Clinical Psychology Review 32 (2012) 26–33

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Clinical Psychology Review

Review

A meta-analysis of experimental studies of diversion programs for juvenile offenders

Craig S. Schwalbe a,⁎, Robin E. Gearing a, Michael J. MacKenzie a, Kathryne B. Brewer a, Rawan Ibrahim b

a Columbia University School of Social Work, 1255 Amsterdam ave., New York, New York, 10027, USAb Columbia University Middle Eastern Research Center, Amman, Jordan

⁎ Corresponding author. Tel.: +1 212 851 2228; fax:E-mail address: css2109@columbia.edu (C.S. Schwal

0272-7358/$ – see front matter © 2011 Elsevier Ltd. Alldoi:10.1016/j.cpr.2011.10.002

a b s t r a c t

a r t i c l e i n f o

Article history:

Received 20 July 2011Revised 12 October 2011Accepted 25 October 2011Available online 4 November 2011

Keywords:Juvenile justiceDiversionMeta-analysis

Objective: Research to establish an evidence-base for the treatment of conduct problems and delinquency inadolescence is well established; however, an evidence-base for interventions with offenders who arediverted from the juvenile justice system has yet to be synthesized. The purpose of this study was to conducta meta-analysis of experimental studies testing juvenile diversion programs and to examine the moderatingeffect of program type and implementation quality.Method: A literature search using PsycINFO, Web of Science, and the National Criminal Justice Reference Servicedata-bases and research institute websites yielded 28 eligible studies involving 57 experimental comparisonsand 19,301 youths.Results: Recidivism was the most common outcome reported across all studies. Overall, the effect of diversion

programs on recidivism was non-significant (k=45, OR=0.83, 95%CI=0.43–1.58). Of the five program typesidentified, including case management (k=18, OR=0.78), individual treatment (k=11, OR=0.83), familytreatment (k=4, OR=0.57), youth court (k=6, OR=0.93), and restorative justice (k=6, OR=0.87), onlyfamily treatment led to a statistically significant reduction in recidivism. Restorative justice studies that wereimplemented with active involvement of researchers led to statistically significant reductions in recidivism(k=3, OR=0.69). Other outcomes, including frequency of offending, truancy, and psycho-social problemswere reported infrequently and were not subjected to meta-analysis.Conclusions: High levels of heterogeneity characterize diversion research. Results of this study recommendagainst implementation of programs limited to case management and highlight the promise of family interven-tions and restorative justice.

© 2011 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.1. Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.2. Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.2.1. Research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.2.2. Diversion program characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.2.3. Effect sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.3. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.1. Recidivism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.2. Other outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

+1 212 851 2204.be).

rights reserved.

27C.S. Schwalbe et al. / Clinical Psychology Review 32 (2012) 26–33

1. Introduction

The development of an evidence-base for the treatment of de-linquent youths has progressed rapidly over the past 30 years. In-terventions such as multisystemic therapy (Henggeler, Borduin,Schoenwald, Rowland, & Cunningham, 2009), functional familytherapy (Alexander, Pugh, & Parsons, 1998), and aggression replace-ment training (Goldstein, Glick, & Gibbs, 1998), among others(Landenberger & Lipsey, 2005; Lipsey & Wilson, 1998), have growingbodies of empirical support to demonstrate their effectiveness for thispopulation. Typically, the effectiveness of these interventions has beenevaluated with youths who have been formally adjudicated delinquentby the courts and who are either at risk of institutional placement orwho reside in residential institutions. However, less attention hasbeen given to juvenile offenders who are diverted from the juvenilejustice system despite their large numbers and forwhom the evidence-base is less certain. National estimates indicate that 25% of all youthsreferred to the juvenile justice system are diverted (similar esti-mates describing the magnitude of police diversion are not avail-able) (Puzzanchera & Kang, 2008). Because of their large numbers,evidence-based interventions are needed to disrupt their offendingtrajectories and increase their resilient functioning.

Modern diversion practices trace their origin to the President'sCommission on Law Enforcement and Administration of Justice(Gibbons & Blake, 1976; The Challenge of Crime in a Free Society,1967). The commission advocated the creation of formal channels toremove status offenders and first time offenders from traditionaljuvenile justice processing. Originally, diversion programs wereconceived to minimize the effects of labeling associated with offend-ing propensity (Lundman, 1976). Diversion programs ranging fromlow intensity warn-and-release programs to complex psychosocialprograms involving psychoeducation and therapy were offered toyouthful offenders in lieu of formal court processing, allowing them toavoid the criminogenic stigma of being labeled ‘delinquent.’ However,experimental research provided only weak support for the labelingperspective and for the destigmatizing impact of diversion (Klein,

Table 1Characteristics of included studies (k=28).

