Assessing risk for imminent violence in the elderly: the Brøset Violence Checklist
Bibliographic record
Abstract
OBJECTIVE: The Brøset Violence Checklist (BVC) assesses confusion, irritability, boisterousness, verbal threats, physical threats and attacks on objects as either present or absent. It is hypothesised that an individual displaying two or more of these behaviours is more likely to be violent in the next twenty-four hour period. This study aims to test the validity of the instrument in geriatric settings and to report on the predictive value of an easy-to-use risk assessment instrument. METHOD: Eight thousand eight hundred and thirty-five BVC observations were completed in two psychogeriatric wards (n = 42 patients) and two special care units for patients with dementia (n = 40 residents). To measure violent incidents the study group was monitored using the Staff Observation Aggression Scale-Revised (SOAS-R). RESULTS: This study disclosed that patients in geriatric wards and residents in nursing homes who are aggressive have higher BVC scores than the non-violent subjects indicating that the BVC does predict violent episodes in these settings. CONCLUSION: From a clinical perspective, it is most important that a prediction aid has good sensitivity, so that most cases are detected and have a high negative predictive value so that most non-cases on the measure are indeed non-cases. Our results indicate that the BVC was able to achieve this goal.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".