Risk reduction treatment of high-risk psychopathic offenders: The relationship of psychopathy and treatment change to violent recidivism.
Bibliographic record
Abstract
The relationships of psychopathy, therapeutic change, and violent recidivism were examined in a sample of 152 high-risk violent offenders treated in a high-intensity violence reduction program at the Regional Psychiatric Centre (RPC) in Saskatoon, SK. The Violence Risk Scale (VRS; Wong & Gordon, 1999-2003) and Psychopathy Checklist-Revised (PCL-R; Hare, 1991, 2003) were rated on the sample. As an extension on a prior psychometric study of the VRS (Lewis, Olver, & Wong, 2012), the associations of therapeutic change scores, obtained from pre- and posttreatment ratings of VRS dynamic items, and violent recidivism were examined among high-risk psychopathic offenders (mean PCL-R >25) over approximately 5 years' follow-up. Positive therapeutic change correlated negatively with the PCL-R, particularly Factor 1 and the Affective facet, and was significantly associated with reductions in violent recidivism after controlling for psychopathy. The association of change to violent outcome decreased, however, when controlling for the Affective facet. Taken together, the present results suggest that risk-related treatment changes demonstrated by high-risk psychopathic offenders can be predictive of reductions in violent recidivism, and that reliable measurements of therapeutic change may be informative about treatment outcome in a high-risk violent offender group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".