Citation Comparisons Design

Binder, Schumacher, Kurz, and Moulson (1985) 2 QEBlechman et al. (2000) 2 QECarney and Buttell (2003) 1 RCTDavidson et al. (1987) 8 RCTDembo et al. (2000) 1 RCTDembo, Wareham, Poythress, Cook, and Schmeidler (2006) 1 RCTEmshoff and Blakely (1983) 2 RCTFantuzzo, Grim, and Hazan (2005) 2 QEForgays and DeMilio (2005) 1 QEJason et al. (2007) 1 QEKlein (1986) 4 RCTMcGarrell (2001) 1 RCTMcGarrell and Hipple (2007) 1 RCTMiller et al. (1998) 1 QEMyers et al. (2000) 1 QEOsgood (1983) 1 RCTPatrick, Marsh, Bundy, Mimura, and Perkins (2004) 4 RCTPatrick and Marsh (2005) 4 RCTRausch (1983) 3 QERegoli et al.(1985) 6 QERodriguez (2007) 1 QESevery and Whitaker (1982) 2 RCTSloan, Smykla, and Rush (2004) 2 QESterwart (2010) 1 QEStickle, Connell, Wilson, and Gottfredson (2008) 1 RCTUmbreit (1994) 2 QEWalker (2002) 1 QEWilson et al. (2009) 1 RCT

*Note. Comparisons are the number of experimental comparisons reported in each stuprocessing with court referral, Min=minimal intervention, Both=two comparison conditi

1986; Rausch, 1983). Subsequently, the pace of empirical researchon diversion slowed through the 1980s and 1990s during the ‘gettough’ era of juvenile justice policy until a resurgence of empiricalresearch published during the 2000s (see Table 1). Today, diversionresearch places less emphasis on labeling theory while invoking alter-native conceptual frameworks such as restorative justice and sociallearning theory in addition to practical concerns about the benefits ofredirecting youths with specific psychosocial needs to alternative treat-ment systems (e.g., mentally ill offenders, substance abuse) and theeconomic benefits of reserving justice resources for the more serious,chronic, and high risk offender population.

Despite the temporal overlap between the diversion and evidence-based practice movements, the evidence base for diversion is unclearand consensus about models that are more or less effective remainsunder investigated. The most recent meta-analysis of diversionprograms was published in 1986 (Gensheimer, Mayer, Gottschalk, &Davidson, 1986). This study (k=44) showed that early diversionprograms were no more effective at reducing offending than standardcourt processing. Average effect sizes were in the small range (d=.23 - .30) with wide confidence intervals precluding definitive con-clusions about diversion effectiveness. Effect sizes were not moder-ated by intervention characteristics (e.g., service brokerage, grouptherapy, academic) but were moderated by youth age (r=−.35),hours of contact (r= .69), and investigator involvement in thedesign and implementation of the interventions (r= .76).

Gensheimer et al.'s (1986) meta-analysis was published beforethe proliferation of empirically supported psychosocial interventionsfor delinquent youths. New developments in diversion since theGensheimer et al. review include the use of diversion for youths withspecific conditions such as mental health problems or substance abuseproblems (Cuellar, McReynolds, & Wasserman, 2006; Hamilton,Sullivan, Veysey, & Grillo, 2007), the development of family-basedtreatment interventions specifically tailored for the diversion context(Gavazzi, Wasserman, Partridge, & Sheridan, 2000), youth courts (i.e.,quasi-judicial proceedings in which adolescent volunteers conducthearings and determine sanctions; Wilson, Gottfredson, & Stickle,

Control Follow N Age Male White Black Latino Asian

Justice 12 233 0.77Min 30 245 15.25 0.718 0.767 0.171Min 18 141 14.85 0.6175 0.5055 0.48Both 24 220 14.2 0.83 0.74Min 12 115 0.55 0.59 0.39Min 194 14.4 0.52 0.61 0.33 0.25Justice 73 14.5 0.67 0.67Justice 567 13.5 0.48 0.153 0.636 0.172 0.039Min 6 52 15.42 0.654 0.85Justice 3 17 0.824 0 0.118 0Both 6 306Min 6 458 13 0.63 0.39Min 24 782 12.6 0.621 0.398Justice 4 81 16 .804 .597 .374 .028 0Justice 12 60 14.9 .48 .333 .633 .033 0Min 12 1300Both 12 234 0.546 0.915 0.0085 0.056 0.0214Both 36 298 15.8 0.546 0.727 0.007 0.044 0.017Justice 6 350Justice 6 834Justice 24 4970 14.05 0.575 0.533 0.0725 0.3945Both 6 2279 14.74 0.88 0.33Justice 24 311 15.9 0.8895 0.6685Min 533 14.66 0.65 0.15 0.84Justice 18 107 14.92 0.707 0.65Justice 12 193Justice 6 184Justice 75

dy; QE=quasi-experiment, RCT=random clinical trial, justice=traditional justiceons (justice and min); follow is follow-up time in months.

