Gender Comparisons in a Forensic Sample: Patient Profiles and HCR-20:V2 Reliability and Item Utility
Bibliographic record
Abstract
Given documented gender differences in risk factors and manifestations of violence, researchers have advocated for gender-sensitive approaches to violence risk assessment. This study compares male ( n = 292) and female ( n = 68) forensic psychiatric patients on an array of demographic, clinical, behavioral, and legal variables to gain a clearer understanding of the prevalence of different risk factors and types of violence in this population. We investigate the interrater reliability and item utility of the Historical, Clinical, Risk Management-20 (HCR-20:V2), and examine whether individual HCR-20 items exhibit differential relationships to the tool's summary risk rating across gender. More women carried a diagnosis of borderline personality disorder, whereas antisocial personality disorder, substance use problems and extensive criminal histories were more often noted in men. These clinical differences were reflected in the distribution of HCR-20 item and subscale scores across gender. Interrater reliability was excellent, especially for women. A lack of personal support increased the odds of being deemed high risk in women to a greater extent than in men. We discuss the utility of structured professional judgment tools such as HCR-20 in women, and consider the importance of a gender-sensitive approach to risk assessment.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".