At Risk of What? Understanding Forensic Psychiatric Inpatient Aggression through a Violence Risk Scenario Planning Lens
Bibliographic record
Abstract
Violence risk assessment is an essential component of forensic mental health services designed to help mitigate and manage the re-occurrence of violence. Although there is large body of evidence supporting structured risk assessments, there is no empirical evidence regarding scenario planning—a specific component of the structured professional judgment approach to violence risk assessment. The purpose of this study was to investigate the base rates and concurrent validity of inpatient aggression scenarios to provide information about risk scenarios. Among a large representative sample of forensic psychiatric patients ( N = 1240), the prevalence of risk scenarios ( Repeat, Escalation, Twist, and Improvement scenarios) was investigated by retrospectively coding changes in inpatient aggression. The results suggested that Improvement scenarios, including a continued desistance of aggression, were common. Aggression scenarios shared significant pattern of associations with the Historical-Clinical-Risk Management-20 and the Psychopathy Checklist—Revised. Overall, this study presents initial empirical evidence related to violence risk scenario planning. Implications from these findings include how scenario planning may intersect with evaluator bias in forensic mental health assessments.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".