28 C.S. Schwalbe et al. / Clinical Psychology Review 32 (2012) 26–33

2009), and the expansion of restorative justice interventions (e.g., pro-grams like family group conferences and victim–offender mediationwhich facilitate restitution by the offender and reconciliation with thevictim; Bazemore & Schiff, 2005). As more recent literature on juvenilediversion has not been synthesized, it is unclear how these advances inintervention methodologies have or have not influenced diversioneffectiveness.

Severalwell-developedprograms of diversion researchdemonstratethe promise of diversion programs for treating juvenile offenders.For instance, the Adolescent Diversion Project (ADP), an innovativeprogram of behavioral contracting and advocacy using highly trainedvolunteers, has been evaluated in four random trials (Davidson, Redner,Amdur, & Mitchell, 1990; Davidson, Redner, Blakely, Mitchell, &Emshoff, 1987; Emshoff & Blakely, 1983). Key findings in this programof research include positive program effects across multiple trials andthe importance of the intervention host setting, with university-hosted interventions outperforming a justice-based implementationof the ADP. More recently, Dembo and colleagues have developed andtested several diversion models through their work in the Miami-Dade Juvenile Assessment Center (Cocozza et al., 2005; Dembo,Wareham, Poythress, Cook, & Schmeidler, 2007; Dembo, Wareham, &Schmeidler, 2005; Dembo, Wareham, Schmeidler, & Chirikos, 2005;Dembo et al., 2008; Waters et al., 2005). Their research suggests thatprograms combining sanctions, restitution, and psychosocial interven-tions prevent recidivism more efficiently compared to intensive casemanagement, monitoring, and sanction-only programs.

Yet a cursory review of the literature suggests that emerging dataon evidence-based practices has been slow to penetrate diversionprogram development. For example, contrary to negative findingsfor case management reported in Gensheimer et al. (1986) andDembo, Wareham, and Schmeidler (2005), justice agencies continueto implement diversion programs of this type. And, despite clearevidence that manualized interventions delivered with fidelity areeffective with delinquent youth (Henggeler, 2011), such rigorousapproaches to implementation and validation are uncommon.

This study synthesized the experimental research evaluations of di-version programs published between 1980 and 2011. It examined theoverall effects of diversion developed since Gensheimer et al. (1986)and sought to expand the evidence base for diversion by examiningthe moderating effect of program types and implementation rigor.

Screened(K=471)

Detailed evaluationK=45

IncludedK=28

Fig. 1. Flow diagram of the s

2. Methods

2.1. Sample

Experimental and quasi-experimental studies that evaluate theeffectiveness of diversion programs for youthful offenders were identi-fied through an electronic search of PsycINFO, Web of Science, andthe National Criminal Justice Reference Service data-base using thekeywords: diversion, diverted, youth court, restorative justice, victimoffender mediation, drug court, mental health court, first time offender,experiment, and juvenile justice. The websites of private research andpublic policy organizations known to conduct evaluation research onjuvenile justice program were also searched (e.g., Vera Institute ofJustice, RAND, NCCD, Washington State Institute for Public Policy,NCJJ). A supplemental searchwas also conducted using Google Scholar.Studies were selected based on the following inclusion criteria: experi-mental or quasi-experimental design comparing at least one activetreatment condition against a minimal intervention or standard justiceprocessing condition, published 1980–2011, served youths under18 years old who were referred to diversion by law enforcement orthe juvenile justice system prior to adjudication. Studies were excludedwhen their only comparison groupwas comprised of treatment refusersbecause of the selection bias inherent in the decision to accept or rejectservices. A total of 471 article abstracts were reviewed, 28 studies metinclusion criteria and were included in the analysis (see Fig. 1).Table 1 shows the characteristics of these studies.

