Implementing violence and incident reporting measures on a forensic mental health unit
Bibliographic record
Abstract
The assessment of risk and prediction of violence in mental health units can play a large role in creating a safer environment for both the staff and the patients. Nurses in forensic units are in a unique position in regards to assessment of violence as they spend a great deal of time with the patients. Nurses on a forensic mental health unit scored the Brøset Violence Checklist (BVC) twice daily for 12 weeks for all patients either resident on or admitted to the unit (N = 46). The Staff Observation Aggression Scale-Revised (SOAS-R) was used to report any adverse incidents (N = 51). Data were examined at the both the item and scale level. Main results showed the area under the curve values of the BVC score, slide rule, and the sum of BVC and slide rule score in turn demonstrated strong predictive accuracy for inpatient aggression (0.68–0.73). Through logistic regression analyses the BVC uniquely predicted inpatient aggression but adding the slide rule did not improve prediction. Predictive accuracy was found across three diagnostic groups – dementia, psychosis and substance use disorders. These results provide further support on the predictive accuracy of the BVC for short-term violence in forensic mental health settings.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".