A Long-Term Outcome Assessment of the Effects on Subsequent Reoffense Rates of a Prison-Based CBT/RNR Sex Offender Treatment Program With Strength-Based Elements
Bibliographic record
Abstract
This article describes an evaluation of the effects of an early version (1991-2001) of Rockwood’s prison-based Cognitive Behavioral Therapy/Risk–Needs–Responsivity (CBT/RNR) sex offender program that had emerging elements of a strength-based approach. This program was implemented under contract to Correctional Service of Canada (CSC) and continued to evolve in response to emerging evidence until it closed in 2013. Thus, the program as evaluated here did not involve a fixed approach as did the comparison CSC program (hereafter referred to as SOTP). Long-term reoffense data, from Rockwood’s program ( n = 579), were compared with SOTP ( n = 625) and with a group of untreated men ( n = 107) sentenced for sex offenses. A modified brief actuarial risk scale (BARS-M) was used to control for baseline risk among the three groups, along with additional controls for age at release, victim type, and individual differences in the length of long-term follow-up period. Both treatment groups displayed lower rates of both sexual and violent reoffending when compared with the no-treatment offenders. Overall, the Rockwood program generated the lowest recidivism rates. The results demonstrate that prison-based sex offense–specific treatment can be effective. We discuss the strengths and limitations of the current design through the Collaborative Outcome Data Committee’s guidelines.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".