Care Pathways, Health Service Use Patterns and Opportunities for Justice Involvement Prevention Among Forensic Mental Health Clients
Bibliographic record
Abstract
OBJECTIVES: The objective of the present study is to describe the patterns of health service use and of prescription claims in the year preceding an offense leading to a verdict of not criminally responsible on account of a mental disorder (NCRMD). METHODS: Provincial health administrative databases were used to identify medical services, hospitalizations, and ambulatory prescription claims among 1,014 individuals found NCRMD in Québec. Contacts in the year preceding the offense were analyzed using descriptive analyses and latent class analysis. RESULTS: Overall, 71.4% of subjects were in contact with services for mental health reasons within a year of their NCRMD offense. Among those that received services and not hospitalized for psychiatric reasons at the time of the offense, 20.7% committed the NCRMD offense within a week of the most recent mental health contact. Among those that had at least one prescription claim for an antipsychotic, 45.8% were not taking any antipsychotic at the time of the offense. Latent class analysis provided a multidimensional representation of mental health service use and showed that 58.4% of subjects had no or very rare contact with services. CONCLUSIONS: Many forensic patients are likely to have experienced service disruption or discontinuity while in the community, for reasons that may relate to perceived need for care, to service organization, or to the acceptability, availability, and accessibility of services. Given the serious impact of the "forensic" label on the lives of service users, not to mention the increased pressure on resources, the considerable economic costs, and the impact on victims, there is reason to advocate for a greater involvement of mental and physical health service providers in early prevention of violence, which requires reorganizing resources to share the forensic knowledge upstream, before an offense is committed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".