Out of the shadows: Addressing resident‐to‐resident aggression in long‐term care
Bibliographic record
Abstract
Abstract Background Resident‐to‐resident aggression is a behaviour concern in long‐term care (LTC). Incidents of resident‐to‐resident aggression can have serious consequences and may result in poorer quality of life for residents, increased staff turnover, harmful psychological and physical effects, or death. Many people shy away from talking about resident‐to‐resident aggression because it can be a sensitive topic. This silence can result in missed opportunities to reduce the risk. Discussing it openly is an important step in working towards solutions. Method Recognizing resident‐to‐resident aggression as an urgent public health issue, the Alzheimer Society of Canada (ASC) has embarked on a multiyear project to understand the underlying needs that can trigger this behaviour, and what LTC staff need to address it. The outcome for the first phase of this project was the creation of an information booklet for LTC staff. To create the booklet an environmental scan was conducted that examined existing literature and tools on resident‐to‐resident aggression, subject‐matter experts were consulted, and focus groups were conducted with staff and families at a Canadian LTC home. Result The information booklet that has been developed addresses three main topic areas. First, it provides an overview of the issue of resident‐to‐resident aggression and outlines the potentially harmful consequences this can have on residents, families, staff and management if not dealt with appropriately and openly. Second, it outlines the importance of talking openly about resident‐to‐resident aggression as a first step in addressing this issue. Finally, the resource provides practical, person‐centred strategies for reducing incidents of resident‐to‐resident aggression; including structural and environmental approaches. In particular, it includes practical tips and strategies for how to respond to incidents when they do occur and how to reduce the risk of them happening again in the future. Conclusion Educating LTC staff about resident‐to‐resident aggression can support them to think creatively and apply effective strategies to reduce the number of incidents. Doing so can improve quality of life for residents, staff, management and families. ASC’s next steps will involve creating practical tools for staff to help them address the issue of resident‐to‐resident aggression in their own LTC home.
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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 | 0.000 |
| Open science | 0.001 | 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".