2.2. Coding

All studies were coded by two authors (CS, KB). Codes werereviewed in weekly meetings and any discrepancies were resolvedthrough consensus. Agreement of the coders exceeded 90%. The follow-ing study characteristics were coded:

2.2.1. Research designStudieswere coded for type of experimental design (randomassign-

ment or quasi-experiment). For quasi-experimental designs, studieswere also coded for the strategy employed to assure equivalent controlgroups (i.e., matching, statistical control for pre-treatment variables,none). Two types of control groups were coded: referral to traditional

Excluded (K=17)• Correlational design (k=2)• Post-adjudication (k=5)• Duplicate study (k=3)• Cost benefit study (k=2)• Non-equivalent control group

(k=4)

Excluded(K=426)

tudy selection process.

29C.S. Schwalbe et al. / Clinical Psychology Review 32 (2012) 26–33

justice processing and diversion withminimal services (e.g., warn-and-release). Studies were also coded for length of follow-up and samplecharacteristics (N, age, gender, race/ethnicity). All study outcomeswerecoded, including recidivism (arrest, adjudication), number of arrests,number of charges, program completion rates, and truancy rates. Forstudies that included standardized scales, the scale name and outcometype were coded.

2.2.2. Diversion program characteristicsAn initial review of the study descriptions indicated five mutually

exclusive program categories, including case management/brokeredservices only, individually oriented treatment provided by the programwith or without case management, family-based treatment providedby the program with or without case management, youth court, andrestorative justice. Other program characteristics included eligibilitycriteria (e.g., status offense, first time offense, substance use problems,mental health problems) and intervention setting (e.g., justice agency,community agency, university/hospital clinic). Implementation qualitywas coded using three codes — presence of a manual (defined as a setof specific program principles and philosophies as well as an actualwritten document), researcher involvement (defined as involvementas a trainer or direct supervisor or as the intervention developer), andfidelity monitoring (steps for monitoring fidelity through direct obser-vation were reported).

2.2.3. Effect sizesTwo types of effect sizes were coded — proportions and percent-

ages for outcomes treated as discrete events (e.g., re-arrest) andmeans and standard deviations for other outcome types. Odds ratioscomparing experimental and non-experimental conditions werecalculated from event rates for discrete outcomes, OR=pe/(1−pe)/pc/(1−pc), and transformed to a linear scale for analysis, Log(OR).Cohen's d was calculated for all other outcomes, d=(me−mc)/spooled.When effect sizes could not be directly calculated, Cohen's d wascalculated by transforming f-statistics (Rosenthal, 1991).

Table 2Effect sizes of methodological moderators.

k OR 95%CI Q I2

All studies 45 .83 .43–1.58 63.13 30.31Methodological moderators

RCT 27 .82 .42–1.59 34.81 25.30Quasi-experiment 18 .83 .44–1.57 28.02 39.34Nbp75 a 34 .69 .25–1.89 48.66 32.18N>p75 a 11 .89 .64–1.24 15.64 37.07%male — below median b 13 .84 .55–1.28 11.15 0%male — above median b median=.65 14 .89 .48–1.65 16.65 21.92Average age — below median c =14.2 11 .84 .54–1.30 18.58 46.17Average age — above median c 12 .93 .67–1.28 6.33 0%White — below median d 12 .89 .69–1.16 11.61 5.26%White — above median d 15 .64 .24–1.71 17.65 20.69Control type — min 19 .84 .47–1.52 20.68 12.96Control type — justice 26 .82 .41–1.60 41.45 39.69Follow — 6 months 13 .74 .29–1.87 20.74 42.13Follow — 12 months 12 .83 .46–1.52 14.50 24.14Follow — 24 months 14 .86 .53–1.42 23.77 45.32Follow — 36 months 6 .83 .53–1.31 2.61 0

a 75th percentile for sample size was 238.b Median %male of samples was 65%.c Median average age of samples was 14.2.d Median %white of samples was 74%.

