Exploring Gender Differences in the Utility of Strength-Based Risk Assessment Measures
Bibliographic record
Abstract
The generalizability of risk assessment measures to female populations remains up for debate; in particular, few studies have made direct comparisons between male and female civil psychiatric patients on protective factors and risk factors relevant to violence risk assessments. To address this gap in the literature, we conducted a prospective study with 102 civil psychiatric patients (60.8% male) to investigate strength-based risk assessments. Outcome data (i.e., verbal, physical, and sexual aggression) was collected after 6 and 12 months. We found a number of potentially interesting gender differences in the predictive validity of the START, HCR-20 V2 , and SAPROF. Findings are generally supportive of the use of established Structured Professional Judgement (SPJ) risk assessment measures with male civil psychiatric populations, and with the exception of the START, caution is warranted when using these measures with female civil psychiatric patients. Findings suggest that SPJ assessments that utilize both strengths and vulnerabilities generally performed better than SPJ assessments relying on either strengths alone or vulnerabilities alone.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".