings and Case Examples. The PASS intervention program is currently in development. The following section describes findings related to service utilization and program satisfaction for the first cohort of families participating in PASS. Also, we include case examples to illustrate how PASS has been implemented with two families. Service Utilization and Program Evaluation During the first year of the program, PASS received 26 referrals from PCPs working in an urban primary care setting. An analysis of service utilization data for children in kindergarten through grade 6 with attention and behavior problems who had received at least two months of intervention was conducted (n = 18). Eight referred children were excluded from the analysis for the following reasons: one student was in grade 10; two were involved in treatment for less than two months; one child was referred because he had a moderate to severe cognitive disability; two families were not interested in service; and two families did not respond to repeated efforts to contact them by telephone. All of the 18 children met criteria for ADHD. Fourteen (78%) were male, 17 (94%) were African American, 12 (67%) attended public schools, and about 80% were on Medicaid. Mean age of the sample was 9.6 years (SD=2.3), and families were involved in intervention for an average of 5.2 months (SD=2.0). Table 19.2 indicates the percentage of families receiving each module of PASS and the mean contact time for families in each module. Because the Engagement and Progress Monitoring component of PASS is embedded in each of these modules, the table does not report separately participation in this component. The findings demonstrated that a high percentage of the families received psychosocial intervention for the treatment of ADHD and related difficulties. In general, the families received a substantial amount of treatment; the mean number of face-to-face sessions with families was 5.2, and the mean number of contact hours (including face-to-face and telephone encounters) with families was 8.7. As indicated, a considerable amount of time was spent on family–school consultation activities (about 50% of total contact hours). Only three families (17%) participated in the medication management module of PASS. However, an additional six families had their children on medication at some point during the intervention (total of

Partnering to Achieve School Success 385 Table 19.2 Family Involvement in Each Component of PASS Intervention Component

% of families receiving component*

Mean # of face to face sessions per family (SD)

Mean # of contact hours per family (SD)

Family Family–school Medication Crisis Intervention Evaluation Total

88.9 77.8 16.7 27.8 22.2

2.6 (1.8) 2.5 (2.0) 1.7 (1.2) 1.0 (0.0) 1.5 (1.0) 5.2 (4.1)

2.9 (2.0) 4.3 (3.0) 2.1 (1.0) 1.7 (0.63) 3.1 (2.7) 8.7 (5.8)

Note * Families may receive more than one component; therefore, total percentages do not equal 100%. Family = Brief Family Intervention; Family–school = Family–school Conjoint Behavioral Consultation; Medication = Medication Management.

50% medicated). Lack of involvement in the formal medication monitoring component of PASS seemed to be related to a lack of awareness of this element of the program among some PCPs and a preference among a few pediatricians to monitor medication using procedures with which they were accustomed. Our team was able to contact nine of the 18 families by telephone to obtain program satisfaction information. Seven of the nine parents (78%) reported that PASS was helpful in addressing their child’s school problems. Six of eight parents (75%) indicated that PASS was useful in addressing difficulties at home; one parent reported that there were no real problems at home to work on. The parents discussed medication issues with their clinician in eight of nine cases (89%), and six of the eight parents who discussed medication indicated that these discussions were very helpful. In five of nine cases, the parents reported that PASS could have been more helpful in teaching them to be more effective advocates for their children in school and community settings. This feedback has been useful to our team in improving the PASS program. In addition, our team was able to elicit feedback from eight of the 16 PCPs in the practice via an online survey. Six of the eight providers (75%) found the PASS service to be very helpful for their patients, and the remaining two PCPs viewed the intervention as helpful. Also, six of the eight providers (75%) perceived the communications from PASS clinicians to be very helpful in understanding the child’s behavioral health treatment plan; the other two PCPs viewed the communications as helpful. Some providers indicated that they would like the intervention to be accessible to more of their patients. The Case of Kevin Kevin is an 11-year-old, African American, sixth-grade student enrolled in a charter school operating under the auspices of a large, urban public school district. He was referred by his PCP because he was having academic difficulties and displaying problem behaviors at home and school. At the time of referral, Kevin was diagnosed with ADHD, Predominantly Inattentive Type, and he was prescribed stimulant medication. Kevin lived with his maternal aunt, who is his legal guardian, his two cousins (ages seven years and two years), and two foster children (ages 12 years and two years). Kevin’s aunt was working full-time and pursuing her Bachelor’s degree in early childhood education in the evening. During the initial session, the aunt identified Kevin’s distractibility, difficulty concentrating, academic performance, and tendency to be easily influenced by peers as her primary concerns. Therefore, the care plan initially included a focus on school performance, using the

386 Thomas J. Power et al. Family–School Consultation component. However, repeated efforts by the clinician to prepare the family and teachers for conjoint consultation were not successful during the initial months of intervention, which occurred in the last three months of the school year. In the meantime, Kevin was identified by the school psychologist as a student with a learning disability, and his school-based concerns were addressed through special education and related services. At the end of the school year, Kevin’s aunt and school team decided to retain him in sixth grade. In addition, Kevin’s PCP requested support in monitoring his response to stimulant medication, so Kevin, his aunt, and his teachers participated in the Medication Management module. After several weeks of treatment, Kevin’s aunt noted that he was displaying disruptive behavior at home, particularly during homework time, so the Brief Family Intervention module was included in the care plan. As part of the Medication Management module, Kevin’s response to medication treatment was monitored using parent and teacher behavior rating scales. In addition, during one faceto-face session with the PASS clinician, Kevin reported that he was unhappy with the medication treatment because the medication made him “feel funny” and interfered with his performance on the basketball court. Parent and teacher rating scale data as well as Kevin’s selfreport were shared with his PCP via the electronic health record system, and the team decided to change Kevin’s prescription to another stimulant medication. Medication monitoring continued; Kevin’s aunt, Kevin, and his teachers reported behavioral improvements on the new medication. After several months on the new medication, it became evident that Kevin was not taking his medication on a consistent basis, so medication adherence became a target for treatment. The PCP, PASS clinician, and Kevin’s aunt worked closely to develop and implement a plan to monitor Kevin’s adherence to treatment. Kevin’s aunt became more directly involved in medication administration (Kevin had been taking medication independently before school), and Kevin received incentives for taking medication daily without arguing. Brief Family Intervention sessions focused on teaching strategies for using positive reinforcement and giving effective instructions. Sessions began at the end of the school year, so goals were related to completion of chores and following directions rather than homework completion. During an initial face-to-face session, Kevin and his aunt agreed that he would earn points for following directions the first time and completing chores. He would exchange points for rewards, such as video game time and participation in a hip-hop dance class. Shortly after the initial face-to-face Brief Family Intervention session, Kevin’s aunt became increasingly busy with her studies and full-time employment. Therefore, contact with the family was conducted primarily over the telephone. During telephone contacts, Kevin and his aunt reported behavioral improvements at home. Kevin was following directions, completing chores, and earning rewards on a regular basis. During the following school year, Kevin repeated sixth grade in the same charter school. At the start of the school year, the PASS clinician met with the school team to begin the Family– School Consultation module. The clinician learned that school staff recently began implementing a school-wide behavior plan that included behavioral expectations and opportunities for students to earn points for meeting expectations. In addition, students received demerits for specific misbehaviors. Kevin received mostly demerits and rarely earned rewards. Within the context of family–school consultation, the PASS clinician, Kevin’s aunt, and Kevin’s teachers developed an individualized behavior support plan for Kevin at school that would likely lead to much higher rates of positive reinforcement. The PASS clinician is continuing to work with the family and school to implement and evaluate the plan. Overall, Kevin and his aunt participated in about 10 hours of Brief Family Therapy, five hours of Family–School Consultation, and an hour of Medication Management. In addition,

Partnering to Achieve School Success 387 the family was involved in numerous phone contacts with the clinician throughout the course of the intervention to promote their sustained engagement and to monitor progress. Throughout treatment, the PASS clinician was in regular contact with the PCP through the use of the electronic health record regarding treatment goals and progress toward goals. The family continued to participate in PASS, with an increased focus on family–school communication and self-empowerment and independence, in preparation for a transition out of PASS and to community-based services, if needed. Over the course of treatment, Kevin’s aunt became increasingly consistent with her use of behavior management strategies, and the caregiver–child relationship improved (as reported by Kevin and his aunt). In addition, Kevin and his aunt both reported satisfaction with medication treatment. The Case of Marquis At the time of intake, Marquis was a 10-year-old African American male, living with both parents and his younger brother. A PCP referred Marquis to PASS after she diagnosed the child with ADHD, Predominantly Inattentive Type. Both parents reported primary concerns related to inattentive behavior at the public school he attended. He had been evaluated for special education services at age six but did not qualify. At the initial session, his mother reported concerns about attention, organization, and reading skills. Marquis and his parents reported no concerns with peer relationships at the time of intake. Although Marquis had been prescribed a stimulant medication by his PCP, his mother reported that she had not filled the prescription due to concerns about using medication to treat his learning and behavior problems. Marquis had no history of psychotherapy. At the onset of treatment, goals were discussed and agreed upon by Marquis’ parents and the clinician. First, consultation with the school was needed to better address Marquis’ attention and learning problems. Second, given teacher and parent concerns about reading, Marquis was in need of a psychoeducational evaluation and comprehensive educational plan. At the beginning of treatment, the clinician contacted Marquis’ PCP through the electronic health record system and informed her of the treatment plan, and she indicated her agreement. During treatment, the clinician forwarded progress notes to the PCP for review following pertinent therapy sessions (e.g., during the crises discussed below). Marquis initially received the Family–School Consultation component of PASS. For the school-based intervention, his clinician first observed him in class to conduct a functional assessment of behavior and determine appropriate classroom behavioral goals. He often left his seat and experienced difficulty completing assignments. His mother unfortunately was unable to attend a school meeting; therefore, the construction of the home-school note had to be discussed separately with the teacher and parents. His mother was very supportive of the idea and determined appropriate home rewards, such as extra video game or television time. Following a discussion with his teacher, behavioral goals were determined (e.g., staying in seat with few reminders) and monitored using a home-school note. In addition, a psychological evaluation was conducted, which resulted in a decision to provide Marquis with special education services to address his reading and behavioral difficulties. The clinician attended the school meeting to develop his individualized education plan (IEP), which indicated the need for additional reading and behavioral supports within and outside of the regular education classroom. During the course of intervention, two crises arose that required the application of the Crisis Intervention component of PASS. Close to the start of his involvement with PASS, Marquis severely burned his hand after deciding to make a “fire ball” in his bathroom using a

388 Thomas J. Power et al. lighter and lighter fluid. His family rushed him to the emergency department of a nearby hospital for medical care. Following this incident, his mother voiced a strong concern about his safety. The clinician discussed the events that preceded and may have contributed to the incident, such as boredom, miscommunication about child supervision, access to unsafe items, and peer pressure from other children with conduct problems. The clinician also spent time discussing the ways in which impulsivity may manifest itself at home and school, sometimes in dangerous ways. The clinician and Marquis’ mother developed strategies for better monitoring Marquis in the home and providing him with access to safe games and opportunities to play with peers in adequately supervised settings. A similar incident occurred when Marquis brought an unloaded BB gun to school after being teased by a young peer. Unfortunately, the timing of this incident was poor; it occurred shortly after a school shooting that made national headlines. School officials expressed a clear intention to expel Marquis from school. However, after the clinician provided education about ADHD to the school officials and support to the family, the school decided instead to suspend him from school for several days. In order to respond to these crises, the clinician needed to deviate from work on goals that had been targeted for treatment. During the summer, the family decided to initiate a trial of stimulant medication. The mother collaborated with the PCP to coordinate the medication trial. Marquis transitioned into the fourth grade and did well. According to his mother, the medication helped him to focus and stay seated in the classroom. Moreover, she stated that the implementation of his new IEP was helping him to better complete assignments during the school day. She stated that Marquis was displaying no real difficulties with home behavior. Thus, the PASS clinician found a combined pharmacological and psychosocial treatment to be the best fit for Marquis’ needs at home and school. Overall, Marquis and his family participated in about two hours of Brief Family Therapy, four hours of Family–School Consultation, one hour of Crisis Intervention, and 2 hours of Psychological Evaluation. These services were provided in addition to numerous phone conversations to monitor progress and promote engagement. Throughout treatment Marquis’ family remained engaged, attending most sessions and calling when they could not attend. They were highly responsive to the interventions provided during the program. Additionally, Marquis’ school professionals were highly cooperative in working with the clinician. His teacher was willing to begin and continue a home-school note. His principal and school counselor incorporated the clinician’s recommendations into the IEP developed for Marquis. Although the two crises that arose during the previous school year were very upsetting to the family, the parents gained a better understanding of the impact of Marquis’ impulsivity and the importance of more intensive adult monitoring and supervision.

Challenges and Proposed Solutions Although there are numerous challenges in the implementation of PASS, the most significant issue is getting families engaged and keeping them engaged in intervention. It is common for families not to come to sessions without calling to cancel and not to return phone calls. PASS clinicians typically contact families before sessions to remind them of appointments, and call repeatedly at different times of the day to reach families. Persistence in contacting families has been helpful to some extent in keeping families engaged. Applying motivational interviewing strategies (e.g., empathic responding, affirmation of parental attempts to follow through, and strategies to assist families in overcoming barriers to care) have also been useful.

Partnering to Achieve School Success 389 A noteworthy limitation of the PASS model is that clinicians, including the psychologist and PCP, may be at a disadvantage in forming partnerships that are essential for success due to cultural or socioeconomic differences with families. An alternative approach is for clinicians to enlist professionals or paraprofessionals who reside in the surrounding communities being served to serve as co-interventionists. These individuals, whom we call community partners, can serve a critical role in developing trusting relationships with families and acting as cultural brokers for providers (Cowen et al., 1996). A partnership between the clinicians and community partner may serve to integrate the expertise of the providers, who are trained in providing evidence-based care, with the expertise of the community partner, who understands the culture of the families being served and knows how to relate effectively with these families (Power, Dowrick, Ginsburg-Block, & Manz, 2004). Thus, incorporating community partners into the service delivery team may be an additional strategy to promote the development of strong partnerships with families leading to greater engagement in treatment. Another challenge with PASS, specifically related to Family–School Consultation, is getting teachers invested in the process of intervention. Sometimes a lack of teacher investment may be related to the hardship teachers face working in a school environment that is stressful and under-resourced. Relatedly, teachers may be reluctant to work with families whom they perceive as non-supportive or critical of their efforts. Using motivational interviewing strategies may be as helpful with teachers as they are with parents. It is important for clinicians to empathize with the challenges teachers face working with difficult-to-teach students in stressful environments. Taking the time to directly observe the child in the classroom and listening to teachers’ descriptions of the problems posed by the child may help the clinician to communicate empathic understanding. Further, teachers undoubtedly have tried numerous strategies to educate the child and manage his or her behavior. Acknowledging teachers’ efforts and selectively reinforcing strategies that have the potential to be effective are also useful approaches. Quite often, clinicians work with teachers who are reluctant to form partnerships with parents and collaborate in developing intervention plans. In these cases, it has often been helpful to work with other professionals (e.g., counselor, nurse, special education teacher) who may be key opinion leaders in the school, to support the teachers and frame out the consultation in a manner that is more acceptable to them (Atkins, Graczyk, Frazier, & Abdul-Adil, 2003). These individuals, whom we call school champions, can be invaluable not only in helping with a particular case, but in assisting with other cases that may arise in the future (Blom-Hoffman, 2008). Identifying school champions and forming strong and enduring partnerships with them has become a cornerstone of the PASS program. An additional challenge with PASS is evaluating the effectiveness of this program. Because PASS is designed to be responsive to family preferences and readiness for change, the intervention can vary greatly from family to family. For example, families may receive treatments in different combinations or sequences. Also, the amount of each component provided may vary substantially. Intervention research typically involves implementation of a manualized protocol that standardizes treatment across cases, but it is difficult to standardize the application of PASS across the diverse group of families served by this program. Therefore, innovative research designs are needed to evaluate the effectiveness of programs such as PASS in realworld community practice.

Conclusions Success in school is critical for child development and sustained health. Early school success depends to a great extent upon family involvement in education, including involvement in

390 Thomas J. Power et al. home learning experiences as well as collaborative family–school relationships. Children with attention and behavior problems are at risk for early school failure, which is related in part to disrupted parent–child relationships and contentious family–school relationships. The risks faced by low-income, urban families coping with attention and behavior problems, including ADHD, may be particularly salient given the multiple stressors associated with poverty and the lack of resources available in many inner-city schools. The major settings for service delivery for children with attention and behavior problems are schools and primary settings. Although each of these settings has advantages with regard to service delivery for this population, there are a number of challenges with the delivery of interventions that commonly arise in these systems. A collaborative care approach that links the family, school, and primary care practice to address the needs of children with attention and behavior problems is strongly needed. The Conjoint Behavioral Consultation model is a useful framework for connecting systems to deliver effective services for children with attention and behavior problems. Key elements of collaborative care for children with attention and behavior problems include strategies to strengthen the parent–child relationship, improve children’s selfregulation skills, build family–school partnerships for problem-solving, and strengthen the relationship between family and primary care provider. In addition, when working in lowincome settings where stress is high and resources may be limited, it is critical to include strategies to engage families and maximize teacher investment in intervention. The PASS program is an early intervention program designed to prevent serious outcomes for children with emerging signs of risk. PASS was developed to provide integrated, multisystemic intervention for families residing in low-income, urban settings who are coping with attention and behavior problems, including ADHD. The main components of PASS are: Engagement and Progress Monitoring, Brief Family Intervention, Family–School Consultation, Medication Management, Psychological Evaluation, and Crisis Intervention. This chapter describes feasibility and program satisfaction data for the first cohort of families who received this intervention program. The data suggest that PASS can be effectively delivered to low-income families coping with attention and behavior problems and that generally it is well received by parents and PCPs. The challenges with the delivery of this intervention, as expected, include sustaining family engagement and enhancing teacher investment in the intervention. The PASS program is currently in the relatively early stages of program evaluation. A study of its effectiveness using a control group design and including an examination of moderators and mediators is currently underway.

Note The development of this chapter was supported by the Chair’s Initiative funded through the Department of Pediatrics at The Children’s Hospital of Philadelphia.

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20 Dissemination of Evidence-Based Programs in the Schools The Coping Power Program John E. Lochman, Nicole R. Powell, Caroline L. Boxmeyer, and Rachel Baden University of Alabama Schools are increasingly recognized as a critical setting for providing prevention programs to foster healthy child social and emotional development and to prevent onset or exacerbation of mental health problems (President’s New Freedom Commission on Mental Health, 2003). Schools are uniquely poised to provide programming to a wide array of children and families. In addition to being able to provide classroom- and school-wide programming to promote positive skill development in large numbers of youth, schools are also in a key position to identify early risk markers for mental health problems in individual youths and to provide links to early preventive interventions. A wide range of school-based prevention curricula is now available and an important task is identifying and disseminating the most effective programs for specific types and levels of need. Prevention approaches are classified at different levels based on the population served (Weisz, Sandler, Durlak, & Anton, 2005). Universal preventive interventions target entire groups or populations (e.g., all third-graders at an elementary school) and are designed to address specific developmental risk factors, without attempting to discern which youths are at elevated risk. In contrast, indicated or targeted preventive interventions target individual youths whose risk of developing mental health problems is significantly higher-than-average, but do not yet fall in the diagnosable range (e.g., third-graders who score in the at-risk range on a measure of classroom behavior problems). Universal and targeted prevention approaches each offer advantages and disadvantages. Because universal programs typically include positive skill-building strategies that promote well-being, they are seen as beneficial for all and may be more readily accepted and less stigmatizing than other levels of intervention (Greenberg, Domitrovich, & Bumbarger, 2000). However, such programs may not provide sufficient duration or intensity to alter the developmental trajectory of children who are already at significant risk. A central advantage of targeted preventive interventions is that they direct resources toward the individuals in greatest need (Offord, 1996). Targeted preventive interventions typically require a screening process to identify at-risk youth. Thus a potential disadvantage of targeted approaches is that children or families may feel labeled or resist intervention because they did not actively seek it out themselves. Exemplar programs of each type have been shown to have a lasting positive impact on children’s social and emotional development (e.g., Greenberg et al., 2000; Kutash, Duchnowski, & Lynn, 2006); thus wide-scale dissemination of these evidence-based prevention programs to school settings is a major priority. Preventive interventions for youth mental health problems are typically based on ecological and developmental models of child social and emotional functioning. Developmental research has shown that most mental health problems are multiply-determined, meaning that

394 John E. Lochman et al. there is not one specific cause or risk factor for any disorder. Coie and colleagues (1993) identified a number of factors that place children at increased risk for developing mental health problems, including individual (e.g., biological deficits, behavioral and skill deficits, emotional difficulties), family (e.g., low socioeconomic status, family history of mental health problems, exposure to violence, family conflict, maladaptive parenting), and contextual (e.g., interpersonal problems, school problems, neighborhood violence, low community support) risk factors. Research has shown that these risk factors are inter-related and a developmental stacking of risk factors can occur over time, multiplying children’s risk of moving on an escalating trajectory toward serious mental health problems. Later interventions must address multiple risk factors, thus it is best to provide preventive interventions as early as possible to alter children’s developmental trajectories and prevent this stacking of risk factors from occurring. Given the central role of family, school, and contextual factors on children’s development, it is also important to intervene at multiple levels. Empirical reviews have found that multicomponent prevention programs that seek to instill positive changes in home and school environments in addition to changing child behavior are generally more effective than child-only interventions (e.g., Greenberg et al., 2000; Kutash et al., 2006). These reviews also show that multi-year programs are important for intervening with particularly treatment-resistant problems, such as serious child conduct problems. Extended interventions recognize the fact that such difficult-to-treat problems typically develop over an extended period of time, and can target contributing factors that are relevant at various developmental timepoints, leading to gradual improvements that ultimately reduce negative outcomes (Conduct Problems Prevention Research Group [CPPRG], 2002). A number of universal and targeted prevention programs have demonstrated preventive effects in school settings in randomized controlled trials. Several exemplar programs are described below. Exemplar Universal Preventive Interventions. The Promoting Alternative THinking Strategies (PATHS) program is an example of a universal prevention program that seeks to promote general social-emotional competencies and cognitive skill-building in elementary school children (Greenberg & Kusché, 2006). The program is designed as a universal intervention, delivered by teachers in the classroom setting. The curriculum has three major domains which include self-control and problem identification (the “Turtle Unit”), emotional and interpersonal understanding (the “Feelings Unit”), and interpersonal cognitive problem-solving. The PATHS curriculum has been implemented with children in regular education classrooms as well as with children with special needs (e.g., behaviorally at-risk children, deaf children). In a series of randomized controlled trials, significant child behavioral improvements were maintained two years post-intervention in children in regular education classrooms and in children with special needs (e.g., Greenberg, Kam, Heinrichs, & CPPRG, 2003; Kam, Greenberg, & Kusché, 2004) The Life Skills Training Program is an example of a universal prevention program designed to prevent substance abuse in adolescents (Botvin & Griffin, 2004). The program was developed for middle school students. It is implemented across 15 class periods during the first year of middle school, with supplemental booster sessions during the following two years. Cognitive behavioral skills-training techniques are used to teach students personal selfmanagement skills, social skills, and drug resistance skills. The program has been shown to be highly effective in reducing alcohol, tobacco, marijuana, and polydrug use in a series of randomized controlled efficacy trials and in two effectiveness studies (Botvin, Baker, Dusenbury, Tortu, & Botvin, 1990; Botvin, Baker, Dusenbury, Botvin, & Diaz, 1995; Botvin et al., 1992;

Dissemination of Evidence-Based Programs in the Schools 395 Botvin, Griffin, Diaz, Ifill-Williams, 2001; Botvin et al., 2000; Botvin, Schinke, Epstein, Diaz, & Botvin, 1995). Evaluation results support the long-term effectiveness of the program as well as its generalizability to diverse geographic, socioeconomic, and racial/ethnic groups. As described above, multi-component programs that address multiple domains of risk factors are generally more effective than child-only programs. The Linking the Interests of Families and Teachers (LIFT) program is an example of a multi-component universal preventive intervention designed to prevent conduct problems (Reid & Eddy, 2002). The program targets elementary-age schoolchildren and their families who are living in high-risk neighborhoods. LIFT is a 10-week intervention that includes: (1) parent training in consistent limit-setting, effective discipline practices, and active involvement in children’s school and social activities; (2) a 20-session classroom-based social skills program designed to increase children’s social problem-solving skills and to help children resist negative peer groups; (3) a behavioral program to reduce playground aggression based on the Good Behavior Game (Embry & Straatemeier, 2001); and (4) systematic communication between teachers and parents regarding school work and classroom behavior. In a randomized trial with 671 families, significant improvements in family problem-solving and playground aggression were found following intervention. Children in the intervention group were also significantly less likely to have been arrested 30 months following the intervention. A fairly new, but rapidly growing, approach to universal promotion of students’ positive social and academic functioning is the school-wide application of Positive Behavior Support (PBS, also known as Positive Behavior Interventions and Supports; Sugai & Horner, 2002). The PBS approach has an established record of reducing challenging behaviors in children with developmental and intellectual disabilities, and research is beginning to emerge supporting the effectiveness of PBS as a school-wide preventive intervention to reduce the incidence of problem behaviors and increase student learning. An example of a school-wide or universal application of PBS is the “teaching recess,” in which workshops are held for the entire school, including staff and students, outlining positive behavioral expectations for recess. Following such an intervention, recess-related office referrals were found to decrease by 80% (Todd, Haugen, Anderson, & Spriggs, 2002). The implementation of PBS practices has also been found to be effective in reducing office referrals among high school students (Bohanon et al., 2006), in reducing office referrals and after-school detentions among elementary school students (George, White, & Schlaffer, 2007), and in reducing the need for interventions such as seclusion and restraint among students receiving special education services for autism or emotional disturbance (George et al., 2007). Exemplar Targeted Preventive Interventions. A number of targeted preventive interventions are available for children who are at-risk for more serious problem behaviors. Consistent with findings that programs with the strongest empirical support intervene with multiple stakeholder groups and across multiple years, the majority of evidence-based targeted interventions are multi-component and multi-year programs. The Incredible Years Training Series was originally developed as a parent training intervention, but has now been adapted for use as a targeted prevention program for preschool and early elementary age children. The expanded program now includes teacher and child components and seeks to prevent onset and exacerbation of child disruptive behavior problems (Webster-Stratton, 2005). During therapist-facilitated parent groups, parents view and discuss video vignettes demonstrating social learning and child development principles and how parents can use child-directed interactive play, praise, incentive programs and non-violent discipline techniques. An advanced version of the parenting program incorporates video vignettes promoting parents’ personal self-control, communication skills, problem-solving

396 John E. Lochman et al. skills, social support, and self-care. An adjunctive parent curriculum also helps parents learn to foster children’s academic success. The Incredible Years teacher component targets teachers’ use of use of effective classroom management strategies, reinforcement of prosocial child behaviors, and positive school-home communications. The Incredible Years child training program teaches children social skills, emotional awareness, self control strategies, and effective problem-solving. Multi-component versions of the program have been shown to have the strongest outcome effects in randomized controlled trials (Reid, Webster-Stratton, & Hammond, 2007). While the majority of targeted school-based mental health prevention programs have emphasized prevention of externalizing behavior problems, a few programs have focused on preventing internalizing problems. The Queensland Early Intervention and Prevention of Anxiety Project studied a school-based targeted intervention to prevent onset and exacerbation of anxiety problems (Dadds et al., 1999). The program was implemented with students ages 7–14 who had elevated anxiety symptoms or who met diagnostic criteria for a mild anxiety disorder. The program comprises primarily individual child sessions in which children learn cognitive, behavioral, and physiological coping strategies while being exposed to increasingly anxiety-provoking situations. Supplementary parent sessions are also provided in order to teach parents strategies to manage their children’s anxiety, familiarize them with the skills being taught to their children, and assist parents in managing their own anxiety using the same set of skills. In a randomized controlled trial, intervention effects were most apparent six months post-intervention, at which time at-risk anxious youth had developed significantly fewer internalizing symptoms. The Coping Power program is a multi-component, multi-year targeted preventive intervention for at-risk aggressive elementary and middle school children (Lochman, Wells, & Murray, 2007). Coping Power will be described in detail to highlight the process of developing, testing, and disseminating an evidence-based prevention program in school settings.

