Comparing Recidivism Rates of Treatment Responders/Nonresponders in a Sample of 413 Child Molesters Who Had Completed Community-Based Sex Offender Treatment in the United Kingdom
Bibliographic record
Abstract
Analysis of psychometric data from a sample of 413 child molesters who had completed a U.K. probation-based sex offender treatment program was carried out to assess (a) the effectiveness of therapy in the short term and (b) the longer term implications of treatment in relation to sexual recidivism. It was found that 12% (51 offenders) of the sample had recidivated within 2 to 4 years. Of these recidivists, 86% (44 offenders) had been reconvicted for a sexually related offense. One hundred thirty-five offenders (33%) demonstrated a treated profile (i.e., demonstrated no offense-specific problems and few, or no, socioaffective problems at the posttreatment stage). This group was compared with a sample of offenders deemed as not responding to treatment, matched by their levels of pretreatment risk/need. It was found that a significantly smaller proportion (n = 12, 9%) of treatment responders had recidivated, compared to the treatment nonresponders (n = 20, 15%), indicating a 40% reduction in recidivism in those who had responded to treatment (effect size = .18). Matching length of treatment to the offenders' level of pretreatment risk/need (i.e., higher risk/treatment-need offenders typically undertook longer treatment) reduced the rate of recidivism among this group to the level of recidivism observed among the lower risk/need offenders.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".