Alcohol Consumption among Sexual Offenders in the Context of Analysis of Forensic Psychiatric Assessments.
Bibliographic record
Abstract
Introduction: The association of alcohol use with committing sexual offences is well established. However, there are still gaps in knowledge about the mechanisms which lead to alcohol overuse and related sexual violence occurring under the influence of alcohol among this group of perpetrators. Goal: The goal of this paper is to describe characteristics of perpetrators of sexual offences with regards to their self-declared alcohol use status: “overusing” – declaring overusing alcohol, “not-overusing” – declaring not overusing alcohol, and “abstaining” – declaring abstinence from alcohol. Material and Methods: Material for this study consisted of 180 individual forensic psychiatric reports issued by the experts from the Mental Health Clinic at the 10th Military Clinical Hospital in Bydgoszcz. The reports were reviewed by the study authors. Relevant data from was extracted using the survey tool developed for the purpose of this study: “Survey of Factors Determining Sexual Criminal Behaviour” Results: Statistically significant associations between several studied variables were noted and presented in table format, see tables 1-3 for details. Conclusions: Disinhibiting effect of alcohol on sexual offending is associated with coexisting personality disorders and organic personality disorders. Perpetrators of sexual offences overusing alcohol are characterized by poor levels of social functioning and personal history of being raised in dysfunctional families with alcohol problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".