High-psychopathy men with a history of sexual offending have protective factors too: But are these risk relevant and can they change in treatment?
Bibliographic record
Abstract
OBJECTIVE: Psychopathy is a serious personality disorder reputed for resistance to correctional and forensic mental health treatment and synonymous with being high risk for different recidivism outcomes; it is not readily associated with an abundance of positive qualities or protective factors. Research has yet to examine the presence of protective factors as a function of psychopathy in correctional samples and the risk-relevance of protective factors for high-psychopathy men. METHOD: , 2011, 10, 171) were rated from institutional files and recidivism data were obtained from official criminal records. RESULTS: PCL-R scores were inversely related to SAPROF scores; however, even men scoring high on the PCL-R made significant pre-post changes on protective factors. PCL-R and SAPROF scores predicted sexual, violent, and general recidivism; treatment-related changes in protective factors, controlling for PCL-R score, were significantly associated with decreased violent recidivism. CONCLUSIONS: Protective factors can and do change with purposive change agents (e.g., correctional treatment), even among individuals with substantial psychopathic traits. The role and risk relevance of protective factors in sexual violence risk assessment and management with high psychopathy clientele are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".