Multidimensional evaluation of a mental health court: Adherence to the risk-need-responsivity model.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".