The effectiveness of mental health courts in reducing recidivism and police contact: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Mental health courts were created to help criminal defendants who have a mental illness that significantly contributes to their criminal offense. Despite the increasing number of mental health courts around the world, data about their effectiveness have only begun to emerge in the past decade. The purpose of this systematic literature review is to assess the current evidence on the effectiveness of mental health courts. Specifically, this review will address the question, "How effective are mental health courts in reducing recidivism and police contact?" METHODS/DESIGN: Eight electronic databases will be searched, specifically PsycINFO, Medline, Medline In-Process, Embase, Web of Science, CINAHL, Social Work Abstracts, and Criminal Justice Abstracts. A multi-phase screening process will be used to identify relevant search hits. Articles that pass the three-stage screening process will then be assessed for risk of bias and have their reference lists hand searched. Full-text articles that are rated to have low to moderate risk of bias will be summarized into two tables, one containing a brief description of the study and the other reporting the results of relevant outcomes measured. DISCUSSION: By synthesizing the results of the studies, this systematic review will help illuminate gaps in the literature, direct future research, and inform policy makers. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016036084.
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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 | 0.072 | 0.071 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.016 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.009 |
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".