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Record W2586171314 · doi:10.1177/0306624x17690449

Examining the Effects of Intensive Supervision and Aftercare Programs for At-Risk Youth: A Systematic Review and Meta-Analysis

2017· review· en· W2586171314 on OpenAlexaff
Jessica Bouchard, Jennifer S. Wong

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2017
Typereview
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRecidivismPsychological interventionIntervention (counseling)Meta-analysisReentryPsychologyOutcome (game theory)Juvenile delinquencyPoison controlSuicide preventionClinical psychologyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.464
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.632
GPT teacher head0.469
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2017
Admission routes1
Has abstractyes

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