Supportive housing and forensic patient outcomes.
Bibliographic record
Abstract
In Canada, Review Boards are mandated to evaluate individuals found Not Criminally Responsible on Account of Mental Disorder (NCRMD) on an annual basis and render 1 of 3 dispositions: (a) custody, (b) conditional discharge, or (c) absolute discharge. To promote social reintegration, conditional discharge can be ordered with the condition to live in supportive housing. However, NCRMD accused face great barriers to housing access as a result of the stigma associated with the forensic label. The goal of this study was to evaluate the role of housing in the clinical and criminal trajectories of forensic patients as they reintegrate into the community. Data for this study were extracted from a national study of individuals found NCRMD in Canada (Crocker, Nicholls, Seto, Côté, et al., in press). The present study focuses on a random sample of NCRMD accused in the province of Québec, who were under a conditional discharge disposition during the study period (n = 837). Controlling for sociodemographic, clinical, and criminal variables, survival analysis showed that individuals placed in independent housing following a conditional discharge from the Review Board were 2.5 times more likely to commit a new offense, nearly 3 times more likely to commit an offense against a person, and 1.4 times more likely to be readmitted for psychiatric treatment compared with individuals residing in supportive housing. These results point to the influence housing can have on the trajectories of forensic patients, above and beyond a range of clinical, criminological, and sociodemographic factors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".