Psychopathy and alcohol abuse in relation to the recidivism of sexual offenders
Bibliographic record
Abstract
The abuse of alcohol is a fundamental component to consider when assessing the risk of \nreoffending in sexual offenders. Previous research with sexual offenders has demonstrated that \nbeing diagnosed with an alcohol use disorder almost doubles their chance of reoffending. While \nthere has been much research on alcoholism and sexual offenders, there remains a gap in \nliterature considering the impact of alcohol abuse and drug abuse with psychopathic sexual \noffenders. A review article briefly indicated that elevated scores on the Michigan Alcohol \nScreening Test (MAST) might moderate the Hare Psychopathy Checklist-Revised (PCL-R) \nability to predict recidivism. No, follow up study was discovered to observe further results. The \ncurrent study evaluated the impact of these findings using a long-term recidivism database \ncollected in the Ontario Region of Correction Service Canada (CSC). The database included over \n500 high-risk sexual offenders from the Regional Treatment Center, Sex Offender Treatment \nProgram (RTCSOTP). The database contained information on men, over 18 years of age, who \nhad served at least two years in custody for a sexual offense. The PCL-R, MAST, and DAST \nwere utilized to measure psychopathy, alcohol abuse, and drug abuse, respectively. A Cox \nregression analysis revealed that the PCL-R and the DAST were significant predictors of sexual \nand violent recidivism, but the MAST was not a significant predictor of sexual and violent \nrecidivism. The MAST did not impact the PCL-R’s capability of predicting recidivism in a \nmoderate to high-risk sample, as previously observed. While the result of non-significant MAST \nprediction was contrary to previous findings, these results indicate that targeting drug abuse \nshould continue to be a component in sexual offender treatment programs. Alcohol abuse cannot \nbe excluded definitively from the study of recidivism.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".