2.3. Analysis

Thirteen of 28 studies reported multiple comparisons because theyincluded both types of comparison groups (standard court or minimalintervention) or they included multiple experimental conditions. Forthis study, each comparison between an experimental condition and acontrol condition were considered separately (k=57). High levels ofheterogeneity in the most common outcome, recidivism (k=45),supported a random effects model (Q=63.13, I2=30.3) (Hunter &Schmidt, 2004). Hunter and Schmidt (2004) random effects modelswere employed to calculate inverse-variance weighted effect sizes,correct standard errors for sampling error variance, and to calculatecredibility intervals. Log (OR) was meta-analyzed for outcomesinvolving rates. Categorical moderators (e.g., gender, interventiontype) were tested by estimating average effect sizes and credibilityintervals for each category; continuous moderators (e.g., averageage, sample size) were first dichotomized using a median split. Hunterand Schmidt recommended data sub-setting over multivariateapproaches to moderator analysis to avoid capitalizing on chancein meta-analyses with low statistical power. Outliers were detectedusing the sample-adjusted meta-analytic deviancy statistic (SAMD)(Winfred, Winston, & Huffcutt, 2001). The SAMD statistic, calculatedfor each research report, is a measure of change in average effect sizeadjusted for sample size when the research report is excluded fromthe analysis. SAMD statistics follow an approximate t distribution.Following Winfred et al., scree-plots were used to identify potentialoutliers. All analyses were conducted in SAS (v. 9.2).

3. Results

The 28 studies yielded 57 comparisons involving 19,301 youths(Mean=585, SD=913) in 33 independent samples. The averageages of the study samples ranged from 12.6 to 15.9 years old(k=15, Mean=14.2, SD=20.6). Up to 88% of the samples weremale (k=18, Mean=0.66, SD=3.47) and up to 91.5% were White(k=17, Mean=0.47, SD=4.39). Fourteen studies employed randomassignment (42.4%) with the remainder utilizing a quasi-experimental design (k=15, 57.6%). Follow-up times ranged from 3to 36 months (k=28, Mean=12.5, SD=8.86, Median=9.0). Fivestudies (15.2%) reported comparisons involving both justice referraland minimal service diversion control groups; 19 studies (57.5%)reported comparisons involving only justice referral and nine studies(27.3%) reported comparisons involving only minimal service diver-sion. Sixteen studies (57.1%) restricted eligibility to minor and/orfirst-time offenders; eligibility in three studies (10.7%) was restrictedto high risk, chronic, or serious offenders; 2 studies (7.1%) employedthe discretion of police officers or county attorneys to establish eligi-bility; 1 study used substance use problems as its sole eligibility cri-teria; and 6 studies (21.4%) did not report their eligibility criteria.Active experimental conditions included case management (k=21,36.8%), individual treatment with or without case management(k=12, 21.1%), family treatment with or without case management(k=10, 17.5%), youth court (k=7, 12.3%), and restorative justice(k=7, 12.3%). Thirteen studies implemented a manualized interven-tion (44.8%), ten involved researchers in roles such as interventiondeveloper, monitor, or trainer (34.5%), and eight reported active fidel-ity monitoring (27.6%). A majority of studies were published duringthe 2000s (k=18, 64%), with only two studies published during the1990s (7%) and eight studies published in the 1980s (29%).

3.1. Recidivism

Recidivism rates were reported for 45 comparisons. Overall, theeffects of diversion programs on recidivism were non-significantacross all studies (OR=0.83, 95%CI=0.43–1.58; see Table 2). Aver-age recidivism rates for experimental and control conditions weresimilar (31.4% vs. 36.3%, respectively). Analysis of outliers identifieda large number with SAMD scores greater than 4.0 (k=18, 40%). Ex-clusion of outliers did not change the pattern of results; so, all studieswere retained in all analyses. Effect sizes were not moderated by

30 C.S. Schwalbe et al. / Clinical Psychology Review 32 (2012) 26–33

research design characteristics (e.g., random assignment, length offollow-up, sample size, type of control group, type of recidivism mea-sure [i.e. arrest, referral, adjudication]), sample demographic charac-teristics (age, race, gender), eligibility criteria, nor by interventionquality indicators (manual, researcher involvement, fidelity).

Table 3 presents the results of the moderator analysis for diversionprogram type and implementation quality. Whereas non-significanteffect sizes were associated with case management programs(k=18, OR=0.78), individual programs (k=11, OR=0.83), youthcourt (k=6, OR=0.93), and restorative justice programs (k=6,OR=0.87), family-based programs were associated with statisticallysignificant reductions in recidivism (k=4, OR=0.57). The familystudy experiments were from three research programs includingthe Adolescent Diversion Project (Davidson et al., 1987; Emshoff &Blakely, 1983) and Project Back-on-Track (Myers et al., 2000), eachof which was administered in non-community settings under strictmonitoring by their respective research teams, and an evaluation ofa community-based drug treatment program (Miller, Scocas, &O'Connell, 1998) that included family counseling and which wasevaluated using a matched sample design. Low heterogeneity statisticsfor family interventions (I2=1.20) and youth court (I2=0.00) indi-cates that variation in effect sizes within these categories is largelyattributable to random sampling error.

