Effect of Mental Health Courts on Arrests and Jail Days
Bibliographic record
Abstract
CONTEXT: Mental health courts are growing in popularity as a form of jail diversion for justice system-involved people with serious mental illness. This is the first prospective multisite study on mental health courts with treatment and control groups. OBJECTIVES: To determine if participation in a mental health court is associated with more favorable criminal justice outcomes than processing through the regular criminal court system and to identify defendants for whom mental health courts produce the most favorable criminal justice outcomes. DESIGN: Longitudinal study. SETTING: Four mental health courts in San Francisco County, CA, Santa Clara County, CA, Hennepin County (Minneapolis), MN, and Marion County (Indianapolis), IN. PARTICIPANTS: A total 447 persons in the mental health court (MHC) and 600 treatment-as-usual (TAU) controls. INTERVENTION: Eighteen months of pre-entry and postentry data for 4 jurisdictions. All subjects were interviewed at baseline, and 70% were interviewed at 6 months. Objective outcome data were obtained on all subjects from Federal Bureau of Investigation arrest records, jails, prisons, and community treatment providers. MAIN OUTCOME MEASURES: Annualized rearrest rates, number of rearrests, and postentry incarceration days. RESULTS: The MHC and TAU samples are similar on the major outcome measures in the pre-entry 18-month period. In the 18 months following treatment, defined as entry into mental health court, the MHC group has a lower annualized rearrest rate, fewer post-18-month arrests, and fewer post-18-month incarceration days than the TAU group. The MHC graduates had lower rearrest rates than participants whose participation was terminated both during MHC supervision and after supervision ended. Factors associated with better outcomes among the MHC participants include lower pre-18-month arrests and incarceration days, treatment at baseline, not using illegal substances, and a diagnosis of bipolar disorder rather than schizophrenia or depression. CONCLUSIONS: Mental health courts meet the public safety objectives of lowering posttreatment arrest rates and days of incarceration. Both clinical and criminal justice factors are associated with better public safety outcomes for MHC participants.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".