Reintegration After Incarceration for People with Mental Illness: A Pilot Community Mental Health Bridging Service
Bibliographic record
Abstract
The prevalence of serious mental illness in correctional facilities is approximately eight times higher than in the general population. The difficulties experienced by people with serious mental illness and correctional involvement are often compounded by substance abuse, homelessness, lack of support and stigma. While psychiatric treatment is provided in custody, rapid access to mental health services upon release is essential to ensure continuity of prescribed medications, monitoring and support in areas such as finances and housing. However, there are barriers to accessing services upon release, resulting in high rates of return to custody. In addition, there is a shortage of community psychiatrists and often long waiting lists for assertive community treatment teams. In this paper, we describe the development of an innovative community service to fill this gap. For the past 10 years, the Forensic Early Intervention Service (FEIS) has provided mental health consultation and case management services within two correctional centres in Toronto. The service has now been expanded into the community to provide continuity of care for individuals released from custody who otherwise have no existing mental health service provider. We describe the structure of the new service and the gap it seeks to fill.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".