International Journal of Offender Therapy and
Comparative Criminology 2018, Vol. 62(6) 1509 –1534
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Examining the Effects of Intensive Supervision and Aftercare Programs for At- Risk Youth: A Systematic Review and Meta-Analysis
Jessica Bouchard1 and Jennifer S. Wong1
Abstract Community correctional sentences are administered to more juvenile offenders in North America than any other judicial sentence. Particularly prominent in juvenile corrections is intensive supervision probation and aftercare/reentry, yet the effects of these supervision-oriented interventions on recidivism are mixed. The purpose of this meta-analysis is to determine the effects of intensive supervision probation and aftercare/reentry on juvenile recidivism. An extensive search of the literature and application of strict inclusion criteria resulted in the selection of 27 studies that contributed 55 individual effect sizes. Studies were pooled based on intervention type (intensive supervision probation or aftercare/reentry) and outcome measure (alleged or convicted offenses). The pooled analyses yielded contradictory results with respect to outcome measure; in both cases, supervision had a beneficial effect on alleged offenses and negatively affected convicted offenses. These patterns across intervention type and outcome measure, as well as recommendations for future research, are discussed.
Keywords aftercare, reentry, intensive supervision probation, meta-analysis, recidivism
1Simon Fraser University, Burnaby, British Columbia, Canada
Corresponding Author: Jennifer S. Wong, Associate Professor, School of Criminology, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada V5A 1S6. Email: firstname.lastname@example.org
690449 IJOXXX10.1177/0306624X17690449International Journal of Offender Therapy and Comparative CriminologyBouchard and Wong research-article2017
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Despite common perception of increasing crime rates, juvenile delinquency has been on the decline for a number of years. For instance, recent statistics indicate that in 2013, juvenile arrests for violent crime reached their lowest level in 30 years, and, in the last 10 years, the number of cases handled by juvenile courts decreased for almost all types of offenses (Hockenberry & Puzzanchera, 2015; Puzzanchera, 2013). Although these statistics are impressive, they do not necessarily represent the effec- tiveness of the criminal justice system, nor do they suggest that the number of juvenile cases is acceptable. Despite overall decreases in the juvenile justice system, juvenile courts continue to assess more than 1 million cases per year (Hockenberry & Puzzanchera, 2015). As the use of incarceration for young offenders is typically reserved as a last resort and for youth who are at high risk to reoffend, a large propor- tion of convicted juvenile offenders serve at least some or all of their sentence in the community. In addition, of the available community-based correctional options for juvenile offenders, variations of probation and intensive supervision probation (ISP) are the most commonly used by the criminal justice system (Caputo, 2004; Hockenberry & Puzzanchera, 2014). This deinstitutionalization of young offenders has presented the opportunity to develop new and innovative policies and practices for treating and rehabilitating juveniles in the community (Annie E. Casey Foundation, 2013). Extensive research has been conducted on the effectiveness of intermediate sanctions in preventing and deterring criminal activity for youth. The effectiveness of these interventions, however, has been highly debated, particularly with respect to its (poten- tially faulty) theory, high rates of failure, and net widening (Caputo, 2004). As the conceptualization and development of intermediate sanction interventions dates back to the 1980s (discussed in further detail below), it is possible that the conceptual and theoretical frameworks of these interventions are not as relevant (and consequently no longer as effective) as they once were. If this is true, intermediate sanctions may need to be revisited, reconceptualized, and revised to be consistent with current criminal justice issues and fit the needs and responsivity of modern-day juvenile offenders. As such, comprehensive research that systematically reviews and quantitatively pools evidence on correctional interventions for juvenile offenders is an important endeavor so that practitioners and policymakers can make evidence-based decisions regarding what is in the best interest for young offenders in the current juvenile justice system.
Prior to the 1980s, there were very few viable sentencing options other than probation and incarceration; probation was considered more lenient and was reserved for less serious offenders (whose crimes were not severe enough to warrant incapacitation), whereas incarceration was reserved for more serious offenders (Morris & Tonry, 1990). However, within a 10-year period (1975-1985), incarceration rates in the United States nearly doubled, and available resources were quickly exhausted (Wakefield & Uggen, 2010). As a response to the vast increase in incarcerated offenders and the
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limited capacity of prisons to accommodate the influx of offenders, community-based correctional programs emerged as a potential alternative to the punitive approach. Thus, the need for “tough” community alternatives (what is known today as “interme- diate sanctions”) became evident (Clear & Hardyman, 1990). This new option held the promise of alleviating logistical and economic pressures while holding offenders accountable for their actions and maintaining public safety (Petersilia, 1999). As such, intermediate sanctions offered an appealing solution; they would alleviate pressures on jails and prisons and provide a continuum of sentencing choices (Caputo, 2004).
As intermediate sanctions were designed to offer a range of correctional options that fall in between probation and incarceration (Morris & Tonry, 1990), a spectrum of interventions was developed to reflect the varying severity in crimes and risk levels of offenders. Caputo (2004) identified eight principal forms of intermediate sanctions: intensive supervision programs, monetary penalties/restitution (including fines), com- munity service, day reporting centers, home confinement (including electronic moni- toring), boot camps, day halfway houses, and aftercare/postrelease supervision.
Three sequential conditions are said to have contributed to the emergence of inter- mediate sanctions: prison overcrowding, overuse of probation (and subsequent per- ception of correctional failure), and a lack of sentencing choices (Caputo, 2004; Clear & Hardyman, 1990).
Prison overcrowding. Although the prison population increased dramatically over a short period in the 1980s, systemic resources were not expanded, leading to system overcrowding that eventually exhausted available resources (Caputo, 2004; Wakefield & Uggen, 2010). Although building additional facilities to adequately house the grow- ing population of offenders was an apparent response, the endeavor would be costly (Morris & Tonry, 1990; Petersilia & Turner, 1993). This predicament led policymakers to set their eyes on the community and consider the possibility of community-based initiatives as viable options to relieve the pressures in areas that the criminal justice system was lacking.
