Revisiting “what works”: A systematic review of Correctional Service of Canada offender treatment programs across time
Bibliographic record
Abstract
Rehabilitation and successful reintegration of offenders into the community is one of the primary goals of the Canadian justice system. As such, a comprehensive examination of the effectiveness of offender treatment programs across time will help to identify those factors that are directly related to successful offender treatment implementation and effectiveness. This review involves a systematic statistical examination of the past 40 years of offender treatment programs delivered through Correctional Service of Canada, using publically available documents. The effect size of several variables are examined to assess their impact on overall recidivism rates, including training of correctional personnel, referral criteria, and program drop-out rates. The results of this review critically assess advances in federal correctional programming practices and evaluation. Results are compiled to examine how program effect sizes have shifted with the changing landscape of offender rehabilitation over the last four decades, which started as small-scale efforts to build effective programming before advancing to the widespread implementation of manualized programs. An integrative discussion outlines lessons learned for future approaches to offender rehabilitation and successful implementation of contemporary offender programming.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".