Treating offenders with mental illness: A research synthesis.
Bibliographic record
Abstract
The purpose of this research synthesis was to examine treatment effects across studies of the service providers to offenders with mental illness. Meta-analytic techniques were applied to 26 empirical studies obtained from a review of 12,154 research documents. Outcomes of interest in this review included measures of both psychiatric and criminal functioning. Although meta-analytic results are based on a small sample of available studies, results suggest interventions with offenders with mental illness effectively reduced symptoms of distress, improving offender's ability to cope with their problems, and resulted in improved behavioral markers including institutional adjustment and behavioral functioning. Furthermore, interventions specifically designed to meet the psychiatric and criminal justice needs of offenders with mental illness have shown to produce significant reductions in psychiatric and criminal recidivism. Finally, this review highlighted admission policies and treatment strategies (e.g., use of homework), which produced the most positive benefits. Results of this research synthesis are directly relevant for service providers in both criminal justice and mental health systems (e.g., psychiatric hospitals) as well as community settings by informing treatment strategies for the first time, which are based on empirical evidence. In addition, the implications of these results to policy makers tasked with the responsibility of designating services for this special needs population are highlighted.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".