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 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 | 0.010 |
| 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 | 0.001 | 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".