Problems with probation. To alleviate prison overcrowding, more serious offenders were sentenced to probation, which resulted in an increase in the number of high-risk offenders in the community (Morris & Tonry, 1990; Torbet, 1996). The probation pop- ulation continued to grow and diversify, and probation officers could not keep up with the heightened demand as these new offenders required more intensive supervision and programming than could be allocated (Caputo, 2004; Petersilia, 1999). Over time, concern for public safety became widespread and probation began to lose credibility as a viable alternative to incarceration as recidivism rates for probationers increased.
Lack of sentencing choices. The lack of viable sentencing options other than probation and incarceration resulted in a polarizing choice between “soft” and “tough” alterna- tives (Caputo, 2004; Petersilia, 1999). In short, a sentencing option that struck a bal- ance between “too harsh” and “too lenient” was required—an option that fell in between probation and incarceration for youth with criminal activity severe enough to
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warrant attention and intervention but not severe enough for incarceration (Morris & Tonry, 1990; Petersilia, 1999).
The analyses presented herein will focus on a subset of intermediate sanctions— aftercare/reentry and ISP—and will examine the effectiveness of these programs on juvenile offenders as they relate to recidivism.
Aftercare/reentry. Aftercare/reentry programs were developed through the theoretical lens that increased supervision and monitoring will lead to a decrease in criminal activity. The goal of aftercare/reentry is to prevent recidivism among offenders who are released from custodial institutions (Caputo, 2004). Generally, aftercare/reentry programs aim to help offenders transition from the prison setting to the community setting. Aftercare/reentry programs include supervision as well as any service that is deemed to assist the successful transition and reintegration of prisoners from custody to the community (Petersilia, 2004).
ISP. ISP programs can be highly diverse in design and overall goals. As a “tough” community alternative, these programs highlight increased intensity, control, and supervision in comparison with traditional probation (Taxman, 2002). Although ISP programs are diverse, they are characterized by three primary features: small casel- oads, intensive surveillance, and strict conditions of compliance (Tonry, 1990).
Criticisms of Aftercare/Reentry and ISP
As discussed above, intermediate sanctions were developed in the 1980s and imple- mented with the expectation that the “tough” interventions (increased supervision and control) would decrease the prison population and reduce recidivism. However, although many offenders continue to be diverted to community-based interventions, the number of youth involved in the juvenile justice system remains high, creating scepticism toward the effectiveness of these “tough” interventions (Hockenberry & Puzzanchera, 2015; Jalbert, Rhodes, Flygare & Kane, 2010; Lowenkamp, Flores, Holsinger, Makarios & Latessa, 2010; Merrington, 2006). The literature highlights two possible reasons for ineffectiveness: (a) faulty theory and (b) high rates of failure.
Faulty theory. ISP programs were developed with the theoretical reasoning that smaller caseloads and more frequent contacts would lead to more intensive supervision. In turn, the intensive supervision would serve as a specific deterrent for youth and result in a reduction in recidivism (Baird, 1991; Bennett, 1988). The main criticism of inter- mediate sanctions in general and ISP programs in particular is that they lead to increased recidivism. That is, heightened supervision (offenders being watched more closely) may lead to increased detection of criminal activity (a higher likelihood of being caught) and subsequently result in increased (official) rates of recidivism (Caputo, 2004; Petersilia, 1999). As such, the theory that “more supervision will be better” may not be sound (Clear & Hardyman, 1990).
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High rates of failure. Intensive supervision is typically accompanied by strict condi- tions and strict enforcement (Caputo, 2004; Clear & Hardyman, 1990). Hence, in addi- tion to the watchfulness associated with intense monitoring, the vigilance related to strict conditions may lead to more technical violations or detection of deviant/criminal behavior that would have otherwise gone unnoticed (Caputo, 2004; Petersilia & Turner, 1993). If the point regarding increased technical violations and detection of deviant behavior is valid, then, altogether, ISP programs may appear to be working against their goal of reducing recidivism.
The Evidence for Community-Based Supervision Interventions on Juvenile Recidivism
The following section highlights findings from the most recent meta-analyses of com- munity-based supervision programs for juveniles. Notably, there has been a vast increase in the number of studies evaluating aftercare/reentry and ISP evaluations. Furthermore, there is little consistency with respect to positive or negative treatment effects, and, generally, researchers have not found statistically significant effects.
Aftercare/reentry. Overall, recent meta-analytic results are small and positive but vary with respect to statistical significance. Weaver and Campbell (2015) assessed the effects of 30 independent aftercare programs on the recidivism of juvenile offenders. The results demonstrated a small, positive, but nonsignificant effect (relative risk [RR] = 0.931, p = .117). In addition, James, Stams, Asscher, De Roo, and van der Laan (2013) examined the effects of 22 independent aftercare/reentry programs on recidi- vism for juveniles and young adult offenders. The analysis demonstrated a small, posi- tive, and significant effect, meaning that in comparison with the control group, youths who participated in an aftercare program post custody were significantly less likely to recidivate (d = 0.12, p < .001).
The studies above show that there is variability in effect sizes across studies; some programs are effective at reducing recidivism, while some are not. Notably, two recent meta-analyses on aftercare programs differed in their operationalization of the inter- vention as well as selection criteria, which resulted in differing samples and findings. Given the inconsistent findings between studies, it is important to be aware of opera- tionalization and selection criteria, and draw conclusions accordingly.
