The Brøset Violence Checklist: clinical utility in a secure psychiatric intensive care setting
Bibliographic record
Abstract
Accessible summary • Fear of violence from patients may affect the quality of care mental health nurses provide. • The Brøset Violence Checklist (BVC), a six-item instrument, has the potential to assist health-care providers in identifying patients who may become aggressive. • A trial of the BVC on a secure psychiatric intensive care unit suggested that the tool was well accepted by staff and may have contributed to reduced seclusion rates. • Five-year follow-up has revealed an incorporation of the BVC into routine practice on the psychiatric intensive care unit. Violence towards health-care workers, especially in areas such as mental health/psychiatry, has become increasingly common, with nursing staff suggesting that a fear of violence from their patients may affect the quality of care they provide. Structured clinical tools have the potential to assist health-care providers in identifying patients who have the potential to become violent or aggressive. The Brøset Violence Checklist (BVC), a six-item instrument that uses the presence or absence of three patient characteristics and three patient behaviours to predict the potential for violence within a subsequent 24-h period, was trialled for 3 months on an 11-bed secure psychiatric intensive care unit. Despite the belief on the part of some nurses that decisions related to risk for violence and aggression rely heavily on intuition, there was widespread acceptance of the tool. During the trial, use of seclusion decreased suggesting that staff were able to intervene before seclusion was necessary. The tool has since been implemented as a routine part of patient care on two units in a 92-bed psychiatric centre. Five-year follow-up data and implications for practice are presented.
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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 | 0.004 | 0.020 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".