Aggressive Behaviour Risk Assessment Tool for newly admitted residents of long‐term care homes
Bibliographic record
Abstract
AIM: The aim of this study was to revise the 10-item Aggressive Behaviour Risk Assessment Tool for predicting aggressive events among residents newly admitted to long-term care homes. BACKGROUND: The original tool had acceptable sensitivity and specificity for identifying potentially aggressive patients in acute care medical-surgical units, but its usefulness in long-term care homes is unknown. DESIGN: A retrospective cohort study design was used. METHODS: All residents admitted to 25 long-term care homes in western Canada were assessed for the risk of aggression using the original tool within 24 hours of admission from January 2014 - December 2014 (n = 724). Incident reports of aggressive events occurring within 30 days of admission were collected. Multiple logistic regression and receiver operating characteristics analyses were performed. RESULTS: Fifty-three residents of 724 exhibited aggressive behaviours. The demographic variable of age less than 85 years was found to be a positive predictor of aggressive events in multivariate logistic regression model and was added to the tool. The revised six-item Aggressive Behaviour Risk Assessment Tool for Long-Term Care consists of one new item, age less than 85 years and five items from the original tool: History of physical aggression, physically aggressive/threatening, anxiety, confusion/cognitive impairment and threatening to leave. The receiver operating characteristics of the revised tool with weighted scoring showed a good discriminant ability with satisfactory sensitivity and specificity at the recommended cut-off score of 4. CONCLUSION: The revised six-item tool may be useful in identifying potentially aggressive residents newly admitted to long-term care homes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".