Outcome of an Institutional Sexual Offender Treatment Program: A Comparison Between Treated and Matched Untreated Offenders
Bibliographic record
Abstract
Data from a sexual offender treatment program operated by the Correctional Service of Canada at the Regional Psychiatric Center (Saskatoon) supported the conclusion that cognitive behavioral treatment can reduce sexual offense recidivism. The study compared 296 treated and 283 untreated offenders followed for a mean of 6 years after their release. An untreated comparison subject was located for each treated offender on three dimensions: (a) age at index offense, (b) date of index offense, and (c) prior criminal history. Data were analyzed using tests of proportion, survival analysis, and analysis of offender Criminal Career Profiles. Over a mean follow-up period of almost 6 years, convictions for new sexual offenses among treated offenders were 14.5% versus 33.2% for untreated offenders. During the follow-up period, 48% of treated offenders remained out of prison compared to 28.3% of untreated offenders. Time series comparisons of treated and comparison samples also showed that treated men reoffended at significantly lower rates after 10 years. A Criminal Career Profile (CCP) was constructed by taking the Age at First Conviction and plotting the offender's successive lengths of time free against time incarcerated. Pre- and posttreatment slopes of the CCP were lower for both groups posttreatment; however, the degree of change was significantly greater for the treated group, indicating a greater reduction in criminal activity among these offenders. Taken together, the results of all three analytic techniques supported the efficacy of appropriate correctional treatment for effective reduction 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".