Predictive validity of dynamic factors: Assessing violence risk in forensic psychiatric inpatients.
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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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".