A meta-analysis of the effect of violence intervention programs on general and violent recidivism
Bibliographic record
Abstract
Individuals with convictions for violence are likely to have both violent and nonviolent subsequent reoffences. Individuals who have committed violent offences are often required to participate in violence treatment programming prior to release. The aim of this study was to examine whether violence intervention programs offered in community or institutional correctional settings are effective for reducing general and violent recidivism among individuals with previous histories of violence. In total, 21 controlled studies with data from 17,223 violent offenders (99% men) were included in the meta-analysis for general recidivism, and 19 controlled studies with data from 8,863 offenders (99% men) were included in the meta-analysis for violent recidivism. This article extends an earlier meta-analysis by Papalia et al. (Clinical Psychology: Science and Practice, 26(2), 1–28 [2019]) by adding seven new studies to the meta-analysis of general recidivism and five new studies to the meta-analysis of violent recidivism. The results of the meta-analysis indicate that the odds of general recidivism were 25% lower, and the odds of violent recidivism were 24% lower for individuals who participated in interventions compared with the control groups. The results of the present study are consistent with previous meta-analyses, which support the use of correctional violence treatment programs. Implications for future research are identified, considering these findings.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.054 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".