ISP. Generally, recent meta-analyses on ISP programs for youth suggest nonsignificant effects on reducing recidivism. Sarver, Molloy, and Butters’s (2012) meta-analysis produced an odds ratio of 0.88 (p < .01), suggesting that youth participating in ISP are significantly more likely to commit a new offense in comparison with youth on stan- dard probation. Lipsey (2009) also examined the effects of probation/parole on juve- nile offenders and found a slightly positive but nonsignificant standardized regression coefficient of 0.01. Similarly, Farrington and Welsh (2005) meta-analyzed ISP and aftercare programs, finding a positive yet nonstatistically significant effect for ISP (d = 0.02) in reducing recidivism.
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In sum, recent meta-analyses on the effects of aftercare programs and intensive supervision programs for youth have demonstrated inconsistent results with respect to juvenile recidivism. Notably, however, there are important distinctions between the existing meta-analyses and the current meta-analysis. With respect to aftercare, although Weaver and Campbell (2015) and James et al. (2013) assessed the effects of aftercare programs on juvenile recidivism, the differences between the studies lie primarily in the operationalization of “aftercare” interventions and the inclusion criteria. Specifically, Weaver and Campbell include programs in which the intervention serves as aftercare to shock incarceration programs as well as boot camp programs. Furthermore, James et al. only included aftercare programs that “incorporated a treatment modality such as skills training, counseling, and cognitive behavioral therapy” and excluded studies with an intervention focused on discipline or surveillance (p. 265). Thus, although the goals of Weaver and Campbell, James et al., and the meta-analysis presented herein appear sim- ilar, the differing operationalization of “aftercare” and differing inclusion criteria resulted in different sets of studies, which subsequently resulted in different findings with different implications. The current meta-analysis represents and includes the most up-to-date, pertinent, and complete set of aftercare programs for youth. For example, because shock incarceration and boot camp programs for youth are no longer common- place, the present analysis does not include these programs. Furthermore, in addition to including treatment-focused aftercare programs, the current meta-analysis also includes surveillance-oriented programs. Finally, with respect to ISP, the most recent literature on ISP programs demonstrates mixed findings. As can be seen in the description of the existing meta-analyses, of the studies that were selected to be included in the analyses, the control groups appear to vary from study to study (e.g., some use probationers, parolees, other programs, or a combination of these). This is an important issue to con- sider because interpretation of results and implications of the findings vary greatly depending on which comparison group is being referred to.
Aim of the Study
Despite the widespread endorsement and use of community-based sanctions for juvenile offenders, uncertainty remains regarding which community-based correctional interven- tions for youth are effective, which have no effect at all, and which are producing poten- tially destructive outcomes. As such, synthesized evaluations of community-based intensive supervision practices for youth are integral to developing an understanding of what works for juvenile offenders in the community setting. The benefits of synthesized research, however, not only extend far beyond the immediate understanding of the effects of intermediate sanctions on recidivism but also carry the potential for drawing broader lessons and improving future correctional policies and practices.
This article seeks to expand upon the existing evidence to determine the effect of ISP and aftercare/reentry on juvenile recidivism. This research is particularly impor- tant for juvenile offenders because, if the criticisms of intensive forms of supervision (potentially faulty theory, high rates of failure, and net widening) are shown to be true, then programs providing intensive forms of supervision may be working against their goals and perpetuating a cycle of violence.
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The current research seeks to expand upon the existing evidence as well as address some of the methodological concerns that are prevalent in the literature on this topic. Specifically, the stringent methodology (exhaustive search strategy and strict selection criteria), the generalizability of results to the “typical” offender, and the unique bifur- cated analyses on recidivistic outcome measures (discussed below) differentiate the current study from existing meta-analyses and underscore its importance. In addition, existing meta-analyses on the topic typically focus on aftercare or ISP programs in isolation, preventing a comparison of the relative effects of these different approaches with intermediate sanctions for youths. As the current study uses the same search strat- egy, selection criteria, and analytic approach for both program types, a discussion of the effectiveness of the two interventions is more meaningful. First, we investigate the effects of aftercare/reentry programs on juvenile recidivism compared with the recidi- vism rates of youth who receive “care as usual” post incarceration. Second, we assess the effects of ISP programs on juvenile recidivism compared with the recidivism of youth on standard probation.
To begin, we present and explain the steps and procedures used in the systematic review and meta-analysis, beginning with the literature search strategy and selection criteria employed. We also provide information on data collection and the analytical techniques used.
