Individual Community-Based Treatment of Offenders With Mental Illness
Bibliographic record
Abstract
This study explores the effectiveness of psychological intervention at reducing the risk of recidivism among a group of high-risk, high-need offenders housed in a Community Correctional Centre (CCC) operated by the Correctional Service of Canada (CSC). File reviews on 136 male federal offenders living in a CCC in a large metropolitan area were included in the present investigation. Previous research on this sample by our team indicated that the majority of this sample met diagnostic criteria for a variety of psychiatric conditions. Data on the number of individual counseling sessions received and progress in treatment were collected from official file information for the purpose of the present investigation. After accounting for actuarially assessed risk, moderate doses of treatment were found to be associated with 7.7 times less likelihood of recidivism, and high doses of treatment were found to be associated with 11.6 times less likelihood of recidivism, when compared with offenders who received no treatment or were only assessed for treatment. These results are discussed in the context of correctional models of offender risk assessment and rehabilitation. It is notable that a very simple measure of global mental health treatment attendance, with no consideration of such factors as responsiveness, added considerable incremental predictive validity to the results after having statistically accounted for actuarially assessed risk of recidivism.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".