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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".