Inclusion criteria. Interventions were limited to ISP and aftercare/reentry programs that targeted juvenile offenders who were primarily between the ages of 12 and 18 years.1 Studies must have also been published in English and between the dates of January 1, 1990, and April 21, 2015. Studies were restricted to rigorous or moderately rigorous control group designs, including randomized controlled trials and quasi-experimental (QE) designs in which participants were matched on at least some variables (e.g., crimi- nal history, age, sex). In addition, measured outcomes had to be quantitative and crimi- nogenic in nature, report on at least one individual-level outcome measure of crime, and the reported outcomes must also have provided sufficient numerical or graphical data to allow for computation of an effect size. Furthermore, the program must have been delivered, at least partially, in a nonclosed setting in the community (i.e., school, youth custody, hospital).2 Finally, a minimum sample of 20 subjects in both the treatment and control groups was required, and the study had to have been conducted in Canada, the United States, Australia, New Zealand, or a Western European country.3
Exclusion criteria. Where the primary intervention in a study was “traditional” supervision (i.e., standard probation), the study was excluded.4 With respect to program participants, very specific types of programs targeting offenders such as perpetrators of domestic vio- lence, those with serious mental health problems, substance users, sex offenders, and known gang members were excluded. As this analysis focused on criminal recidivism,
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studies limited to status offenses (truancy, antisocial behavior, traffic violations) or techni- cal probation violations were not selected. In addition, studies that measured substance use (tobacco, alcohol, illicit substances) as the only outcome measure were not consid- ered.5 Finally, studies were excluded if the outcome measures were not measured for the youth who participated in the program or when the primary target of the intervention was someone other than the youth himself or herself (e.g., parent, family, sibling).6
Search Strategy for Identification of Studies
The steps listed below outline the process that was used in the systematic review and identification of relevant studies. (a) A systematic search for studies was conducted across 20 electronic databases (see Appendix A for a complete list of databases). (b) Four constructs were used in the development of a comprehensive list of key search terms: juvenile, criminogenic outcome, intervention, and evaluation. The search terms were applied to all 20 electronic databases, and key terms were searched in the abstract (see Appendix B for the complete search strategy). (c) Efforts toward conducting a comprehensive literature search also involved hand-searching the grey literature for reports, theses, dissertations, or individual papers that were potentially relevant to the present study.7
Data Collection and Analysis
One reviewer read through the titles and abstracts of identified hits to determine studies that appeared potentially relevant and to be retrieved for further review. Once the studies were retrieved in full, two reviewers used the inclusion and exclu- sion criteria to determine whether articles met the full list of selection criteria. Discrepancies between coders were discussed until a consensus was reached. Two reviewers participated in data extraction and the coding of studies, and the extracted data for each study were used to calculate an effect size. Next, data from each indi- vidual study were standardized so that studies could be meaningfully analyzed (Lipsey & Wilson, 2001; Sánchez-Meca, Marín-Martínez, & Chacón-Moscoso, 2003 ). As the majority of the selected studies in the current analysis used dichoto- mous data to report outcomes, effect sizes were calculated as odds ratios.8 The stan- dardized mean difference was used in the computation of effect sizes for continuous data, with the Cox logit transformation applied to enable commensurability with the odds ratios for dichotomous data.
Two approaches were used to assess sources of heterogeneity. The Q statistic indi- cates whether the calculated heterogeneity among studies is statistically significant, whereas the I2 statistic communicates the magnitude of heterogeneity and addresses the percentage of variability that can be attributed to factors other than chance (Lipsey & Wilson, 2001). Due to the small samples in the current meta-analyses, the primary analyses focus on the findings from the fixed effects (FE) models,9 with results from random effects (RE) models shown in a summary table in the “Discussion” section. Subgroup analyses were used to explore heterogeneity in the models. Four study
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characteristics were selected for closer examination: publication type, research design, follow-up period, and sample size of the treatment group.
Finally, sensitivity analysis was performed to assess the influence of outliers on the overall mean of the pooled treatment effect. When outliers were identified (through visual inspection of the funnel plot), an influence analysis was conducted to assess the degree of influence the outliers had on biasing the pooled estimate. When outliers were found to have a high degree of influence, they were removed in subsequent exploratory analyses (see Table 5).10
The next section demonstrates the findings from four separate meta-analyses and the related moderator analyses. The findings are separated by program type (aftercare/ reentry or ISP). Each program type is also separated by outcome measures/type of offense. With respect to outcome measure, recidivism was measured by both alleged offenses (arrests, charges, referrals, court/police contacts) and convicted offenses (con- victions, sustained petitions, adjudications, incarceration). The distinction by outcome measure in this analysis was meant to differentiate between alleged criminal acts ver- sus “true”/convicted criminal acts.
Table 1 presents the number of hits selected in each successive stage of the search. A total of 12,199 hits were identified in the initial search of electronic databases. The application of selection criteria resulted in a final sample of 41 individual program sites (from 27 studies).
Table 1. Search Strategy Result.
Step Classification Number of hits
1 Total number of search hits for “intermediate sanctions” 12,199 Total number of search hits once duplicates were
removed within databases 9,934
2 Total number of hits selected for review 385 Total number of hits selected for review once duplicates
were removed between databases 310
3 Total number of hits selected for inclusiona 106 4 Aftercare/reentry and ISP studies selected for inclusiona 52
Other “intermediate sanctions” studies 54 5 Number of individual studies/sites codeda 43
Final number of studies/sites coded (two studies excluded after coding)a
Note. ISP = intensive supervision probation. aDenotes when two reviewers participated.
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Table 2. Characteristics of Included Studies for Aftercare/Reentry Meta-Analysis.
Study characteristics Alleged offenses (n = 13), n (%)
Convicted offenses (n = 11), n (%)
Publication year 1990-1994 2 (15.38) 1 (9.09) 1995-1999 3 (23.08) 3 (27.27) 2000-2004 0 (0.0) 0 (0.0) 2005-2009 7 (53.85) 7 (63.64) 2010-2014 1 (7.69) 0 (0.0) Publication type Journal article 2 (15.38) 0 (0.0) Report 11 (84.62) 11 (100.0) Program delivery year 1980-1989 2 (15.38) 1 (9.09) 1990-1999 6 (46.15) 6 (54.55) 2000-2010 5 (38.46) 4 (36.36) Follow-up perioda
Less than 1 year 3 (25.0) 0 (0.0) Exactly 1 year 7 (58.33) 7 (70.0) More than 1 year 2 (16.67) 3 (30.0) Type of research design Randomized control trial 5 (38.46) 4 (36.36) QE (matched comparison) 2 (15.38) 4 (36.36) QE (weakly matched) 6 (46.15) 3 (27.27) Sample gender mix All males 6 (46.15) 6 (54.55) Mixed 7 (53.85) 5 (45.45) Sample race/ethnicity Caucasian/mixed 3 (23.08) 1 (9.09) Predominantly minority 10 (76.92) 10 (90.91) Sample size in treatment group Less than 100 10 (76.92) 8 (72.73) 100+ 3 (23.08) 3 (27.27)
Note. QE = quasi-experimental. aOne value missing for alleged offenses; one value missing for convicted offenses.
Characteristics of included studies. A total of 15 independent program sites (from 10 studies) were selected for inclusion and contributed 24 effect sizes. Thirteen sites reported on alleged offenses, whereas 11 sites reported on convicted offenses.11 Table 2 provides a comparison of study characteristics, by outcome measure.