Coping Power: A Targeted Prevention Program The Coping Power program is conceptually grounded in a contextual social-cognitive model (Lochman & Wells, 2002a). This model posits that factors within the child and factors within the child’s environment contribute to the development and maintenance of aggressive behavior in children. Contextual factors most relevant to the aims of the Coping Power program relate to the family, school, and peer environments. In the family context, negative parenting behaviors consistently emerge as significant predictors of negative child outcomes. More specifically, research suggests that parenting that is verbally or physically aggressive, inconsistent, and lacking in warmth and involvement are all directly linked to children’s behavior problems (Stormshak, Bierman, McMahon, Lengua, & CPPRG, 2000). While much research focuses on the effects of negative parenting on children, some work highlights the effects of positive parenting behaviors such as parental monitoring in curtailing child and adolescent delinquent behaviors (Pettit, Laird, Dodge, Bates, & Criss, 2001). Children’s aggression and related behavior problems are also influenced by factors within the school and peer contexts. Research indicates, for example, that children who are placed in classrooms with highly aggressive peers will exhibit higher levels of aggressive behavior (Barth, Dunlap, Dane, Lochman, & Wells, 2004). This finding suggests that children’s peer groups are extremely influential in shaping their behavioral choices. Research also suggests that high levels of classroom management—defined in terms of teachers’ caring, teaching, monitoring, and intervention behaviors—are negatively related to children’s bullying of others (Roland &

Dissemination of Evidence-Based Programs in the Schools 397 Galloway, 2002). Mayer (1995) reviewed additional school-level correlates of children’s behavior problems. He specifically noted the ways in which vague school rules and policies, lack of follow-through and administrative support regarding discipline issues, and a one-sizefits-all approach to learning contribute to children’s behavior problems (Mayer, 1995). The contextual social-cognitive model maintains that the aforementioned contextual factors wield direct and indirect effects on children’s behavior (Lochman & Wells, 2002a). Contextual factors are believed to wield indirect effects on children’s behavior by shaping their affective and social-cognitive processes. In truth, aggressive children exhibit many difficulties in these two domains. Research suggests, for example, that children who evidence high levels of emotionality and low levels of regulation exhibit concurrent difficulties in social functioning, defined in terms of their parent-reported problem behaviors and teacher-reported social skills, popularity, and prosocial or disruptive behaviors at school (Murphy, Shepard, Eisenberg, & Fabes, 2004). Moreover, research demonstrates that children with externalizing behavior problems experience higher levels of anger than their non-externalizing peers (Eisenberg et al., 2001). Notably, these emotion processes have important implications for how children cognitively process social information and solve interpersonal problems (Lemerise & Arsenio, 2000). Indeed, aggressive children exhibit many deficits and distortions in their social information processing (Crick & Dodge, 1994). For example, in appraising social situations, aggressive children tend to selectively attend to and encode more hostile social cues than their nonaggressive counterparts (Dodge, Pettit, McClaskey, & Brown, 1986). Moreover, relative to their non-aggressive peers, aggressive children—particularly those who are more severely aggressive—tend to recall fewer relevant cues for social situations (Lochman & Dodge, 1994) and to encode fewer cues before making inferences about others’ behavior (Dodge & Newman, 1981). Not only do aggressive children exhibit the aforementioned cue-encoding difficulties, but they are also more apt to attribute hostile intent to others’ behaviors (Dodge, Price, Bachorowski, & Newman 1990), to over-perceive others’ aggressiveness, and to underperceive their own aggressiveness (Lochman & Dodge, 1998). These distortions at the appraisal stage of social information processing are thought to fuel children’s aggressive behaviors. Children’s aggressive behaviors have also been linked to deficiencies in social problemsolving. For example, research has demonstrated that aggressive children provide fewer solutions to hypothetical conflict scenarios and specifically offer fewer compromise and helpseeking solutions than their non-aggressive peers (Lochman & Dodge, 1994). This may stem, in part, from aggressive children’s belief that aggressive solutions will result in positive outcomes. These positive outcome expectations for aggressive behavior are more evident in aggressive children than non-aggressive children (Lochman & Dodge, 1994). In addition, aggressive children have lower self-efficacy regarding their ability to successfully enact prosocial solutions (Erdley & Asher, 1996), and, in fact, they appear less skilled in actually enacting competent, prosocial responses to provocation (Dodge et al., 1986). The contextual social-cognitive model provides a framework for understanding and organizing the contextual and child-level factors that contribute to child aggression. This conceptual model also helps to clarify potential targets for interventions. The Coping Power intervention directly targets the child-level social-cognitive and affective processes and the contextual risk factors highlighted by the contextual social-cognitive model. In specifically targeting these child-level and contextual factors, Coping Power aims to curb child aggression and related behavior problems. Coping Power consists of both parent and child components. The child component includes 34 sessions that are generally conducted with groups of four to

398 John E. Lochman et al. six students and are led by two co-leaders. The sessions occur at school during the school day and last from 40 to 60 minutes. Coping Power was designed for students in the fourth through sixth grades, though, in practice, experienced clinicians have modified the program for use with slightly younger and older students. When working with older students, for example, leaders might leave more time for discussion of student ideas and revise activities geared toward younger students to be more developmentally appropriate (e.g., substituting role plays for puppet activities). The child sessions are specifically designed to target the aforementioned affective and social-cognitive processing difficulties evident in children with behavior problems. As such, sessions focus on helping students in areas such as (1) goal-setting, (2) emotion recognition and awareness, (3) anger management, (4) organizational and study skills, (5) social problemsolving, (6) perspective-taking and attribution-retraining, and (7) positive peer affiliation and resistance to peer pressure. The curriculum in these areas is designed with an eye towards assisting students with difficulties in their family, school, and peer contexts. The curriculum aims to engage students in a variety of interactive games, role plays, and group projects to teach them new skills. In order to promote the generalization of these skills and to nurture progress, students are asked to develop weekly behavioral goals with help from their teacher and to solicit daily feedback from their teacher regarding their progress. The students’ progress is then rewarded in the group sessions. The Coping Power parent component parallels the child component. The parent component includes 16 sessions that are conducted with groups of approximately 10 parents or parent dyads. Sessions are led by two co-leaders and last approximately 90 minutes. The parent component aims to teach parents, in part, how to reinforce the skills that their children are learning in the Coping Power child component. The essence of the parent sessions, however, derives from well-established, empirically-based parent training programs (e.g., Forehand & McMahon, 1981) and centers on issues such as (1) identifying positive and negative child behaviors and reinforcing positive behaviors; (2) ignoring minor misbehaviors; (3) giving clear instructions; (4) establishing age-appropriate rules and expectations; and (5) providing effective consequences for misbehavior. As such, the Coping Power parent sessions focus on bolstering positive parenting skills. Additionally, the Coping Power parent sessions aim to coach parents in managing their stress and negative moods, fostering family cohesion (e.g., through family game nights), implementing a model for family problem-solving, and increasing family communication (e.g., through weekly family meetings). Like the Coping Power child sessions, the Coping Power parent sessions are highly interactive and often include role plays, group discussions, and homework assignments. The efficacy of the Coping Power intervention has been established in several randomized controlled trials. Lochman and Wells (2002a) found that the Coping Power intervention produced significant changes in parent and child processes and that these changes were associated with decreases in delinquency, school misbehavior, and substance use at post-intervention. Notably, intervention-related changes in parents’ consistency with discipline and children’s attributional biases were particularly critical in curbing children’s delinquent behavior. These positive results were maintained at one-year follow-up: children who participated in the full Coping Power program with both parent and child components evidenced lower rates of self-reported covert delinquency and parent-reported substance use than participants in the control and Coping Power child-component-only conditions (Lochman & Wells, 2004). Furthermore, children in both the child-component-only intervention and the full-scale intervention evidenced greater improvements in their teacher-reported school behavior than children in the control condition at follow-up. More importantly, the Coping Power

Dissemination of Evidence-Based Programs in the Schools 399 intervention successfully moved the aggressive intervention boys into the normative range on the aforementioned outcome variables (Lochman & Wells, 2004). Another study examined the degree to which nesting the indicated Coping Power intervention program within a universal program targeting all students would yield differential improvements in various outcomes (Lochman & Wells, 2002b). In this study, 245 aggressive fifth- and sixth-graders were randomly assigned to one of four groups: (1) a universal classroom intervention plus an indicated intervention; (2) a universal classroom intervention only; (3) the indicated intervention only; and (4) a control condition with no universal intervention and no indicated intervention. All three of the intervention groups resulted in lower rates of substance use and improved social competence and self-regulation at the end of sixth grade. Moreover, the Coping Power intervention tended to increase parents’ supportiveness in their interactions with their children at post-intervention (Lochman & Wells, 2002b). While the indicated Coping Power program produced positive results in its own right, its positive effects appeared to be enhanced when delivered in combination with a universal, classroom-wide intervention. More specifically, children who received both the indicated and universal interventions evidenced significant increases in their perceptions of their social competence and their teacher-rated prosocial behaviors, relative to children in the other three groups (Lochman & Wells, 2002b). Compared to control students, Coping Power students had greater teacher-rated improvements on aggressive behavior and lower rates of self-reported delinquency and substance use at a one-year follow-up (Lochman & Wells, 2003).

Dissemination Issues Promoting or Interfering with Successful Dissemination After the efficacy and effectiveness of an intervention have been established, an important next step is the dissemination of the program for “real-world” implementation. Although use of evidence-based programs (EBPs) remains infrequent in treatment settings (Addis & Krasnow, 2000; Henderson, MacKay, & Peterson-Badali, 2006), support for their implementation is increasing through high-level recommendations and mandates (Glisson & Schoenwald, 2005). As EBPs move from carefully controlled research studies to clinical practice, there is a need to identify the factors that might promote or interfere with their successful dissemination. A growing body of research has examined this topic, and this section will review the current literature. Implementation Integrity vs. Flexibility. EBPs, such as Coping Power, are implemented in efficacy and effectiveness trials under carefully controlled conditions. For example, participants are selected according to specific criteria, interventionists typically undergo intensive, ongoing training, and the interventions themselves are clearly laid out in detailed manuals. Such practices support treatment integrity, which is essential for meaningful program evaluation. Manuals developed for intervention studies have the added benefit of promoting the transportability of the program. That is, the content of the program is presented in a userfriendly manner, allowing subsequent clinicians to apply the program in a variety of settings. In addition, by specifically delineating the program’s key features, manuals increase the likelihood that future replications of the program will yield effects similar to those of intervention trials (Kendall & Chu, 2000). These benefits notwithstanding, practicing clinicians have raised concerns about the use of manualized treatments in “real-world” clinical work (Addis, Wade, & Hatgis, 1999). Criticisms involve beliefs that manuals have a limited focus on one therapeutic perspective, de-emphasize common factors such as positive engagement and the therapeutic alliance, do not attend to client-specific characteristics (including comorbidity), may

400 John E. Lochman et al. prolong treatment due to required adherence to a linear, invariant protocol, and may limit therapists’ ability to use their clinical judgment during treatment sessions (Kendall & Beidas, 2007). It appears that such concerns have led to dampened enthusiasm for implementation of EBPs in clinical settings. However, according to Kendall and Beidas (2007), this need not be the case. These authors propose that evidence-based, manualized treatments can be designed to promote “flexibility within fidelity,” allowing practitioners to use their clinical judgment to individualize an EBP for a given client, while retaining the critical features of the program. Our own dissemination efforts with the Coping Power program have indicated that clinicians appreciate the opportunity to adapt the program content to suit case-specific needs. For example, clinicians might supplement session content with favorite activities from their clinical repertoire (e.g., adding a “feelings bingo” game to the component on emotional awareness), or adapt activities to better match client characteristics (e.g., conducting roleplays rather than discussions for children demonstrating ADHD symptoms). Coping Power includes several activities that are completely individualized for each participant or group of participants, including making a video to reinforce problem-solving skills and creating a pictorial representation of specific peer groups in the child’s school or community. It is important to note that flexible adaptations such as these do not include omissions of program content or radical departures from the protocol, which would instead be considered a lack of adherence and would be expected to detract from the effectiveness of an EBP. We believe that appropriate adaptations to EBPs have the potential to enhance effectiveness, possibly through improving engagement and increasing personal relevance; however, empirical research is needed to determine how various types of modifications impact on outcomes. Parent Engagement into School-Based Programs. Many EBPs for children include a strong focus on parents, either through involving parents in their child’s treatment (Kendall, 2000) or by providing a separate parenting component aimed at addressing child behaviors through enhancing parenting skills (e.g., Lochman & Wells, 2002a; Webster-Stratton, 1989). Outcome research studies conducted to determine the effectiveness of such programs are performed under relatively ideal conditions, in which, compared to the typical treatment setting, more resources are available, therapists have smaller case loads, and parents are often more motivated to participate. In our own Coping Power intervention studies, participation in the parenting component is encouraged through the provision of child care, meals, and a cash stipend to cover transportation expenses. Unfortunately, it appears that, even with substantial efforts to promote attendance, many parents will not join intervention groups (e.g., Reid, Eddy, Fetrow, & Stoolmiller, 1999), raising concerns for the transportability of these programs into typical resource-strapped intervention settings. Dissemination research investigating parent involvement in EBPs is needed to provide empirically-based recommendations for promoting attendance and engagement. Until this obstacle can be effectively addressed, problems with parent engagement will remain a significant challenge to the dissemination of EBPs for children. Anecdotal reports from our own dissemination efforts have yielded several low-cost strategies for encouraging attendance at parent meetings including flexible scheduling that accommodates parents’ work and family obligations, providing personalized reminders of meetings (phone calls, notes), facilitating pot-luck meals at meetings, and planning a student presentation (e.g., a skit illustrating program concepts) during a parent meeting. The most effective strategy for promoting parent attendance we have encountered came from an elementary school guidance counselor who achieved near 100% parent participation for her group of nine students. In addition to the

Dissemination of Evidence-Based Programs in the Schools 401 strategies suggested above, this counselor reported that she developed positive relationships with all of the students’ parents through phone calls and emphasizing the children’s positive attributes during meetings (e.g., by starting each meeting by asking each parent to brag about his/her child). By creating an environment of acceptance and warm concern, this counselor appeared to achieve the near-impossible for parent participation. Efforts to disseminate EBPs might find that promoting clinicians’ relationship-building strategies might offer a costeffective way to address parent engagement issues. School Characteristics and School Climate Factors. When EBPs are disseminated into school settings, a new set of challenges are introduced. Schools represent established systems with characteristic organizational features that may impact on program implementation. Working environments, including schools, have been conceptualized as encompassing a relationship dimension (involvement, peer cohesion, staff support), a personal growth and development dimension (autonomy, task orientation, work pressure), and a system maintenance and change dimension (clarity, control, innovation, physical comfort), all of which may influence individual staff members’ enthusiasm for and involvement in intervention implementation (Moos, 2002; Vincent & Trickett, 1983). Further, some research has demonstrated an association between features of the organizational climate (e.g., low conflict, cooperation, role clarity, and personalization) and improved child psychosocial outcomes (Glisson & Hemmelgarn, 1998). In regard to dissemination of programs, research suggests that new programs are disseminated faster within a hierarchical structure in which the decision to implement an EBP is made solely by administrators in a top-down manner; however, dissemination efforts made under such conditions are less successfully maintained than those made under more collaborative circumstances (Henggeler, Lee, & Burns, 2002). Sustainability of new programs is also influenced by organizations’ turnover rates for clinicians and administrators (Schmidt & Taylor, 2002). Individual staff members who perceive their school climate as negative have been found to have more burn-out (McClure, 1980), while positive perceived school climate has been associated with successful implementation of new practices in schools (Bulach & Malone, 1994). Collegiality, shared authority among colleagues, and positive leadership by principals have been shown to be relevant factors in facilitating positive changes and improvements in schools, which might include the addition of EBPs into the curriculum (Peterson, 1997). Other school-level factors that may have an impact on the implementation of EBPs include school size, the ethnic composition of schools, the socioeconomic level of the student body, and school-wide aggression levels among students (Barth, Dunlap, Dane, Lochman, & Wells, 2004; Kellam, Ling, Merisca, Brown, & Ialongo, 1998). Although these particular factors may not be readily amenable to change, their identification can help to inform issues related to program implementation, such as the need for ongoing consultation and support. Several researchers have also outlined some of the practical implications of implementing EBPs in school settings (e.g., Crisp, Gudmundsen, & Shirk, 2006; Mufson, Dorta, Olfson, Weissman, & Hoagwood, 2004). Challenges include limited staff time and availability, lack of funds to cover training and implementation costs, limited physical space in which to hold sessions, interruptions or cancellation of meetings due to school-wide events, and inability to complete the program due to student absences, expulsions, or transfers (Crisp et al., 2006; Mufson et al., 2004). Interestingly, Crisp et al. (2006) found that students reported very low levels of psychological barriers (e.g., concern about stigmatization, breaches of confidentiality) when receiving services in the school setting; instead, student-reported barriers focused on the relevance of the treatment and interference of life stressors. Clearly, it is important to

402 John E. Lochman et al. consider this group of practical obstacles to implementation and to problem-solve potential solutions prior to disseminating EBPs into school settings. Individual Staff Characteristics. Clinicians charged with the actual implementation of a given program may facilitate or impede the program’s success, depending on their attitudes and behaviors. Clinicians’ implementation efforts are related to their perceptions of a new program (Schmidt & Taylor, 2002; Stirman, Crits-Christoph, & DeRubeis, 2004), and fostering favorable attitudes appears critical in effective dissemination. Clinician resistance to EBPs may present a significant obstacle to dissemination efforts and may have its basis in concerns such as the degree of appropriateness or flexibility of a given intervention (Stirman et al., 2004). Practitioners need to be convinced that a new EBP offers advantages over their established practices (Stirman et al., 2004) Other factors proposed to relate to practitioners’ abilities to effectively implement EBPs include their levels of confidence, self-efficacy, prior experience, perceived barriers to implementation, and familiarity with the intervention and its theoretical model (Turner & Sanders, 2006). Cynicism about organizational change, which may develop after exposure to unsuccessful change efforts, also appears to affect practitioners’ openness to new programs and their willingness or ability to implement new programs effectively (Wanous, Reichers, & Austin, 1994). Effects of Training. Characteristics of the training process have the potential to influence implementation of a program and, through this mechanism, can impact on outcomes (Henggeler, Melton, Brondino, Scherer, & Hanley, 1997). Many agencies and systems provide training to staff members through one- or two-day workshops and, while this arrangement may be efficient and economical, it does not appear to be adequately effective. For example, in a study investigating the effectiveness of three different types of training in CBT procedures, Sholomskas, Syracuse-Siewert, and Rounsaville (2005) found that training workshops alone, in the absence of other techniques and supports, were not effective in establishing clinician competency. However, when workshops are followed by continuing supports such as ongoing supervision, expert consultation, and the provision of feedback on cases, competence and adherence to the EBP protocol can be improved (e.g., Stirman et al., 2004). Schoenwald, Sheidow, and Letourneau (2004) have shown that consultant practices can promote or detract from therapist adherence and resulting youth outcomes. Specifically, consultant competence and consultants’ focus on specific strategies and principles of the intervention appear to be beneficial, while consultants’ efforts to support therapists may dampen adherence and outcomes. Attention to non-specific factors also appears to be important in the training process. For example, clinicians’ openness to new programs appears to be influenced by characteristics of “change agents,” a group that includes trainers and inter-agency proponents of the new program (Stirman et al., 2004). In fact, Schmidt and Taylor (2002) found that credibility and personal characteristics of the change agents were some of the most powerful influences on practitioners’ adoption of new practices. Experts also recommend that practitioner attitudes be addressed early in the training process, as clinicians’ feelings and beliefs about new programs can affect their receptiveness and implementation (Gotham, 2006). Aarons (2005) has developed a measure that can be used to assess practitioners’ general attitudes toward EBPs, information which can then guide the training process. Clinician attitudes and beliefs can also be addressed less formally throughout the training process, with trainers taking a proactive role in eliciting discussion of practitioner concerns (e.g., that EBPs are rigid and ineffective) and providing education on EBPs’ flexibility and demonstrated benefits (e.g., Stirman, 2004).

Dissemination of Evidence-Based Programs in the Schools 403

Coping Power Dissemination As a case example of dissemination, we will overview how these dissemination issues can be evident in our implementation of an evidence-based program (Coping Power) in new settings. We will explore factors which affect the implementation of the program, and explore how much adaptation and sustained use occurs. Dissemination of Program to New Settings We have been involved with efforts to transfer Coping Power to a variety of other settings. Two examples are evident in research-based efforts to examine how Coping Power can be used in clinic settings and in specialized residential settings. It is clear that a range of relevant adaptations need to be made to insure the program’s relevance in different settings. Coping Power Effectiveness Study with Aggressive Deaf Children. The Coping Power program, a social-cognitive intervention which has produced effects with hearing children in the prior studies, was adapted in this study for use with deaf children (Lochman et al., 2001). Deaf children in a residential school were screened for aggressive behavior (n = 49), using teacher ratings, and were randomly assigned by classroom to the Coping Power program or to a waitlist control condition. The children in the multi-component Coping Power program attended group sessions. Because these students were not living at home, the parent component was not delivered; however, the students’ teachers and dormitory staff received training to influence the context around the children. The Coping Power program was adapted to meet the unique needs of deaf and hard-of-hearing children, and the intervention was delivered using a revised manual. Each group was led by two co-leaders, both of whom were proficient in sign language and one of whom was deaf. Several sections of the Coping Power program were expanded to address the areas of socialcognitive functioning that have been shown to be particularly problematic for deaf youth (e.g., perspective-taking, understanding emotions; see Lochman et al., 2001). Deaf students participated in extended sessions on affective education, physiological awareness, perspective taking, and anger management. In addition, specialized materials were developed to help teach the basic social-cognitive skills for both in-group instruction and also for use in the classroom and dorm setting to help generalize skills learned. Group leaders used a variety of visual materials to teach complex coping skills to the students. For example, the Coping Power program focuses on “self-talk” as one technique for dealing with anger during social conflict. This technique was taught to the students in a stepwise fashion. Group facilitators used visual drawings of speech bubbles with written comments to introduce the idea of self-talk. Students could choose from a variety of prewritten statements to insert into a character’s speech bubble to show what that character was saying to him/herself in response to a certain situation. Groups then differentiated between “positive thoughts” and “negative thoughts” and the feelings that could result from each. These techniques supported later use of speech bubbles in role-playing common school or dorm situations to emphasize the coping power of self-talk. Both negative and positive coping statements were presented to show the students that both have an impact on how a situation is resolved. To help with the concept of physiological cues, group leaders developed human figure drawings and drawings of different physical body cues related to the basic emotions of happy, sad, scared, and mad (e.g., pictures of ice cubes, fire, broken heart, as well as drawings of respective facial features, such as raised eyebrows, clenched teeth), which could be pasted to the appropriate physical area on the human figure. A word bank was kept while different

404 John E. Lochman et al. physical cues were introduced to assist the students in their language development. Teaching words to describe various facial expressions (e.g., furrowed brow, pursed lips, squinting eyes, “puppy dog eyes”) not only helped language development, but also reinforced awareness of differential cues. Group leaders used the concept of spatial perspective to help students understand what perspective meant. Activities used concrete objects that provided group members the opportunity to visually and physically experience different perspectives. For example, in one activity, a box was placed in the middle of a table with two students seated on opposing sides. Each side of the box had the same number and types of shapes but in different colors. The two students took turns describing what he or she saw and were surprised to learn that, while they were looking at the same box, their descriptions were not the same. Another activity involved placing a variety of objects on a table and having each student sit in different angles from the table. They were to draw what they saw on the table and then share their drawings. The pictures emphasized the different views of the same set of objects. These concrete spatial activities helped to establish the concept of perspective from which social perspective activities could be built upon. A significant part of the program involves use of the PICC (Problem Identification, Choices, Consequences) model, a social problem-solving model. This model pulls together the concepts and skills taught in preceding activities. Additionally, a variety of visual activities were used to help the students to understand the sequence of thought and action in the PICC model. A “PICC Road” was created to use with the students as a visual and tactile reinforcement of the skills. The students were able to walk on it and manipulate the problems, choices, and consequences along the way. The road split at one point to emphasize that different choices lead to different “paths” and consequences. A variety of problems, choices, and consequences were presented visually through written and signed language, pictures, and drawings. Coping Power children displayed behavioral improvement across the intervention year in comparison to control children (effect size: .5), according to teacher ratings, and the Coping Power children displayed significant improvements in their social problem-solving skills and in their communication skills. This small-scale effectiveness study suggested that the Coping Power program could be adapted to meet the needs of specialized populations, such as deaf children who had unique communication difficulties. The success of this dissemination project was bolstered by excellent administrative support by the school’s principal. Overall, teachers were also supportive of the project, though some issues arose around identifying times to run the groups that did not conflict with classroom schedules, and some teachers were less reliable in reporting on students’ progress toward their daily goals. These issues were addressed on an ongoing basis through continued communication with the teaching staff. A Coping Power Effectiveness Study with Disruptive Behavior Disorder Children in the Netherlands. Following training from Dr. Lochman in the Coping Power program, Dr. Walter Matthys and his colleagues at Utrecht University developed briefer Dutch versions of the Coping Power Child and Parent Components. The program was delivered from newly developed manuals, and program effects were examined in a treatment study including 77 children with a Disruptive Behavior Disorder diagnosis who were randomly assigned to Coping Power or to a Care-as-Usual condition. Because the Coping Power program, originally developed for implementation in the school setting, was adapted for use with an outpatient sample, the Utrecht Coping Power Program child sessions had proportionally fewer discussions and more activities to suit the short attention span of the children. Children in both conditions displayed significant improvements in disruptiveness at the end of treatment and at a six-month followup, but the Coping Power children had significantly greater reduction in overt aggressive

Dissemination of Evidence-Based Programs in the Schools 405 behavior by post-treatment (van de Wiel et al., 2007). These positive treatment outcomes of the Coping Power program occurred even though the Coping Power intervention staff had significantly less clinical experience than the Care-as-Usual therapists, indicating the costeffectiveness of the Coping Power model (van de Wiel, Matthys, Cohen-Kettenis, & van Engeland, 2003). A four-year follow-up study of this sample has found that Coping Power had a preventive effect in producing significantly lower marijuana and tobacco use, in comparison to the control condition, indicating long-lasting effects of the intervention on substance use (Zonnevylle-Bender, Matthys, van de Wiel, & Lochman, 2007). Coping Power Field Study A NIDA-funded study of the dissemination of the Coping Power prevention program has been implemented in a field trial in 57 schools within five school districts. This field trial is examining whether the Coping Power prevention program can be usefully taken “to scale” and delivered in an effective manner by existing staff in a range of urban school sites within Tuscaloosa, Alabama, and the Birmingham, Alabama, metropolitan area. In this field study, existing school staff members (school counselors) have been trained to use the Coping Power program with high-risk children at the time of transition to middle school. This field study was designed to address three primary gaps in the literature: (a) whether this type of prevention program can be taken “to scale” and produce positive outcomes in the reduction of youth violence, delinquency and substance use, in attaining good intervention integrity, and in sustained use in the years following training; (b) whether the intensity level of training (Coping Power—Intensive Training: CP-IT; versus Coping Power—Basic Training: CP— BT) impacts the intervention outcomes, intervention integrity, and sustained intervention use; and (c) whether organizational characteristics of the schools, and characteristics of the school site staff who implement the intervention, influence intervention outcomes, intervention integrity, and sustained intervention use. To address these gaps, this field trial has randomly assigned 57 elementary schools to one of three conditions: CP-IT, CP-BT, or Control. Up to 10 children in each school were screened as being at-risk because of third-grade teachers’ ratings of students’ aggressive behaviors, resulting in a total sample of 531 target children. Nineteen schools are in each condition, with 183 children in CP-BT, 168 children in CP-IT, and 180 in Control. The Coping Power program was delivered during the fourth- and fifth-grade years. Two annual cohorts of schools and children were recruited. School Selection. Five school systems in the greater Birmingham and Tuscaloosa areas are involved in this study. These school systems, and the schools they contain, have student populations which range from heavily impoverished, inner-city, and African American, to suburban, middle-class and largely white. After the superintendents’ office of a school system agreed to be involved in the study, we contacted principals of the schools, and they decided to participate or not, typically after consulting with their counselors (who would be trained to deliver the program if their school was randomly assigned to one of the two intervention conditions) and teachers. We used a series of incentives to enhance school participation, including annual stipends for school involvement in the research (going to general school-wide activity or supply funds), and payment for a portion of school personnel’s time to plan and provide the Coping Power child component, which included weekly group sessions and monthly individual contacts. In addition, school staff earned additional funds for leading parent groups in the evening. Eighty percent of the schools which we directly contacted in these five school systems agreed to participate.