Given high levels of heterogeneity within case management studies(I2=52.80), individually oriented programs (I2=30.72), and restor-ative justice programs (I2=32.86), two follow-up analyses wereundertaken. First, effect sizes for interactions between interventionmonitoring quality indicators (i.e., manual, researcher involvement,and fidelity) and intervention type were explored. Second, sourcearticles were reviewed qualitatively within each category.

Among casemanagement studies, onewas coded as having includedan interventionmanual (i.e., wraparound, (Carney & Buttell, 2003). Thisstudy included observation by researchers aswell asfidelitymonitoringand was associated with a statistically significant reduction in recidi-vism (OR=0.59, 95%CI=0.45–0.79). The remaining studies providednon-specific descriptions of their experimental condition usuallylimited to service brokerage by non-justice community agencies.Researchers had limited roles as evaluators, usually employing admin-istrative data to evaluatemultiple programs involvingnodirect involve-ment with the programs. Odds ratios ranged from 0.17 to 1.70suggesting high levels of heterogeneity which could not be explainedby data provided in the articles.

Effect sizes for individually oriented studies were similarly notmoderated by intervention monitoring quality indicators. Odds ratioswithin this category ranged from 0.30 to 1.87. Mirroring the hetero-geneity of effect sizes (Q=14.43, I2=30.72), these studies were het-erogeneous in their intervention strategies, utilizing behavioralapproaches (k=5, OR=0.66, 95%CI=0.27–1.64), mentoring (k=4,OR=0.96, 95%CI=0.34–2.71), and crisis intervention (k=2,OR=0.91, 95%CI=0.91–0.91). Due to the small number of studiesin this category, the average effects of individually oriented

Table 3Effect sizes by intervention program type and implementation quality indicators.

k OR 95%CI Q I2

Intervention typeCase management 18 .78 .34–1.82 36.03 52.81Individual Treatment 11 .83 .35–1.97 14.43 30.72Family treatment 4 .57 .40–.82 3.04 1.20Youth court 6 .93 .58–1.50 2.37 0Restorative justice 6 .87 .67–1.12 7.36 32.06

Implementation qualityManualized 17 .69 .36–1.33 15.71 15.71Researcher 14 .64 .35–1.20 17.03 23.68Fidelity 13 .65 .34–1.23 15.79 24.00

interventions were sensitive to extreme effect size values. For example,two of the five comparisons involving behavioral approaches had oddsratios greater than 1.0 (1.24 and 1.87) favoring the control condition(Davidson et al., 1987). These programswere administered fromwithina juvenile justice agency compared to the other behavioral interven-tions which were administered in community agencies (Blechman,Maurice, Buecker, & Helberg, 2000) or university clinics (Davidson etal., 1987). When the justice-agency programs were removed, theaverage odds ratio for the remaining three behavioral interventionsdeclined and was statistically significant (k=3, OR=0.54, 95%CI=0.33–0.89, I2=0).

Effect sizes for restorative justice studies were moderated by levelof researcher involvement. When researchers were directly involvedin intervention monitoring, the effect size dropped from OR=0.87(k=6) to OR=0.69 (k=3) and the credibility interval indicated astatistically significant effect (95%CI=0.51–0.95). These three studiesrepresented two research projects in which victim–offender confer-ences were tightly controlled. Both had high levels of researcherinvolvement which included active fidelity monitoring in one case(McGarrell, 2001; McGarrell & Hipple, 2007) and administrationthrough a dedicated research center in the other (Umbreit, 1994).The remaining three studies involved implementation of uncertainquality by justice-community partnership agencies.

3.2. Other outcomes

Eight studies involving twelve comparisons examined outcomesbeyond recidivism rates. Four studies reported the average numberof arrests or charges during the follow-up period, with all effects inthe small range (d=−0.03). Other effect sizes reported includeself-reported delinquency (k=2), drug use (k=2), and school truancyand absenteeism (k=2) and a small range of other outcomes thatwerereported in single studies (k=4). Because of the small sample sizes,these outcomes were not meta-analyzed.