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1a: Alleged offenses Pooled effect. Results from aftercare/reentry programs that presented outcomes
such as criminal contacts, charges, and arrests (n = 13) were pooled together. The overall mean estimate favored the treatment group with a pooled effect of 0.179 (z = 2.18, p = .029). This finding suggests that youth who participate in aftercare/reentry programs are less likely to be charged or arrested upon release from custody in com- parison with the control group. A significant Q statistic demonstrates a heterogeneous sample (43.20, df = 12, p < .001), and the I2 statistic indicates that 72.2% of this hetero- geneity can be attributed to factors beyond sampling error. The forest plot in Figure 1 provides a visual representation of the findings for the meta-analysis. Each individual data point plotted in the figure demonstrates the effect size of each study. The diamond and the broken vertical line show the overall pooled effect for the entire set of studies.
Subgroup analysis. All the variables examined here were significant moderators (see Table 4 for an overview of the findings). First, the logged odds ratio (LOR) for moder- ate research designs is indicative of weakly matched designs being related to stronger and statistically significant effects (LOR = .277, z = 2.88, p < .05). Second, the analy- sis for follow-up period suggests that short follow-ups are related to stronger effects (LOR = .390, z = 1.84, p = .066) and that, over time, treatment effects may dissipate. Finally, large sample sizes (100+ participants) are related to stronger and statistically significant effects (LOR = .342, z = 3.12, p < .05).
Figure 1. Forest plot for the effect of aftercare/reentry on alleged offenses (fixed effects). Note. CI = confidence interval.
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Figure 2. Forest plot for the effect of aftercare/reentry on convicted offenses (fixed effects). Note. CI = confidence interval.
1b: convicted offenses Pooled effect. Figure 2 demonstrates that the summary treatment effect for after-
care/reentry on convicted offenses (adjudication, conviction, incarceration; n = 11) is negative and nonsignificant (LOR = −.029, z = 0.27, p = .784), suggesting that there is no difference between the aftercare and comparison groups when it comes to convicted offenses. In addition, the sample is fairly homogeneous (Q = 14.16, df = 10, p = .166, I2 = 29.4%).
Subgroup analysis. Research design, follow-up period, and treatment group sample size were examined as potential moderator variables (Table 4). Length of follow-up period was the only study characteristic shown to be important at moderating treat- ment impact (LOR = −.523, z = 2.44, p < .05), suggesting that longer follow-ups (12+ months) are related to negative treatment effects. This finding is of interest because it suggests that the impact of aftercare/reentry programs may not have a lasting effect over time when it comes to reducing recidivism.
Characteristics of included studies. A total of 26 individual program sites (from 15 studies) were selected for inclusion and contributed 31 effect sizes. Twelve sites
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reported on alleged offenses, and 19 sites reported on convicted offenses.12 Table 3 offers a comparison of characteristics of included studies by outcome measure.
Table 3. Characteristics of Included Studies for Intensive Supervision Probation Meta- Analysis.
Study characteristics Alleged offenses (n = 12), n (%)
Convicted offenses (n = 19), n (%)
Publication year 1990-1994 2 (16.67) 2 (10.53) 1995-1999 1 (8.33) 0 (0.0) 2000-2004 6 (50.0) 14 (73.68) 2005-2009 3 (25.0) 3 (15.79) Publication type Journal article 6 (50.0) 4 (21.05) Report 6 (50.0) 15 (78.95) Program delivery yeara
1980-1989 1 (9.09) 2 (10.53) 1990-1999 6 (54.55) 13 (68.42) 2000-2010 4 (36.36) 4 (21.05) Location United States 11 (91.67) 16 (84.21) England 1 (8.33) 3 (15.79) Follow-up periodb
Less than1 year 3 (25.0) 0 (0.0) Exactly 1 year 5 (41.67) 4 (22.22) More than 1 year 4 (33.33) 14 (77.78) Type of research design Randomized control trial 10 (83.33) 15 (78.95) QE (matched comparison) 1 (8.33) 3 (15.79) QE (weakly matched) 1 (8.33) 1 (5.26) Sample gender mixc
All males 0 (0.0) 0 (0.0) Mixed 10 (100.0) 7 (100.0) Sample race/ethnicityd
Caucasian/mixed 4 (40.0) 5 (71.43) Predominantly minority 6 (60.0) 2 (28.57) Sample size in treatment group Less than 100 5 (41.67) 9 (47.37) 100+ 7 (58.33) 10 (52.63)
Note. QE = quasi-experimental. aOne value missing for alleged offenses. bOne value missing for convicted offenses. cTwo values missing for alleged offenses; 12 values missing for convicted offenses. dTwo values missing for alleged offenses; 12 values missing for convicted offenses.
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2a: alleged offenses Pooled effect. Twelve studies presenting outcomes for ISP programs and alleged
offenses (i.e., criminal contact, charges, arrests, etc.) were pooled together. As shown in Figure 3, the estimate for the FE model yielded a pooled effect of 0.088 (z = 1.25, p = .210), a very small and nonsignificant effect, suggesting that there are no significant differences between youth in the treatment and comparison groups with respect to alleged offenses. The significant Q statistic demonstrates a significantly heterogeneous sample (30.86, df = 11, p = .001, I2 = 64.4%).
Subgroup analysis. Subgroup analysis was performed to examine publication type (journal article or report), follow-up period (12+ months vs. 12 months or less), and treatment group sample size (less than 100 vs. 100+) as possible variables that mod- erate results. As shown in Table 4, for each of the variables examined, the Q-between statistic was nonsignificant, suggesting that variables other than the methodological characteristics investigated here are responsible for the heterogeneity present in the model.
