Stopping the revolving door: A meta-analysis on the effectiveness of interventions for criminally involved individuals with major mental disorders.
Bibliographic record
Abstract
Faced with high and increasing rates of mental disorder within the criminal justice system (CJS), a range of interventions have been implemented in an effort to prevent continued involvement in criminal activities among this population. A meta-analytic review was undertaken to consider the effectiveness of interventions for criminally involved adults with a mental disorder targeting either improved criminal justice or mental health outcomes. Furthermore, characteristics that were hypothesized to predict better outcomes were examined. Studies that considered sex offender interventions, or focused solely on antisocial personality, intellectual and cognitive, or substance use disorders were excluded. Results assuming a fixed-effects model combining 37 effect sizes from 25 studies (N = 15,678) support the effectiveness of these interventions in terms of reductions in any CJS involvement (d = 0.19 excluding one outlier). Interventions had no significant effect on an aggregate mental health outcome (d = 0.00). However, when considering distinct mental health outcomes, intervention participants had significantly better functioning (d = 0.20) and fewer symptoms (d = 0.12). There were no significant effects of the interventions on mental health service or medication use. Moderator analyses identified seven sample, intervention, and design characteristics that were related to the magnitude of the effect sizes for criminal justice outcomes, and suggest implications for service provision, policy, and research. Results suggested some relationship between intervention effects on mental health and criminal justice reinvolvement, although future research is needed in this area, especially given the absence of mental health outcome data in many studies.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".