Patterns and Predictors of Reincarceration among Prisoners with Serious Mental Illness: A Cohort Study: Modèles et prédicteurs de réincarcération chez les prisonniers souffrant de maladie mentale grave : Une étude de cohorte
Bibliographic record
Abstract
BACKGROUND: A small proportion of people who have serious mental illness and rapid and frequent incarcerations account for a disproportionate amount of overall service use and cost. It is important to describe such individuals, so that services can respond more effectively. METHODS: We investigated a cohort of 4,704 incarcerated men and women who were discharged from a correctional mental health service and followed for a median of 535 days. We investigated social, clinical, demographic, and offense characteristics as predictors of return to the service using Cox survival analyses. Secondly, we characterized individuals as high-frequency service users as those who had 3 or more incarcerations during a 1-year period and investigated their characteristics. RESULTS: We found that a higher rate of return to custody was associated with schizophrenia spectrum/bipolar affective disorder (BPAD), personality disorder traits, crack cocaine and methamphetamine use, and unstable housing. Charges of theft/robbery and breach of probation were also positively associated, and sex assault was negatively associated with return to custody. Within a 1-year time period, we found 7.2% of individuals were high-frequency service users, which accounted for 19.5% of all reincarcerations. CONCLUSION: Identification of the characteristics of those with mental illness in custody, especially those who have high-frequency returns to custody, may provide opportunity to target resources more effectively. The primary targets of intervention would be to treat those with schizophrenia/BPAD and substance use problems, particularly those using stimulants, and addressing homelessness. This could reduce the problem of repeated criminalization of the mentally ill and reduce the overall incarceration rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".