2b: convicted offenses Pooled effect. The results for the pooled effect for ISP on convicted offenses (adju-
dication, conviction, incarceration; n = 19) are presented in Figure 4. Similar to the
Figure 3. Forest plot for the effect of intensive supervision probation on alleged offenses (fixed effects). Note. CI = confidence interval.
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Table 4. Summary of Subgroup Analyses.
Model Study characteristic Effect size and Q statistics
Aftercare/reentry, alleged offenses
Research design Strong (RCT or QE matched;
n = 7) LOR = −.087, z = 0.55, p = .584
Moderate (QE weakly matched; n = 6)
LOR = .277, z = 2.88, p = .004*
Between-studies heterogeneity QB = 3.84 ~ χ1 2 , p = .050*
Follow-up period Long (12 months+; n = 9) LOR = −.136, z = 1.34 p = .180 Short (<12 months; n = 3) LOR = .390, z = 1.84, p = .066 Between-studies heterogeneity QB = 5.01 ~ χ1
2 , p = .025* Treatment group sample size Less than 100 (n = 10) LOR = −.031, z = 0.25, p = .806 100+ (n = 3) LOR = .342, z = 3.12, p = .002* Between-studies heterogeneity QB = 5.04 ~ χ1
2 , p = .021* Aftercare/reentry,
Follow-up period Long (12 months+; n = 3) LOR = −.523, z = 2.44, p = .015* Short (12 months; n = 7) LOR = .021, z = 0.13, p = .893 Between-studies heterogeneity QB = 4.26 ~ χ1
2 , p = .039* Research design ns Treatment group sample size ns
ISP, alleged offenses
Publication type ns Follow-up period ns Treatment group sample size ns
ISP, convicted offenses
Publication type Journal article (n = 4) LOR = .245, z = 1.63, p = .103 Report (n = 15) LOR = −.224, z = 2.75, p = .006* Between-studies heterogeneity QB = 7.52 ~ χ1
2 , p = .006* Research design Strong (RCT; n = 15) LOR = −.021, z = 0.24, p = .814 Moderate (QE match/weak
match; n = 4) LOR = −.296, z = 2.45, p = .014*
Between-studies heterogeneity QB = 3.37 ~ χ1 2 , p = .066
Follow-up period Long (12 months+; n = 14) LOR = .056, z = 0.60, p = .550 Short (12 months; n = 4) LOR = −.450, z = 3.96, p = .000* Between-studies heterogeneity QB = 11.82 ~ χ1
2 , p < .001* Treatment group sample size Less than 100 (n = 9) LOR = .352, z = 2.18, p = .029* 100+ (n = 10) LOR = −.232, z = 2.91, p = .004* Between-studies heterogeneity QB = 10.54 ~ χ1
2 , p = .001*
Note. RCT = randomized control trial; QE = quasi-experimental; LOR = logged odds ratio; ISP = intensive supervision probation. * p < .05
1524 International Journal of Offender Therapy and Comparative Criminology 62(6)
Figure 4. Forest plot for the effect of intensive supervision probation on convicted offenses (fixed effects). Note. CI = confidence interval.
results for alleged offenses, the analysis for convicted offenses suggests that there is no difference between the treatment and control groups (LOR = −.117, z = 1.64, p = .101). The test for homogeneity revealed a heterogeneous sample (43.17, df = 18, p = .001), where the I2 statistic indicates that 58.3% of the heterogeneity can be attributed to nonrandom factors.
Subgroup analysis. The analysis shows that all four variables are significant modera- tors. The analysis for publication type suggests that technical reports are significantly related to negative effects (LOR = −.224, z = 2.75, p < .01). Furthermore, QE research designs are related to significantly negative effects (LOR = −.296, z = 2.45, p < .05). That is, the direction and magnitude of an effect size is tempered by whether the researchers used a randomized controlled design or a QE design. Treatment group sample size is also an important variable in moderating results as studies with large sample sizes (100+) have a larger effect than do smaller studies (LOR = −.232, z = 2.91, p < .01). Shorter follow-up periods are significantly related to negative effect sizes (LOR = −.450, z = 3.96, p < .001). Table 4 provides a summary of the findings from the moderator analyses discussed herein.
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Altogether, community-based supervision programs are promising when it comes to reducing juvenile recidivism. Although only one of the analyses demonstrated a statis- tically significant pooled effect, a clear pattern emerged across models with respect to the directionality of effects (shown in Table 5). The effect of the interventions (both aftercare/reentry and ISP) on alleged offenses favored the treatment group, whereas the effect of the interventions on convicted offenses favored the control group. More specifically, the results suggest that in comparison with “supervision as usual,” youth receiving intensive supervision (either through ISP or aftercare/reentry) are less likely to be arrested or charged for an offense but are more likely to be convicted of an offense. Due to length constraints, the full analyses for the alternative exploratory models were not presented herein (see Bouchard, 2015, for full details). The findings from all the analyses conducted on the data (FE models with and without outliers, and RE models) are summarized in Table 5. The finding that this pattern was consistent across intervention types is telling of the importance in making a distinction when it comes to recidivism as an outcome measure.
Furthermore, the nonstatistically significant findings presented herein for intensive supervision programs provide important policy implications and suggest that intensive forms of community-based supervision are not necessarily more effective in reducing recidivism compared with standard forms of supervision. In addition, this finding lends support to the existing literature and the criticism that the theoretical basis for aftercare/reentry and ISP programs is potentially faulty. To reiterate, intermediate
Table 5. Summary of Pooled Analyses.
