Living environment and resident-to-resident aggression in long-term residential care facilities
Bibliographic record
Abstract
Living environmental conditions can pose great impacts on the health and well-being of older adults receiving care from residential care facilities (RCFs). Despite the growing concern on resident-to-resident aggression (RRA) worldwide, little is known about what environmental factors, and how these factors affect RRA among RCF residents. This study examined the correlates of RRA, with a special emphasis on the environmental and structural features of the RCFs. Cross-sectional data collected from a quota sample of 412 personal care workers (PCWs) working at 29 RCFs in Hong Kong were analysed using linear mixed-effects modelling. Guided by a survey, PCWs reported the most recent RRA incident they witnessed, and provided details about the perpetrator, victim, and the RCF involved. Effects of different individual characteristics of PCWs and residents, as well as environmental and structural factors of RCFs were included to predict RRA witnessed by PCWs. Results show that RRA is associated most strongly with residents’ behavioural disturbances (perpetrator: B = 0.19, SE = 0.04, p < .001; victims: B = 0.16, SE = 0.03, p < .001). Among all environmental factors, cleanliness of the indoor areas of RCFs is the only significant predictor of RRA ( B = -0.06, SE = 0.03, p < .05). Overall, findings did not support the impacts of most environmental features on RRA in the current settings. Yet, the significant effects of residents’ behavioural disturbances and cleanliness of RCFs on RRA advocate for integrated prevention and intervention strategies that address both individual health needs and organisational management. • Living environment is linked to the likelihood of resident-to-resident aggression (RRA) witnessed by personal care workers (PCWs) in residential care facilities. • Residents living in facilities with clean indoor areas are less likely to experience RRA. • Greater behavioural disturbances of both perpetrators and victims are associated with more severe RRA incidents.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".