Managing correctional treatment for reduced recidivism: A meta‐analytic review of programme integrity
Bibliographic record
Abstract
Purpose. Although issues surrounding programme integrity and implementation seem intuitively appealing as important contributors to effective correctional programming, they have been relatively ignored within the extant literature. The present meta‐analysis provided the first systematic examination of these issues by exploring their impact on recidivism reduction in correctional treatment programmes. Methods. A meta‐analysis was conducted on 273 tests of the effectiveness of correctional treatment programmes that were extracted from various human service programmes. Indicators of programme integrity reviewed included several management variables (i.e. selection, training and clinical supervision of service deliverers), evaluator involvement, presence of training manuals, monitoring of treatment delivery, and using a small sample of clients. Results. Overall, the meta‐analyses revealed that programme integrity provided an independent source of enhanced programme effectiveness, even when controls were introduced for other variables (e.g. involved evaluator and sample size). Conclusions. Consistent with previous research, the present study demonstrated that the positive contributions of programme integrity were limited to the enhancement of the effects of human service programmes consistent with the principles of risk, need, and general responsivity. However, the relatively poor reporting of programme integrity indicators within primary studies necessitates that evaluators and programme deliverers alike ensure that this information is included in future evaluations to provide an even greater understanding of the influences of integrity.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".