4. Discussion

This study documents the substantial heterogeneity that continuesto characterize diversion program research. High levels of heterogene-ity permeates all aspects of this literature including research design,programdesign, and the quality of programmonitoring and implemen-tation, and is reflected in the wide range of effect sizes observed in thissample. The lack of statistically significant effect sizes appears to indi-cate that the field has advanced little beyond Gensheimer et al. (1986)and its overall conclusion that formal diversion programs prevent recid-ivism no better than simple warn-and-release models or formal courtprocessing. This finding suggests the provocative hypothesis that thebest intervention for most diverted youths is no intervention. This hy-pothesis draws attention to the problem of net-widening, in whichyouths who would otherwise be diverted informally are insteaddiverted to a formal program and exposed to intensive, and potentiallyiatrogenic, interventions. However, the wide credibility interval aroundmost effect sizes observed in this study suggests otherwise. The averageeffect of active diversion programs are as likely to be highly positive(e.g., OR= .43) as they are to be highly negative (i.e., OR=1.58). Thusthe results of this study, far from recommending warn-and-releasediversion models for all diversion-eligible offenders, challenges thefield to strengthen programdesign and implementation, and offers spe-cific directions for future research in this arena.

First, results of this study echo Gensheimer et al. (1986) whichfound that broker-only programmodels as currently conceived providean uncertain benefit for participating youths. In each of these meta-analyses, service broker models were themost common among all pro-grammodels and it now appears that research on programs of this typespanning the years 1971 through 2011 cast doubt on their promise.When considered in light of similar findings in allied fields like mental

31C.S. Schwalbe et al. / Clinical Psychology Review 32 (2012) 26–33

health and child welfare where broker models of case management arecommon (Bedell, Cohen, & Sullivan, 2000; Bickman, Lambert, Andrade,& Penaloza, 2000; Ryan, March, Testa, & Louderman, 2006; Sorensen etal., 2003), the prospects for casemanagement appear to bedim.Howev-er, the findings of Carney and Buttell (2003) referenced in the currentstudy highlight an alternative explanation that finds support inevidence-based models of case management. Typical broker-onlymodels of case management emphasize service coordination as thekey mechanism of change whereas growing bodies of evidence indi-cates that specific interventions designed to foster client engagementin services, like active outreach in assertive community treatment forseriously mentally ill clients and for substance-abusing parents of chil-dren in foster care (Bedell et al., 2000; Ryan et al., 2006), are an addi-tional key ingredient to their success. Similarly, Carney and Buttell(2003) showed that thewraparoundmodel emphasizing client engage-ment and leadership in conjunction with careful matching of clientswith formal and informal community-based resources can improveoutcomes for delinquent youths. To advance research in this area, futureinvestigations on diversion case management programs should focuson the outreach and engagement dimensions of case management inaddition to servicematching to align these programsmore fullywith in-novations in the evidence-based practice movement.

Second, results of this study affirm that evidence-based interven-tions are not routinely included as components to diversion pro-grams. Cognitive–behavioral interventions and family-basedinterventions based on an ecological framework have been fullyestablished as key ingredients for successful interventions with delin-quent youths (Andrews & Bonta, 2010; Landenberger & Lipsey, 2005).Yet the result of this review yielded few trials testing evidence-basedpractices such as these. Those that did bore promising results andsuggest the need to increase the rigor of intervention design in futurediversion planning efforts (Blechman et al., 2000; Davidson et al.,1990; Emshoff & Blakely, 1983; Myers et al., 2000).

Third, because so few experimental evaluations of specialized di-version programs for youths with mental health or substance useproblems were located for this review, there is limited evidence insupport of these programs. Only one evaluation of a specialized diver-sion program targeting youths with substance use problems wasavailable for this review (Miller et al., 1998). Three other studies in-volving diverted youths with mental health or substance use prob-lems were located but were excluded for methodological reasons:Hamilton et al. (2007) reported the results of a longitudinal study ofyouths who participated in a 10-county diversion initiative in NewYork State; Cuellar et al. (2006) reported a quasi-experimental studyof a Texas State initiative focused on diverted and adjudicated youthswith mental health problems; Henggeler et al. (2006) reported anrandomized controlled trial of a juvenile drug court which also servedboth adjudicated anddiverted youths. These studies suggest that youthswho receive specialized diversion programs have reduced recidivismand reduced rates of out-of-home placement compared with non-diverted youths. Therefore, additional research into the potential bene-fits of specialized programs focused on diverted youth is warranted.

Remaining conclusions are tentative owing to high heterogeneityin study results and limited statistical power when the set of includedstudies was subdivided by program type and moderators. Neverthe-less, the subgroup analyses presented here raise hypotheses thatneed further exploration in subsequent research. For instance,among any programmodels, diversion programs that included familyinvolvement had the largest effect sizes with little residual heteroge-neity. However, due of their small number, conclusions about thetype of family interventions that may be more or less effective cannotbe made and leave open avenues for further research. The adaptationof models utilizing multiple family group formats (Quinn & Van Dyke,2004), parent management training (Kazdin, 2005), and intensivefamily-based therapies (Henggeler, 2011) are all promising foroffenders referred to diversion programs.