406 John E. Lochman et al. In this study of the school-based violence prevention program, we used a teacher-rating approach to screen the risk students who were eligible for the indicated intervention (Lochman & CPPRG, 1995; Hill, Lochman, Coie, Greenberg, & CPPRG, 2004). Using a similar screening system, we have found screening scores to be valid and stable over time, and have found subsequent aggressive behavior problems to be primarily predicted by first-gate teacher ratings, with second-gate parent ratings adding only 3% of the variance in predicting to outcomes (Hill et al., 2004; Lochman et al., 1995). Thus, in this study, we used a teacher rating screen. Our prior research has found aggressive samples selected in a similar manner to have significantly higher parent and teacher ratings of aggressiveness (as expected), more observed off-task behavior in the classroom, more social-cognitive difficulties associated with aggressive behavior, and a higher risk for later violence, delinquency and substance use than nonaggressive samples (e.g., Lochman & Dodge, 1994). During screening, third-grade teachers were asked in the spring of the year to rate how reactively and proactively aggressive all of the children in their classes were using a six-item scale. Based on these ratings, we determined the 30% most aggressive children across all classes. The selection criterion (30%) was based on the distribution of teacher ratings across all of the fourth-grade classrooms, rather than identifying the 30% most aggressive children in each classroom. Some classes contained a larger percentage of target children than others, since aggressive children are not usually evenly distributed across classrooms. Across the two cohorts, 3,774 children were screened and the scores of approximately 1,200 children fell within the range for inclusion in the study. Contact to schedule interviews was made with a total of 670 potential participants. Consent was obtained for 531 (79%) of these participants and they were assessed at baseline. Sixty-five percent were male. Eighty-four percent were African American, 14% were Caucasian, and 2% were of other race/ethnicity. Retention was 98% at Time 2 across both cohorts. Coping Power Intensive Training (CP-IT). CP-IT had four training components. First, the school counselors received a total of three initial workshop training days in the fall prior to the beginning of intervention. Second, the school counselors participated in monthly ongoing training sessions (2.0 hours) in which the trainers provided concrete training for upcoming sessions, debriefed previous sessions, and problem-solved about barriers and difficulties involved in the implementation of the program. Third, individualized problem-solving about barriers and difficulties in the implementation of the program was available only to school site intervention staff in the CP-IT condition through a technical assistance component. This component included access by the implementation staff to an email account in which they could raise implementation concerns and problems and through which they could receive trainers’ responses, and also included a telephone “hotline” in which trainers were available for telephone consultation about these same concerns. Fourth, the trainers reviewed the rate of completion of objectives and provided individualized feedback through email and telephone contacts with the school counselors to enhance the intervention integrity. Coping Power Basic Training (CP-BT). CP-BT had the first two training components that were included in the CP-IT condition: a three-day initial workshop, and monthly ongoing training sessions. These sessions were conducted separately for CP-BT school site intervention staff, but were equivalent to the ongoing monthly training sessions for the CP-IT condition. School and Counselor Level Predictors of Program Implementation and Program Outcomes at Mid-Intervention. Lochman (2007) used HLM analyses to examine several school, training and counselor level predictors of good implementation of the program (counselor engagement in sessions as rated by trainers and by independent coders of audiotapes of sessions) and of target children’s aggressive behavior outcomes (rated by parents and teachers). School-level

Dissemination of Evidence-Based Programs in the Schools 407 characteristics and counselor characteristics were associated with program implementation and mid-intervention outcomes in this dissemination study, with somewhat different patterns of predictors emerging for basic versus intensive training. In terms of school-level characteristics, counselors who were in schools with positive personal relations among staff (assessed by counselor and teacher ratings) had better counselor engagement in their use of the program with children (as rated by trainers) in the BT condition (but not in the IT condition) and had greater reduction in their students’ parent-rated aggressive behavior by mid-intervention in the IT condition (but not in the BT condition). In addition, the IT condition counselors from schools with lower levels of work stress (more staff autonomy; less rigid managerial control; clearer work expectations) had lower levels of teacher-rated aggressive behavior by mid-intervention. In terms of counselor personality characteristics, greater engagement in the implementation of the program (as rated by trainers and by independent coders) was evident for counselors who were more conscientious, agreeable and less cynical (in BT) and who had lower neuroticism (in IT condition). These results suggest that the organizational climate of the schools has notable impact on the delivery and initial outcomes of this type of targeted preventive intervention, regardless of the level of intensity of training that counselors received. Counselors’ personality characteristics also played an important role, but primarily for counselors in the BT condition, suggesting that the more intensive form of training assisted the counselors who would have had trouble with the basic form of training to implement the program with greater enthusiasm and with better initial outcomes. Sustained Use of Intervention. We have collected information about sustained use of Coping Power one year after completion of training for the first cohort of counselors. Eighty-three percent of the counselors continued to use at least portions of the child component of Coping Power in the next year, with greatest use of components addressing goal-setting, peer relationships, organizational and study skills, and emotional awareness and management. Counselors had found it more difficult to get parents to attend parent sessions during the Field Trial, and the counselors had lower rates of sustained use of the parent component of Coping Power, with 55% of counselors using at least some portion of the parent program. Overall, it appears that counselors are generally sustaining use of most of the child component of the program, with some adaptation, providing policy-level support for the importance and utility of training regular school counselors in these procedures.

Conclusion In this chapter, we have described an empirically-supported prevention program, Coping Power, and our efforts to disseminate the program in a variety of settings. The program has been disseminated as part of a large-scale research project to “real-world” practicing clinicians in school settings. Information from this project has elucidated the roles of school-level factors, such as the school climate, and individual clinician factors, such as personality and attitudes, in determining how well the program is disseminated and implemented. In addition, Coping Power has been adapted for use outside of the regular school setting, and has been disseminated to deaf students in a residential school and to children and adolescents attending outpatient mental health treatment for diagnosed disruptive behavior disorders in the Netherlands. While modifications were necessary to suit the unique needs of these populations, the essence of the program was retained, illustrating the concept of “flexibility within fidelity.” Successes in these dissemination trials of Coping Power suggest that, with appropriate modifications, the program has potential for broad dissemination to diverse populations

408 John E. Lochman et al. including older and younger students, those with clinical diagnoses, and students with special needs (e.g., educational or learning problems). In addition, the program might be implemented in a range of settings outside of school, including inpatient units, day treatment programs, and community centers. Such efforts to expand the program might help to address the need for high-quality, effective services for children with behavior problems, who are often underserved. Although the field is at an exciting place in beginning to identify research-based universal and targeted preventive intervention programs for schools, we remain at only a beginning stage in understanding the range of factors that can influence successful dissemination and implementation of programs. Future research investigating the dissemination process might further explore the concept of “flexibility within fidelity” to identify the types of adaptations that enhance, rather than detract from, a program’s effectiveness, and the extent to which a program can be modified and still retain its benefits. Such research may also help to address the concerns of practicing clinicians who feel that structured, manualized treatments are too rigid to be useful, ultimately leading to wider acceptance and implementation of evidencebased programs. We would also welcome additional research on factors that may potentially influence dissemination and implementation of empirically-based programs, including organizational variables, individual clinician traits, and training practices and procedures. Factors relating to the targeted population, including child variables (e.g., comorbid diagnoses, cognitive functioning, social skills) and family-level characteristics (e.g., parental psychopathology, poverty, single parenthood and other psychosocial stressors) may also affect the success of dissemination efforts and warrant further study. Ideally, converging evidence from multiple studies of various programs will lead to specific, empirically-based recommendations to guide the dissemination process, resulting in broader and more successful implementation of programs that can effectively address the mental health needs of troubled children and adolescents.

Note This chapter has been completed with support from grants from the National Institute for Drug Abuse, and the Office of Juvenile Justice and Delinquency Prevention to the first author.

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21 Prevention, Early Childhood Intervention, and Implementation Science Samuel L.Odom, University of North Carolina at Chapel Hill Marci Hanson, San Francisco State University Joan Lieber, University of Maryland Karen Diamond, Purdue University Susan Palmer, University of Kansas Gretchen Butera, Indiana University Eva Horn, University of Kansas Some children are at a disadvantage when they enter the public school system. Poverty, ability, home/community language, and/or cultural differences often create a mismatch between children’s skills and the learning experiences they will encounter when they start school. The mismatch results in a disparity in educational achievement that may extend across school years. Program developers have designed early childhood education programs that might prevent or reduce such disparities by promoting skills children will need for early school success. Research has documented the efficacy of such prevention programs, but the eventual utility of the programs depends on the ways in which they are employed in “real-world” settings. Researchers in the emerging field of “implementation science” (Durlak & DuPre, 2008; Fixsen, Naoon, Blase, Friedman, & Wallace, 2005) are determining factors that influence the use of practices, such as school readiness curricula, and their effects on the recipients of the practices. The purpose of this chapter is to examine the application of implementation science to prevention and early childhood intervention. We will first briefly review the history of early childhood intervention designed to prevent or ameliorate conditions that put children at risk for school failure. Next, we will review the definitions of implementation and fidelity, propose a definition that will guide our discussion of the literature, describe research that has assessed implementation and fidelity in programs for young children, and examine research that has found associations between implementation and child or family outcomes. In the latter sections of the chapter, we will describe factors that influence implementation and provide examples from a study of implementation of a school readiness curriculum and adoption of school reform.

Risk and Early Childhood Intervention: Giving Children a Positive Start Early development is a complex transaction between children, their caregivers, and multiple contexts in which both participate (Bronfenbrenner, 1979; Sameroff & Chandler, 1975). By the time children reach kindergarten age and begin their trek through schooling, many factors will have affected their capacity to take advantage of the learning opportunities presented in kindergarten and beyond. Individual and multiple factors will affect children’s development

414 Samuel L. Odom et al. and create risks for poor developmental outcomes. Three factors are prominent: poverty, the language spoken in the home and community, and identified developmental disabilities. Poverty Limited personal and material resources may make everyday life difficult for families and negatively impact child growth and development, child safety, and nutrition (Landry & Menna, 2006). Some children living in poverty are exposed to dangerous, unhealthy, and less than adequate settings that affect negatively cognitive or social development. Of course, not all poor children have difficulty, as the descriptive study of child language reported by Hart and Risley (1995) confirms, but growing up and becoming successful in school is more difficult under less-than-ideal circumstances. Importantly, childhood poverty is widespread. According to a recent report, 17.4% of all children under age 18 or 12.8 million children in the United States lived in poverty in 2006 (DeNavas-Walt, Proctor, & Smith, 2007). English Language Learners The population of young children who are growing up in families with a home language other than English has increased dramatically in the United States. As children enter child care and school systems, they may experience a mismatch between home and school languages and practices, which may put them at-risk developmentally (Genesee, Paradis, & Crago, 2004). Children who come from families that do not speak English as their primary language may not have the same opportunities for learning about literacy as do other children (Espinosa & Burns, 2003). These children may need more or different support in order to achieve optimal learning outcomes in school. As Tabors (1997) has argued, the type of programs needed to support individual children is based on a variety of factors including the child’s age, motivation to learn English, exposure to English, support in the home for language, and the child’s individual personality characteristics. Specialized training and culturally sensitive curricula are needed to support children who are learning English. Disabilities Infants and young children with disabilities, such as intellectual disability, autism spectrum disorders, and sensory impairments, are often identified early in life (Dunst, 2007). Early delays in cognitive, communication, sensory, or social development are markers for such disabilities and they limit children’s ability to take advantage of learning opportunities that may exist in daily life and in school settings. A primary initiative in early childhood and traditional public schools has been to include children with disabilities in regular education settings (Odom, 2000), with accommodations provided to allow them as much participation and achievement as possible. Nevertheless, their disabilities often lead to the educational disparity noted previously.

Intervention for At-Risk Populations Efforts to prepare young children for success in later life have a long history in education and prevention science. For poor children, recent early intervention history may be traced back to the 1960s War on Poverty and the initiation of the Head Start program. At that time, influential early education programs operated by researchers such as Susan Gray in Tennessee (Gray

Prevention, Early Childhood Intervention, and Implementation Science 415 & Klaus, 1970) and Ed Zigler in Connecticut (Zigler & Butterfield, 1968) had shown promising results for providing early childhood education experiences to children living in poverty. A program of research that examined the planned variation of early childhood education curricula having different theoretical bases extended this early research, with comparative analysis finding differences in outcomes for different approaches (Datta, McHale, & Mitchell, 1976; Smith, 1974). In an evaluation across programs, Lazar and Darlington (1982) analyzed the immediate and long-term effects of 11 intervention projects and demonstrated the general efficacy of early intervention for children living in poverty. The Abecedarian project and the Perry Preschool project are two of the most prominent programs to have examined long-term effects. In the Perry Preschool project, African American children with IQs less than 85 were randomly assigned to intervention or control groups. A long-term follow-up of former students at age 40 showed continued difference on outcome measures favoring the treatment group (Schweinhart, 2004). In the Abecedarian project, children were also randomly assigned to intervention and control conditions. Followup studies have shown higher scores for children who participated in the intervention in reading and math, a trend toward less grade retention and special education needs during the school years and continued difference on outcome measures to age 30 (Campbell, Ramey, Pungello, Sparling, & Miller-Johnson, 2002). Current Head Start programs are the curricular descendants of the early work by Campbell, Ramey, Weikart, Schweinhart, Zigler, Gray, and others. Evaluations of outcomes for Head Start are mixed; however, a recent study using random assignment to control and implementation groups shows modest gains in cognitive and social outcomes for three- and four-yearolds (Administration for Children and Families, 2005) in Head Start programs. As Head Start evolved, services to children and families moved to an earlier age through the initiation of Early Head Start (Kamerman & Kahn, 2004). Research with Early Head Start participants has shown that children receiving services perform better on cognition, language, and social-emotional functioning than their peers who did not receive services (Administration for Children and Families, 2006). However, children from families at the very lowest level of poverty showed no effects. Although a program may have positive outcomes for some of the population, it may not be universally effective. Discussions of optimal preschool instructional practices often identify the need for support of young children who are English learners (Chang et al., 2007) and curricular and instructional guidelines are being developed to address the needs of this group (California Department of Education, 2007). However, few empirical investigations of instructional practices targeting this group of learners have been conducted. One recent and promising effort is the use of an intensive professional development program aimed at teaching skills on classroom practices and the use of specific language and literacy strategies for young English learners (Buysse, Castro, & Peisner-Feinberg, 2008). Infants and young children with identified disabilities, when identified early, may receive early intervention services from state agencies (Dunst, 2007), and/or they may enter programs operated by public schools at three years of age (Carta & Kong, 2007). Often young children with disabilities receive their programs in inclusive settings, which may be in a public school setting, in private child care settings in the community, in Head Start, or in state pre-k programs (Odom et al., 1999). An array of intervention approaches have been examined for use with young children having disabilities, with various levels of efficacy demonstrated (Odom & Wolery, 2003). In most cases, these services and programs are directed toward secondary and tertiary prevention, with less emphasis on primary prevention of or recovery from a disability and more emphasis on maximizing outcomes.

416 Samuel L. Odom et al. Two areas have been particularly prominent in primary or secondary prevention. A particular emphasis has been placed on children with challenging behaviors and early prevention efforts that might be efficacious. The preschool version of the Incredible Years curriculum developed by Webster-Stratton (Webster-Stratton, Reid, & Stoolmiller, 2008) has been applied as a class-wide curriculum that promotes social competence and prevents the occurrence of problem behavior for young children in Head Start programs. Also, the First Step program, developed by Walker (Walker et al., 1998) is a broad-based approach involving parents, community, and school resources and designed to prevent the deterioration of prosocial behavior and escalation of delinquent or criminal behavior as youth. Randomized studies have found positive effects for child participants for both of these approaches (Walker et al., 1998; Webster-Stratton et al., 2008). The broader application of the multi-tiered approach of Positive Behavior Support, as defined by Dunlap, Fox and colleagues (Powell, Fixsen, Dunlap, Smith, & Fox, 2007), has as its foundation the prevention of challenging behavior through provision of high-quality educational settings and services. For children with autism spectrum disorder (ASD), early identification and intervention are critical components of an effort to promote positive outcomes for children and families. A number of comprehensive treatment models have been developed, with some reporting positive outcomes and even recovery (Odom, Rogers, McDougle, Hume, & McGee, 2007).

Scaling Up Early Childhood Intervention A natural evolution of the pioneering work on early childhood education and intervention programs has been movement from model programs closely monitored by researchers (i.e., often defined as efficacy research) and program developers to the wider use of the programs in conditions under which the researcher has less control (i.e., often called effectiveness research.) Rohrbach, Grana, Sussman, and Valente (2006) have called this process “scaling-up.” As programs scale up, concerns exist about how closely the scaled-up program model will approximate the prototypic program when delivered to more children and by individuals outside the original developer group. As a result, early childhood researchers and program providers have become interested in assessing the implementation of programs. The Abecedarian Project and subsequent iterations of this model are examples of this evolution. As noted previously, Ramey and Campbell (1984) started the Abecedarian Project in the 1970s as an approach for preventing mental retardation. The intervention model consisted of a clearly articulated curriculum (Learning Games by Sparling and Lewis, 1979) and prescribed method of care. The project took place in one location (i.e., North Carolina) and was under close control of the research staff. In its original application, measures of implementation were not reported. Primary components of the Abecedarian Project served as the foundation for the Infant Health and Development Project (IHDP), which was a large, national, multi-site study (i.e., a scaling-up project) involving low-birth-weight infants and their families (Gross, Spiker, & Haynes, 1997). The purpose of IHDP was to prevent, through early developmental intervention, health and developmental problems that often accompany prematurity and low birth-weight. A main effect (i.e., effect for all low-birth-weight groups across all sites) of the type found in the Abecedarian Project was not found in IHDP. In IHDP, however, measures of implementation were collected, and sustained effects were found with children for whom a sufficient level of implementation occurred (Hill, Brooks-Gunn, & Waldfogel, 2003).

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Implementation and Early Childhood Programs Because of the great interest in early childhood education for at-risk children and the intent to base educational practice on scientific evidence of efficacy, the scaling-up issue has become critical. The study of implementation has become a central feature of efficacy and effectiveness research on early childhood programs. Yet, to study implementation, one must begin with an accepted definition and conceptualization of the process. In this section, we will discuss definition and suggest a consistent terminology, present a rationale for studying implementation in efficacy and effectiveness studies, describe methodologies for data collection described for evidence of process/product outcomes, and identify issues that linger in our consideration of the concept. Definition and Meaning One of the major challenges facing the application of fidelity/implementation to prevention science and early childhood education is agreement on a common definition. Conceptualizations and descriptions of fidelity and implementation vary, with the terms sometime being used synonymously and at other times having unique meanings. Durlak and DuPre (2008) provided a very simple definition of implementation being “what a program consists of when it is delivered in a particular setting” (p. 329), and Fixsen and colleagues (2005) added that implementation is “a specified set of activities designed to put into practice a . . . program of known dimensions” (p. 5). In this chapter, implementation will be the organizing construct. We will define implementation as the program delivered to and experienced by participants, in our case “at-risk” children and their families. In this review of studies assessing fidelity of implementation in K–12 curriculum intervention research, O’Donnell (2008) noted that definitions have often varied between structural features (i.e., adherence to a criterion number or set of procedures) and process features (i.e., quality with which a curriculum is used by participants). Current researchers often draw from the Dane and Schneider (1998) multi-component conceptualization of fidelity, which consists of adherence to the program, dose (i.e., the number of lessons or amount of content that the implementer provides), quality of program delivery, participant reactions or acceptance, and program differentiation (i.e., discriminating between programs that are implementing well or poorly). To this list, Durlak and DuPre (2008) added the concepts of monitoring of control or comparison conditions (i.e., in treatment research), program reach (i.e., involvement and representativeness of participants), and adaptation (i.e., changes made when program is delivered by practitioners or service providers.

Conceptual Model of Implementation Building on the work of these implementation scientists, we offer a conceptual model of the implementation process, which appears in Figure 21.1 (from Odom, in press). The initial step in this process is the development of the intervention, the efficacy testing by the developer, and the preparation of intervention materials to be delivered to prospective implementers. At this point, developers usually have an ideal prototype for how the intervention should be implemented. When the intervention is delivered to users (e.g., teachers), the developer becomes a purveyor (i.e., a person advocating for and perhaps supporting the adoption of the intervention). When users receive the intervention, they often adapt procedures in the intervention to fit their context; adaptation will be discussed more fully in subsequent sections. The actual

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Child engagement Attendance

Researcher/ purveyor ideal

Family outcomes

Process Practitioner adaptation

Child outcomes

Implementation Structure

Program differentiation Program acceptability

• Practitioner values • Community values • Administrative support

Program scope and reach

Figure 21.1 Elements of the Implementation Process.

implementation is measured by the purveyor or users with structural variables (i.e., the amount of intervention delivered and/or received) and/or process variables (i.e., the quality of program delivery). Child attendance is another variable that affects implementation at this point also. The implementation of the intervention may affect a range of variables, such as participant response (e.g., children’s engagement in the curriculum activity), child outcomes, family outcomes, program differentiation (e.g., discriminating between high and low implementers), program acceptability (e.g., implementers’ attitude about the program), and program reach (e.g., scope and representativeness of implementers relative to the entire organization/school district/community).

Rationale for Studying Implementation A primary reason for studying implementation is, by definition, to determine precisely what occurred when an intervention or treatment was delivered. Such documentation allows the researcher to examine the relationship between the degrees or features of implementation and outcomes that occur for children. Alternatively, it could allow the researcher to document that an intervention had no significant effect even when it was implemented properly. An example of the former use of implementation is the Love and colleagues study (2005) that documented positive impact of Early Head Start home- and center-based combination programs that fully implemented performance standards, in contrast to those that partially implemented the standards. Alternatively, an example of the latter is findings by St. Pierre, Ricciuti, and Rimdzius (2005) of no educationally important impact for Even Start programs when they were fully implemented. In efficacy and effectiveness research, assessment of implementation may also take place in control or contrast groups, in order to monitor the features of the treatment that may also be occurring in the control group. When core features of a program are implemented in control classrooms, it may result in the absence of or diminished effects for the treatment (Greenberg, Domitrovich, Graczyk, & Zins, 2005). In the Preschool Curriculum Evaluation Research Consortium (2008) studies, the Vanderbilt site conducted a randomized study of the relative effectiveness of the Creative Curriculum and Bright Beginnings curricula. Their assessments of fidelity indicated that, despite random assignment, teachers in both conditions were

Prevention, Early Childhood Intervention, and Implementation Science 419 implementing both core components of both curricula at nearly equivalent levels. As might be expected, there were no differences between the treatment and control groups on any of the dependent measures. Without the implementation assessments, one might conclude that there were no differences between the curricula, but in fact, one cannot draw any conclusions about treatment differences because it appeared that very similar activities were happening in classes in both conditions. Adaptation of interventions by service providers, as noted, is a primary issue in implementation research (Durlak & DuPre, 2008), as will be addressed more fully in a subsequent section. To assess adaptation, one must have a clear standard of comparison that is provided by implementation measures. Further, clear and well-articulated implementation measures allow adopters to more easily replicate interventions or treatments, provide a means for monitoring their own replication efforts, and are tools for purveyors to assess the replicators’ replication. Fixsen and colleagues (2005) proposed that a systematic process for providing feedback to replicators or adopters is critically important for ensuring high levels of and sustained implementation. Last, in setting standards of evidence for prevention programs, Flay and colleagues (2005) noted that a system for documenting and using implementation information is a necessary feature of effectiveness research. In fact, several meta-analyses of different types of prevention programs (i.e., drug prevention, aggression reduction and bullying prevention) have documented that studies in which researchers monitored implementation yielded greater effect sizes as compared to studies in which no implementation was assessed (Dubois, Holloway, Valentine, & Cooper, 2002; Tobler, 1986; Wilson, Lipsey, & Derzon, 2003).

Methodology for Studying Implementation Given that constituent features of implementation are different, methodologies for assessing the features vary also. In two recent reviews, Durlak and DuPre (2008) and O’Donnell (2008) analyzed different dimensions of research studies of implementation for mental health prevention programs (59 studies) and K–12 curriculum research in education (23 studies), respectively. They reported four primary methods of assessing implementation: observation (42% and 34% of the methods employed, respectively), self-report (44% and 12%), interview (6% and 31%), and archival records (6% and 15%). Observational assessment of implementation usually consists of a person external to the program watching program operations and completing an assessment of program features that occurred. For example, in their study of a school readiness and primary prevention program for socioemotional problems for preschool children, Webster-Stratton and colleagues (2008) collected direct observational data on teachers’ behavior and interaction in classrooms. To examine the effects of a coaching and web-based intervention to promote delivery of a school readiness curriculum, Pianta, Downer, Mashburn, Hamre, and Justice (2008) analyzed videotapes of teachers’ interactions with children, finding that the quality of teacher–child interaction occurred in their coaching prevention/intervention model. In both of these examples, researchers examined the quality of teacher behavior. In addition, Webster-Stratton and colleagues (2008) documented the number of lessons completed by the teacher, the use of video vignettes (a key implementation component), and small group activities. Program participants may also provide information about implementation. Often this consists of service providers who are using an intervention or curriculum completing an implementation checklist. In an examination of factors moderating the class-wide use of a peer tutoring program in elementary classrooms, Greenwood, Terry, Arreaga-Mayer, and Finney

420 Samuel L. Odom et al. (1992) had teachers complete a procedural checklist that reported the features of the intervention implemented on randomly selected days. Also, in some studies, evaluators conduct interviews with program implementers to determine whether intervention procedures are employed. For example, Mihalic, Irwin, Fagan, Ballard, and Elliott (2004) used both questionnaires and phone interviews to determine adherence and quality of implementation of a violence and drug prevention program and Mills and Ragan (2000) used a 45-minute taped interview, subsequently coded by program staff, to assess fidelity of a integrated learning system in an educational context. Researchers sometimes use archival records to gauge some features of implementation. For example, adherence to national program standards is a commonly accessible form of information that Love and colleagues (2005) found strongly associated with the impact of Early Head Start. To assess factors affecting math achievement in schools, Ysseldyke and colleagues (2003) used the number of objectives accomplished and mean number of math problems attempted by each class as gauges of implementation of curriculum-based instruction. As noted in the conceptual model described previously, program attendance and/or participation by children and families have been used as a measure of implementation (i.e., sometimes described as a dosage feature of implementation). In their examination of the sustained effects of the IHDP program, Hill and colleagues (2003) found an association between number of days children participated in the program and outcomes for children at age eight. With Head Start children, Hubbs-Tait and colleagues (2002) documented the positive relationship of child attendance to teacher ratings of social competence. Measurement of implementation usually generates a level or percentage of implementation present. Complete adherence to an intervention plan with exacting quality of delivery could ideally lead to 100% implementation, yet in reality implementation rarely approaches this ideal. In their review of 59 implementation studies of mental health and education interventions, Durlak and DuPre (2008) found that studies were generating positive effects with implementation levels of approximately 60% and that only a few studies had levels greater than 80%. In preschool prevention programs that occur in the community, the level of implementation will undoubtedly vary with the teacher and community. A challenge for purveyors and program implementers is to determine the minimum degree of implementation needed to achieve positive effects. The search for the “key ingredients” of prevention/ intervention models that lead to positive effects is an important direction for program providers. Paired with the issue of variance in intervention is the acknowledged tension that exists between the need for high levels of implementation and the adaptation, mentioned previously, that may occur in implementation settings (Fixsen et al., 2005). For programs to be sustainable, they undoubtedly will need to fit the context, and in such cases, teachers and service providers may adapt procedures. Such adaptations result in lower levels of measured implementation, but could be functional in the modification for local sites, perhaps even leading to stronger effects (Durlak & DuPre, 2008). Alternatively, such adaptations could lead implementers to choose only a small number of treatment features that they want to use in their program. In their examination of the implementation of the DARE drug prevention program, Lynam et al. (1999) reported that the program itself was popular and teachers reported wide use of the program, but implementation data documented that only a small number of treatment features were routinely employed in the classes. They also reported that, despite its popularity, the effects of the program were marginal. Accurate, reliable, valid measurement is a central feature of efforts to promote implementation of prevention and intervention programs for children. Multiple organizations and

Prevention, Early Childhood Intervention, and Implementation Science 421 professional groups have included assessment of implementation in their criteria for studies of efficacy and effectiveness (Flay et al., 2005; Mowbray, Holter, Teague, & Bybee, 2003; Odom et al., 2004; Smith et al., 2007). However, Durlak and DuPre (2008) note that “science cannot study what it cannot measure accurately and cannot measure what it does not define” (p. 342). Two implications for future research may be drawn from this literature. First, researchers and evaluators need to reach consensus about the constructs associated with implementation, and second, standards for measurement for the construct should be established.

Association of Implementation and Outcomes A primary reason for documenting implementation is to determine that there is an association between the prevention/intervention program, treatment, or innovation and outcomes for the recipients of the program, in our case children and families. This association is usually demonstrated in efficacy and effectiveness research through randomized experimental designs, in which case the information on implementation substantiates that the treatment program occurred. In an early article on treatment efficacy of early childhood programs, however, Dunst (1987) noted that one may examine the correlational relationship between measures of implementation and outcomes and draw some inferences about the effects of the program. With the increasing sophistication of statistical modeling, for example through structural equation modeling, one may anticipate variables that may moderate, mediate, or confound interpretations of outcome and statistically account for them in analyses. Although without experimental designs one cannot make causal inferences, one can make arguments for “causal-like” inferences if sufficient statistical controls are employed (Thompson, Diamond, McWilliam, Snyder, & Snyder, 2005). For prevention and early intervention programs, investigators have examined relationships between different measures of implementation and child and family outcomes. For example, Wilson and colleagues (2003) conducted a meta-analysis of school-based programs designed to reduce and prevent aggressive behavior and found that programs implemented well and intensively had the greatest impact. Individual studies noted previously in this review have documented child and family outcome effects associated with number of days children participated in the IHDP program (Hill et al., 2003), adherence of program quality standards in Early Head Start programs (Love et al., 2005), and child attendance (Hubbs-Tait et al., 2002). In their examination of implementation features of a preschool readiness curriculum for fouryear-old children with different risk variables, Odom and colleagues (2008) found significant associations between the implementation variables, such as quality ratings, amount of curriculum completed, and child attendance, and child outcomes. The type of analysis that documents the association between implementation features and child outcome is also noteworthy. In the Odom and colleagues (2008) study, the implementation variables were continuous and statistical associations between implementation and outcome variables were analyzed. Alternatively, some studies have characterized classes or programs as “high” and “low” implementers and examined group differences between these groups. For example, in the Ysseldyke and colleagues (2003) study noted previously, classrooms/teachers were categorized as high and low implementers, and child math performance differences were analyzed between groups. Although this was a successful approach in the Ysseldyke and colleagues (2003) study, Durlak and DuPre (2008) noted that such betweengroup analyses may be less sensitive to implementation-outcome associations than the analysis of implementation as a continuous variable.

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Factors That Influence and Facilitate Implementation The recognition of the importance of implementation has been heightened by the interest in factors that affect implementation and facilitation strategies. Several groups of researchers have proposed conceptual models of variables affecting implementation, with each of these models reflecting the ecological systems theory of human development proposed by Bronfenbrenner (1979). The primary work with regard to early childhood and prevention has focused on implementation of a specific intervention/prevention treatment, curriculum, or service. However, the broader literature on school reform and adoption of innovation also has relevance for the examination of factors affecting implementation and is included in this discussion.