Intervention Model Pooled estimate z
Aftercare, alleged offenses
Fixed effects (with outliers) 0.179 2.18, p = .029* Random effects (with outliers) 0.065 0.38, p = .075 Fixed effects (outliers
removed) −0.038 0.42, p = .678
Aftercare, convicted offenses
Fixed effects −0.029 0.27, p = .784 Random effects −0.074 0.55, p = .585
ISP, alleged offenses Fixed effects (with outliers) 0.088 1.25, p = .210 Random effects (with outliers) 0.047 0.35, p = .723 Fixed effects (outliers
removed) 0.037 0.39, p = .697
ISP, convicted offenses Fixed effects (with outliers) −0.117 1.64, p = .101 Random effects (with outliers) −0.015 0.12, p = .904 Fixed effects (outliers
removed) −0.094 1.21, p = .226
Note. ISP = intensive supervision probation. * p < .05
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sanctions were developed with the theoretical reasoning that intensive supervision (small caseloads and frequent contacts) would serve as a specific deterrent and decrease recidivism. The main criticism of intermediate sanctions is that the height- ened supervision increases the likelihood of a youth engaging in criminal/deviant behavior that would not be detected under standard supervision, and therefore leads to greater contact with the criminal justice system. Consistently, although the findings shown here do not suggest that youth participating in intensive forms of supervision are significantly more likely to engage in criminal activity, the findings do suggest that more supervision and strict conditions of compliance are not necessarily more effec- tive in reducing recidivism for juvenile offenders compared with traditional and less restrictive forms of supervision. Future research should investigate these patterns further.
The subgroup analyses herein highlight the importance of examining the moderating effect of methodological variables. The findings from the current study suggest that methodological factors should be carefully considered in evaluation design, and rigor- ous methodology should be used whenever possible. It is important to note that due to concerns of commensurability, small sample sizes, missing data, and inconsistent reporting across studies, subgroup analyses were limited to four variables. As such, potentially important moderator variables (e.g., program and sample characteristics) that have been identified as important moderator variables in previous meta-analyses could not be examined here. Notably, Weaver and Campbell (2015) suggested that offender type (violent or nonviolent) and quality of program implementation may be responsible for heterogeneous results, and James et al. (2013) noted that sample char- acteristics (e.g., age, ethnicity, gang involvement, drug use), implementation quality (see Goense, Assink, Stams, Boendermaker, & Hoeve, 2016), treatment intensity, and treatment design (favoring individual treatment over group treatment) are variables related to effect size heterogeneity. As there is some evidence that suggests program and sample characteristics can moderate the effects of aftercare/reentry and ISP inter- ventions, greater effort needs to be put forth by authors to consistently report details on program characteristics so that the effectiveness of interventions is not reduced to statements about programs being holistically effective or ineffective and so that het- erogeneity of effect sizes can be better explained.
In line with existing literature, the results do not present consistently favorable effects for either aftercare/reentry or ISP programs for youth. However, this does not diminish the importance of the new analyses. The current study differs from previ- ous meta-analyses on the topic in two primary ways: First, this meta-analysis is unique because it provides a bifurcated analysis of recidivism as an outcome mea- sure—a distinction that is demonstrably important when evaluating the treatment effects of community-based supervision programs. Second, the study differs from existing studies through the application of strict selection criterion. Specifically, the
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current analysis focuses on supervision-oriented interventions, excludes programs that focus on family-based interventions, and excludes programs that target a spe- cific population of youth such as substance users, sex offenders, and offenders with mental illness.
It can be argued that the strict exclusion criteria used herein prevent a broad sum- mary of evidence. That is, as the meta-analyses exclude very specific populations of offenders (substance users, issues with mental health), the findings are not represen- tative of the literature as a whole on ISP and aftercare/reentry programs. In addition, ideally, the study would have allowed for a larger sample size and a pooled analysis of the set of 55 effect sizes. Finally, the moderator analysis was confined by the inconsistent reporting of important variables, particularly program-level character- istics. As a result, the investigation of potentially moderating variables was limited to mostly study-level characteristics, which are important variables to consider in a moderator analysis but carry little potential to inform practical changes to policies and practices. Due to the limitations associated with conducting moderator analyses on certain variables, substantial heterogeneity in effect sizes remains to be explained in some of the models.
The evaluation and improvement of community-based supervision programs for juvenile offenders should be of utmost importance to correctional research. The findings from the four analyses provide important insight concerning the effective- ness of community-based supervision programs at reducing juvenile recidivism— results that carry important implications for future policies and practices. As the pooled results for ISP demonstrate varying degree of program effectiveness and no significant difference between the treatment and control groups, the findings sug- gest that intensive forms of probation are perhaps not necessary for effective super- vision. However, one analysis (the effect of aftercare/reentry on alleged offenses) did demonstrate a significant reduction among treatment group participants, pro- viding support for the deterring effect of supervision. Considering the mixed evi- dence (demonstrated here and in previous research), one could certainly argue that the inconsistent evidence is suggestive that, overall, these interventions do not reli- ably reduce recidivism and should therefore be replaced with new interventions or, at the very least, reconsidered. This is an important finding, and one that is worthy of serious consideration, as strict enforcement and conditions of compliance that are associated with some forms of intensive supervision are expensive, resource intensive, and may perpetuate a cycle of criminal activity. If synthesized research consistently demonstrates that there is no significant benefit to intensive forms of supervision, then altogether, these interventions may inadvertently contribute to the
1528 International Journal of Offender Therapy and Comparative Criminology 62(6)
revolving door of youth cycling through the criminal justice system, in adolescence and beyond. Furthermore, in consideration of the changes that have occurred since the emergence of intermediate sanctions (i.e., changes in policies, sentencing guidelines, and practices, as well as evolving ideologies, attitudes, and a general understanding of the criminal justice system), it may be prudent to revisit the con- cept of intermediate sanctions and reconsider whether these practices hold the same promise and theoretical value as they once did. Future correctional policies and practices can be rebuilt accordingly.