It is also important to recognize that strong researcher involvementin the family intervention studies may have confounded the consistenteffects of family interventions. Across all studies, the use of treatmentmanuals, researcher involvement, and active fidelitymonitoring tendedto reduce odds of recidivism and shift credibility intervals in the direc-tion of greater effectiveness. In the overall analysis involving all diver-sion programs, the credibility interval was symmetrical around 1.0(0.4–1.6) whereas the credibility interval for studies with active fidelitymonitoring was weighted toward the left of 1.0 and in the direction ofpositive effects (0.3–1.2). Moreover, the importance of researcherinvolvement was most strongly observed for restorative justice studieswhere active researcher involvement reduced heterogeneity of pro-gram effects to a degree that statistically significant decreases in recid-ivism were observed. These findings mirror the moderator analysis ofGensheimer et al. (1986) and point to the need for diversion programsto attend to the level of active supervision and fidelity monitoringneeded to assure a strong program implementation.

Given the developing state of the literature on diversion, it may beof little surprise that diversion research has not yet begun to examinequestions about matching interventions to clients based on popula-tion and disorder characteristics. While the field continues to searchfor effective interventions, attention to these factors may provide akey to future development. One area warranting attention is thelevel of risk for reoffending, where empirical research with justice-involved populations shows that active interventions for low-riskoffenders produce poor outcomes while these same interventionsdelivered to high-risk offenders demonstrate greater success (Andrews& Bonta, 2010; Andrews, Bonta, & Hoge, 1990; Andrews & Dowden,2006). Here the literature suggests that it is best to focus interventionsat high-risk clients and to leave low-risk clients alone. For diversionprograms, these findings suggest that a stepped approach based onrisk assessment may be needed, where low-risk offenders are divertedwith minimal services while higher risk offenders are engaged in moreactive psychosocial programs to address key criminogenic needs. Otherareas to address in matching interventions with specific client charac-teristics are gender (Bloom, Owen, Deschenes, & Rosenbaum, 2002;Goodkind, 2005) and race or ethnicity.

Other study findings deserve mention. Except when moderated byprogram supervision and monitoring, results of the restorative justicestudies were less strong than those presented in two previous meta-analyses of family group conferencing and victim–offender mediation(Bradshaw, Roseborough, & Umbreit, 2006; Nugent, Williams, &Umbreit, 2003). In these analyses, Bradshaw et al. found an overall ef-fect size in the moderate range (k=15, d= .34) and Nugent et al. ob-served odds ratios that strongly favored mediation over treatment asusual (k=15, OR= .59). In part, the difference in findings for the cur-rent study is a result of the sampling design employed by the threemeta-analyses. In the current study, inclusion was restricted to stud-ies that explicitly served children at the pre-adjudication stage of jus-tice processing, a restriction not imposed on Bradshaw et al. nor onNugent et al. When studies were not clear on this point, they were ex-cluded from the current study. Further, Bradshaw et al. and Nugent etal. both included studies that compared program completers withprogram non-completers (e.g., Carr & Nelson, 2000); comparisonsthat were expressly excluded from the current study on the groundsthat program non-completers vary systematically from completersin unmeasured characteristics that are likely to bias study results infavor of program effectiveness. Indeed, this appears to be the caseand led to the more modest conclusions of this current study.

Finally, results of the current study suggest that neither youthcourts nor mentoring programs reliably reduced recidivism amongdiversion participants. In the case of youth courts, whereas only sixstudies were represented in this category, low levels of heterogeneityacross these studies indicate a broad consistency in their findings andare consistent with the findings of other studies in the field (Wilson etal., 2009). In the case of mentoring programs, high levels of variability

32 C.S. Schwalbe et al. / Clinical Psychology Review 32 (2012) 26–33

in effect size and program design precluded further analysis of thesestudy types.

5. Conclusion

Results of this study provide a succinct evaluation of the currentstate of evidence for juvenile diversion programs. Based on theseresults, program planners and researchers are advised to advancethe development of programs that offer a direct psychosocial inter-vention such as evidence-based family-interventions and behavioralprograms in addition to case management, to offer opportunities forrestorative justice interventions like family group conferencing andvictim–offender mediation, and to implement these programs withhigh levels of supervision to attend to the fidelity of program imple-mentation. Broker-only models, mentoring models, and youth courtsfind little support in the literature for reducing rates of recidivismamong diverted youth.

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