Predictive validity of dynamic factors: Assessing violence risk in forensic psychiatric inpatients.
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Bibliographic record
Abstract
There is general consensus that dynamic factors ought to be considered in the assessment of violence risk, but little direct evidence exists to demonstrate that within-individual fluctuations in putative dynamic factors are associated with changes in risk. We examined these issues in a sample of 30 male forensic psychiatric inpatients using a pseudoprospective design. Static and dynamic factors were coded on the basis of chart review using 2 structured measures of violence risk: Version 2 of the Historical-Clinical-Risk Management-20 (HCR-20; C. D. Webster, K. S. Douglas, D. Eaves, & S. D. Hart, 1997, HCR-20: Assessing risk for violence, Version 2, Vancouver, BC, Canada: Mental Health, Law, and Policy Institute, Simon Fraser University) and the Short-Term Assessment of Risk and Treatability (START; C. D. Webster, M. L. Martin, J. Brink, T. L. Nicholls, & S. L. Desmarais, 2009, Short-Term Assessment of Risk and Treatability [START], Version 1.1, Coquitlam, BC, Canada: British Columbia Mental Health and Addiction Services). HCR-20 and START assessments were repeated every 3 months for a period of 1 year. Institutional violence in the 3 months following each assessment was coded using a modified version of the Overt Aggression Scale (S. C. Yudofsky, J. M. Silver, W. Jackson, J. Endicott, & D. W. Williams, 1986, The Overt Aggression Scale for the objective rating of verbal and physical aggression, The American Journal of Psychiatry, Vol. 143, pp. 35-39). Dynamic risk and strength factors showed predictive validity for institutional aggression. Results of event history analyses demonstrated that changes in dynamic risk factors significantly predicted institutional violence, even after controlling for static risk factors. This is one of the first studies to provide clear and direct support for the utility of dynamic factors in the assessment of violence risk.
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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 | 0.000 | 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 | 0.000 | 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 it