Evaluation of a cognitive‐behavioral intervention for high‐ and medium‐risk probationers
Bibliographic record
Abstract
Reducing recidivism is a central goal of treatment programs for offenders. Preliminary evidence suggests that cognitive-behavioral group interventions based on the National Institute of Corrections curriculum (Bush, Glick, & Taymans, 1997) may be effective in reducing recidivism rates among adult probationers. We evaluated the effectiveness of a program based on this curriculum among 167 high- and medium-risk probationers assigned to this program and a comparison group of 120 high- and medium-risk probationers matched on age and number of prior criminal charges. Improvements over prior studies included use of survival analytic methods and propensity score matching, a longer follow-up interval, and examination of treatment effectiveness within ethnic groups. Relative to the comparison group, treatment group probationers were more likely to complete probation satisfactorily and survive longer before rearrest. Moreover, supplementary analyses suggested that ethnicity was associated with differences in intervention effectiveness. Treatment was predictive of lower recidivism rates among European Americans and African Americans but was less effective among Latino American probationers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".