Conceptual Models: The Context of Implementation To understand the context of implementation, Fixsen and colleagues (2005) articulated a framework for developing evidence-based intervention practices within organizations. The components of the framework are identified as the source, destination, communication link, feedback and influences. Source refers to the core intervention components or “package” itself. Destination represents the practitioner (e.g., teacher) who works with the consumer (e.g., student). The communication link is the core implementation components or “drivers” of the process that ensure that the practitioners have the knowledge and resources to provide the implementation. These components include staff selection, pre-service and in-service training, ongoing consultation and coaching, staff and program evaluation, facilitative administrative support, and systems interventions. The feedback mechanisms refer to evaluations of staff, program, and fidelity. Finally, influences are the local and state professional and sociopolitical factors that can impact policies that determine practices such as licensing, funding, and/or staff and agency collaboration. These factors are arranged in a Bronfenbrenner systems-like model with source at the center and influence at the most macro level. To describe the contextual factors that may affect the quality and process of implementation, Chen (1998) offered a conceptual framework. In this framework, the Actual Intervention is comprised of both the planned intervention and the implementation supports. Factors that can influence the Planned Intervention include the program model, quality of delivery, target audience, and participants’ responsiveness. Components of Planned Implementation Support are the pre-planning activities, quality of materials, technical support model, quality of the technical support and the implementer readiness. Factors at the classroom (e.g., implementer characteristics and behaviors, classroom climate, peer relations), school and district (e.g., administrative stability and leadership/support, awareness of student needs, school goals, climate, and communication), and community levels (e.g., school-community relations, school-family relations, community support/readiness) likewise have an impact on the success of the implementation. In order to further to understand factors associated with implementation quality, Domitrovich et al. (2008) also proposed a multi-tiered, conceptual model. This model outlines three interdependent factors that influence the quality of implementation for preventive interventions in schools: macro, organizational, and individual factors. Macro-level factors, the most distal influences, are policies and practices (fiscal, regulatory, and administrative) at federal, state, and district levels that can exert an impact on the implementation of evidencebased programs. Examples may include standards-based reforms or monetary supports for professional development. Organizational-level factors include components of the shared

Prevention, Early Childhood Intervention, and Implementation Science 423 school environment that influence implementation such as characteristics of the school (e.g., size, geography, student mobility), administrative leadership, the school’s mission or policies, the capacity of the organization (funds, materials, equipment, knowledge and skills), the decision-making structure (roles and responsibilities), the school climate and organizational health, the classroom climate such as relationships between teachers and students, cooperation among staff members, sense of belonging, and the organizational culture (i.e., norms, beliefs and assumptions of the membership). Individual-level factors are the third level of the framework and they include professional training of teachers, teachers’ psychological characteristics (e.g., burn-out, willingness to implement or persist, and so on); and staff attitudes about the implementation and their histories with previous programs.

Phases of Implementation and Differential Strategies Implementation is not only influenced by multiple factors but it also is a dynamic process that may occur in phases. Strategies designed to promote implementation may be most appropriate and effective at specific phases of the process. Greenberg and colleagues (2005) identified phases of implementation as (a) pre-adoption (i.e., the period before training when service providers prepare for implementation), (b) delivery (i.e., provision of training and technical assistance—a time when the intervention is introduced and initial training is provided to the teacher), and (c) post-delivery (i.e., period of time after initial delivery of training when the intervention continues or does not continue to be used). At each phase where specific strategies have been found to be facilitative, the implementation has a greater chance of success. Early and Joint Planning. During the pre-adoption phase, service providers prepare for implementation, and detailed planning during this stage enhances implementation. Detailed plans describe processes for “dialogue, project management, setting benchmarks for progress, gathering and communicating feedback, and making decisions about significant changes” (Elias, Zins, Graczyk, & Weissberg, 2003, p. 312). Clearly, the teachers who are implementing the intervention need to be involved at the pre-planning stage to ensure that they “buy into” the intervention (Greenberg et al., 2005). Quality of Materials. Most interventions that are implemented in early childhood programs have instructor manuals for the teachers. In fact, “manualization” is a critical feature of implementation (Smith et al., 2007). Manuals that are visually appealing, user-friendly, age-appropriate and culturally sensitive are more likely to be used by the teachers, thus contributing to implementation success (Greenberg et al., 2005). Access to these curriculum materials may positively affect implementation at the delivery phase of the intervention. Availability and Quality of Technical Support. Technical support is the means to ensure that teachers implement well the interventions for which they have received training. It has its greatest impact during the pre-adoption and delivery phases. Such technical support is provided to teachers through professional development activities. Garet, Porter, Desimone, Birman and Yoon (2001) identified characteristics of professional development that led to teacher learning. Those characteristics were: (1) activities that were sustained and of long duration, (2) a clear focus on the content of the intervention, (3) teachers’ active involvement in learning the content, and (4) ongoing professional development that was part of the daily life of the school. Showers and Joyce (1996) specified additional strategies that contribute to successful implementation. Among the strategies is professional development that includes coaching (from either experts or peers) when the teachers implement the new program in their classroom and feedback about the success of their implementation. In fact, Joyce and Showers (2002) described a study conducted in which 95% of the teachers who received

424 Samuel L. Odom et al. in-class coaching in addition to training that included demonstration, practice, and feedback used their new skills in the classroom. In contrast, only a small percentage of teachers who received training without the coaching actually used the newly trained skills in their classroom. Implementer Readiness. Greenberg and colleagues (2005) suggested that teachers’ likelihood of implementing an intervention depends in part on their readiness for the intervention. Readiness refers to teachers “feel[ing] positive about a program, value what it contributes . . . and [showing a commitment] to its goals” (p. 37). Elliott (1988) found that there was a moderate to strong relationship between whether teachers believed an intervention would be effective and whether or not they perceived it to have the desired effect. In their study of preschool inclusion for children with disabilities, Lieber and colleagues (1998) noted that teachers’ beliefs about the inclusion process affected strongly their ability to implement and success in implementing strategies to support learning for children with disabilities. Clearly, assessing teachers’ beliefs about or attitudes toward intervention approaches may well affect implementation at the delivery and post-delivery stages of intervention. Contextual Factors. Greenberg and colleagues (2005) indicated that the larger classroom or school context also affects teachers’ ability and willingness to implement a new intervention. Within the classroom itself, a positive classroom climate is associated with successful implementation. That may include the teacher’s relationship with the children and with the other adults that work in the classroom. Understanding and designing strategies for promoting a positive social climate may impact implementation at the delivery and post-delivery phases of implementation. Like Greenberg and colleagues (2005), Fixsen and colleagues (2005) also proposed stages of the implementation process. During the initial Exploration phase, the purveyors of the program examine community needs and create agreements with those who will implement the program. The Installation phase involves the preparatory activities and arrangements necessary to do the work. In the Initial Implementation (beginning phase of operation) and Full Implementation phases the program itself becomes fully operational and all facets or components are delivered. The Sustainability phase focuses on factors that may lead to the program’s continuation or demise once the implementation practices are established. Finally, Innovation refers to the adaptations at a local site that are necessary for shaping the program for the unique features of a site while maintaining the core features of the program.

Factors Associated with Adoption and Implementation Implementing preventive programs in real-life settings such as schools is influenced by a variety of factors. Rohrbach and colleagues (2006) conducted a comprehensive review of the implementation literature and identified major factors that predicted success. At the organizational level, major factors included the administrative leadership’s commitment to and support of innovation, decentralized management and participatory decision-making, open communication patterns, the stability and adequacy of school resources, a shared vision and goals, a willingness to initiate the change, the positive school climate, and culture (trust, collaboration). At the provider level factors identified were the teachers’ or providers’ attitudes toward, comfort with, and commitment to making the innovation work as well as their teaching skills and self-efficacy. Innovation-level factors focused on the program being well specified, attractively packaged and easy to use, and incorporating teaching methods that were familiar to the teacher/provider. Finally, the fourth level, the training level, described training

Prevention, Early Childhood Intervention, and Implementation Science 425 and technical assistance prior to and during implementation that may be delivered using a variety of formats.

Two Case Examples of Factors Affecting Implementation In early childhood programs, implementation can be described for the provision of a specific prevention curriculum or treatment, or it can be thought of more broadly as adoption of a school reform. Two case examples are provided to illustrate the facilitators of and barriers to implementation for a preschool readiness curriculum in a randomized efficacy study and more broadly the adoption, use, and sustainability of preschool inclusion. Children’s School Success: Implementation of a Prevention Curriculum The Children’s School Success (CSS) project aimed to improve the educational outcomes for young children considered at risk for school failure due to poverty, limited English fluency or identified disability. The five-year (2003–2008) project was funded by the National Institute on Child Health and Development, the Department of Education and the U.S. Department of Health and Human Services (#HD046091, PIs Odom, Butera, Diamond, Hanson, Horn, Lieber, & Palmer, 2003). CSS investigators developed a curriculum for four-year-old children in classrooms and implemented it in five distinct geographic regions in the United States, and conducted a multi-site experimental study investigating its effectiveness. Based on research about children’s early learning and activities that promote the development of skills children need to be successful during the early elementary grades, the CSS curriculum focused on academic content and social competence. The academic content included curricular goals related to math (beginning numbers and operations, geometry and spatial sense, measurement, pattern/algebraic thinking and displaying and analyzing data), science (measurement and mapping, properties of matter, color and light and neighborhood habitat) and language and early literacy (oral language, phonological awareness and letter/print knowledge). The social-emotional curricular component included content related to emotional literacy, empathy and perspective-taking, friendship skills, anger management, interpersonal problem-solving, and skills related to being successful in school. Preventing children’s problem behavior and promoting prosocial problem-solving was also emphasized. The curriculum content also included a focus on individualization so that teachers could accommodate all learners. Between 2004 and 2008, teachers in 48 Head Start, state preschool or community-based classes were provided with an initial three days of professional development about implementing CSS and a follow-up day later in the year to help them individualize the curriculum activities. Throughout their implementation year, CSS teachers were assisted by curriculum coaches who, visiting them at least weekly in their classrooms, collaborated in planning lessons, modeled the use of various curriculum activities, and provided feedback about their implementation. To assess implementation, CSS staff completed seven fidelity of treatment observational ratings in each class. These ratings assessed the quality of implementation for the science/math, literacy and social components of the curriculum. In addition, CSS staff calculated the percentage of the curriculum that each teacher completed. In order to understand facilitators of and challenges to implementation, CSS investigators conducted a qualitative study of factors associated with teachers’ high or low levels of implementation (Lieber et al., in press). Employing a case study methodology that included field notes of observations collected throughout the intervention year along with interview, artifact, survey and

426 Samuel L. Odom et al. questionnaire data, a reiterative yearly cross-case analysis yielded a total of 17 themes that clustered into three areas (Teacher, Curriculum and Instruction, and Beyond the Teacher). Using a ranking system described by Miles and Huberman (1994), teachers were rated as high, middle or low implementers. Nine themes were identified as particularly descriptive and robust in explaining teachers’ level of implementation, comprising two related to the “Teacher”, three related to “Curriculum and Instruction” and four related to “Beyond the Teacher”. Although themes related to Curriculum and Instruction and Beyond the Teacher were identified as important factors serving to facilitate or hinder CSS implementation, teacher characteristics were identified as particularly influential across the study. Teachers with high levels of CSS implementation were motivated to undertake the changes required in their teaching, innovative in integrating the curriculum into their daily teaching activities, and stated that they appreciated the opportunity to participate in the project as a partner. Conversely, teachers with low levels of implementation demonstrated a lack of motivation and had difficulty integrating CSS concepts into their teaching. The findings of this study support the importance of implementer readiness in the implementation of early childhood interventions (Elliott, 1988; Greenberg et al., 2005; Lieber et al., in press). Lieber and colleagues (in press) suggest that teachers’ perception that they had exercised choice in participating in CSS and that their own expertise and preferences were acknowledged may have influenced the degree to which they viewed themselves as partners in the implementation process and affected their motivation to implement the curriculum. Preschool Inclusion: Factors Affecting Adoption and Sustainability of Innovation Inclusion of preschool children with disabilities in classes with typically developing children is an organizationally complex practice. Because traditional public schools do not begin until kindergarten, but local education agencies are required to provide services to children with disabilities down to the age of three, school system service providers must search for inclusive options either outside the schools system (e.g., private preschools or Head Start programs) or in early childhood programs within their system (e.g., state pre-kindergarten program). Although preschool inclusion takes different forms in different school programs, it is often an innovation introduced into a school system (i.e., if inclusive options were not provided previously). The process and many of the factors that affect specific treatment implementation also affect broader adoption of innovations such as preschool inclusion in school systems (Fullan, 2001). To examine barriers and facilitators to preschool inclusion, investigators with the Early Childhood Research Institute on Inclusion (ECRII) conducted a study of inclusion in 16 nationally selected preschool programs. The study, called the ecological systems study of inclusion (Odom, 2002), tracked the factors affecting implementation of preschool inclusion across a five-year period. Data were collected at the child, teacher, family, administrative, and state/national policy levels. Data were summarized in case studies and case summaries. Analyses of the case studies revealed common factors that affected the success with which local programs and school systems were able to initiate and maintain inclusive programs for preschool children, as well as factors leading to the sustainability of the programs. As ECRII began, inclusion at the preschool level was becoming more widespread around the nation. This allowed investigators to identify and examine the factors that facilitated the “innovation” of preschool inclusion (Lieber et al., 2000). These factors, at the same time, also often served as barriers to inclusion when they were absent. The strongest facilitator was the

Prevention, Early Childhood Intervention, and Implementation Science 427 presence of key personnel to influence policy. An individual school or community leader in a decision-making position often provided the impetus for systems change. Another and related influence was a shared vision (philosophy and definition of inclusion) among staff members and agencies in both regular early childhood education and special education systems. Also, state and national policies were identified as providing the impetus for the creation and implementation of inclusive programs. Other factors found to facilitate inclusive preschool practices included the provision of training and/or external support that provided opportunities and funding for training and collaboration, as well as organizational structures that linked services through interagency agreements. Finally, community influences also played a role; these were often parents who advocated for the establishment of inclusive services. To examine the viability and sustainability of the inclusive programs, ECRII researchers visited the programs again at the end of the five-year period. Of the 16 original programs, four had experienced growth (i.e., increased number of children with disabilities in inclusive settings), six of the programs were stable (i.e., ongoing strong commitment to inclusion), six of the programs exhibited minimal change (i.e., weak commitment to inclusion and reduced numbers of children in inclusive settings) and one program was disbanded, which we called regression (Odom, Wolery, Lieber, & Horn, 2002). [Please note that this total number of programs is 17 because one program divided into two types of inclusion programs, and we characterized them separately at the end of the study.] Programs that experienced growth or stability typically were characterized as having a “critical mass” of individuals who were committed to and supportive of inclusive services. They were able to balance support and pressure from different parts of the system and adapt beliefs and practices in response to program and community change. They had a broad-based ownership of inclusion. Programs in which there was minimal change or regression had begun with a limited commitment to inclusion from the school system and/or limited pressure for and support for inclusion from various local systems. These systems did not provide the training and support to sustain collaborative efforts. For instance, some programs established collaborative arrangements between preschool Head Start and public school agencies. Lack of opportunities and resources for training and collaboration in some cases became serious barriers to the maintenance of inclusion. These services, thus, were observed to be dynamic enterprises that required sustained leadership and collaborative supports to enable them to be maintained and adapt to changing needs. Themes in Case Summaries Although they represent different forms of implementation (i.e., in an efficacy trial and in school reform), factors affecting implementation in both CSS and the inclusive programs in the ECRII study were apparent and exemplified some of the themes emerging from the implementation science literature. For in-class implementation to occur, training, professional development, and ongoing coaching support (for CSS) were important, although the success of these was moderated by teachers’ beliefs, motivation, and “readiness” to implement. Teachers’ participation in the decision-making processes was a critical feature of both CSS and ECRII. For both studies, classrooms were clearly seated within broader ecological systems, and factors operating outside the classroom (e.g., district or program policies, support from key administrator) had major impacts on the degree to which teachers implemented the CSS curriculum or an inclusion model. These projects were relatively short-term, but the phases of implementation, mentioned by several researchers (Fixsen et al., 2005; Greenberg et al., 2005) probably did have an effect. For inclusion models, implementation evolved both positively and negatively, and factors affecting the directions have been discussed. For CSS,

428 Samuel L. Odom et al. implementation only occurred for one year, and the CSS researchers have indicated that being able to implement a curriculum for more than a single year would affect both implementation and sustainability of the curriculum, which again is a theme identified by multiple implementation scientists (Fixsen et al., 2005; Greenberg et al., 2005).

Conclusion Prevention of school failure and promotion of school success, intertwined and inversely related goals, are primary concerns for this country. Early childhood education programs specifically designed for preschool children at risk for school failure have been proposed by individuals as lofty as the U.S. president, as a way of preventing school failure and fostering children’s success in school. Research is beginning to reveal the types of early childhood curriculum models that are producing positive outcomes (e.g., Bierman et al., 2008). The likelihood of these models having a more general positive impact on society will depend on implementation. The emerging science of implementation will inform purveyors of early childhood models and service providers interested in taking these models to scale about the processes necessary for promoting successful implementation and positive outcomes for children.

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Prevention, Early Childhood Intervention, and Implementation Science 429 De Navas-Walt, C., Proctor, B. D., & Smith, J. (2007). Income, poverty and health insurance coverage in the United States: 2006. Washington, DC: Census Bureau. Domitrovich, C. E., Bradshaw, C. P., Poduska, J. M., Hoogwood, K., Buckley, J. A., Olin, S., Romanelli, L. H., Leaf, P. J., Greenberg, M. T., & Ialongo, N. S. (2008). Maximizing the implementation quality of evidence-based preventive interventions in schools: A conceptual framework. Advance in School Mental Health Promotion, 1, 6–28. DuBois, D. L., Holloway, B. E., Valentine, J. C., & Cooper, H. (2002). Effectiveness of mentoring programs for youth: A meta-analytic review. American Journal of Community Psychology, 30, 157–198. Dunst, C. J. (1987). Overview of the efficacy of early intervention programs. In D. Weatherford & L. Bickman (Eds.), Evaluating early intervention programs for severely handicapped children and their families (pp. 79–148). Baltimore: Paul H. Brookes. Dunst, C. J. (2007). Early intervention for infants and toddlers with developmental disabilities. In S. Odom, R. Horner, M. Snell, & J. Blacher (Eds.), Handbook of developmental disabilities (pp. 161–180). New York: Guilford Press. Durlak, J. A., & DuPre, E. P. (2008). Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology, 41, 327–350. Elias, M. J., Zins, J. E., Graczyk, P. A., & Weissberg, R. P. (2003). Implementation, sustainability, and scaling up of social-emotional and academic innovations in public schools. School Psychology Review, 32, 303–319. Elliott, S. N. (1988). Acceptability of behavioral interventions: Review of variables that influence treatment selection. Professional Psychology: Research and Practice, 19, 68–80. Espinosa, L., M., & Burns, M. S. (2003). Early literacy for young children and English-language learners. Teaching 4- to 8-year olds: Literacy, math, multiculturalism, and classroom community (pp. 47–69). Baltimore: Paul H. Brookes. Flay, B. R., Biglan, A., Boruch, R. F., Castro, F. G., Gottfredson, D., Kellan, S., Moscicki, E. K., Schinke, S., Valentine, J. C., & Ji, P. (2005). Standards of evidence: Criteria for efficacy, effectiveness, and dissemination. Prevention Science, 6, 151–175. Fixsen, D. L., Naoom, S. F., Blase, K. A., Friedman, R. M., & Wallace, F. (2005). Implementation research: A synthesis of the literature. Tampa, FL: University of South Florida, Louis de la Parte Florida Mental Health Institute, The National Implementation Network (FMHI Publication #231). Fullan, M. G. (2001). The new meaning of educational change (3rd ed). New York: Teachers College Press. Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Yoon, K. S. (2001). What makes professional development effective? Results from a national sample of teachers. American Educational Research Journal, 38, 915–945. Genesee, F., Paradis, J., & Crago, M. B. (2004). Dual language development and disorders: A handbook on bilingualism and second language learning. Baltimore: Paul H. Brookes. Gray, S. W., & Klaus, R. A. (1970). The Early Learning Project: A seven year report. Child Development, 41, 909–924. Greenberg, M. T., Domitrovich, C. E,, Graczyk, P. A., & Zins, J. E. (2005). The study of implementation in school-based preventive interventions: Theory, research, and practice. Promotion of Mental Health and Prevention of Mental and Behavior Disorders (Vol. 3). Washington DC: U.S. Department of Health and Human Services. Greenwood, C. R., Terry, B., Arreaga-Mayer, C., & Finney, R. (1992). The class-wide peer tutoring program: Implementation factors moderating students’ achievement. Journal of Applied Behavior Analysis, 25, 101–116. Gross, R. T., Spiker, D., & Haynes, C. W. (1997). Helping low birth weight, premature babies: The Infant Health and Development Program. Palo Alto, CA: Stanford University Press. Hart, B., & Risley, T. R. (1995). Meaningful differences in the everyday experience of young American children. Baltimore: Paul H. Brookes. Hill, J. L., Brooks-Gunn, J., & Waldfogel, J. (2003). Sustained effects of high participation in an early intervention for low-birth-weight premature infants. Developmental Psychology, 39, 730–745.

430 Samuel L. Odom et al. Hubbs-Tait, L., Culp, A. M., Huey, E., Culp, R., Starost, H., & Hare, C. (2002). Relation of Head Start attendance to children’s cognitive and social outcomes: Moderation by family risk. Early Childhood Research Quarterly, 17, 539–558. Joyce, B., & Showers, B. (2002). Student achievement through staff development (3rd Ed.). Alexandria, VA: Association for Supervision and Curriculum Development. Kamerman, S. B., & Kahn, A. J. (2004). Early Head Start, child care, family support, and family policy. In E. Zigler & S. Styfco (Eds.), The Head Start debates (pp. 415–422). Baltimore: Paul H. Brookes. Landy, S., & Menna, R. (2006). Early intervention with multi-risk families: An integrative approach. Baltimore: Paul H. Brookes. Lazar, I., & Darlington, R. (1982). Lasting effects of early education: A report from the Consortium for Longitudinal Studies. Monographs of the Society for Research in Child Development, 47(2–3), Series No. 195. Lieber, J., Butera, G., Hanson, M., Palmer, S., Horn, E., Czaja, C., Diamond, K., Goodman-Jansen, G., Daniels, J., Gupta, S., & Odom, S. (in press). Factors that influence the implementation of a new preschool curriculum: Implications for professional development. Early Education and Development. Lieber, J., Capell, K., Sandall, S. R., Wolfberg, P., Horn, E., & Beckman, P. (1998). Inclusive preschool programs: Teachers’ beliefs and practices. Early Childhood Research Quarterly, 13, 87–105. Lieber, J., Hanson, M. J., Beckman, P. J., Odom, S. L., Sandall, S. R., Schwartz, I. S., Horne, E., & Wolery, R. (2000). Key influences on the initiation and implementation of inclusive preschool program. Exceptional Children, 67, 83–98. Love, J. M., Kisker, E. E., Ross, C., Raikes, H., Constantine, J., Boller, K., Brooks-Gunn, J., ChazanCohen, R., Tarullo, L. B., Brady-Smith, C., Fuligni, A. S., Schochet, P. Z., Paulsel, D., & Vogel, C. (2005). The effectiveness of Early Head Start for 3 year-old children and their parents: Lessons learned for policy and programs. Developmental Psychology, 41, 885–901. Lynam, D. R., Milich, R., Zimmerman, R., Novak, S. P., Logan, T. K., & Martin, C. (1999). Project DARE: No effects at 10-year follow-up. Journal of Counseling and Consulting Psychology, 67, 590–593. Mihalic, S., Irwin, K., Fagan, A., Ballard, D., & Elliott, D. (2004). Successful program implementation: Lessons from blueprints (Electronic report. U.S. Department of Justice, Office of Justice Programs). Retrieved December 23, 2008, from http://www.ncjrs.gov/pdffiles1/ojjdp/204273.pdf. Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis, 2nd edition. Newbury Park, CA: Sage. Mills, S. C., & Ragan, T. J. (2000). A tool for analyzing implementation fidelity of an integrated learning system. Educational Technology Research and Development, 48(4), 21–41. Mowbray, C. T., Holter, M. C., Teague, G. B., & Bybee, D. (2003). Fidelity criteria: Developmental, measurement, and validation. American Journal of Evaluation, 24, 315–340. Odom, S. L. (2000). Preschool inclusion: What we know and where we go from here. Topics in Early Childhood Special Education, 20, 20–27. Odom, S. L. (Ed.). (2002). Widening the circle: Including children with disabilities in preschool programs (Appendix A, pp. 173–185). New York: Teachers College Press. Odom, S. L. (in press). The tie that binds: Early intervention, implementation, and child outcomes. Topics in Early Childhood Special Education. Odom, S. L., Brantlinger, E., Gersten, R., Horner, R. D., Thompson, B., & Harris, K. (2004). Quality indicators for research in special education and guidelines for evidence-based practices: Executive summary. Arlington, VA: Council for Exceptional Children Division for Research. Odom, S. L., Butera, G., Diamond, K. E., Hanson, M. J., Horn, E., Lieber, J., & Palmer, S. (2003). Children’s school success: An experimental study of an early childhood education model. Bloomington, IN: Indiana University Press. Odom, S. L., Fleming, K., Lieber, J., Diamond, K., Butera, G., Hanson, M., Horn, E., Palmer, S., & Marquis, J. (2008). Implementation and child outcomes in an early childhood curriculum efficacy study. Manuscript submitted for publication. Odom, S. L., Horn, E. M., Marquart, J., Hanson, M. J., Wolfberg, P., Beckman, P. J., Lieber, J., Li, S.,

Prevention, Early Childhood Intervention, and Implementation Science 431 Schwartz, I., Janko, S., & Sandall, S. (1999). On the forms of inclusion: Organizational context and individualized service delivery models. Journal of Early Intervention, 22, 185–199. Odom, S. L., Rogers, S., McDougle, C. J., Hume, K., & McGee, G. (2007). Early intervention for children with autism spectrum disorder. In S. Odom, R. Horner, M. Snell, & J. Blacher (Eds.), Handbook of developmental disabilities (pp. 199–223). New York: Guilford Press. Odom, S. L., & Wolery, M. (2003). A unified theory of practice in Early Intervention/Early Childhood Special Education: Evidence-based practice. Journal of Special Education, 37, 164–173. Odom, S. L., Wolery, R. A., Lieber, J., & Horn, E. (2002). Social policy and preschool inclusion. In S. Odom (Ed.), Widening the circle: Including children with disabilities in preschool programs (pp. 120–136). New York: Teachers College Press. O’Donnell, C. L. (2008). Defining, conceptualizing, and measuring fidelity of implementation and its relationship to outcomes in K–12 curriculum intervention research. Review of Educational Research, 78, 33–84. Pianta, R. C., Mashburn, A., Downer, J. T., Hamre, B. K., & Justice, L. (2008). Effects of web-mediated professional development resources on teacher–child interactions in pre-kindergarten classrooms. Early Childhood Research Quarterly, 23, 431–451. Powell, D., Fixsen, D., Dunlap, G, Smith, B., & Fox, L. (2007). A synthesis of knowledge relevant to pathways of service delivery for young children with or at risk of challenging behavior. Journal of Early Intervention, 29, 81–106. Preschool Curriculum Evaluation Research Consortium. (2008). Effects of Preschool Curriculum Programs on School Readiness (NCER 2008–2009). Washington, DC: National Center for Education Research, Institute of Education Sciences, U.S. Department of Education. Washington, DC: U.S. Government Printing Office. Ramey, C. T., & Campbell, F. A. (1984). Preventive education for high-risk children: Cognitive consequences of the Carolina Abecedarian Project. American Journal of Mental Deficiency, 88, 515–523. Rohrbach, L. A., Grana, R., Sussman, S., & Valente, T. W. (2006). Type II translation: Transporting prevention interventions from research to real-world settings. Evaluation & the Health Professions, 29, 302–333. Sameroff, A. J., & Chandler, M. J. (1975). Reproductive risk and the continuum of caretaking casualty. In F. Horowitz (Ed.), Review of child development research (187–244). Chicago: University of Chicago Press. Schweinhart, L. J. (2004). The High/Scope Perry preschool study through age 40: Summary, conclusions, and frequently asked questions. Monographs of the High/Scope Educational Research Foundation, 14. Ypsilanti, MI: High/Scope Press. Showers, B., & Joyce, B. (1996). The evolution of peer coaching. Educational Leadership, 53(6), 12–17. Smith, M. S. (1974). Findings of the second year of the Head Start planned variations study. Paper presented at the annual meeting of the American Psychological Association, New Orleans, LA. Smith, T., Scahill, L., Dawson, G., Guthrie, D., Lord, C., Odom, S., Rogers, S., & Wagner, A. (2007). Designing research studies on psychosocial interventions in autism. Journal of Autism and Developmental Disorders, 37, 354–366. Sparling, J. J., & Lewis, I. (1979). Learning games for the first three years: A guide to parent-child play. New York: Walker. St. Pierre, R. G., Ricciuti, A. E., & Rimdzius, T. A. (2005). Effects of a family literacy program on lowliterate children and their parents: Findings from an evaluation of the Even Start Family Literacy Program. Developmental Psychology, 41, 953–970. Tabors, P. (1997). One child, two languages. Baltimore: Paul H. Brookes. Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Synder, S. W. (2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children, 71, 181–194. Tobler, N. S. (1986). Meta-analysis of 143 adolescent drug prevention programs: Quantitative outcome

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22 Taking Effective Prevention to Scale Brian K. Bumbarger, Daniel F. Perkins, and Mark T. Greenberg, Penn State University

Great strides have been made in the science of preventing youth problems such as delinquency, aggression and violence, substance use, and mental health problems. As our understanding of how these problems develop improves, a growing number of preventive interventions have been developed and tested in well-designed studies, giving the youth services field a menu of choices for effective prevention programming. However, as we attempt to scale-up these programs (i.e., to broadly replicate them across many diverse communities under natural conditions) we are faced with a new set of challenges for research, policy, and practice.

