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Record W4408648371 · doi:10.1177/0306624x251327574

The Effects of Halfway Houses on Criminal Recidivism: An Updated Systematic Review and Meta-Analysis

2025· article· en· W4408648371 on OpenAlexaff
Jennifer S. Wong, Kia Neilsen, Kelsey Gushue, Chelsey Lee

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRecidivismConvictionMeta-analysisPsychologyIntervention (counseling)Best practiceSystematic reviewCriminologyActuarial sciencePublic relationsApplied psychologyMedicinePolitical scienceBusinessPsychiatryMEDLINELaw

Abstract

fetched live from OpenAlex

Halfway houses operate as a form of community supervision, offering a unique opportunity for individuals who have offended to receive housing, support, and other resources to aid in navigating the challenges of re-entry from closed custody. Despite being controversial in the eyes of the public, they have long been viewed by stakeholders as a worthwhile intervention. However, existing literature presents mixed findings on their utility. The current study provides a systematic review and meta-analysis of nine studies providing 17 effect sizes on the effects of halfway houses on recidivism. Findings indicate that halfway houses do not result in any differences for treatment versus comparison group participants with respect to outcomes of arrest ( k = 6), conviction ( k = 5), or incarceration ( k = 6). Additional research is needed to inform best practices for structure and services, and how best to respond to differing participant needs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.038
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.024
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.238
GPT teacher head0.417
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2025
Admission routes1
Has abstractyes

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Same venueInternational Journal of Offender Therapy and Comparative CriminologySame topicCriminal Justice and Corrections AnalysisFrench-language works237,207