Does specialized psychological treatment for offending reduce recidivism? A meta-analysis examining staff and program variables as predictors of treatment effectiveness
Bibliographic record
Abstract
A meta-analysis was conducted to examine whether specialized psychological offense treatments were associated with reductions in offense specific and non-offense specific recidivism. Staff and treatment program moderators were also explored. The review examined 70 studies and 55,604 individuals who had offended. Three specialized treatments were examined: sexual offense, domestic violence, and general violence programs. Across all programs, offense specific recidivism was 13.4% for treated individuals and 19.4% for untreated comparisons over an average follow up of 66.1 months. Relative reductions in offense specific recidivism were 32.6% for sexual offense programs, 36.0% for domestic violence programs, and 24.3% for general violence programs. All programs were also associated with significant reductions in non-offense specific recidivism. Overall, treatment effectiveness appeared improved when programs received consistent hands-on input from a qualified registered psychologist and facilitating staff were provided with clinical supervision. Numerous program variables appeared important for optimizing the effectiveness of specialized psychological offense programs (e.g., arousal reconditioning for sexual offense programs, treatment approach for domestic violence programs). The findings show that such treatments are associated with robust reductions in offense specific and non-offense specific recidivism. We urge treatment providers to pay particular attention to staffing and program implementation variables for optimal recidivism reductions.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".