RESIDENT-TO-RESIDENT AGGRESSION: PREVALENCE AND RISK FACTORS
Bibliographic record
Abstract
Abstract This study examines the rates and risk factors of resident-to-resident aggression in long term care facilities in Hong Kong. A total of 800 personal care worker participated. Participants averaged 42.03 years of age (SD=7.63), were mostly female (92.7%), married (79.1%) and reported an average of 6.28 years of experience in long term care. 96.9 percent of the participants provided care to residents with dementia but 58.9% considered the training they received insufficient. Resident-to-resident aggression was common: All participants reported having witnessed verbal aggression (100%), 18% disruptive behaviors, 11.8% physical violence, and 3.1% sexual aggression.Resident-to-staff aggression was commonly reported with verbal aggression being the most common (97.6%) following by other disruptive behaviors (13.7%), physical violence (10.7%), and sexual aggression (8.5%). Logistic regression analysis indicated that disruptive behaviors and physical violence were associated with perpetrator male gender, dementia, and neuropsychiatry symptoms, as well as staff prior and current experience of taking care of persons with dementia, not having received training in dementia care, and perceived insufficiency of training. Sexual aggression was associated with perpetrator male gender and staff female gender. There is an urgent need to provide supportive services to prevent and intervene resident-to-resident aggression in long term care facilities. Improving management of behavioral and psychological symptoms of dementia through sufficient staff training and adequate staffing ration may be helpful in this aspect.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".