Real-world use of the risk–need–responsivity model and the level of service/case management inventory with community-supervised offenders.
Bibliographic record
Abstract
The risk-need-responsivity model (RNR; Bonta & Andrews, 2017) has become a leading approach for effective offender case management, but field tests of this model are still required. The present study first assessed the predictive validity of the RNR-informed Level of Service/Case Management Inventory (LS/CMI; Andrews, Bonta, & Wormith, 2004) with a sample of Atlantic Canadian male and female community-supervised provincial offenders (N = 136). Next, the case management plans prepared from these LS/CMI results were analyzed for adherence to the principles of risk, need, and responsivity. As expected, the LS/CMI was a strong predictor of general recidivism for both males (area under the curve = .75, 95% confidence interval [.66, .85]), and especially females (area under the curve = .94, 95% confidence interval [.84, 1.00]), over an average 3.42-year follow-up period. The LS/CMI was predictive of time to recidivism, with lower risk cases taking longer to reoffend than higher risk cases. Despite the robust predictive validity of the LS/CMI, case management plans developed by probation officers generally reflected poor adherence to the RNR principles. These findings highlight the need for better training on how to transfer risk appraisal information from valid risk tools to case plans to better meet the best-practice principles of risk, need, and responsivity for criminal behavior risk reduction. (PsycINFO Database Record
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".