Postcharge mental health diversion: characteristics of clients and predictors of success
Bibliographic record
Abstract
How to best serve criminal offenders who have mental health issues is of ongoing concern within the justice system in Canada. Mental health diversion has become a popular option that allows mentally disordered offenders to be diverted from custodial sentences to community treatment and supports. However, research on this type of intervention, particularly in Canada, is scant. In order to address this gap, the current set of studies examined mental health diversion in a multisite sample obtained from court support programs in the Greater Toronto Area. In Study 1, it was found that individuals who successfully completed their diversion programming were less likely to have a criminal history and had fewer clinical and psychosocial issues. These results were borne out in the multivariate analyses in Studies 2 and 3 as well, with individuals who had a criminal history, more clinical needs, and who committed more severe nonviolent index offences having lower odds of successfully completing their diversions. In Study 4, when these predictors were developed into a screening tool to determine the likelihood of diversion success, they still predicted diversion outcome at better than chance levels, but the overall predictive accuracy was lower than that found in the multivariate models from Study 3.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".