Prevention Science and the Public Health Model The last three decades of the 20th century saw incredible progress in research and practice aimed at promoting better outcomes for children and adolescents. A clear science of preventing youth problems and promoting positive youth development has emerged, and owes much to the public health model (Coie et al., 1993; Mrazek & Haggerty, 1994). In the public health model, basic research seeks to identify factors which are causally related to a negative outcome, and then works to prevent or reduce the prevalence of the negative outcome by reducing the prevalence of the associated risk factor(s). This model has been utilized effectively in addressing such problems as cardiovascular disease and lung cancer, and in recent decades has been increasingly applied to the prevention of youth mental health problems, substance abuse, violence, and delinquency. Cumulative research has informed our understanding of the risk and protective mechanisms associated with an increased risk for youth problems, as well as the multiple pathways and developmental processes involved with maladaptive outcomes. Risk and protective factors have been identified across multiple ecological domains from the child and his or her peer group to family environmental factors and to the broader community context. The identification of risk and protective factors, and the subsequent change in focus from addressing problems to preventing causal factors, represents a significant paradigm shift that has informed both the practice of prevention and the development of preventive interventions based on theoretical models that recognize this relationship between risk and behavioral outcomes. As a unifying approach, the risk-focused public health model also recognizes that in many cases risk factors operate across multiple outcomes. Research has increasingly indicated that many maladaptive outcomes (e.g., delinquency, substance use, mental health problems, and academic failure) have shared or overlapping risk factors and substantial comorbidity (Greenberg, Domitrovich, & Bumbarger, 2001). For instance, family conflict, lack of commitment to school, and poverty have each been found in longitudinal research to be associated

434 Brian K. Bumbarger et al. with a number of poor outcomes in adolescence. Thus, focusing on risk and protective factors rather than specific behavioral outcomes can potentially serve to prevent a broad range of youth problems, and may also serve to promote resilience and well-being generally. Its impact on the improvement of prevention practice notwithstanding, one criticism that has been leveled against the public health model is its bias toward risk rather than positive well-being. It is often argued that the absence of illness is not the same as health, and thus the absence of maladaptation is not necessarily a reflection of positive development (Bumbarger & Greenberg, 2002). Clearly the field of prevention science has much to gain from an increased focus on the promotion of well-being, regardless of risk (Perkins & Caldwell, 2005). Another important landmark in the development of the science of prevention was the Institute of Medicine’s watershed 1994 report (Mrazek & Haggerty, 1994) which initiated the conceptualization of a continuum of universal, selective, and indicated levels of prevention. While this model may now be viewed as common vernacular it is useful to keep in mind the utility of a prevention continuum, as it recognizes a necessary approach to promoting public health at a population level. Children fall along a continuum of risk and thus require differing levels of intensity and focus in intervention. Durlak (1995) and Offord (1996) have articulated both the strengths and weaknesses of universal versus targeted models of intervention. Universal interventions (e.g., school curricula) are generally less expensive, avoid stigmatizing or labeling participants, do not require recruitment (and thus avoid problems with attrition and retention), avoid the need for screening and the concomitant issue of misdiagnosis and “false negatives” present with targeted intervention, and represent the potential to address ecological change including the macro-level. On the other hand, because of the relatively low prevalence of maladaptation, especially in pre-adolescence, much of the effort of universal programs may be spent on children who might not have developed problems anyway, and thus targeted interventions may be more efficient. In addition, because they must serve whole cohorts or populations, universal interventions often lack the dosage and duration necessary to improve the developmental trajectory of children who may be at increased risk (although findings from several studies of universal interventions have shown stronger effects with the highest risk subgroups (Greenberg, Domitrovich, & Bumbarger, 2001)). The fact remains that both approaches are necessary, and a comprehensive effort that combines universal and targeted interventions is called for to achieve a broad public health impact.

The Development of Effective Interventions As increased knowledge of risk and protective mechanisms has informed our understanding of the etiology of a variety of adolescent behavior problems, a growing number of preventive interventions have been developed and tested. With a more sophisticated understanding of developmental pathways and the interplay of these risk and protective factors came the development of more sophisticated and potentially more robust interventions. The field has gone from naïve information-only approaches and purely behaviorist “scare-tactics” to a more holistic and ecological view of the skill-building and increased supports necessary to positively impact youth outcomes. In recent decades a large number of new prevention programs have been developed, focusing on a wide variety of outcomes through diverse models of intervention in a variety of settings. Collectively, reviews and meta-analyses have concluded that these interventions, both universal and targeted, can be effective in preventing and reducing substance use (Blitz, Arthur &

Taking Effective Prevention to Scale 435 Hawkins, 2002; Tobler et al., 2000; Gottfredson & Wilson, 2003; Lochman & van den Steenhoven, 2002), violence and anti-social behavior (Wilson, Gottfredson, & Najaka, 2001; Wilson, Lipsey, & Derzon, 2003; U.S. Department of Health and Human Services, 2001), mental health problems (Durlak & Wells, 1997; Greenberg et al., 2001; Hoagwood et al., 2007), and promoting positive youth development (Catalano et al., 2002; Eccles & Gootman, 2002). Although effect sizes in the study of these programs are often small to moderate, recent metaanalytic review by the Campbell Collaboration has concluded that the results are both statistically and practically significant, representing reductions of 1/4 to 1/3 in base rates in some cases (Wilson & Lipsey, 2007).

Research Questions Still To Be Addressed As more interventions are tested in well-designed longitudinal evaluative studies, a growing armamentarium of efficacious programs has evolved. The ability for policymakers and practitioners to differentiate programs that “work” represents a sea-change that has dramatically altered the landscape of prevention policy and practice. Although both research and practice have improved, however, there remain important areas that warrant further attention. In a recent report to the Institute of Medicine, Greenberg (2007) outlines a number of important recommendations for improving the current state of outcome research in this area: 1

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Few of the studies demonstrating the efficacy of these programs include long-term follow-up. It is important to know not only that outcomes have improved at immediate post-intervention but that programs actually change the long-term developmental trajectory of the target population. Few of the studies demonstrating program efficacy have been independently replicated. As the original research is often conducted by the program developer this raises concerns, especially among policymakers and practitioners who may not understand the safeguards of scientific peer review process, about bias and objectivity. Furthermore, independently replicated findings are critical to demonstrate the feasibility of the program. Especially with intervention at the school or community level, it is important for studies to have sufficient statistical power to analyze effects at the level of randomization. This becomes increasingly difficult when the focus of intervention is an entire community or when studying environmental approaches to intervention. As we think about wide-scale dissemination of these programs, it is important to know more about their cultural and contextual generalizability. This generalizability has direct implications for the often-perceived need to adapt programs to local populations and the subsequent need to differentiate between “cosmetic” changes in the look and feel of a program versus “structural” changes that literally alter the program’s underlying theory of change. Although an increasing number of programs have demonstrated efficacy in improving outcomes for children and youth, we often know very little about the “critical components” of these sometimes complex interventions. Greater knowledge about the critical components could lead to improvements in efficiency and reductions in cost through the ability to fine-tune programs to their core elements. This “trimming of the fat” might also improve our ability to disseminate effective programs into already demanding environments (e.g., school classrooms). Similarly, we seldom know for whom and under what circumstances these programs are most effective. Because programs do not impact each individual in the same way,

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additional research on the moderators of program impact could both improve the efficiency and effectiveness of programs as well as informing our basic understanding of the causal mechanisms for program impact. More sophisticated control group designs could: (a) address potential placebo effects in studies of targeted interventions; and (b) provide opportunities to compare the relative benefits of two or more efficacious interventions within a single study—as opposed to the common “no treatment” comparison condition. More research is needed on the cost-benefit ratio of these programs. The seminal work of Aos and colleagues (Aos, Mayfield, Miller, & Yen, 2006) has opened the door to demonstrating the incredible cost-saving potential of effective prevention programming and foreshadowed the considerable impact of such economic analyses on public policy. Additional examination of this aspect of prevention is called for. Although considerable advances in methodology have created new opportunities for investigating the effectiveness of preventive interventions, effect size—the common metric for measuring relative impact—on its own may not be an adequate yardstick, especially for universal programs. Because it is likely that a significant proportion of the population being measured in a preventive intervention begins as symptom-free, it is unlikely the effect size will be large even in the event of program impacts that would by all practical measure be considered significant.

Despite these challenges, the field of prevention has benefited greatly from recent advances in theory, methodology, and program design. A broad menu of empirically-validated preventive interventions has been developed, enabling prevention policy and practice to progress even as prevention science continues to address important unanswered questions.

“Type 2” Translational Research and the Challenges of Going to Scale The progress in developing and establishing the efficacy of so many prevention programs has subsequently brought about important policy and legislative changes at both the state and federal level. Descriptive language has been written into legislation, regulation and policy outlining the need for communities to look to evidence-based programs and strategies, and funding is increasingly restricted to programs with documented empirical evidence. As the movement to promote the wide-scale use of efficacious prevention programs grows, a new set of research questions arises. Contrasted with research to translate basic theoretical knowledge into preventive interventions (“Type 1 Translational Research”), we now must move to the study of program adoption, implementation, and effectiveness under real-world conditions. “Type 2 Translational Research” therefore seeks to create a new science of adoption and implementation of empirically-validated interventions as they are scaled-up. This emerging science focuses on identifying factors that influence the adoption, quality and fidelity of program implementation, including the important issues of program adaptation, sustainability, and cost-effectiveness. Several conceptual models have been theorized to guide this line of inquiry (Fixsen et al., 2005; Glasgow et al., 2004; Greenberg, Domitrovich, Graczyk, & Zins, 2006). Implementation research in particular has moved beyond a basic view of whether program content was or was not delivered to a more holistic view that encompasses the intervention as planned, the intervention as delivered, the context in which the intervention is delivered, and the implementation support system (Bumbarger & Perkins, 2008; Livet, Courser, & Wandersman, 2008).

Taking Effective Prevention to Scale 437 Although a minority of efficacy studies to date have sought specifically to assess these constructs, the study of implementation is growing and it is clear there is a nascent “implementation science” (Dane & Schneider, 1998; Durlak, 1998; Domitrovich & Greenberg, 2000). There is increasing knowledge of the factors that influence implementation quality (Dariotis, Bumbarger, Duncan & Greenberg, 2008; Kam, Greenberg & Walls, 2003) and clear evidence that better-quality implementation leads to improved prevention outcomes (Durlak & DuPre, 2008). A recent review of more than 500 studies of the implementation of evidencebased programs by Durlak and DuPre (2008) found a strong, positive correlation between high-quality implementation and better outcomes, and effect sizes on outcomes that were two to three times higher in cases where quality monitoring protocols were used. Theory and some empirical evidence have identified a variety of elements thought to account for variation in program implementation. In addition to fidelity (how closely the program corresponds to the original design), these elements also include: adaptation (whether, how, and how much the program has been altered or adapted); dosage (how much of the original program was delivered); quality (how well the program was delivered in a qualitative sense); reach (the extent to which the program reached the target population); responsiveness (how much the target audience was engaged during delivery of the program); and differentiation (how easily the program can be distinguished from other practices). Though fidelity and adaptation are often thought of as contradictory (i.e., adaptation equals a lack of fidelity), they are actually theoretically separate and sometimes mutually exclusive constructs. At times there has been tension within the prevention science community, as well as between program developers and practitioners, about whether and to what extent adaptation is desirable or even acceptable. This is the result of not knowing exactly which components of an intervention are responsible for its effectiveness, requiring practitioners to implement with complete fidelity in order to maximize the likelihood of positive outcomes. However, some practitioners and researchers emphasize that allowing flexibility and adaptation recognizes the expertise of local implementers and may increase the likelihood of program adoption and reach—by more accurately tailoring the program to the needs of the local community (Backer, 2001). This polarization implies that all adaptation is the same (either good or bad), when in practice what we find are two very different types of adaptation—innovation and program drift—that may have very different effects on program efficacy. High levels of fidelity have been achieved in well-controlled studies (e.g., Mihalik, Fagan, Irwin, Ballard, & Elliott, 2004), demonstrating that it is feasible to implement even complex interventions as designed. However, there is considerable evidence that under natural conditions there is rarely complete fidelity and there is often great variability between implementers. Contrary to the scenario presented by proponents of local adaptation, much of the variation seen in non-research settings is not intentional alteration to improve the program (innovation), but rather reactive adaptation in response to barriers (program drift). This reality has implications for addressing the debate of absolute fidelity versus adaptation. If we know that absolute fidelity does not occur in natural conditions, arguing for it is futile. Conversely, if we know that most adaptation is not innovation, but rather program drift, then the call for implementer flexibility is unsubstantiated. Viewing the question as “either/or” is unnecessarily polarizing and has prevented the field from addressing fidelity and adaptation in a way that promotes best practice (e.g., recognizing that adaptation will inevitably occur and providing implementers with the necessary training and technical support). Along these lines prevention scientists and program developers should prepare implementers to make informed decisions about adaptation, whether faced with implementation barriers or when innovation is desired. This requires a stronger partnership between

438 Brian K. Bumbarger et al. practitioners and prevention scientists in the development, testing, and refinement of interventions. Greater fidelity can be promoted when implementers are permitted flexibility in areas of program delivery not hypothesized to be directly responsible for program outcomes, while adhering to program components that are core to the EBI’s theory of change. As pointed out by Greenberg and colleagues in their implementation model (Greenberg, Domitrovich, Graczyk, & Zins, 2005), there can be “adaptation with fidelity.” Reinforcing the research agenda posited above, this recognition of the potential for adaptation with fidelity only increases the need for continued research beyond efficacy trials to effectiveness trials, to clearly elucidate what the critical core components of an intervention are and how much flexibility is tolerable.

A New Paradigm for Prevention Program Dissemination Given the limitations of prevention practice as it currently exists, prevention scientists and practitioners must also re-examine the current model of program dissemination. Consider the way programs are most often adopted in schools and communities. Once a decision has been made to adopt a program there is usually a brief (one- or two-day) training for potential program implementers. The training generally focuses on the “mechanics” of delivering the intervention (“what” to deliver), often with little emphasis on the theory upon which the intervention is based (the “why” behind each lesson, for example). Even when program theory is part of the training, it is only natural that implementers new to the program will be more interested in learning what they will be expected to do. In many cases, this training is the only contact the implementers have with the program developer or trainer; the expectation is that one or two days of training is sufficient to effectively deliver the program. There is increasing recognition that the conventional approach of “train them and send them on their way” is ineffective in promoting high-quality implementation because of the lack of instruction on theory and because there is generally little or no follow-up after training (Fixsen et al., 2005). In addition to providing implementers with an occasion to process their experiences after having had an opportunity to deliver the program, follow-up consultation and technical assistance can also assist implementers in navigating barriers. Pre-implementation trainings are often held months before actual program implementation, increasing the likelihood that knowledge and skill acquisition might be lost before implementation can occur. Moreover, scheduling limitations of these pre-implementation trainings typically mean that some implementers may not have attended the training at all, making it unlikely that they fully understand the program’s underlying theory of change and reducing the chances for quality implementation. Finally, the common passive information dissemination or purely didactic style of training is not sufficient to have programs be fully adopted or implemented with quality. Thus a new model of program dissemination is called for. Effective program adoption and implementation requires initial training that is interactive and engaging, provides opportunities for behavioral rehearsal, and is followed up with ongoing coaching, technical assistance, and support. Both the initial training and the follow-up consultation should focus as much on theory as on practice, so that implementers develop a clear conceptual understanding not only of what they are required to do, but more importantly why. The conventional training model is widely-used and will be challenging to redefine, and cost and time constraints are already barriers to program adoption. This means new innovative low-cost and low-intensity mechanisms for providing this critical implementation support must be created. Experimental research to develop and test the effectiveness of coaching

Taking Effective Prevention to Scale 439 and technical assistance over and above pre-implementation training is called for (Rohrbach, Grana, Sussman, & Valente, 2006).

Site Readiness and Program Adoption: The Prevention Delivery System The challenge of taking effective prevention to scale also involves ensuring an environment conducive to innovation (Rogers, 1995); assessing community and organizational readiness and developing an infrastructure to support prevention. There is increasing awareness of the need to create “fertile ground” prior to program implementation in order to increase the likelihood of program adoption, quality implementation, positive outcomes, and sustainability. Several frameworks have been developed to engage communities and promote greater site readiness (Hawkins & Catalano, 1992; Wandersman et al., 2000; SAMHSA, 2007). Site readiness in turn promotes pre-implementation planning critical to avoiding or addressing barriers in program delivery, and helps communities make better choices about program selection and adoption. Readiness is an often overlooked aspect of program dissemination that is critical because it solidifies the causal link between prevention programs and public health outcomes. Especially in this time of greater accountability it is important that the adoption of a specific preventive intervention is tied to a quantifiably identified need. The concept of epidemiology of risk is an aspect of the public health model that has been helpful for the assessment of community-specific needs. Armed with the knowledge of risk and protective factors, communities can (and are increasingly required by funders to) conduct an epidemiological risk assessment to quantify and prioritize their prevention needs, and to inform the selection of the most appropriate prevention strategies. In the absence of such a framework, communities often make uninformed decisions about which prevention strategies to implement, guided by popular opinion, aggressive marketing, “pet” programs, or simply the availability of funding for a certain intervention. The result is the implementation of a program that may or may not be logically tied to the cause of the poor outcome the community is trying to prevent or reduce. When this haphazard manner of decision-making and resource allocation is repeated by service providers and youth-serving organization in a community, the results are predictably disappointing. Thus, a framework for the prevention delivery system that includes some form of “needs assessment” can both encourage more thoughtful program choices and create a synergy in the way the various youth-serving organizations in a community approach prevention collectively.

Sustainability Often there exist grant dollars to initiate these EBIs, with an assumption that the community will figure out how to sustain them (Cornerstone Consulting Group, 2002). Thus, many program implementers are left scrambling to find strategies that enable them to sustain their interventions past those initial grant dollars. This has proven to be a daunting task for local practitioners. Moreover, given scarce and limited resources, policymakers and funders are also very concerned about how to allocate resources into effective programs that will be sustained (Shediac-Rizkallah & Bone, 1998). This concern has led to many capacity-building programs that have been developed to provide training and technical assistance for sustainability (Justice Research & Advocacy, Inc., 2008; The Finance Project, 2002; Waverly Group Midwest, 2008). However, our understanding about sustainability is superficial at best because we have limited empirical studies from which to draw conclusions about what “elements” or factors predict sustainable programs.

440 Brian K. Bumbarger et al. In its most basic form, sustainability is the capacity of programs to continuously respond to community issues (Mancini & Marek, 2004). Therefore, the key elements of sustainability are providing continued benefits, regardless of the particular activities delivered or the format (National Center for Community Education, 2002; Shediac-Rizkallah & Bone, 1998). Nonetheless, this simple definition does not accurately describe sustainability of evidencebased interventions given that EBIs have specific standards and detailed protocols associated with high-quality implementation of EBIs, involving scripted-out content and activities which need to be followed with high fidelity if desired outcomes are to be achieved. Therefore, within the prevention science field sustainability has been defined by Johnson and colleagues (Johnson, Hays, Center, & Daley, 2004) as “the process of ensuring an adaptive prevention system and a sustainable innovation that can be integrated into ongoing operations to benefit diverse stakeholders” (p. 137). Our own experience concurs with this definition that sustainability is a change process which is multi-faceted, ongoing, and cyclical in nature. Scholars have identified several reasons why program sustainability is important and why failure to sustain programs may be problematic (Pluye, Potvin, & Denis, 2004; ShediacRizkallah & Bone, 1998). Before we address the reasons to sustain programs; however, it is important to acknowledge that some programs should be discontinued if the program was unable to demonstrate positive results or it addressed a need that no longer exists. Though Altman (1995) argues that it may be premature to address program sustainability without first addressing whether the program is efficacious, since EBIs have demonstrated efficacy in previous trials the emphasis shifts to whether or not they are effective locally and, if not, whether poor implementation quality and fidelity are the problem. The first reason for promoting sustainability is that program termination means that the potential effects will be short-lived and the program may be counterproductive when the behavior being addressed remains (Shediac-Rizkallah & Bone, 1998). Few preventive interventions “inoculate” the target population against poor outcomes. Sustained effects require sustained intervention. The second reason is that the start-up phase of a program is like a latency phase (Pluye et al., 2004). During this period, the program’s budget will incur large one-time costs associated with program training and materials, and the program may not be immediately functioning at a level that will generate positive outcomes (Pluye et al., 2004; Shediac-Rizkallah & Bone, 1998). A third reason is that when effective programs are discontinued it can potentially have a negative impact on the future adoption of other programs, particularly for the most under-resourced communities (Ackerlund, 2000). The discontinuance of successful programs may erode a community’s trust and decrease their willingness to try new programs in the future (Goodman & Steckler, 1989). Potential factors that facilitate sustainability have been theorized but few of them have even been assessed. We provide then a preliminary examination of potential sustainability factors that have been presented by others. First, Johnson and colleagues (Johnson et al., 2004) postulate that alignment of the outcomes addressed by the EBI and the implementing organization or community group goals and mission is directly linked to the likelihood of sustainability. Second, the development of a sustainability plan early in the program development process is considered critical according to numerous scholars (Altman, 1995; Denton, Vaughn, & Fletcher, 2003; Goodman & Steckler, 1989; Johnson et al., 2004; Mancini & Marek, 2004; Pluye et al., 2004; Shediac-Rizkallah & Bone, 1998). A sustainability plan should happen early in the implementation process and sustainability efforts should occur concurrently with implementation (Pluye et al., 2004). According to Mancini and Marek (2004), practitioners need to be

Taking Effective Prevention to Scale 441 intentional about their sustainability efforts by virtue of the development and implementation of a sustainability plan. The third element theorized to be important to sustainability is implementing organization infrastructure capacity (Fixsen et al., 2005; Johnson et al., 2004; Pluye et al., 2004). Within the infrastructure element the following have been identified as key: administrators’ support, organizational policies and procedures that foster ease of implementation, organizational resources designated to support the effort, and expertise within the organization to foster integration of the EBI into the routine organizational operations (Fixsen et al., 2005; Johnson et al., 2004; Marek, Mancini, & Brock, 1999; Pluye et al., 2004). Staff buy-in to implementation and to the EBI’s theory of change has also been identified as a critical element for program sustainability (Denton et al., 2003; Mancini & Marek, 2004). Finally, sustainability is more likely to occur if the EBI has champions, influential and proactive individuals inside or outside of an organization (Akerlund, 2000; Fixsen et al., 2005; Goodman 2000; Mancini & Marek, 2004; Shediac-Rizkallah & Bone, 1998). As effectiveness trials are needed to identify core elements of EBIs and quality implementation, so too research is needed to better understand the essential elements needed to sustain high-quality implementation of EBIs. This type of research can lead to the development of practical tools and strategies to assist practitioners and organizations in sustaining EBIs that are implemented with high quality.

Conclusion There has been considerable progress in our understanding of the risk and protective mechanisms associated with delinquency, youth violence, adolescent drug use and related behavior problems. Armed with this knowledge, basic translational research has led to the development of a growing number of preventive interventions that have been subsequently evaluated in rigorous studies and found to be effective. The subsequent emergence of “lists” of evidence-based programs has led to changes in policy and legislation aimed at encouraging the greater use of these programs. While all of this collectively represents great evolution in the field of prevention, some important research and practice challenges remain. Simply knowing which programs are efficacious is not sufficient. To achieve broad public health impact, these programs must be carefully selected to fit a specific need and context, implemented with sufficient quality and fidelity, and be sustained long-term. To do so will require continued research and scholarship to understand the process of moving effective prevention to scale, and a reconsideration of existing models for program dissemination.

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23 Youth Policy and Politics in the United States Toward an Increased Focus on Prevention Siobhan M. Cooney, Department of Human Development and Family Studies, University of Wisconsin–Madison Thomas R. Kratochwill, Department of Educational Psychology, University of Wisconsin–Madison Stephen A. Small, Department of Human Development and Family Studies, University of Wisconsin–Madison Introduction Over two centuries ago, Benjamin Franklin coined the famous phrase “An ounce of prevention is worth a pound of cure” (Triefeldt, 2007, p. 11). Long before the days of rigorous, longitudinal program evaluations and state-of-the-art cost-benefit analyses, these words of wisdom were spoken without the empirical support one finds today. In modern times, an increasing amount of attention has been devoted to examining the value of prevention. The growing empirical base in the prevention science field suggests that many mental and behavioral health concerns in children and youth can be prevented (Durlak, 1997; Tolan & Dodge, 2005; Weisz, Sandler, Durlak, & Anton, 2005). Prevention and early intervention programs with children and adolescents who have not developed serious problems can be quite effective in the long term in the academic, social, and behavioral realms of development (Biglan, Mrazek, Carnine, & Flay, 2003; Fletcher, Lyon, Fuchs, & Barnes, 2007; Greenberg, Lengua, Coie, & Pinderhughes, 1999; Nation et al., 2003; Weissberg, Kumpfer, & Seligman, 2003). Additionally, empirical evidence suggests that prevention can make good financial sense: A number of studies indicate that the implementation of effective youth prevention programs can produce positive cost savings. For example, educational programs that prevent youth from dropping out of high school yield benefits to society that are two and a half times greater than their costs (Levin, Belfield, Muenning, & Rouse, 2007), and programs that effectively prevent youth substance abuse return between $3 and $102 for every dollar spent (Aos, Lieb, Mayfield, Miller, & Pennucci, 2004). In addition to the growing empirical base demonstrating the value of prevention, the prevention science field has gained momentum with the advent of both evidence-based programs (Cooney, Huser, Small, & O’Connor, 2007) and the Response to Intervention (RtI) initiative (Kratochwill, 2007). Evidence-based programs are well-documented, theory-driven programs that have demonstrated their effectiveness through rigorous, peer-reviewed evaluations (Cooney et al., 2007). Two decades ago, the American Psychological Association could identify only 10 such programs for children and youth (Price, Cowen, Lorion, & RamosMcKay, 1988), but today, they number in the hundreds (see Blueprints for Violence Prevention, 2008; Office of Juvenile Justice and Delinquency Prevention Model Program Guide, 2008; Substance Abuse and Mental Health Services Administration National Registry of Evidence-Based Programs and Practices, 2008). The recent growth in the number of

446 Siobhan M. Cooney et al. evidence-based programs, as well as vocal calls for their dissemination, has underscored the idea that prevention initiatives have real potential to positively impact youth. Likewise, RtI has garnered increased support for prevention, particularly in the field of education. Within the context of serving children’s academic and mental health needs, RtI adheres to a prevention science philosophy (see Brown, Chidsey, & Steege, 2005; Kratochwill, Clements, & Kalymon, 2007). RtI involves the implementation of multi-tiered services (typically spanning universal, selected, and indicated) in a school in response to a student’s academic and/or behavioral problems. The multi-tiered framework is aligned with public health models that involve three levels of mental health services. Although the RtI movement has some limitations—it has primarily been associated with special education (see National Association of State Directors of Special Education, 2005), has limited empirical support, and some professionals may question its narrow focus on the risk model of prevention—the movement has tremendous implications for wide-scale adoption of prevention models and specific prevention and early intervention practices in educational settings. The RtI movement has prompted increased interest in prevention beyond its origins in special education, and has helped many school professionals expand on the concept of prevention from a single, narrow focus or target group (e.g., school dropout, drug abuse prevention) to a multi-tiered model of prevention. Despite raised awareness of prevention’s value in educational and other applied settings, our experiences working within the education, mental health, and juvenile justice fields collectively suggest that among publicly-funded youth programming, the treatment of disorders, rather than the prevention of them, remains a greater priority. The focus on the treatment of disorders, rather than the prevention of them, leads one to ask, if research indicates that a variety of programs can not only work to prevent a range of emotional, behavioral, and physical problems in youth but also save valuable resources, why are so few monies invested in preventing negative outcomes for children and adolescents? Why do federal, state, and local government entities and some private agencies continue to provide more attention to the treatment of problems than to their prevention? This chapter is primarily concerned with the intersection of prevention science and youth policy in the United States. Our discussion focuses on understanding how prevention scholars can better influence the course of youth policy and funding decisions. We examine reasons why policies currently being funded and implemented in the United States do not tend to incorporate the most relevant and recent research findings. In addition, we suggest reasons why the policies and funding decisions affecting our country’s youth population are not more prevention-oriented. Our discussion draws on literature on the link between social science research and the policymaking process. We also comment on our own experiences that speak to the inherent difficulties in getting more public resources allocated for prevention efforts. Finally, we delineate several strategies that scholars in the prevention field can use to help bring an increased focus on prevention for our country’s youth.

