Sex Offenders' Response to Treatment and its Association with Recidivism as a Function of Psychopathy
Bibliographic record
Abstract
This study examined the relationship between recidivism and ratings of response to specialized cognitive behavioral treatment conducted in a prison setting among 418 sex offenders released to the community for an average follow-up period of over 5 years. As well as testing for a main effect for treatment ratings, the potential role of psychopathy assessed using the Psychopathy Checklist--Revised (PCL-R) as a moderator of response to treatment was investigated. Ratings of response to treatment failed to predict either serious (violent including sexual) or sexual recidivism. For the more inclusive outcome of serious recidivism, there was no significant interaction between psychopathy and treatment ratings; however, the ubiquitous effect of psychopathy on recidivism was found to be significant. For sexual recidivism, psychopathy was not significant as a main effect, but a significant interaction between psychopathy and treatment ratings was found. Among sex offenders with PCL-R scores of 25 or higher, those with ratings reflecting a more negative response to treatment recidivated sexually at a faster rate than others. This interaction effect was not significant when treatment noncompleters were removed from the data set. The results were discussed in terms of the methodology involved in the assessment of response to treatment among sex 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".