Examining Characteristics Associated with Mental Health Service Utilization among Forensic Patients in Central Canada
Bibliographic record
Abstract
Identifying characteristics and mental health service use patterns among forensic mental health patients in Manitoba can have serious implications for intervention and treatment management. By describing a sample of forensic mental health patients and their psychiatric care pathways, we can examine if individual differences in a sample of forensic mental health patients are associated with the intensity of acute mental health service use. Forensic mental health patients often present with complex mental health needs and, subsequently, frequent or intense use of acute psychiatric services. By better understanding the characteristics of this complex group, we can also aim to identify social risk factors related to minimal or no contact with mental health services to explore existing barriers to accessing proactive or preventative mental health care. The present study aims to describe the characteristics and trajectories of psychiatric hospitalization among forensic patients by doing a secondary data analysis of a retrospective chart review of 71 individuals found NCR or Unfit to Stand Trial under the Manitoba Review Board between 2000 and 2015.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".