The Role of Gender in Mental Health Court Admission and Completion
Bibliographic record
Abstract
Mental Health Courts (MHCs) have emerged across North America in an effort to address the criminalization of persons with mental illness. Despite a growing body of literature examining MHCs, research on the role of gender in MHCs remains scarce. For this study, secondary data were analysed to examine whether gender differences in mental illness and crime affected the likelihood of MHC admission and completion in referrals to the Nova Scotia MHC (507 men, 243 women). Consistent with predictions, MHC admission and completion rates were similar between men and women. Higher rates of psychotic and substance use disorders were observed among men, whereas women had higher rates of mood and personality disorders. Although cases with psychotic and mood disorders were more likely to be admitted to the MHC, and those with substance use and personality disorders were less likely to be admitted, these differences did not vary by gender. Contrary to prediction, men had higher rates of violent index offences than women; however, this difference was only present for those who were not admitted to the MHC. Findings are discussed in terms of contributions to the literature surrounding the role of gender in MHCs, as well as notable implications for MHC practices and research.
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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 | 0.003 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".