Does gender matter? Exploring mental health recovery court legal and health outcomes
Bibliographic record
Abstract
BACKGROUND: Based upon therapeutic justice principles, mental health courts use legal leverage to improve access and compliance to treatment for defendants who are mentally ill. Justice-involved women have a higher prevalence of mental illness than men, and it plays a greater role in their criminal behavior. Despite this, studies examining whether women respond differently than men to mental health courts are lacking. Study goals were to examine gender-related differences in mental health court participation, and in criminal justice, psychiatric and health-related outcomes. METHODS: This study utilized a quasi-experimental pre-posttest design without a control group. The data were abstracted from administrative records of Kalamazoo Community Mental Health and Substance Abuse agency, the county jail and both county hospitals, 2008 through 2011. Generalized estimating equation regression was used to assess gender-differences in pre-post program outcomes (jail days, psychiatric and medical hospitalization days, emergency department visits) for the 30 women and 63 men with a final mental health court disposition. RESULTS: Program-eligible females were more likely than males to become enrolled in mental health court. Otherwise they were similar on all measured program-participation characteristics: treatment compliance, WRAP participation and graduation rate. All participants showed significant reductions in emergency department visits, but women-completers had significantly steeper drops than males: from 6.7 emergency department visits to 1.3 for women, and from 4.1 to 2.4 for men. A similar gender pattern emerged with medical-hospitalization-days: from 2.2 medical hospital days down to 0.1 for women, and from 0.9 days up to 1.8 for men. While women had fewer psychiatric hospitalization days than men regardless of program involvement (2.5 and 4.6, respectively), both genders experienced fewer days after MHRC compared to before. Women and men showed equal gains from successful program completion in reduced jail days. CONCLUSIONS: Despite similar participation characteristics, findings point to greater health gains by female compared to male participants, and to lower overall psychiatric acuity. Mental-health-court participation was associated with decreased psychiatric hospitalization days and emergency department visits. Successful program completion correlated to fewer jail days for both women and men.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".