The Ontario Forensic Mental Health System: A Population-based Review
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to increase the understanding of the Canadian forensic psychiatry system by providing a population-based overview of the Ontario forensic mental health system. METHODS: Data were collected on 1,240 accused adults who were subject to the jurisdiction of the Ontario Review Board (ORB) between 2014 and 2015. Archival data were retrieved from annual ORB hearing hospital reports for accused supervised by all nine adult forensic psychiatry facilities across Ontario. RESULTS: The sample included not criminally responsible (NCR; 91.6%) and unfit to stand trial (UST; 8.4%) accused. The majority of the sample was male (85.7%), single (70.1%), unemployed (63.6%), with a high school education (48.8%). Most were on a detention order (78.5%) and almost half were living in the community at the time of the report (48.8%). The majority had prior contact with psychiatric services (83.1%) and/or the criminal justice system (70.6%) before entering the forensic system. A history of elopement (31.5%) and inpatient aggression was high (60.6%). Most had a psychotic spectrum disorder (81.6%) and over half had a substance use disorder (57.2%) in the reporting year. A range of index offences was observed (69.9% violent, 20.3% general, 9.8% sexual), and the majority of the sample (61.0%) had an index offence that resulted in no injury or a minor injury to the victim. CONCLUSION: The Canadian forensic psychiatry system is comprised of a unique subset of justice-involved individuals. This study provides a detailed examination of accused who are subject to the jurisdiction of the ORB and provides key insight into risk factors associated with offending behaviour in this population. The results of this study will provide a framework for future studies examining the association between mental disorder and violence and the treatment trajectories for those in the forensic psychiatry system.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".