Substance Abuse Among High-Risk Sexual Offenders: Do Measures of Lifetime History of Substance Abuse Add to the Prediction of Recidivism Over Actuarial Risk Assessment Instruments?
Bibliographic record
Abstract
There has been relatively little research on the degree to which measures of lifetime history of substance abuse add to the prediction of risk based on actuarial measures alone among sexual offenders. This issue is of relevance in that a history of substance abuse is related to relapse to substance using behavior. Furthermore, substance use has been found to be related to recidivism among sexual offenders. To investigate whether lifetime history of substance abuse adds to prediction over and above actuarial instruments alone, several measures of substance abuse were administered in conjunction with the Sex Offender Risk Appraisal Guide (SORAG). The SORAG was found to be the most accurate actuarial instrument for the prediction of serious recidivism (i.e., sexual or violent) among the sample included in the present investigation. Complete information, including follow-up data, were available for 250 offenders who attended the Regional Treatment Centre Sex Offender Treatment Program (RTCSOTP). The Michigan Alcohol Screening Test (MAST) and the Drug Abuse Screening Test (DAST) were used to assess lifetime history of substance abuse. The results of logistic regression procedures indicated that both the SORAG and the MAST independently added to the prediction of serious recidivism. The DAST did not add to prediction over the use of the SORAG alone. Implications for both the assessment and treatment of sexual offenders are discussed.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".