Violence risk prediction in mental health inpatient settings using the Dynamic Appraisal of Situational Aggression
Bibliographic record
Abstract
Introduction/Background: Aggression and violence are common problems in healthcare settings and affects both patients and healthcare staff. The Dynamic Appraisal of Situational Aggression (DASA) is an assessment tool to guide assessment for short term risk in inpatient settings. There have been no large-scale studies examining the performance of the DASA across different clinical settings. Our objective is to examine the performance of the DASA using a large longitudinal patient sample on different clinical units. A secondary objective was to examine alterative risk categories of the DASA. Methods: All consecutive mental health hospital admissions to a large hospital in Toronto, Canada between 2016 and 2019 were included. Time-to-event analysis and Receiver Operating Characteristics Area Under the Curve (AUC) was conducted with the outcome variable being the occurrence of the first violent incident or first restraint event. Results: We included 3819 patients, of which 17% had at least one violent incident. We analysed 88,124 DASA scores and found a significant association with violence (HR 1.79 (95% CI), AUC 0.73). We found that the AUCs were similar for subspecialized forensic, schizophrenia and acute care units (0.71, 0.73 and 0.75 respectively), and lower for geriatric units (0.66). We propose new violence risk categories based on the frequency of violence at each score. Discussion: Higher DASA scores are associated with higher risk of violent incidents in both forensic and non-forensic inpatient psychiatric units. The proposed violence risk groups help rule out patients at low risk of violence and may help identify patients who would most benefit from interventions to reduce violence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".