The Risk–Need–Responsivity Model and Justice-Involved Persons with Serious Mental Illness
Bibliographic record
Abstract
The assessment and rehabilitation of justice-involved persons with serious mental illness (SMI) present unique challenges to the criminal justice system. For persons without mental health challenges, the risk–need–responsivity (RNR) model has had an enormous impact on what to assess and how best to deliver treatment to those in need. This paper poses the general question, Is RNR relevant to justice-involved persons with SMI? We argue that the critical risk/need factors, called the Central Eight, are as important to those with SMI as they are to those with no SMI. Unfortunately, assessment protocols that incorporate the Central Eight are quite rare in the literature. When we turn to lessons from the RNR model for the rehabilitation of justice-involved persons with SMI the research is even more scant. On a positive note, we provide an illustration of an RNR-based model of community supervision that demonstrates reductions in general and violent recidivism, STICS. The STICS model serves as an example of how effective treatment can be applied with persons with SMI.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".