To draw more conclusive evidence about successful interventions, future research should focus on the detailed reporting of sample characteristics and inter- vention characteristics so that moderator analyses can investigate heterogeneity in effect sizes and the characteristics that are related to strong (or weak) results. If reported on reliably and consistently, moderator analyses will provide insight on the circumstances under which interventions are most effective (i.e., the presence or absence of certain intervention characteristics). Collectively, applying these modifications to evaluation research and reporting can pay off in dividends by cre- ating a better understanding “what works” in community-based programs for juve- nile offenders.
Electronic searches included the following 20 databases:
1. Academic Search Premier 2. Canadian Research Index 3. Cochrane Central Register of Controlled Trials 4. Cochrane Database of Systematic Reviews 5. Criminal Justice Abstracts 6. Database of Abstracts of Reviews of Effects 7. Education Resources Information Center (ERIC) 8. MEDLINE 9. National Criminal Justice Reference Service (NCJRS) 10. Open Access Theses and Dissertations 11. ProQuest Dissertations and Theses Full Text 12. PsycARTICLES 13. PsycBOOKS 14. PsycINFO 15. Public Affairs Information Service (PAIS) International 16. Social Sciences Abstracts 17. Social Sciences Full Text 18. Social Services Abstracts 19. Sociological Abstracts 20. Web of Science.
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(youth* OR juvenile* OR adolesc* OR teen*)
AND (crime* OR criminal* OR devian* OR violen* OR delinquen* OR offend* OR offense* OR recidiv* OR gang OR gangs)
AND (diversion* OR probat* OR parole OR aftercare OR reentry OR surveillance OR supervis* OR deterrence OR “alternative to imprisonment” OR “alternative to incar- ceration” OR “alternative to detention” OR restitution OR rehabilitat* OR “intensive community program” OR “graduated sanction*” OR “intermediate sanction*” OR “shock program*” OR “shock incarceration” OR “boot camp*” OR retribution OR counseling OR mentor* OR wilderness OR “day center*” OR “day reporting” OR “reporting center” OR “early release” OR “pretrial release” OR “supervised release” OR “electronic monitoring” OR “home confinement” OR “house arrest” OR “home detention” OR “community tracking” OR “community service” OR “halfway house” OR “transitional center*” OR “community correctional center*” OR “community release center*” OR “work release” OR “electronic release” OR “work camp” OR “residential treatment” OR “residential placement*” OR “residential service*” OR “residential program*” OR “wraparound program” OR “wraparound service*”)
AND (evaluat* OR effect* OR impact* OR outcome*)
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publication of this article.
1. When the age range of participants was slightly above or below 12 to 18 years (maximum age 21 years) but the average age of study participants fell in between 12 and 18 years, the study was eligible for inclusion.
2. Eligible programs could involve community-based supervision that was followed by a supervision component in a closed setting (i.e., prison, jail). Any other form of “closed” or “partially closed” setting (e.g., school based, secured-facility boot camp, or wilderness camp) that preceded the supervision, or in which the intervention itself was carried out, was excluded.
3. To maximize the generalizability of the results, studies were restricted to those that were conducted in countries that are reasonably similar in terms of types of offenders, general approach to criminal justice, and intervention types.
1530 International Journal of Offender Therapy and Comparative Criminology 62(6)
4. Standard probation served as the comparison for many of the studies and was therefore not appropriate to include.
5. However, when substance use was the primary outcome measure but the study also reported on other recidivistic outcome measures, the study was eligible for inclusion. The intention behind this exclusion criterion was to maximize study commensurability as well as gener- alizability of findings. As substance abuse programs target very specific types of offenders and related behaviors, interventions specifically targeting substance use are arguably of a different variety than the supervision-focused interventions analyzed here.
6. For example, in some circumstances, the primary focus of the program was parenting- related skills or workshops, or family-related interventions (e.g., Functional Family Therapy), where, in essence, the study was evaluating the effect of the intervention on the youth; however, the outcome measure was related to an entity other than the youth them- selves and the youth did not participate in the program directly.
7. Hand searches were conducted in relevant journals (e.g., Criminology, Evaluation Review, Federal Probation), the curriculum vitae of pertinent authors (e.g., Peter Greenwood, Joan Petersilia, Faye Taxman), and websites of relevant organizations (e.g., Office of Juvenile Justice and Delinquency Prevention [OJJDP], Crime Solutions).
8. Odds ratios were log-transformed such that a logged odds ratio of 0 demonstrates no dif- ferential treatment impact between groups. Studies were coded, so a value below 0 demon- strates that the intervention favors the control group and the treatment group is more likely to experience the event, whereas a logged odds ratio with a value above 0 indicates a beneficial impact with the treatment group being less likely to recidivate (Lipsey & Wilson, 2001). David Wilson’s online effect size calculator was used for the computation of all effect sizes (http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php).
9. When high levels of heterogeneity are present in a meta-analytic model, the general litera- ture calls for the use of a random effects (RE) model to portray the data. Recent work by Guolo and Varin (2015) and Schulze (2007) suggests that with small samples, the results from the RE model may present an imprecise measurement of the pooled effect.
10. Visual inspection of funnel plots was also used to examine publication bias. For all models, the funnel plots (not presented here) were symmetrical, indicating an absence of publica- tion bias. Further detail on analyses of publication bias for each of the models can be found in Bouchard (2015). The authors excluded studies that reported abnormally large or small effect sizes due to the possibility that, for various reasons (e.g., implementation validity, program characteristics, offender characteristics, etc.) of which we were unable to examine through moderator analysis, these studies may be substantively different than the other studies in the pooled analysis. This decision was further validated by the significant decrease in levels of heterogeneity when the outliers were removed (full details available in Bouchard, 2015).
11. As the majority of the sites reported on both outcome measures, the effect sizes are not independent of each other.
12. Multiple sites (n = 5) reported on both outcomes, and thus, effect sizes are not independent of each other.
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