Multidimensional evaluation of a mental health court: Adherence to the risk-need-responsivity model.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current study examined the impact of a mental health court (MHC) on mental health recovery, criminogenic needs, and recidivism in a sample of 196 community-based offenders with mental illness. Using a pre-post design, mental health recovery and criminogenic needs were assessed at the time of MHC referral and discharge. File records were reviewed to score the Level of Service/Risk-Need-Responsivity instrument (Andrews, Bonta, & Wormith, 2008) to capture criminogenic needs, and a coding guide was used to extract mental health recovery information at each time point. Only mental health recovery data were available at 12 months post-MHC involvement. Recidivism (i.e., charges) was recorded from police records over an average follow-up period of 40.67 months post-MHC discharge. Case management adherence to the Risk-Need-Responsivity (RNR) model of offender case management was also examined. Small but significant improvements were found for criminogenic needs and some indicators of mental health recovery for MHC completers relative to participants who were prematurely discharged or referred but not admitted to the program. MHC completers had a similar rate of general recidivism (28.6%) to cases not admitted to MHC and managed by the traditional criminal justice system (32.6%). However, MHC case plans only moderately adhered to the RNR model. Implications of these results suggest that the RNR model may be an effective case management approach for MHCs to assist with decision-making regarding admission, supervision intensity, and intervention targets, and that interventions in MHC contexts should attend to both criminogenic and mental health needs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it