Review on the effectiveness of Canadian and American mental health courts
Bibliographic record
Abstract
Objective: This systematic review synthesizes mental health court (MHC) research across the United States and Canada. This study reviews and compares the operations and practices of MHCs across both countries, as well as their recidivism rates. Methods: We gathered from existing literature to present common MHC practices used across the United States. However, in response to the lack of literature about Canadian day-to-day practices, we developed a questionnaire and contacted every Canadian MHC. In total, we contacted 36 Canadian MHCs, and 19 courts filled out a questionnaire. With respect to recidivism rates, we conducted a comprehensive literature search in February and March 2019 in PsycINFO, Google Scholar, Web of Science, and National Criminal Justice Reference Service Abstracts using the keywords mental health court, therapeutic justice, serious mental illness, mentally ill offenders, mental health diversion and problem-solving courts. Results: Canadian and American MHCs have similar practices. However, American MHC’s have more robust screening measures and typically admit more participants with schizophrenia, bipolar disorder, and major depressive disorder into their programs compared to Canadian MHCs. MHC participants in both countries typically had lower recidivism rates compared to regular docket court participants. Conclusions: MHC research should inform public policy. Additional research should move in the direction of discovering the predictors for why MHCs reduce 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 | 0.018 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".