Predictors of sex offender treatment dropout: psychopathy, sex offender risk, and responsivity implications
Bibliographic record
Abstract
Abstract We examined sex offender treatment dropout predictors, in particular, the relationship of psychopathy and sex offender risk to treatment dropout in a sample of 154 federally incarcerated sex offenders treated in a high intensity sex offender treatment program. Demographic, criminal history, mental health and treatment-related data as well as data on risk assessment measures including the Static 99, Violence Risk Scale – Sexual Offender version (VRS-SO), and Psychopathy Checklist – Revised (PCL-R) were collected. Logistic regression and discriminant function analyses were used to identify predictors that made significant and unique contributions to dropout among all the variables under study. The Emotional facet of Factor 1 of the PCL-R and never being married were found to be the most salient predictors of treatment dropout and correctly identified about 70% of the cases. The implications of the findings for managing treatment dropout and for the treatment of psychopathic offenders are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".