The Relationship Between Research and Youth Policy Prevention research and policymaking have experienced a troubled relationship in the United States. While researchers’ interests in prevention science and program evaluation derive from an expectation that this work can influence the development, adoption, and amendment of policy and programming in accordance with “what works” (Weiss, 1999), the field has instead witnessed a sizeable disconnect between what researchers might expect to be done with empirical knowledge and what is actually done. Prevention researchers, joining many others in the

Youth Policy and Politics in the United States 447 social sciences, have long lamented the lack of applied use of their findings (e.g., Chelimsky, 1987; Small, 2005; Weiss, 1999). For several inter-related reasons, a very tenuous link exists between the findings of empirical research, such as the type undertaken by prevention scientists, program evaluators, and economists, and decisions regarding which youth policies and initiatives should or should not be developed, adopted, implemented, or funded. One key factor responsible for this disconnect is that in the realm of policymaking, research is only one factor that decision-makers may draw on when debating and ultimately deciding upon courses of action. Policymakers have several priorities beyond simply adopting the recommendations put forth by researchers or otherwise taking into account a body of knowledge, such as that generated by the prevention science field in the past couple of decades. Other factors, including alternative sources of information, can influence the decision-making process, and empirical research must contend with these. First and foremost, policymakers’ political ideologies influence their decisions. Ideologies are especially relevant when considering the actions of elected and appointed officials, who usually hold their positions as a direct result of their values and beliefs and the means by which they demonstrate them (Weiss, 1999). The ways in which policymakers interpret research findings, and the odds that they will consider these findings relevant, exist as a function of their political ideologies and belief systems. Because prevention researchers may also view research findings with an eye toward policy implications, such interpretations and their related recommendations may not coincide with those of any one policymaker. Similarly, elected and appointed policymakers have responsibilities to their constituents and the individuals for whom they work (Lavis et al., 2003; Weiss, 1989). Elected officials tend to focus on making decisions that are favored by the voting public and ultimately will get them re-elected. As such, research findings may not be as influential as anecdotal evidence offered by a constituent (Zervigon-Hakes, 1995). Research with lawmakers indicates that constituents are powerful sources of information, carrying more influence than other potential sources, such as lobbyists and non-partisan, university-based educational seminars (Bogenschneider, Olson, Linney, & Mills, 2000). With an eye toward the next election, policymakers tend to favor actions that they believe will avoid controversy, generate favorable media coverage, and reap noticeable benefits within the next several years (Zervigon-Hakes, 1995). To this end, policymakers may make a policy or funding decision simply to demonstrate that they support a particular position (e.g., “tough on crime”) regardless of whether research has suggested that the strategy being funded and implemented is more effective than others for producing positive impacts. Other factors determining the course of policy include the policymakers’ past experiences; real or imagined expertise in certain areas; and communication networks of issue advocates, lobbyists, interest groups, and research organizations (Chelimsky, 1987; Sorian & Baugh, 2002). Such networks are often well-established for experienced policymakers (Weiss, 1989). Although policymakers deem issue advocates and lobbyists to be questionable sources of unbiased information, they may seek their responses to research findings and proposed legislation in order to better understand all sides of an argument. Finally, tradition, or what is usually done, often plays a role in the formation of policies. Past policies shape and constrain future policies (Lavis et al., 2003). Very often new policies cannot and do not form on a blank slate; rather, changes must be made while cognizant of the structures, processes, and pathways that are currently in place (Weiss, 1999). In the youth prevention field, some of the same factors that help to provide ongoing support to well-established youth programs, such as public perceptions of the program and

448 Siobhan M. Cooney et al. policymakers’ and funders’ preferences, can also make it more difficult to move programs in more innovative directions or eliminate them entirely in favor of more effective programs. As one example, consider the history of the Drug Abuse Resistance Education (DARE) program, one of the most frequently implemented school-based substance use prevention programs in the United States. Several years after empirical evidence strongly suggested the DARE program to be ineffective (Ennett, Tobler, Ringwalt, & Flewelling, 1994), the rate of program implementation in this country remained exceptionally high. In 2002, Hallfors and Godette estimated that 80% of school districts were implementing DARE in some form. As this specific case of disconnect between research evidence and prevention practice continues to incite debate (Des Jarlais et al., 2006; Weiss, Murphy-Graham, & Birkeland, 2005), some policymakers have suggested that DARE should be implemented as part of a larger, more comprehensive effort to prevent youth substance use, rather than as a single program (Birkeland, Murphy-Graham, & Weiss, 2005). In this way, decision-makers intent on maintaining the status quo can continue supporting the DARE program while recognizing that it alone cannot substantially impact youth substance use. The discrepancy between the value of prevention indicated by research and the current focus of youth policy in the United States also stems from factors inherent to prevention research and the environment in which most prevention scientists operate. Researchers usually have several priorities, some of which may be more important than influencing policy decisions. For instance, university tenure practices often do not recognize and reward efforts to inform policymakers, so academic researchers usually receive very little benefit from communicating with a policy audience (Bogenschneider et al., 2000; Small, 2005). Instead, university-based researchers receive tenure and advance in their field when they publish in scholarly journals and obtain grant funding for more research. Researchers may perceive that effectively working with and for the benefit of policymakers is a good use of their time only if influencing policy is a primary goal of their organization. When organizations are mainly focused on other objectives, such communication and any influence it has on policy would be viewed as more accidental than deliberate (Lavis et al., 2003). Perhaps because researchers do not often intentionally interact with policymakers, most are not cognizant of the best ways to communicate information to this audience. Observers of the relationship between social science research and policymaking note the inherent differences between these groups in terms of their communication styles (e.g., Bazelon, 1982; Caplan, 1979; Small, 2005). While policymakers prefer information to be conveyed as precisely, simply, and quickly as possible, researchers are accustomed to writing in a detailed and highly technical jargon that is not easily understood by non-researchers. Furthermore, researchers are trained to qualify each of their statements and focus on the complexity of issues and what information is still needed; however, such complexities do not translate well to nonresearchers (Roos & Shapiro, 1999), and policymakers are more interested in discussing what is known, rather than unknown. The preferred modes of communication also tend to differ between these two groups. An “oral tradition” persists among congressmen, legislators, and other policymakers accustomed to relying on the spoken, rather than the written, word (Weiss, 1989, p. 414). In contrast, researchers tend to follow the “written tradition” that communicates findings primarily to other researchers. As noted by Zervigon-Hakes (1995, p. 180), “few researchers write for the newspapers or the television broadcasts that are the daily media diet of policymakers.” In sum, research evidence does not often reach policymakers, and when it does it is written in a language meant for other researchers and not easily understood by a policy audience (Small, 2005).

Youth Policy and Politics in the United States 449

Additional Impediments to Prevention In addition to these more general barriers hindering social science’s ability to inform policy, other impediments more specifically related to the concept of prevention also exist. Most glaring is the disparity between the time schedules on which policymakers and prevention scientists operate. Policymakers’ needs for information are often immediate (Chelimsky, 1987). Because they are regularly inundated with information, the timeliness of the message is a key determinant of what actually gets read and discussed (Sorian & Baugh, 2002). However, in the prevention science field, researchers must undertake longitudinal and other time-intensive studies to better understand the effects of a program or policy. Prevention, by its very nature, does not show its effects immediately. Effective prevention programs targeting children and youth may not show their largest impacts for years, and possibly even decades. As one example, consider the longitudinal findings of the Abecedarian Project that assessed the impacts of high-quality educational daycare on participants until they were 21 years of age (Campbell, Ramey, Pungello, Sparling, & Miller-Johnson, 2002). Data indicated that the intervention youth, compared to the control group youth, had consistently higher cognitive test scores and academic achievement over the course of the study. However, some of the most compelling results were those evidenced years later when the youth made their transition to adulthood. For example, the treatment youth were twice as likely as the control group youth to be enrolled in school at age 21 (42% and 20%, respectively). Contrast this scenario first with the needs of policymakers, who must receive information on the potential benefits of prevention initiatives in a very timely manner if that information is ever going to be used. Prevention researchers simply do not have enough time to evaluate the effects of various programs and policies when called upon for this information; instead, prevention scholars can only use information already at their disposal that may or may not adequately speak to expected outcomes for youth or cost-to-benefit ratios. In addition, consider the effect that the prospect of re-election has on the decisions made by elected officials. Because most politicians must keep an eye focused on the next election, it can lead to a shortsightedness that emphasizes the here and now of policy decisions over potential long-term consequences. Policymakers may be wary of investing public resources in prevention efforts when the benefits of those efforts will not be demonstrated until after the next election or even later, after the policymaker is out of office. Another barrier faced by the prevention field is that decreasing federal, state, and local budgets effectively work against the funding, adoption, and implementation of prevention initiatives and favor “deeper end” services and programs. Our personal experiences working with decision-makers at all levels point to the perception that prevention is a lower priority— merely “value added”—while interventions for youth’s current problems are of greater necessity and priority. Quite understandably, there is more of urgency when it comes to treating current social, emotional, and behavioral disorders in children and adolescents. In contrast to the noticeable benefits that may be gained when youth are currently experiencing substance addictions, mental illness, or severe learning disabilities, the benefits of prevention occur in the distant future for some unidentified, abstract individuals who are not yet affected. In the research and policy arenas, these abstract people of the future do not demand programming and policy responses the same way that youth in the here and now do. Although preventing problematic outcomes in children and adolescents may save money for society in the long run, our experiences suggest that in times of decreasing budgets, prevention initiatives are much less likely to receive funding due to a lesser sense of urgency.

450 Siobhan M. Cooney et al. The current trend toward defederalization, moving monies and decision-making powers from federal to state to local jurisdictions (Sorian & Baugh, 2002), also works against the funding and implementation of prevention initiatives. While decisions to fund prevention efforts are more often made locally and programs may be funded by a single entity, the benefits are accrued by the larger society, not by the local government or community organization that made the initial financial investment. For example, school-based programs focused on preventing teen pregnancies, substance abuse, and delinquency may be adopted and funded by local school systems, but the public benefits of such programs are borne by a much broader audience in the long term; among others, the juvenile and criminal justice systems, businesses and employers, and state and federal systems (through increased tax revenue) may all benefit. Furthermore, in an age of geographic mobility, locally-funded prevention efforts tend to benefit not only the immediate community and its residents, but the rest of the country and beyond. Thus, policymakers at all levels may grapple with the question of why they should invest valuable resources in prevention initiatives when the benefits, at least to some extent, will be realized elsewhere. A final category of barriers to the greater adoption of prevention as a strategy for promoting youth well-being applies to both prevention research and practice. First, although social and behavioral scientists have been interested in prevention for years, the field continues to experience a dearth of information on the longitudinal outcomes and cost-benefit ratios of youthfocused prevention programs. With the advent of evidence-based programs, the field is in a unique position to capitalize on the influence that cost-benefit analysis can have on policy and funding decisions. Unfortunately, cost-benefit research has not yet progressed to the same extent as rigorous impact evaluation, leaving policymakers to question whether an “effective” program is “cost-effective” and therefore worthy of limited funding resources. Moreover, although there has been significant growth in the number of evidence-based and promising prevention programs in recent years, youth programming currently funded and implemented in the United States still tends toward well-meaning, but often ineffective, interventions (Greenberg et al., 2003; Satcher, 2001). Many locally developed youth programs lack both evidence to suggest their effectiveness and the funding necessary for program improvement and impact evaluation activities. Fortunately, this situation is changing to some extent as a result of increased knowledge of “what works” in youth prevention programming (Small, Reynolds, O’Connor, & Cooney, 2005), the growing availability of evidence-based programs, and state-level adoption of comprehensive youth development models (e.g., social-emotional learning in schools; see Greenberg et al., 2003). However, because no two schools, families, or communities are “built” alike and the prevention programming developed in one setting may not translate well to others, prevention advocates are sometimes left in the awkward position of seeking resources for youth initiatives with questionable impact and cost savings.

Strategies for Bringing a Preventative Focus to Youth Policy If prevention scholars want to have a greater impact on youth policy, they will need to become more sophisticated and strategic in conducting relevant empirical research and communicating this knowledge in the youth policy arena. Researchers may believe that their typical role— that is, testing theories, measuring phenomena, and communicating results to other researchers—is sufficient for contributing to the betterment of society. However, if decisionmakers are unaware of how prevention works and its potential for substantial impacts on children and adolescents, their communities, and society, can the role currently played by many researchers really have much significance? If youth-oriented policies at local, state and

Youth Policy and Politics in the United States 451 Table 23.1 Strategies for Bringing a Preventative Focus to Youth Policy 1 2 3 4 5 6 7

Recognize and exploit the conditions under which prevention research can inform policy. Communicate with multiple audiences, and tailor the message to each. Involve policymakers and their staff in the prevention research process. Build and maintain credibility. Reward prevention researchers for disseminating evidence to non-research audiences. Educate future prevention researchers about policymaking and related topics. Start small.

national levels are to become more prevention-focused, prevention scholars will need to make a more concerted effort to further research in the field and reach policymakers not only in the manner in which they are most comfortable but also at the time that empirical contributions are needed. Simply publishing research, evaluation, and cost-benefit findings in scholarly scientific journals and waiting for policy to follow suit is not and has never been an effective method for improving youth policy. Both the literature and our experiences working with policymakers suggest several key strategies for shifting the current focus on treating youth problems to one on preventing them (see Table 23.1). These strategies have not been empirically tested in the manner of evidencebased practices and programs. Rather, they are primarily based on the experience of scholars who have spent many years working in the policy arena and astutely observing the ways in which researchers in the social sciences can influence the policy process. Although some have made calls to more formally evaluate the effectiveness of various strategies (e.g., Lavis et al., 2003), the field has not yet reached, and perhaps never will reach, such a point. Given the lack of more rigorous evidence, these recommendations draw on the best available knowledge and may help prevention science researchers bring an increased focus on current science and prevention to the youth policy arena. Recognize and Exploit the Conditions Under Which Prevention Research Can Inform Policy Instead of having a direct or instrumental influence on the results of a particular policy debate, some prevention scholars believe that more often scientific findings influence the world view of policymakers over time and affect policy in small increments (Lavis et al., 2003; Weiss, 1989, 1999). Thus, those working to apply their research findings within the policy arena should not expect a radical paradigm shift to occur at any one time point. Rather, research findings can gradually infiltrate the political arena such that a concept—such as the value of prevention— becomes integral to the common mode of thinking. Prevention scholars can contribute to this “enlightenment,” the gradual filtering of new information, ideas, and perspectives into decision-making arenas (Weiss 1989, 1999). In this way, concepts and ideas, not the empirical data themselves, yield the most influence. Weiss (1999), for example, reports that evaluations can tell “stories” and communicate generalizations that can sway policymakers in small but meaningful ways. Researchers occasionally have the opportunity to influence policy decisions more directly, but for them to do so, the timing must be right. According to Chelimsky (1987), sometimes it is less essential to have the strongest study than it is to have an adequate study that delivers results when decision-makers need them. As such, researchers may do well to stay abreast of legislative and election calendars (Zervigon-Hakes, 1995) and attune themselves to the opportunities that “policy windows” offer (Bogenschneider et al., 2000). Policy windows open when

452 Siobhan M. Cooney et al. problems, policies, and politics converge: when a problem is recognized, when adequate policy solutions are available, and when the political climate is ripe for a shift in strategy. Policy windows may be opened by a shift in public opinion, a change in administration, or a major disaster or media event. In the United States, troubling events or trends that garner prolonged media attention, such as the school shootings that occurred in the late 1990s (Satcher, 2001), can lead policymakers in school districts, communities, and state capitols to adopt and fund initiatives that may help prevent such occurrences in the future. Similarly, Weiss (1989) reports that research findings can have an effect on policy when there is a general perception among policymakers that something needs to be done but everyone is very unclear about exactly what path to take. Research on policymakers suggests that when they need information quickly, they turn to people “who either know the answer or know where to find it” (Sorian & Baugh, 2002, p. 269). They are likely to seek out expertise from credible and knowledgeable sources, such as staff members and trusted professionals at state agencies and local, grassroots organizations (Jackson-Elmoore, 2005). When they contact researchers, policymakers and their staff appear most likely to seek out direct but informal exchanges of information (Hy, Venhaus, & Sims, 1995). Less often, they search for a hard copy of a research brief, request a formal committee presentation, or browse the internet for more information (Hy et al., 1995; Jackson-Elmoore, 2005). Given this reality, prevention scholars need to be proactive and versatile in the ways they reach policymakers. They can exploit such opportunities to influence policy by letting policymakers know ahead of time their particular areas of expertise. When called upon, prevention researchers can serve a valuable role by delivering timely, concise, and matter-offact information. Zervigon-Hakes (1995) recommends a two-pronged approach; first, network with policymakers and program administrators and their staffers so they will remember you as an expert in the field, and second, always be prepared to summarize information quickly. Communicate with Multiple Audiences, and Tailor the Message to Each Prevention researchers should consider presenting work to various audiences both within and outside academic circles; only through such widespread dissemination efforts will a focus on prevention become a larger part of a national vision. This strategy is especially important when considering that policymakers who influence the lives of youth fill many different roles. In addition to decision-makers existing at all levels of government, the private and non-profit sectors are also influential in determining which programs are developed, funded, and implemented. Thus, if prevention scholars are ever to bring a stronger preventative focus to youth policy and funding decisions, they need to communicate to a myriad of audiences instead of focusing efforts on only one or two legislative bodies or administrative agencies. Because these audiences are likely to vary in their professional and educational backgrounds, their priorities, and their preferred methods of communication, messages need to be tailored to be maximally effective. One general message for everyone is not likely to be sufficient. The decision-maker’s ability to understand the information is key to it ever being used (Chelimsky, 1987). Policymakers very often will not utilize information that is too long, detailed, technical or theoretical in its presentation, leading many scholars to recommend that researchers banish research and evaluation jargon when talking to policymakers and other professionals unfamiliar with the terminology (Sorian & Baugh, 2002). It is recommended that researchers present information to decision-makers beginning at the most basic level possible (Normandin & Bogenschneider, 2006), perhaps by using bullet points, short sentences,

Youth Policy and Politics in the United States 453 stories, and visual illustrations that require little to no explanation (Bogenschneider et al., 2000). It is also recommended that communication be as brief and to-the-point as possible. According to Chelimsky (1987), researchers tend to communicate each and every research finding in the same tone, leaving it to the receiver of the information to pick out the most essential details. However, “telling all is tantamount to telling nothing” when communicating with a policymaking audience (Chelimsky, 1987, p. 212). Because presentations are likely to be better received by policymakers when they are succinct, researchers may need to prioritize findings, presenting only the most important points that pass the “so what” test (Normandin & Bogenschneider, 2006). Scholars who regularly work with policymakers also recommend presenting the conclusions of the work first, rather than presenting details of the research and building up to the conclusions (Bogenschneider et al., 2000). In other words, researchers should take a strategy that first relays “What we found” and only secondly, “How we got there.” Others suggest relaying “actionable messages” that focus on solutions, rather than the problems themselves (Lavis et al., 2003). Additionally, although prevention research tends to be large-scale and quantitative, researchers can effectively use anecdotes or stories to illustrate broader findings (Chelimsky, 1987). Such anecdotal evidence “put[s] a face on research findings” for policymakers, who are attuned to the messages that such stories communicate (Zervigon-Hakes, 1995, p. 189). One key task that researchers can fulfill for policymakers is providing a framework for understanding prevention and related concepts. Policymakers need to know not just that prevention initiatives can work, but how, why, and in what context they work. Theories of youth development and related phenomena can help researchers clearly articulate and explain these concepts. As one example, representing the life course development of youth antisocial behaviors with the “vile weed” illustration (Patterson, Reid, & Dishion, 1992) can make the known precursors to these behaviors much more salient for policymakers. In this illustration, the “weed” has its roots in the child’s temperament, parental substance abuse and antisocial behaviors, and stressors, grows in the context of poor parental monitoring and discipline, and develops into associations with deviant peers, youth substance abuse, and delinquency. Bogenschneider and her colleagues (2000) report that one state legislator, after learning this analogy, used it to explain to other lawmakers the value of intervening early with troubled families. Another key task for researchers interested in informing policy is to integrate research evidence from a body of literature and generate thoughtful but general knowledge about the subject (Chelimsky, 1987). Such systematic reviews on one particular topic are invaluable for developing policies that are well-grounded in years of prevention research. Because individual researchers are unlikely to generate such a body of knowledge on their own, this strategy for informing policy speaks to the importance of continuing to publish literature reviews and meta-analyses in academic journals in addition to effectively communicating these aggregated findings to policymakers. An important element of effectively communicating with others is framing ideas and concepts so that people take an interest in them (Gilliam & Bales, 2001). Researchers would do well to assess decision-makers’ goals and to frame prevention whenever possible within this light. For example, scholars making the case for investing in youth development and prevention initiatives might frame the issue differently for fiscally conservative state legislators than for local business executives. While the former group may be persuaded by an argument appealing to potential cost savings, the latter may see the value in helping to cultivate a well-prepared workforce.

454 Siobhan M. Cooney et al. The cost savings that prevention can provide is one definite selling point for decision-makers. Weiss (1999) reports that a primary reason policymakers take note of program evaluation findings is the cost and benefit data that are sometimes included. Because so many decisions in the modern policy arena are justified by cost estimates, such as what benefits will be gained or lost by giving up one programming focus for another (Weiss, 1999), we believe this information needs to be gathered and disseminated more deliberately. While a major argument for the importance of prevention rests on the presumed ratio of benefits to costs, in reality, the prevention field currently does not possess a substantial cache of overwhelming evidence. Thus, where cost-benefit findings indicate the positive value of effective youth-focused prevention programs, scholars need to communicate them more often and more broadly. (Aos and his colleagues (2004) provide an excellent resource of cost-benefit estimates for youth programs.) Where cost-benefit information does not exist, the field would do well to investigate it whenever possible. Considering the power that such information could yield for youth policymaking, estimating the cost-effectiveness of prevention initiatives needs to become more standard in a field currently exploding with rigorous longitudinal program evaluations. Beyond directly communicating with policymakers, the literature on the transference of research to the policy arena suggests that targeting constituents should be part of a comprehensive strategy. Policymakers, especially elected officials, will focus on youth prevention if constituents demand it. Elected officials are unlikely to support preventative measures, even when such proposed measures are supported by scientific evidence, if constituents do not see them as a good use of public resources (Bogenschneider et al., 2000). One of the best ways to way to reach constituents is through the media (Roos & Shapiro, 1999; Zervigon-Hakes, 1995). If constituents are informed of an issue by the media and they take interest in it, policymakers will be forced to respond (Roos & Shapiro, 1999; Weiss, 1999). Thus, the media can have a substantial influence on policymakers, both directly and indirectly, through communication with the public. Prevention scholars might consider taking a more active role in engaging the media to educate the general public and policymakers on the value of youth prevention and what programs and practices have empirical support for their effectiveness. The Teen Assessment Project (Small, 1996) offers one example of how the media can serve as an important vehicle for raising awareness and educating policymakers and the public. This university-based research project utilized a community survey process to identify and address issues and concerns among local adolescents. A central activity of the project was to share the survey’s findings with policymakers and the public through local press releases, newsletters, press conferences and community forums. This process helped to increase the community’s awareness of issues facing local youth which in turn motivated community leaders and policymakers to take action to address them. Involve Policymakers and Their Staff in the Research Process Interactions between researchers and policymakers appear to be important for accounting for why some research is used while other research is not (Lavis et al., 2003). When possible, it is recommended that policymakers and their staff are involved in the evaluation research process at key time points, such as when articulating the research questions and when reviewing the results (Zervigon-Hakes, 1995). Researchers can use these interactions to discover what policymakers’ assumptions are, the questions they have, the kinds of data that would be most convincing, and how they plan to use the results of the study (Chelimsky, 1987; Roos & Shapiro, 1999). Such interactions not only attune researchers to the policymaking process,

Youth Policy and Politics in the United States 455 they also educate policymakers about what good prevention research requires, essentially producing paradigm shifts on both sides (Lavis et al., 2003). Prevention researchers can begin the process of influencing youth policy by testing the assumptions of policies directly (Chelimsky, 1987). Prevention scholars can also make research endeavors more policy-friendly by not only searching for practical ways to prevent common problems in childhood and adolescence, but also focusing on issues that are of critical importance to decision-makers. Zervigon-Hakes (1995) recommends that program evaluations ask practical questions—What services are needed? How much will those services cost? Who is most likely to benefit? As such, program evaluation questions are not created at the sole discretion of the researcher; instead, the evaluator helps to bring the best possible information to light on a wide variety of policy and practice questions (Chelimsky, 1987). Build and Maintain Credibility Scholars studying the link between research and policymaking report that researchers need to broker knowledge, not just advocate for certain policy recommendations, to be maximally effective (e.g., Chelimsky, 1987; Normandin & Bogenschneider, 2006). The source of information is very likely taken into consideration by policymakers, who trust some individuals and organizations more than others. Policymakers are well-tuned to pick up on biases in the way research is presented; they work under the assumptions that everyone, including those in the research community, has an agenda, and the “truth” cannot be learned solely from one source (Bazelon, 1982). Moreover, according to Chelimsky (1987), reputations for partisanship persist and are not forgotten. Thus, the burden falls on the prevention researcher to conduct and present his or her work with as little bias as possible. A large part of remaining credible is recognizing and acknowledging that information deficits exist, particularly in the social sciences (Bazelon, 1982; Chelimsky, 1987; Lavis et al., 2003). It is recommended that researchers become comfortable saying that good evidence is not available when in fact it is not, and actively push back against decision-makers who want them to make declarations beyond their expertise (Bazelon, 1982). It is also important to acknowledge that some bodies of research will not generate a take-home message because there is no apparent or credible conclusion. Falsely judging that something works is detrimental to the whole of social science (McCartney & Rosenthal, 2000). Bazelon (1982) eloquently warns researchers against promising too much; when prevention efforts fail to deliver, researchers have an even lower likelihood of influencing future policy decisions. Particularly in evaluation research, it is essential to remember that what works under ideal conditions may not work or may be less effective when transferred to the “real world,” where other factors influence the outcomes of prevention efforts. Program impact evaluations are frequently conducted under ideal conditions with well-trained, supervised staff and ample, consistent funding. In contrast, the effectiveness of a program is measured under “everyday” conditions which are potentially less favorable. As such, the effectiveness of a program often does not reach its demonstrated efficacy, and we should expect the latter impact when the program is implemented in a non-experimental setting (Evidence-Based Intervention Work Group, 2005; Small, 2005). Additionally, it is recommended that researchers acknowledge distinctions between the evidence provided by different types of research. Some research methods are generally considered better than others, and each method has its limitations, so it is important to

456 Siobhan M. Cooney et al. communicate how much confidence one has in a study’s findings (Normandin & Bogenschneider, 2006). For example, meta-analyses are generally preferred to jointly considering the results of only two or three studies, and research with samples representative of the population is often perceived as more desirable than those conducted with more limited samples. Prevention researchers should also have a working knowledge of effect size estimates and the difference between practical and statistical significance (McCartney & Rosenthal, 2000). To build and maintain credibility with a policymaking audience, researchers need to distinguish between new, emerging information that has yet to be replicated and information drawn from a litany of well-respected studies. When presenting the results of only one study, researchers should be clear that individual studies can have conclusions much different from those emanating from a larger body of research. Additionally, in line with the evidence-based practice movement, it is becoming increasingly important for researchers to convey accurate information not only when a particular program is found to be ineffective, but also when a program demonstrates iatrogenic effects (Lilienfield, Lynn, & Lohr, 2003; Norcross, Koocher, & Garofalo, 2006). In instances where we know prevention works, researchers are encouraged to highlight the field’s knowledge on the subject. Of course, in many cases we are unsure of the value of preventative initiatives for youth. Here, more research is needed, and prevention scholars may find themselves in the position to say as much. If policymakers understand that prevention can work and provide cost-savings, it logically follows that investments need to be made in prevention research to assess exactly which programs or types of program should be funded and how and with whom they should be implemented. There exists an ongoing debate in the social sciences about whether researchers should outline policy recommendations emanating from a study or collection of studies. When Sorian and Baugh (2002) surveyed state legislators, they found that the majority (89%) stated they do want to know what the researcher recommends or perceives as implications of the research. When identifying policy options, researchers can be more effective when they provide a balanced perspective of the consequences of each option (Normandin & Bogenschneider, 2006), a process sometimes termed policy alternative education. As one example, when states are deciding which, if any, policies to adopt regarding the type of sex education taught in public schools, a prevention researcher might lay out three options—such as comprehensive education, abstinence-only education, and condom use instruction—and show what the evidence suggests would be the outcomes of each. In this manner, recognizing that additional factors beyond empirical evidence will influence policymakers’ decision, the researcher can forgo advocating for any one policy option and maintain credibility. Some scholars have suggested convening an interdisciplinary group to discuss research findings and their implications before forming policy recommendations, especially for controversial topics (Chelimsky, 1987; Zervigon-Hakes, 1995). When originating with a group rather than one or two people, the recommendations may be less likely to be biased by a particular mode of thinking. If convening a panel of scholars is not possible, a related option is to have others in the field review the recommendations. Such activities will allow researchers and the organizations for which they work to remain credible and effectively inform youth policymaking, no matter which political party or prevailing viewpoint is currently the most powerful (Bogenschneider et al., 2000). A number of well-respected institutions that employ prevention researchers, such as RAND Corporation and Child Trends, have maintained influence in the policy arena through these means.

Youth Policy and Politics in the United States 457 Reward Researchers for Disseminating Evidence to Non-Research Audiences Building credibility and effectively transferring knowledge to others outside of academia and the prevention research community can be time- and labor-intensive. While researchers have become increasingly specialized in their fields of study, policy-oriented research, and the thoughtful dissemination and application of results, necessitates integration and synthesis (Bogenscheider et al., 2000). The ability to speak to and work with a range of audiences is a unique skill that prevention researchers do not typically learn during their graduate training. Such skills need to be developed and cultivated; as such, prevention researchers need to be rewarded for undertaking policy-friendly work if a real shift toward prevention is ever to occur in the youth policy arena. Educate Future Prevention Researchers About Policymaking and Related Topics Given the influence prevention researchers can have on youth policymaking in the United States, universities have a responsibility to adequately prepare students for this task. Graduate training in prevention science might include courses on performing program evaluations, policy simulations, and cost-benefit analyses; working with policymakers; understanding the policymaking process; investigating policy questions; and applying empirical findings to areas of policy interest. Several universities have already established prevention science curriculums that highlight the importance of connecting this work to policy. For example, at the University of Wisconsin—Madison, graduate students enrolled in prevention science courses gain realworld experience by holding “practice” briefings with local legislators and press conferences with members of the local media. Start Small Working in the policy arena can be intimidating, even for the most knowledgeable professionals in the prevention field. We suggest that prevention researchers “start small,” by working with individuals and groups already familiar and accessible. Today, most youth policies are debated, crafted, and implemented at the local level through school boards, local commissions, county boards, community-based organizations and other agencies that might readily welcome outside assistance. Researchers may find these collaborative experiences to be particularly educational and empowering. Prevention researchers who help local decisions-makers with even the smallest of projects might use these opportunities as a springboard, addressing larger and more powerful audiences when the circumstances are right. Prevention researchers influential in the youth policy arena have taken this route; as one example, in Wisconsin, university-based prevention researchers leading the Responsive Education for All Children (REACh) initiative started their work with seven schools before expanding it into a statewide prevention initiative.

