Mentally ill individuals in limbo: obstacles and opportunities for providing psychiatric services to corrections inmates with mental illness
Bibliographic record
Abstract
For more than two millennia, prison reformers and inmate advocates have lamented the ethical and humanitarian injustices experienced by persons with mental illness in correctional settings; yet, we continue to see mentally ill individuals stuck in limbo between health care and custody. Using a case study that exemplifies the challenges that can prevent the provision of efficient mental health care in correctional settings, we identify the gaps and failures in service delivery, and provide specific strategies for streamlining inmates' access to psychiatric assessment and treatment. As a backdrop, we present a brief overview of the reasons why correctional centres experience difficulties in ensuring expedient care (e.g., competence, mental health legislation, waitlists) as well as reviewing the prevalence of mental health needs in correctional settings. Using the partnership and strategic alignment that have been developed for several years between our forensic psychiatric system and our provincial correctional system, we provide a roadmap to successfully reducing wait times and enhancing service delivery to mentally ill inmates. In our view, custody admissions provide a rare opportunity to provide mental health (and other services) to marginalized individuals who often slip through the cracks. imPROVE and related programs and strategies have been found to be effective means of preventing these opportunities from being lost.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".