Forensic Hospitalization Following Diagnosis of Nonaffective Psychotic Disorder: A Retrospective Cohort Study Using Health Administrative Data
Bibliographic record
Abstract
Young people with a psychotic disorder are at higher risk of violence and contact with the criminal justice system, particularly in early psychosis (the first 2 to 5 years of illness), during help-seeking and before receiving symptom-stabilizing treatment. Criminal justice involvement may lead to entering the forensic mental health system. Experiencing a forensic hospitalization has the potential to influence long-term outcomes, given the typically long detention periods in hospital and significant stigma associated. However, there is a dearth of evidence related to the frequency of forensic hospitalization and associated factors. The aim of this project is to examine the incidence, risk factors, and mental health service use pathways associated with forensic hospitalization following a first episode of psychosis. This project will use population-based health administrative data to construct a retrospective cohort of people, aged 14 to 50 years, with first onset nonaffective psychotic disorder in Ontario, Canada. We will estimate the incidence of forensic hospitalization following first diagnosis and will examine the relationship between time with psychosis and risk of forensic hospitalization to identify high-risk periods. We will explore the sociodemographic, clinical, and service use factors associated with forensic hospitalization. We will also identify trajectories of mental health service use in the 5-year period prior to admission. Findings from this study will identify subgroups of people with psychosis at high-risk for forensic hospitalization and could highlight opportunities for earlier intervention in the mental health care 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".