Conclusion For a multitude of reasons, prevention has not been a primary focus of youth policy and funding decisions in the United States. While several of these barriers echo those found in the social sciences more generally, others must be recognized and addressed more deliberately in the prevention field. Literature on the utilization of social science research by policymakers not only speaks to the existing barriers, but also suggests strategies for countering them. As prevention

458 Siobhan M. Cooney et al. researchers become more sophisticated in their work with policymaking audiences, it is our hope that the prevention field will not only generate new information about effectively facilitating these relationships, policy-relevant research, and the knowledge transfer process, but also increase its impact on the programs, institutions, and policies that enhance youth development and well-being.

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Editors

Dr. Beth Doll is a Professor and Director of the School Psychology program at the University of Nebraska–Lincoln. Her research addresses models of school mental health that foster resilience and enhance the well-being of students in naturally-occurring communities, and program evaluation strategies that demonstrate impact and accountability of school mental health services. Her publications on issues of resilience, school mental health, self-determination, the identification of emotional disabilities, and students’ friendships appear in prominent national journals. Dr. William (Bill) Pfohl is a Professor of Psychology at Western Kentucky University in Bowling Green, Kentucky. He has trained school psychologists for 30 years. His professional interests are school safety, school crisis intervention, and research on emotional intelligence in students. He was National Association of School Psychologists (NASP) President twice, is current president of the International School Psychology Association, and serves on NASP’s National Emergency Assistance Team (NEAT) responding to school crises. Dr. Jina Yoon is Associate Professor and Co-Director of the School and Community Psychology program at Wayne State University in Detroit, Michigan. Her research focuses on risk and prevention of aggression, bullying/victimization, and behavior problems in children and adolescents, and the impact of school environments on aggression. She has published many papers on aggression, bullying and victimization in school, peer relationships, school climate and teacher–student relationships.

Contributors

Howard S. Adelman Department of Psychology University of California, Los Angeles Los Angeles, California Rachel Baden Department of Psychology The University of Alabama Tuscaloosa, Alabama Carrie Ball Department of Educational Psychology Ball State University Muncie, Indiana Lisa K. Barrois Department of Educational Psychology Texas A&M University College Station, Texas Nathan J. Blum University of Pennsylvania Children’s Hospital of Pennsylvania Philadelphia, Pennsylvania Caroline L. Boxmeyer Department of Psychology The University of Alabama Tuscaloosa, Alabama Brian K. Bumbarger Prevention Research Center Penn State University University Park, Pennsylvania Gretchen Butera School of Education Indiana University Bloomington, Indiana

Contributors 463 Sandra L. Christenson Department of Educational Psychology University of Minnesota Minneapolis, Minnesota Siobhan M. Cooney Department of Human Development and Family Studies University of Wisconsin–Madison Madison, Wisconsin Helen Cowie Health and Social Care University of Surrey Guildford, Surrey, UK Christy M. Cunningham Department of Psychology Northern Illinois University Dekalb, Illinois Cynthia D’Atrio Department of Special Education and Habilitative Services University of New Orleans New Orleans, Louisiana Michelle K. Demaray Department of Psychology Northern Illinois University Dekalb, Illinois Karen Diamond Department of Child and Family Studies Purdue University West Lafayette, Indiana Panayiota Dimitropoulou Department of Psychology University of Athens Athens, Greece Beth Doll Department of Educational Psychology University of Nebraska—Lincoln Lincoln, Nebraska Erin Dowdy Gervitz Graduate School of Education Department of Counseling, Clinical, and School Psychology University of California Santa Barbara Santa Barbara, California Kevin Dwyer American Institutes for Research Bethesda, Maryland

464 Contributors Katie Eklund Gervitz Graduate School of Education Department of Counseling, Clinical, and School Psychology University of California Santa Barbara Santa Barbara, California Edward G. Feil Oregon Research Institute University of Oregon Eugene, Oregon Karin S. Frey Department of Educational Psychology University of Washington Seattle, Washington Michael Furlong Gervitz Graduate School of Education Department of Counseling, Clinical, and School Psychology University of California Santa Barbara Santa Barbara, California Maribeth Gettinger Department of Educational Psychology University of Wisconsin–Madison Madison, Wisconsin Mark T. Greenberg Prevention Research Center Penn State University University Park, Pennsylvania Marci Hanson Department of Special Education and Communicative Studies San Francisco State University San Francisco, California Chryse Hatzichristou Department of Psychology University of Athens Athens, Greece Leanne Hawken Department of Special Education University of Utah Salt Lake City, Utah Alicia Hoffman Department of Educational Psychology University of Wisconsin–Madison Madison, Wisconsin

Contributors 465 Eva Horn Department of Special Education University of Kansas Lawrence, Kansas Jan N. Hughes Department of Educational Psychology Texas A&M University College Station, Texas Lyndsay N. Jenkins Department of Psychology Northern Illinois University Dekalb, Illinois Diane Carlson Jones Department of Educational Psychology University of Washington Seattle, Washington Antti Kärnä Department of Psychology University of Turku Turku, Finland Kimberly Kendziora American Institutes for Research Washington, DC Thomas R. Kratochwill Department of Educational Psychology University of Wisconsin–Madison Madison, Wisconsin Aikaterini Lampropoulou Department of Psychology University of Athens Athens, Greece Heather Jones Lavin University of Pennsylvania Children’s Hospital of Pennsylvania Philadelphia, Pennsylvania Joan Lieber Department of Special Education University of Maryland College Park, Maryland John E. Lochman Department of Psychology The University of Alabama Tuscaloosa, Alabama

466 Contributors Konstantina Lykitsakou Department of Psychology University of Athens Athens, Greece Christine K. Malecki Department of Psychology Northern Illinois University Dekalb, Illinois Jennifer A. Mautone Children’s Hospital of Pennsylvania Philadelphia, Pennsylvania Kenneth W. Merrell Department of Special Education and Clinical Services University of Oregon Eugene, Oregon Laura Mulford Department of Educational Psychology University of Wisconsin–Madison Madison, Wisconsin Gale Naquin Department of Special Education and Habilitative Services University of New Orleans New Orleans, Louisiana Jodi Burrus Newman Department of Educational Psychology University of Washington Seattle, Washington Samuel L. Odom Frank Porter Graham Child Development Institute University of North Carolina Chapel Hill, North Carolina Bram Orobio de Castro Utrecht University Utrecht, The Netherlands David Osher American Institutes for Research Washington, DC Susan Palmer Beach Center on Disability University of Kansas Lawrence, Kansas

Contributors 467 Daniel F. Perkins Family and Youth Resiliency and Policy Penn State University University Park, Pennsylvania William Pfohl Department of Psychology Western Kentucky University Bowling Green, Kentucky Elisa Poskiparta Centre for Learning Research University of Turku Turku, Finland Nicole R. Powell Department of Psychology The University of Alabama Tuscaloosa, Alabama Thomas J. Power University of Pennsylvania Children’s Hospital of Pennsylvania Philadelphia, Pennsylvania Sandra Prince-Embury The Resiliency Institute of Allenhurst, LLC West Allenhurst, New Jersey Amy L. Reschly Educational Psychology and Instructional Technology The University of Georgia Athens, Georgia Kristin Ritchey Gervitz Graduate School of Education Department of Counseling, Clinical, and School Psychology University of California Santa Barbara Santa Barbara, California Christian Sabey Department of Special Education University of Utah Salt Lake City, Utah Elina Saeki Gervitz Graduate School of Education Department of Counseling, Clinical, and School Psychology University of California Santa Barbara Santa Barbara, California Christina Salmivalli Department of Psychology

468 Contributors University of Turku Turku, Finland Herbert H. Severson Oregon Research Institute and the University of Oregon Eugene, Oregon Stephen A. Small Department of Human Development and Family Studies University of Wisconsin–Madison Madison, Wisconsin Peter K. Smith Goldsmiths University of London New Cross, London, UK Wakako Sogo School of Education University of North Carolina at Chapel Hill Chapel Hill, North Carolina Samuel Y. Song College of Education Seattle University Seattle, Washington Linda Taylor Department of Psychology University of California, Los Angeles Los Angeles, California Oanh K. Tran Department of Educational Psychology California State University, East Bay Hayward, California Erika Van Buren District of Columbia Department of Mental Health Washington, DC Lilian Vliek Institute for Kanjertraining Almere, The Netherlands Utrecht University, Utrecht, The Netherlands Hill M. Walker Oregon Research Institute and the University of Oregon, Eugene, Oregon Jina Yoon Theoretical and Behavioral Foundations Wayne State University, Detroit, Michigan

Index

Abecedarian Project 415, 416, 449 academic achievement 36, 45, 47, 49, 122; bullying victims 219; Check & Connect 335, 337, 338, 344; conditions for learning 129–130, 130; EMERGE literacy program 363–364; poor children 352, 355; Resiliency Scales for Children and Adolescents 154; Responsive Classroom Approach 206; school climate and connectedness 136; social and emotional learning 123–124; social comparison cues 196–197; social support 166; standards-based reform 126; teacherstudent relationships 195 Academic and Behavior Intervention Teams (ABITs) 110–111, 113 Academic Engaged Time (AET) 105, 106 access to services 49, 50, 51–52, 308 accountability 24, 30, 33, 34, 35–36, 55 Achenbach Behavior Checklist 99 Achenbach Teacher’s Report Form 82 active listening 179, 181, 183 adaptability 143, 145, 156, 156 adaptation of interventions 243, 280, 311, 419, 420, 437–438 Adelman, Howard S. 15, 19–44 affiliation with school 330, 331 African Americans 121, 127, 293–294, 307, 333, 355, 415 aggression 290, 294, 364; Coping Power program 396–397, 398–399, 404–405, 406; gender differences 287; preschool children 351, 354; proactive 287–288; reactive 228, 287; relational 223–224, 287; social information processing 289; TIGER program 292, 295–296, 298–299, 299, 300, 302; see also bullying Ahmad, N. 177 AIR Survey of the Social and Emotional Conditions for Learning 8, 125–126, 125, 127–135, 136 Albee, G. W. 1 Albers, C. A. 76, 98, 146–147 alcohol use 8, 51, 116, 204, 207, 394

Algozzine, R. 110 alphabet knowledge 358, 359, 360, 361, 362 Altman, D. G. 440 American Psychological Association 3, 445 Andrès, S. 182, 186, 188 anger 148, 149, 155, 279, 397, 398 Anton, B. S. 3 anxiety 50, 288, 289–290; Aggregate Resiliency Profiles 150, 151; bullying victims 218, 239; Queensland Project 396; Resiliency Scales for Children and Adolescents 148, 149, 155, 155, 156, 158; TIGER program 292 Aos, S. 436, 454 Appleton, J. J. 343 archival school records 101–102, 103, 104, 106 Arreaga-Mayer, C. 419–420 assertiveness training 223 assessment 7–8, 9, 51, 108; Pupil Assistance Model 115; social and emotional learning 124; strengths-based 83, 86–87, 268; Youth Risk Behavior Surveillance Survey 72; see also screening Association for Supervision and Curriculum Development 136 Atkins, Mark 310–311 Atri, A. 166 at-risk groups 123, 352, 375–376; Check & Connect 327, 328, 329; critical behavioral events 100–101; early intervention 116–117, 414–416; preschool children 349, 351, 369; Program for the Promotion of Mental Health and Learning 259; Pupil Assistance Model 114; Resiliency Scales for Children and Adolescents 154; screening 76, 107, 111–112, 113, 393; student engagement 332–333; see also risk factors attachment theory 195, 287 attendance 335, 336–337, 338, 339–340, 342, 344, 420 attention and behavior problems 375–392; adaptations in urban contexts 380–381; evidence-based interventions 378–380; family engagement 377–378;

470 Index attention and behavior problems (cont.): health disparities 376; Partnering to Achieve School Success 381–389, 390; service delivery 376–377; see also conduct disorder; emotional and behavioral disorders attention deficit hyperactivity disorder (ADHD) 375–377, 379–381, 383–388, 400 authentic behavior 292–293, 295, 296, 297 autistic spectrum disorder (ASD) 416 Baden, Rachel 393–412 Ball, Carrie 349–375 Ballard, D. 420 Bandura, A. 143 Barnett, D. W. 365 Barnett, W. S. 356 Barocas, R. 354–355 Barrera, M. 13 barriers 24–26, 27, 29, 136–137, 449–450 Barrois, Lisa K. 6, 11, 194–217 BASC-2 Behavioral and Emotional Screening System (BESS) 77, 79–80, 89 Batson, C. D. 177 Battistich, V. 207, 208, 210 Baugh, T. 456 Bazelon, D. 455 befrienders 178, 183, 186, 187 Behavior Assessment System for Children (BASC) 166 Behavior Assessment System for Children Teacher Rating Scale – Child Version (BASC TRS-C) 87 behavior management 51, 55, 274, 280 behavioral competence 364–369 behavioral observations 103–104, 169, 173, 209 Beidas, R. S. 400 Bell-Dolan, D. 169 “best practice” 55–56, 58, 62, 65 Biglan, A. 12 bipolar disorder 150, 151, 155, 156, 158 Birman, B. F. 423 Blechman, E. 101 Block, J. 157 Block, J. H. 157 Blum, C. 106 Blum, Nathan J. 375–392 Bocian, K. 100–101 Bogenschneider, K. 453 Bond, L. 86 Bourdon, K. H. 78 Bowen, M. 144 Boxmeyer, Caroline L. 393–412 Brehm, K. 153, 256–257 Briggs-Gowan, M. J. 87 Bronfenbrenner, U. 422 Brooks, David 327 Brooks, R. 157

Brophy, J. 343 buddies 178 Bukowski, W. M. 168–169 bullying 6, 218–237; classroom management 396–397; hybrid framework 312–322; KiVa program 238–252; monitoring of interventions 230; onlookers 177, 219, 238–239, 244; outcomes associated with 218–219; peer support 177–193; protective peer ecology 315–316, 317, 318–319, 320–321; school community relational context 229–231; self-esteem 290; social ecology 220–221, 226, 313; social support 163, 166–167; teacher characteristics 222–224; teacher-class relational context 226–229; teacher-student relational context 224–226; transactional model of intervention 221–222; see also aggression Bumbarger, Brian K. 14, 433–444 Butera, Gretchen 413–432 Caldarella, P. 107 Campbell, F. A. 416 Campbell, S. B. 351 capacity-building 43, 44, 341, 439 care, continuum of 21, 23 caring communities 256, 257 Caring School Community (Child Development Project) 200, 202, 207–208, 210 Carter, A. S. 87 Carter, E. 99 case management 51 Cassidy, L. J. 80 Castell, William 70 Castro, F. G. 13 challenge 122–123, 126, 128–133, 134, 134, 135 character 21, 56 Chauhan, P. 181 Check & Connect program 7, 13, 327–348; description of 329–332; empirical base 342; health promotion 345; implementation 335–341; role of mentor 334–335; student engagement 332–334, 343; theoretical underpinnings 332–334; universal application 344–345 Chelimsky, E. 451, 453, 455 Chen, H. 422 Chen, Q. 196 Cheney, D. 106 Chicago Public Schools (CPS) 126, 127 Child and Adolescent Social Support Scale (CASSS) 166, 167, 170, 171, 172 Child Behavior Checklist (CBCL) 73–74, 78, 80, 85, 99 Child Development Project (CDP) 200, 202, 207–208, 210

Index 471 ChildLine in Partnership with Schools (CHIPS) 182, 183 Children’s Depression Inventory (CDI) 80, 86 Children’s School Success (CSS) 425–426, 427–428 Chiu, Y. 206 Chorpita, B. F. 309 Christenson, Sandra L. 13, 110, 327–348 Cicchetti, D. 147 Clark, R. D. 133 class size 134–135, 135, 280 Classroom Assessment Scoring System (CLASS) 198 classroom emotional climate 211–212, 226, 288, 291, 424 classroom-based approaches 27, 28, 42 coaching 13, 58, 225–226, 423–424, 438–439 Coatsworth, J. D. 327 cognitive behavioral therapies (CBT) 60, 156, 330, 402 cognitive-developmental theory 350 Cohen, J. 299 Cohen, S. 165 Coie, J. D. 394 Coleman, J. 332 Coleman, N. 345 collaboration 23, 31, 32, 310, 375; attention and behavior problems 377, 390; FACET 367; parent-teacher 379; preschool interventions 366; team problem-solving 60 Collaborative for Academic, Social, and Emotional Learning (CASEL) 54, 60, 124, 125–126, 198, 275–276 comfort with others 144, 145, 158 Commission on the Prevention of Mental/Emotional Disabilities 1 Committee on the Prevention of Mental Disorders 2–3 communication skills 179, 181, 182, 187, 259, 262, 404 community, sense of 207–208, 257, 260, 266, 266, 267, 268 community prevention 5, 12, 15 competence-enhancement approaches 7, 365 Comprehensive Behavior Support Plans (CBSPs) 367, 368 computer game 240–241 conduct disorder 5, 150, 151, 155, 156, 158; see also attention and behavior problems Conduct Problems Prevention Research Group 203 Conjoint Behavioral Consultation (CBC) 377, 379, 383, 390 consultation 212, 402 Cook, E. T. 201 Cooney, Siobhan M. 14–15, 445–460

Coping Power program 6, 7, 13, 396–399, 400, 403–408 Corrective Action Plan (CAP) 109–110 Cortes, R. C. 203 cost-effectiveness 63, 445, 450, 454 Cottrell, R. 166 counseling 51; Coping Power program 407; parents 400–401; peer support 177–178, 182, 183, 187 Cowen, E. L. 277 Cowie, Helen 6, 177–193, 319 Craven, R. G. 186, 188 Crick, N. C. 289 crime 133–134, 134 crisis assistance and prevention 28, 28, 42–43, 51; Partnering to Achieve School Success 382, 384, 385; Program for the Promotion of Mental Health and Learning 259 Crisp, H. L. 401 critical behavioral events 100–101, 104 Critical Events Index (CEI) 100–101, 105, 107, 112, 113 cultural issues 61, 255, 263, 269, 389 Cummings, J. 141 Cunningham, Christy M. 163–176 Curriculum-Based Measurement (CBM) 113, 114–115 Dadds, M. R. 85–86 Dane, A. V. 417 Darlington, R. 415 data dilemmas 36–37 D’Atrio, Cynthia 96–120 Davidson, L. M. 166–167 deaf children 403–404 defederalization 450 delinquency 46, 204, 364, 396, 450; at-risk groups 116, 352; Coping Power program 398, 399, 406; early school performance 121 Demaray, Michelle K. 6, 163–176, 225 Deployment-Focused Development Model 311, 312 depression 72, 73, 156, 289, 290; Aggregate Resiliency Profiles 150, 151; bullying victims 218, 239; Children’s Depression Inventory 80; gender differences 287; Resiliency Scales for Children and Adolescents 148, 149, 155, 155, 156, 158; screening 74, 86; TIGER program 286, 292, 298, 299, 299, 300, 302 Derryberry, D. 144–145 Desimone, L. 423 developmental models 393–394 Developmental Pathways Screening Program (DPSP) 75 deviancy training 288, 290 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) 2, 4, 78

472 Index Diagnostic Interview Schedule for Children Predictive Scales 8 (DPS-8) 87 Diamond, Karen 413–432 Diffusion of Intervention Theory 14 Dilworth, J. E. 205 disabilities 414, 415, 426–427 disciplinary referrals 101–102, 103, 104, 114 discrimination 166, 167 Dishion, T. J. 104, 381 disruptive behavior 133, 148, 149, 364, 377, 404–405 DiStefano, C. A. 87 districts 29–30, 33 diversity 255, 257, 263, 264, 265, 269 Dodge, K. A. 96, 289 Doll, Beth 1–18, 141, 153, 225, 256–257 Domitrovich, C. E. 203, 422–423 Dowdy, Erin 7, 10, 70–95 Downer, J. T. 419 Drew, S. 337 dropout 9–10, 36, 45–46, 327, 328, 329; alterable predictors 333, 336–337, 336; at-risk groups 116, 352; challenge and 134, 134; Check & Connect 332, 342; cost savings 445; early school performance 121; peer influence 333–334; social and emotional learning 274 Drug Abuse Resistance Education (DARE) 56, 420, 448 Drummond, T. 99 DuBois, D. 188 Dunst, C. J. 421 DuPre, E. P. 417, 419, 420, 421, 437 Durlak, J. A. 3, 417, 419, 420, 421, 434, 437 Dwyer, Kevin 4, 45–69 Early Childhood Research Institute on Inclusion (ECRII) 426–427 Early Head Start 415, 418, 420, 421 early intervention 4, 96, 116–117, 413–432; atrisk populations 414–416; continuum of care 21, 22, 23; implementation 416, 417–428; Partnering to Achieve School Success 381–389, 390; preschool children 349, 350–352, 356–370; scaling up 416, 417; school-based 25, 48, 57, 63–64; screening 70, 89, 141 ecological fit 309–310 Edstrom, L. V. 208 Education through Characters with emotionalIntelligence and Role-playing Capabilities that Understand Social interactions (E-CIRCUS) 276 education/mental health divide 53–54 effectiveness 198–199, 435–436, 441; evidencebased interventions 308, 309; school wellbeing 256, 257, 268; see also evaluation of interventions

Eklund, Katie 70–95 Elgen, I. 78–79 Elias, M. J. 205 Elliott, D. 420 Elliott, S. N. 424 Ellis, L. A. 186, 188 EMERGE program 13, 360–364 emotional and behavioral disorders (EBD) 70, 71, 87, 97–98, 109–115; see also attention and behavior problems emotional climate 197, 198–199, 211–212, 221, 226, 288, 291, 424; see also school climate emotional reactivity 144–146, 147–149, 155; Aggregate Resiliency Profiles 150, 151; outcome assessment 152–153; Personal Resiliency Profiles 150, 150; preventive interventions 154, 155; screening model 152, 152 emotions: Program for the Promotion of Mental Health and Learning 259, 262, 263–264, 266; regulation of 287, 290, 291, 397; Strong Kids programs 278, 279; see also self-regulation empathy 177, 179, 181, 182, 187, 223, 240 “enabling” component 26–29, 26, 27, 65 enrollment 132–133, 132, 134 epidemiological research 4, 141, 439 Erikson, Erik 144 Espelage, D. L. 230 ethnicity 51–52, 56, 61, 111, 127, 128; see also African Americans; Latinos evaluation of interventions 7–8, 10–11, 14, 20, 65, 455; bullying interventions 230, 243–245, 249, 319–321; Check & Connect 341, 342; Coping Power program 398–399, 403–407; data dilemmas 36–37; dissemination 408; early childhood intervention 415; EMERGE literacy program 363–364; evidence-based interventions 309; FACET 368–369, 368; implementation 417–421; intensive interventions 57; Partnering to Achieve School Success 384–385, 385, 389, 390; peer support 180; preschool interventions 369–370; Program for the Promotion of Mental Health and Learning 261, 266–267; social relationship interventions 202, 210–211; Strong Kids programs 281–283; TIGER program 297–303, 304 evidence-based interventions (EBIs) 9, 14, 15, 34–35, 198, 307–311; attention and behavior problems 378–380; bullying 313–314; bullying interventions 317; cost-benefit analysis 450; dissemination 309, 399–402, 441; FACET 366–369; family engagement 378; growth in 445–446; implementation 309–310, 422, 437, 438; Partnering to Achieve School Success 382; preschool children 349, 370; school well-being 259, 268; school-based 58; sustainability 439–441

Index 473 Exemplary Model of Early Reading Growth and Excellence (EMERGE) 13, 360–364 expectations 195–196, 224–225, 257, 279–280, 288, 327, 329 expulsion from school 70 externalizing behavior 73–74, 85, 286, 287, 397; referrals 71, 98; screening 80, 81–82, 104–105, 105, 111–112, 113 extra-curricular activities 122, 331 Ezell, H. K. 359–360 FACET program 366–369 facilitators 34, 58 Fagan, A. 420 family 6–7, 48, 62, 354–355, 375; attention and behavior problems 377–378, 378, 380–381; Check & Connect 329, 340; diversity 255; EMERGE literacy program 363; involvement of 61, 124, 357, 389–390; Partnering to Achieve School Success 382–383, 382, 385–389, 385; student engagement 334; support services 51; see also parents family assistance 28, 28, 43, 44 Fan, X. 206 Feil, Edward G. 96–120 fidelity 12–13, 14, 243, 418–419, 437–438; Check & Connect 341; Children’s School Success 425; definition of 417; dissemination of interventions 250, 400, 407; FACET 367, 368; hybrid model 312; KiVa program 248–249; Pupil Assistance Model 114, 115; school-based programs 56, 57, 58; social diffusion theory 311; Strong Kids programs 282; TIGER program 304; see also implementation Finn, J. D. 332, 333 Finney, R. 419–420 First Step to Success 116, 416 Fixsen, D. L. 417, 419, 422, 424 Flay, B. R. 11, 210, 250, 304, 419 Flood, M. F. 157 Floyd, C. 333 follow-up 75, 88, 435, 438 formative evaluation 36–37 Forness, S. R. 116 Frede, E. C. 357 Freeman, H. E. 245 Frey, Karin S. 208, 218–237 friends 288, 333; see also peer support friendship-building 227, 228 Frisch, M. B. 84 Functional Assessment, Collaboration, and Evidence-Based Practice (FACET) 366–369 functional behavior assessment (FBA) 115, 366, 368 functional support approaches 169, 170 funding 3, 19, 356, 439, 449–450; Medicaid 55,

63; special education 54 Furlong, Michael 70–95 Garbarino, J. 328 Garet, M. S. 423 gender: conditions for learning survey 127, 128; peer support 180–181, 182, 187; risk factors 287 Gettinger, Maribeth 7, 349–375 Gillberg, C. 78–79 Glover, T. A. 76, 146–147 Godette, D. 448 Goldstein, S. 157 Golly, A. 116 Good Behavior Game 395 Good Start, Grow Smart 349, 350 Goodenow, C. 123, 333 Goodman, R. 78 Goodvin, R. 157 Gordon, R. S. 3 grade level 128, 129 grade point average (GPA) 127, 131, 131; see also academic achievement graduation rates 9–10, 135, 135 Graham, B. 116 Grana, R. 416 Gray, Susan 414–415 Greenberg, Mark T. 201, 203, 423, 424, 433–444 Greene, J. R. 133 Greenspan, S. 354–355 Greenwood, C. 104, 419–420 Gresham, F. M. 100–101 Grossman, J. P. 184, 208 group interventions 51, 173 Hallfors, D. 448 Ham, D. 85–86 Hammond, C. 337 Hamre, B. K. 195, 419 Hanson, Marci 413–432 Hart, B. 353, 414 Hatzichristou, Chryse 7, 253–272 Havsy, L. H. 333 Hawken, Leanne 96–120 Hawkins, J. D. 116 Head Start 350–351, 370, 415, 420, 427 health promotion 345 Hennessey, B. A. 205 Henry, D. 228 Higa, C. K. 309 Hile, M. 101 Hill, J. L. 420 Hirschstein, M. K. 208 Hispanics 355 HIV-positive youth 167 Hoagwood, K. 141 Hodges, E. 315

474 Index Hoffran, Alicia 349–375 holistic approach 23–24 home visits 340 home-school connections 27, 28, 28, 43, 340, 375, 387 Hops, H. 104 Horn, Eva 413–432 Horner, R. 102, 106, 275 Horton, K. D. 86 Houlston, C. 182, 183, 185, 186, 187, 188 Hubbs-Tait, L. 420 Huberman, A. M. 426 Huebner, E. S. 85 Hughes, Jan N. 6, 11, 194–217 Hurricane Katrina 307 Hutson, N. 185, 188, 189 hybrid model of intervention 310–322 Hysing, M. 78–79 Ialongo, N. 9 ICPS (I Can Problem Solve) 200, 202, 2009 identification 70, 74, 89, 98, 141, 142 ideologies 447 Illinois 276 immigrants 259, 261, 264, 282 impairment 145, 146, 155 implementation 13, 20, 57, 436–438; bullying interventions 229, 230–231, 243–244, 246, 248–249; case studies 425–428; Check & Connect 335–341; coaches 58; comprehensive approach 63–64; conceptual models 417–418, 418, 422–423; Coping Power program 406–407; definition 417; early childhood intervention 413, 416, 417–428; evidence-based interventions 309–310, 399–402; mental health promotion 49; phases 423–424; Program for the Promotion of Mental Health and Learning 262–263; screening programs 72–76, 88; social and emotional learning 279–281; social relationship interventions 210–211; success factors 424–425; TIGER program 304; see also fidelity implementation science 14–15, 428, 436–437 inclusion 259, 426–427 Incredible Years Training Series 395–396, 416 indicated prevention 3, 21, 23, 337, 381–382, 434; see also targeted interventions Individualized Education Programs (IEPs) 51, 60, 101, 110, 387, 388 individualized intervention 330–331, 330, 335, 339–340 Individuals with Disabilities Education Act (2004) 3, 37, 98, 163 Infant Health and Development Project (IHDP) 416, 420, 421 information, screening 74–75

infrastructure 28, 29–33, 441 Institute of Medicine 2, 21, 341, 343, 434, 435 integrated services 25, 47, 107, 108, 377 intelligence 287, 354–355 intensive interventions 22, 46, 57, 281; Check & Connect 335, 337, 338, 339, 339; Coping Power 405–407; Pupil Assistance Model 113, 115; school-based 48, 51, 52, 63–64 intentional enhancement 357 internalizing behavior 73–74, 85, 286, 287; referrals 71, 98; screening 80, 81–82, 104–105, 105, 111–112, 113; social support 166–167; Strong Kids programs 277, 282; targeted interventions 396; teacher support 225 Irby, M. 334 Irwin, K. 420 Jefferson Parish Public School System (JPPSS) 109–115 Jellinek, M. S. 80 Jenkins, Lyndsay N. 163–176 Johnson, K. 440 Jones, Damon 87 Jones, Diane Carlson 218–237 Joyce, B. 423–424 Justice, L. M. 359–360, 419 Kalberg, J. 99 Kallestad, J. H. 230 Kam, C. 203 Kamphaus, R. W. 87 Kärnä, Antti 6, 238–252 Kauffman, J. 98 Kaya, A. 169 Kazdin, A. E. 82 Kellam, S. G. 9 Kendall, P. C. 400 Kendziora, Kimberly 8, 121–140 Kernis, M. H. 290, 297 Kim, D. 343 KiVa program 7, 13, 238–252; adoption 246, 247–248; background 238–239; computer game 240–241; core components 242, 242; dissemination 246, 247; evaluation 243–245, 249; implementation 246, 248–249; maintenance 246, 249–250; school/parent involvement 241; student lessons 240; training 243, 248–249 Kratochwill, Thomas R. 314, 383, 445–460 Kusché, C. A. 201, 203 Lab, S. P. 133 Lampropoulou, Aikaterini 253–272 Lane, K. L. 87, 99, 106 Lane-Garon, P. 184 language issues 61, 280, 282, 353, 414, 415

