Correlates of Mental Health Diversion Completion in a Canadian Consortium
Bibliographic record
Abstract
Mental health diversion is an important option for offenders with mental illness who do not pose a serious risk to public safety and who would otherwise be better served outside the criminal justice system. Predictors of complete vs. incomplete diversion were examined in a sample of 708 defendants seen in Toronto's mental health diversion programs. Univariate analyses revealed that unsuccessfully diverted defendants were significantly more likely to be younger, homeless, and have more clinical and legal needs compared to those who were successfully diverted. In multivariate analyses, criminological factors (e.g., criminal history) had the strongest association with diversion completion, compared to clinical (e.g., primary diagnosis) and psychosocial (e.g., employment status) factors outside of marital status, which was strongly associated with completion. The results from this research add to previous research on mental health courts and diversion by giving guidance on how to select and prepare diversion candidates. These findings suggest that diversion programs may benefit from adaptations in order to better suit high need clients.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".