950 – Achieving Positive Outcome - Reducing Recidivism Within Toronto's Mental Health Court Support Program: The Mount Sinai Hospital Experience
Bibliographic record
Abstract
This paper investigates the recidivism of Mount Sinai Hospital mental health court support program in Toronto, Canada among patients involved in the criminal justice system. It also looks to find relationships between recidivism and factors including gender, age and ethnicity. Method Follow up periods of up to 48 months after the time of initial admission to the program was conducted and the frequency of re-offense was observed. Comparisons for the significance of risk factors were analyzed using t-tests and Chisquare tests. Results 191 clients were admitted to the Mount Sinai Hospital Court Support Program between September 2001 and June 2007. At first admission, the mean ± s.d. age was 35.8 ± 9.8 years (range=18-74 years; n=184). The median age was 35 years. The modal age was 34 years. Of the 191 clients, 16 (8.4%) reoffended. Two of them (12.5%) had a third offense; and 1 (6.3%) had a total of four offenses within this tracking period. it appears that re-offense is more likely between 13 and 24 months. No re-offense was noted beyond the 48 months. The gender distribution was not significantly different between reoffenders and non-reoffenders. The mean age at first admission also did not differ between reoffenders and non-reoffenders. The distribution of ethnic groups among reoffenders and non-reoffenders did not differ. Conclusions The findings seem to indicate that recidivism has no relationship with gender, age and ethnic groups. The comprehensive and length of support services seem more important in preventing recidivism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| 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".