Index 475 Latinos 61, 282 Lavin, Heather Jones 375–392 Lazar, I. 415 leadership 31, 33, 56, 57, 60, 65, 257 learning 47, 257; barriers to 24, 26, 27, 29; conditions for 121–140; “enabling” component 26, 27, 29; see also social and emotional learning Learning First Alliance (LFA) 126 Least Restrictive Environment (LRE) 110 Leon, A. C. 74 Letourneau, E. J. 402 Levitt, J. M. 141, 147, 280 Lewis, C. 207 Liabo, K. 188 Lie, S. A. 78–79 Lieber, Joan 413–432 Liew, J. 196 life satisfaction 85 Life Skills Training Program 49, 394–395 Likert rating scales 98–100, 104, 105 limited English proficiency (LEP) 127, 128, 353, 414, 415 Linking the Interests of Families and Teachers (LIFT) 395 Linton, D. 337 Lipsey, M. W. 245 Lishner, D. A. 177 literacy 7, 13, 349, 352–353, 355–356, 358–364 Little, M. 80 Lobel, Arnold 278 Lochman, John E. 13, 393–412 Loeber, R. 104 Loukas, A. 86 Love, J. M. 418, 420 low-resource communities 307–308, 311, 322; see also poverty Lucas, P. 188 lunch status 127, 128 Lundervold, A. J. 78–79 Lykitsakou, Konstantina 253–272 Lynam, D. R. 420 MacMillan, D. 100–101 main effect theory 165, 166 Makin, L. 359 Malecki, Christine K. 163–176, 225 Mancini, J. A. 440–441 manualized interventions 12, 13, 60, 399–400, 423; hybrid model 311, 312–321; KiVa program 248–249; Strong Kids programs 277–284 Marek, L. 440–441 Marsh, H. W. 186, 188 Martinez, C. R. 13 Mashburn, A. 419 Massey, S. L. 359

Masten, A. S. 256, 327 mastery, sense of 143, 145, 146, 147–149, 156; Aggregate Resiliency Profiles 150, 151; outcome assessment 152–153; Personal Resiliency Profiles 150, 150; preventive interventions 156–157; screening model 152 Matsumura, L. C. 197 Mautone, Jennifer A. 375–392 Mayer, G. R. 397 Mayer, L. S. 9 McPartland, J. M. 332, 339 measurement 55, 56–57, 59, 420–421; peer support 190; Program for the Promotion of Mental Health and Learning 261–262, 263; social support 168–173, 171–172; training and coaching 61–62 media 454 Medicaid 55, 63 medication 51, 71, 155, 377, 379–380, 381; Partnering to Achieve School Success 382, 383, 384–385, 385, 386–387, 388 Mellor, D. 79 Menesini, E. 186, 188 mental health: definition of 50; developmental models 393–394; dual-factor model 83, 84–85; education/mental health divide 53–54; evidence-based interventions 307–308; historical/current developments 54–56; measurement of outcomes 56–57, 59; positive psychology 254, 268; Program for the Promotion of Mental Health and Learning 259–267; promotion 47–49, 64–65; report card strategy 136; school well-being 258, 268; school-based interventions 45–69, 273–274; screening 70–95, 141; social and emotional learning 275–276; social support 167; status report 49–53; systems interventions 255–256 Mental Health: A Report of the Surgeon General (2000) 49–50, 54 mental illness 50, 83, 273 mentoring 124, 329–332, 334–335, 337, 338–339, 340; see also peer support Merrell, Kenneth W. 7, 13, 99, 273–285 methodological issues 9–14, 35, 209, 261, 369, 419–421, 436; see also reliability; validity Mihalic, S. 420 Miles, M. B. 426 Mills, C. 73 Mills, S. C. 420 modeling 289, 291, 297 Mokrue, K. 205 monitoring: Check & Connect 330, 331–332, 338, 344; EMERGE literacy program 362; Partnering to Achieve School Success 382–383, 382 Montague, R. 85–86

476 Index Mulford, Laura 349–375 Multidimensional Scale of Perceived Social Support (MSPSS) 170, 171, 172 multi-tiered intervention 3, 57, 394; Coping Power program 396–399, 400, 403–408; LIFT program 395; Positive Behavior Support 416; preschool children 358, 361–362, 361, 367; Pupil Assistance Model 113–115; Resiliency Scales for Children and Adolescents 151–152; RtI 97, 446 Murphy, J. M. 80 Myers, C. 185, 188, 189 Najman, J. M. 73–74, 87–88 Naquin, Gale 96–120 narcissism 290 Nation, M. 5, 12, 345 National Association of School Psychologists 55, 96, 142 National Institute of Child and Human Development (NICHD) 355–356 National Institute of Mental Health (NIMH) 3, 4, 87 National Longitudinal Study of Adolescent Health 85 National Mental Health Association (NMHA) 1 Naylor, P. 181, 182–183, 184, 187, 188 needs assessment 439 neighborhood disadvantage 133–134 Network of Relationships Inventory (NRI) 170, 171, 172 networks 5–6; network analysis 168, 173; social support 163, 164–165, 168 New York 276 Newcomb, A. F. 168–169 Newman, Jodi Burrus 6, 9, 13, 218–237 Nimetz, S. L. 357 No Child Left Behind Act (2001) 9, 24, 37, 55, 63 Nolen, S. B. 208, 220 Odom, Samuel L. 14, 413–432 O’Donnell, C. L. 417, 419 O’Dougherty Wright, M. 256 Offord, D. R. 434 Olafsson, R. 181, 183, 185, 187, 188 Olweus, D. 230, 238, 318–319 “on track” status 127, 128, 131–132, 132 Open Circle 200, 202, 205–206 Oppedal, B. 167 Oppositional Defiant Disorder (ODD) 5 optimism 143, 145, 156, 156, 177 oral language 358, 359 organization facilitators 34 Orobio de Castro, Bram 7, 11, 286–306 Osher, David 8, 121–140 outreach: Check & Connect 340, 341;

“enabling” component 28, 28; school prevention programs 25, 32, 44 Oztug, O. 185, 188, 189 Pagano, M. E. 80 Palmer, Susan 413–432 parents 166, 287–288, 291, 354–355; access to services 49; anti-bullying program 241; Check & Connect 344; Coping Power program 396, 398, 407; evidence-based interventions 400–401; Incredible Years Training Series 395–396; LIFT program 395; partnerships with teachers 379, 381; screening by 79, 80; social and emotional learning 124; TIGER program 294, 297; see also family Parks, R. 99 participatory intervention model (PIM) 380 Partnering to Achieve School Success (PASS) 7, 381–389, 390 partnerships 47, 377, 378, 379, 380; Check & Connect 329, 340; Partnering to Achieve School Success 383, 389 Paternite, C. E. 54 Patterson, G. R. 104 pedagogical caring 197 Pediatric Symptom Checklist (PSC) 77, 80–81 peer relationships 196–197, 227, 228–229 Peer Social Behavior (PSB) 105, 106 peer support 177–193, 239, 291; aims of 179; future research 189–191; peer supporters 178, 179, 180–182, 185–186, 187, 190; protective peer ecology 315–316, 317, 318–319, 320–321; research findings 180; school ethos 179, 183–186, 188–189; success of schemes 189; types of 178–179; users of 179, 182–183, 187–188; see also friends; social support Penn, Audrey 278 Pentz, M. A. 14 perceived quality of life (PQOL) 84–85 Perceived Social Support Scale (PSSS) 170, 171, 172 Perkins, Daniel F. 433–444 Perry Preschool Project 415 persistence-plus 330, 331 Peterson, L. 185, 188 phonological awareness 358, 359, 360, 361 Pianta, R. C. 195, 211, 328, 357, 419 Pittman, K. J. 334 planning 423, 440–441 policy 14–15, 21, 445–460; evidence-based practice 34–35; policy windows 451–452; school improvement 24–25, 30; threecomponent framework 26–29 political ideologies 447 Porter, A. C. 423

Index 477 Positive Behavior Support (PBS) 97, 107, 116, 316, 395, 416; preschool children 365–366; Pupil Assistance Model 113, 115; screening case study 108, 112 Positive Behavioral Interventions and Supports (PBIS) 57, 59 positive psychology 82–83, 84, 253–255, 256, 268 Poskiparta, Elisa 6, 238–252 poverty 4, 48, 307, 308, 350, 414; academic achievement 352; attention and behavior problems 376, 380–381, 390; early childhood intervention 415; language delays 353; preschool children 355–356 Powell, Nicole R. 393–412 Power, Thomas J. 13, 375–392 preschool children 22, 349–375; behavioral competence 364–369; critical elements of interventions 356–358; future research 369–370; history and rationale for early intervention 350–352; literacy skills 358–364; resilience and protective behaviors 356; risk factors 352–356; see also early intervention Preschool Curriculum Evaluation Research Consortium 418–419 President’s New Freedom Commission on Mental Health 55, 70 prevention 15, 19–24, 163; conceptual foundations 4–5; effectiveness 198–199; methodological issues 9–14; policy and politics 445–460; programs and practices 5–8; Resiliency Scales for Children and Adolescents 154–158; scaling up 433–444; school-based 45–69, 273–274, 393; social and emotional learning 275; social support 167–168, 173; see also early intervention; primary prevention; screening; secondary prevention; tertiary prevention; universal prevention prevention science 1, 2–4, 275, 321, 433–434, 445–460 primary care providers (PCPs) 376–377, 378, 379, 383, 385, 386–388 primary prevention 2, 19, 107, 108, 167; continuum of care 21, 22, 23, 275; early childhood intervention 415, 416 Prince-Embury, Sandra 7, 8, 141–162 principals 31, 56, 57, 189 print awareness 358, 359 Problem Identification, Choices, Consequences (PICC) model 404 problem-solving 60, 179, 397; Academic and Behavior Intervention Teams 110–111; Check & Connect 330, 330, 335, 338–339, 345; Children’s School Success 425; coaching 225–226; Coping Power program 398, 404;

Incredible Years Training Series 395–396; Interpersonal Problem Solving 200, 202, 209; Pupil Assistance Model 113, 115 professional development 42, 62, 423–424, 427; bullying interventions 229; EMERGE literacy program 362; FACET 367–368; preschool interventions 357, 370; school psychologists 258; Seattle Social Development Project 203 Program for the Promotion of Mental Health and Learning (PPMHL) 259–267 Project ACHIEVE 49, 59 Project Competence group 143 Promoting Alternative Thinking Strategies (PATHS) 200, 201–203, 202, 210, 212, 394 prosocial norms 221, 222, 226, 227, 228 protective peer ecology 315–316, 317, 318–319, 320–321 psychological evaluation 382, 383–384, 385, 387 Psychological Sense of School Membership Scale (PSSM) 85–86 psychopathology 3, 4, 5, 154; dual-factor model of mental health 83, 84; emotional reactivity 144; life satisfaction 85; screening 73 psychosocial development 22, 48 public health model 22, 47–48, 52, 72, 433–434, 439 Public Law 94–142, 54 Pupil Assistance Model (PAM) 109–115, 116 quality of life (QOL) 83, 84 Quamma, J. P. 201 Queensland Early Intervention and Prevention of Anxiety Project 396 Question and Answer Handouts 179 Ragan, T. J. 420 Ramey, C. T. 416 readiness: families 381; site 439; teachers 424, 427 reading 358, 359–360, 361, 363 recovery 145, 146, 155 referrals 44, 51, 71–72, 98, 344–345; disciplinary 101–102, 103, 104, 114; PBS 395 relatedness, sense of 143–144, 145, 146, 147–149, 158; Aggregate Resiliency Profiles 150, 151; outcome assessment 152–153; Personal Resiliency Profiles 150, 150; preventive interventions 157–158; screening model 152 relational aggression 223–224, 287 reliability: Protective Peer Ecology Scale 320–321; Resiliency Scales for Children and Adolescents 147–148, 159; social support measures 170, 172; TIGER program 298, 304 replication 419, 435 report card strategy 136 Reschly, Amy L. 13, 327–348

478 Index resilience 13, 15, 142, 273–285, 327; definition of 274; preschool children 352, 356; Program for the Promotion of Mental Health and Learning 260; school completion 332–333; school well-being 256–257, 256, 268; social and emotional learning 274; stress-buffering theory 165; Strong Kids programs 277–279, 283; teacher-student relationships 196 Resiliency Scales for Children and Adolescents (RSCA) 8, 141–162; academic achievement 154; Aggregate Resiliency Profiles 150, 151; classroom screening 153, 154; core constructs 142–145; description of 145–146; multitiered universal screening 151–152; outcome assessment 152–153; Personal Resiliency Profiles 149–150, 150; preventive interventions 154–158; psychometric properties 146–147, 159; reliability 147–148, 159; unanticipated events 154; validity 148–149, 159 Resnick, W. M. 85 resource councils 32–33 Resource Index 145, 146, 147–149, 151–152, 152, 156 resources: Check & Connect 337–338, 345; enhancing 154, 156–157; mapping 319, 338; school prevention programs 23, 30, 31–32, 33, 36; screening 88; student and family assistance 44 respect for others 294 Responding in Peaceful and Positive Ways (RiPP) 49 Response to Intervention (RtI) 55, 97, 107–108, 116, 163, 445–446; comprehensive approach 27; Pupil Assistance Model 113, 115; screening case study 108, 109, 110–111 responsibility 293–294, 296 Responsive Classroom Approach 200, 202, 206 Responsive Education for All Children (REACh) initiative 457 retraining 33–34 Revok, G. W. 9 Reynolds, A. J. 357 Ricciuti, A. E. 418 Richardson, M. 107 Richardson, T. 184 Rigby, K. 185, 188 Rimdzius, T. A. 418 Rimm-Kaufman, S. E. 206 risk factors 5, 15, 287–291, 328, 394, 433–434, 439; attention and behavior problems 375–376; bullying victims 239; Check & Connect 336, 336, 337, 345; early intervention 96, 116; high school graduation 9–10; mental health prevention 47–48; preschool children 352–356; see also at-risk groups

Risley, T. R. 353, 414 Ritchey, Kristin 70–95 Roberts, H. 188 Rochester Child Resilience Project 143 Rock, D. A. 332 Rodkin, P. 315 Rohrbach, L. 247, 416, 424 Romanelli, K. 141 Rossi, P. H. 245 Rothbart, M. K. 144–145 Rounsaville, B. J. 402 Roysamb, E. 167 Rumberger, R. W. 332 rural poverty 308 Rutter, M. 352 Sabey, Christian 96–120 Saeki, Elina 70–95 Safe School Assessment and Resource Bank (SSARB) 114 Safe Schools, Healthy Students grants 55, 63 Safe Schools, Successful Students Initiative 56–57 safety 122, 126, 128–134, 134, 135, 185 Saka, L. H. 141 Salmivalli, Christina 6, 13, 185, 188, 238–252, 290, 294, 319 Sam, D. L. 167 Samara, M. 183 Sameroff, A. J. 354–355 Sandall, S. 358 Sandler, I. N. 3 Sararson, S. B. 36 Say Yes to Education 136, 137 scaling up 11, 60, 433–444; Coping Power program 405; early intervention 416, 417; efficacy criteria 199; TIGER program 304 Schaefer, B. A. 59 Schaps, E. 207 Schmidt, F. 402 Schneider, B. H. 417 Schoenwald, S. K. 402 School Archival Records Search (SARS) 101 school climate 136, 219, 257, 401; Coping Power program 407; peer support 179, 183–186, 188–189; see also emotional climate school completion interventions 327, 328–329, 332 school connectedness 85–86, 136, 274, 327 school records 9–10, 101–102, 103, 104, 106 School Social Behavior Scale (SSBS) 208, 209 School Wide Information System (SWIS) 102, 103 schools 6, 8, 19–44, 393; accountability mandates 35–36; attention and behavior problems 376, 378; classroom management 396–397; comprehensive approach 25–29,

Index 479 35–36, 63–64; conditions for learning in 121–140; Coping Power field study 405–407; data dilemmas 36–37; “domino effect” 45–46; ecological fit 310; education/mental health divide 53–54; evidence-based interventions 34–35, 401–402; failure of programs 12; improvement policy 24–25; infrastructure 29–33; measurement of outcomes 56–57, 59; mental health 45–69, 273–274; Partnering to Achieve School Success 382, 383; peer support 179, 183–186; personnel retraining 33–34; screening 70–95; social relationships 194–217; status report 49–53; three-component framework 65; TIGER program 294, 297; true potential of 47–49; well-being promotion 253–272 Schwartz, I. 358 Scott, S. 78 screening 7–8, 10, 12, 96–120, 393; archival school records 101–102, 103, 104, 106; behavioral observations 103–104; case study 108–115; Check & Connect 337; comprehensive approach 85–87; Coping Power field study 406; critical behavioral events 100–101, 104; current status of practices 97–98; dual-factor model of mental health 83, 84–85; future research and policy 115–117; implementation of program 72–76; instruments 76–82; integrated student supports 71–72; Likert teacher ratings 98–100, 104, 105; objectives for 72, 88; positive psychology 82–83; Resiliency Scales for Children and Adolescents 141–142; school-based practices 70–95; strengthsbased assessment 83, 86–87; see also assessment Seattle Social Development Project (SSDP) 200, 202, 203–204, 210, 211 Second Step Violence Prevention Program 57, 200, 202, 208–209, 210 secondary prevention 2, 107, 108, 275; Check & Connect 337; early childhood intervention 415, 416; social support 167 Seeley, J. 116 Seifer, R. 354–355 selective prevention 3, 21, 23, 434 self system motivational theory 195 self-affirmation 293–294 self-efficacy 143, 145, 156, 156, 182, 291, 341; aggressive behaviors 397; bullying victims 240; FACET 367, 369; social information processing 289; teachers 222, 223, 229–230, 424 self-esteem 177, 290–291; peer support 182, 187, 239; Program for the Promotion of Mental Health and Learning 259, 262, 263, 264; TIGER program 286, 292–294, 296–298,

299–303, 299, 300, 302 self-regulation 353–354, 356, 365, 376, 379, 399 self-talk 403 sensitivity 145, 146, 155 Serna, L. 116 Severson, H. H. 82, 96–120 sex education 259, 456 sexual activity 116, 204 Shaffer, E. J. 84 shared book reading 359–360, 361, 363 Sharma, M. 166 Sheidow, A. J. 402 Sheldon, A. 188 Sheridan, S. M. 383 Shochet, I. M. 85–86 Sholomskas, D. E. 402 Showers, B. 423–424 Shure, M. B. 209 Siegel, D. J. 145 single-parent households 354 Small, J. 116 Small, Stephen A. 445–460 Smink, J. 337 Smith, D. J. 314 Smith, Peter K. 6, 177–193, 243 Smith, R. S. 144 social acceptance 298, 299, 299, 301, 302 social and emotional learning (SEL) 7, 122, 123–124, 126, 128–133, 134, 135, 135; framework for success 274–275; implementation 279–281; national and international movement 275–276; school well-being 256, 257–258, 268; Strong Kids programs 277–284; see also social skills social capital 332, 341 social comparison cues 196–197 social diffusion theory 311 social ecology 220–221, 226, 313 social information processing 289, 295, 397, 398 social integration 168, 173 social interaction 286–306; risk and protective factors 287–291; TIGER program 295–299, 299, 300, 301, 303–304 social justice 307, 308 social learning 289, 379; see also social and emotional learning social relationships 5–6, 194–217; caring communities 257; Check & Connect 330, 330; Child Development Project 207–208; classroom emotional climate 197, 198–199, 211–212; evaluation of interventions 210–211; future research 211–212; Interpersonal Problem Solving 209; Open Circle 205–206; PATHS 201–203; peers 196–197, 227, 228–229; protective peer ecology 315–316; Responsive Classroom Approach 206; review of programs 199–201,

480 Index social relationships (cont.): 200, 202; Seattle Social Development Project 203–204; Second Step 208–209; Talking with TJ 204–205; see also social support; teacherstudent relationships social skills 54, 60, 96, 157, 291; bullying interventions 227–228, 318; failure to acquire 273, 290; Incredible Years Training Series 395–396; language of 62, 64; LIFT program 395; peer support 181, 187; Program for the Promotion of Mental Health and Learning 259, 261, 262, 265–266, 266; review of studies 200; Talking with TJ 204–205; TIGER program 295; see also social and emotional learning Social Skills Rating System (SSRS) 99, 205, 265–266, 266 social status 168–169, 196–197, 315–316 social support 163–176; measurement 168–173, 171–172; perceived access to 144, 145, 157–158, 158; Tardy’s model of 164–165, 169; theoretical basis of 165; youth outcomes 165–168; see also peer support; social relationships; support Social Support Questionnaire (SSQ) 170, 171, 172 Social Support Scale for Children (SSSC) 170, 171, 172 social-cognitive model 396, 397 Society for Prevention Research 10–11 socioeconomic status (SES) 167, 337, 353, 355 socio-emotional development 20, 49, 51, 64, 137, 144, 355 sociometry 168–169 Sogo, Wakako 6, 7, 12, 307–326 Solomon, D. 207 Song, Samuel Y. 6, 7, 12, 307–326 Sorian, R. 456 special education 3, 30, 115, 128; eligibility for 54; inclusion 259; PBS 395; pre-referral process 110; RtI 446 Spivack, G. 209 Sprague, J. 102, 275 St. Pierre, R. G. 418 Stage, S. 106 standards 10–11, 126 Steps to Respect 6, 9, 13, 225–226, 228, 229, 230–231 Stoep, A. V. 75 Stoiber, K. C. 313, 314, 317–318 Stokes, R. 133 Stormshak, E. A. 381 strength identification 157 Strengths and Difficulties Questionnaire (SDQ) 77, 78–79, 85–86 strengths-based assessment 83, 86–87, 268 stress 21, 287

stress-buffering theory 165, 166–168, 169 Strong Kids programs 277–284 student and family assistance 28, 28, 44 student engagement 332–334, 343 Student Risk Screening Scale (SRSS) 87, 99–100, 100 study/success/support team (SST) 76 subjective well-being (SWB) 83, 84, 84, 254 Substance Abuse and Mental Health Services Administration (SAMHSA) 50–52, 54, 63, 198 substance use 8, 51, 51, 394, 450; at-risk groups 352; Child Development Project 207; Coping Power program 398, 399, 406; cost savings 445; DARE program 56, 420, 448; early school performance 121; Seattle Social Development Project 204 Sugai, G. 102 suicide prevention 276 Suldo, S. M. 84 summative evaluation 36–37 supervision 340–341 support 22, 42, 47, 328; conditions for learning 123, 126, 128–133, 134–135, 134, 135; school completion interventions 329; student and family assistance 44; from teachers 219, 221, 225; transitions 43; see also peer support; social support surveys: conditions for learning 125–126, 125, 127–135, 136; sociometric 169 suspension from school 70, 133, 133, 338, 340 Sussman, S. 416 sustainability 311, 407, 424, 439–441 Suzuki, R. 86 Swearer, S. M. 230 Syracuse-Siewert, G. 402 Systematic Screening for Behavior Disorders (SSBD) 7–8, 81–82, 97, 104–107, 108–115, 116 systems interventions 255–256, 256, 258–267, 268 Tabors, P. 414 “tailoring variables” 13 Talamelli, L. 181 Talking with TJ 200, 202, 204–205 Tardy, C. 164–165, 169, 170 targeted interventions 22, 393, 395–396; Check & Connect 328, 336, 337; Coping Power program 396–399, 400, 403–408; Pupil Assistance Model 113, 114–115; see also indicated prevention Taub, J. 209 Taylor, Linda 15, 19–44 Taylor, T. K. 402 teacher support teams (TST) 59–60

Index 481 teachers 12, 42, 49, 166, 211; behavioral observations 103–104; bullying interventions 218–237, 242, 248–249, 313; “buy-in” 59, 310, 314, 423; characteristics 222–224; Check & Connect 342; Children’s School Success 425–426; classroom emotional climate 197, 198, 288, 291; coaching 58; conditions for learning 122, 123; Coping Power program 406; EMERGE literacy program 362; evidence-based interventions 402; Open Circle 205; Partnering to Achieve School Success 389; partnerships with parents 379, 381; PATHS 201, 212; peer support 181, 184; Program for the Promotion of Mental Health and Learning 262, 266–267, 267; readiness for implementation 424, 427; referrals 98; Responsive Classroom Approach 206; retraining 33–34; rules and expectations 279–280, 288; screening 74, 79, 81, 98–100, 104, 105–106; Seattle Social Development Project 203, 204, 211; Second Step 208; team problem-solving 60; technical support 423–424; TIGER program 294, 297 teacher-student relationships 194–196, 199, 211, 288–289, 291, 375; bullying 219, 220, 221, 224–226; preschool children 354, 359; TIGER program 298, 299, 299, 301, 303 teaching pyramid 107, 108 teams 31, 32; interdisciplinary 65, 456; problem-solving 60 technical support 423–424, 438 Teen Assessment Project 454 teen pregnancies 116, 450 Teen Screen 71 Terry, B. 419–420 tertiary prevention 2, 107, 108, 275; Check & Connect 337; early childhood intervention 415; social support 167 Thomaes, S. 290, 294 Thompson, R. A. 157 three-component framework 26–29, 65 Tierney, J. P. 184 TIGER program 7, 11, 286, 291–304 tobacco use 8, 51, 394 Todd, N. 104 tolerance 144, 145, 158 Torgesen, J. K. 358 training 12, 13, 423–425, 438; bullying interventions 223, 224, 229–230, 243, 248–249; Coping Power program 406, 407; evidence-based interventions 402; measurement of effectiveness 61–62; peer support 179, 180, 182, 187, 189; personnel retraining 33–34; problem-solving 60; Program for the Promotion of Mental Health and Learning 260, 262; see also professional development

Tran, Oanh K. 7, 13, 273–285 transitions 27, 28, 43 treatment 21, 22, 23, 57, 446 trust 144, 145, 158, 225, 296, 335, 345 Tsang, J-A. 177 United Voices for Education 136 universal prevention 3, 8; Check & Connect 344–345; continuum of care 21, 23, 434; exemplar programs 394–395; school-based 48, 49, 51, 393 universal screening 7–8, 10, 12, 70–71, 96–120; archival school records 101–102, 103, 104, 106; behavioral observations 103–104; case study 108–115; critical behavioral events 100–101, 104; current status of practices 97–98; follow-up care 75; future research and policy 115–117; Likert teacher ratings 98–100, 104, 105; objectives for 72; Resiliency Scales for Children and Adolescents 141–142 Valente, T. W. 416 validity: KiVa program 244; Resiliency Scales for Children and Adolescents 147, 148–149, 159; social support measures 170, 172; Strong Kids programs 280, 281, 282; TIGER program 298, 304 Van Buren, Erika 4, 45–69 vicious circles 289–290 violence 4, 208–209, 406; see also aggression; bullying Vliek, Lilian 7, 11, 286–306 Voeten, M. 239 Vulnerability Index 145, 146, 147–149; classroom screening 154; outcome assessment 153; screening model 151–152, 151, 152 Walker, Hill M. 7–8, 9, 82, 96–120, 416 Walsh, D. B. 328 Watson, D. 182, 183, 184, 188 Watson, M. 207 Wayne, N. 133 Webster-Stratton, C. 416, 419 Weersing, V. R. 311 Weide, Gerard 286 Weiss, C. H. 451, 452, 454 Weist, M. D. 54 Weisz, J. R. 3, 12, 312 well-being 83, 84, 84, 85, 434; school 253–272; TIGER program 298, 299–303, 299, 301, 303 wellness promotion 3, 7, 8, 19, 277 Wells, K. C. 398 Welsh, W. N. 133 Werner, E. E. 144 Wessler, A. E. 169

482 Index What Works Clearinghouse (WWC) 342 White, R. W. 143 Wills, T. A. 165 Wilson, N. 207 Wilson, S. J. 421

Young, E. 107 Young, K. R. 107 Youth Risk Behavior Surveillance Survey (YRBS) 72, 73 Ysseldyke, J. 110, 420, 421

Yoon, Jina 1–18 Yoon, K. S. 423 You, W. 206 Young, B. 107

Zax, M. 354–355 Zervigon-Hakes, A. M. 448, 452, 455 Zigler, Ed 415 Zucker, S. 153, 256–257