DESIGNING AND IMPLEMENTING A RESIDENT-TO-RESIDENT AGGRESSION PREVALENCE STUDY IN A NEW CONTEXT
Bibliographic record
Abstract
Abstract Resident-to-resident aggression (RRA) is a prevalent issue in long-term care (LTC) settings with serious consequences. Research from the U.S. has found that approximately 20% of residents experience RRA each month and this issue is associated with several physical/mental health morbidities. Very little RRA research has been conducted in LTC settings in Canada. This study sought to estimate the prevalence of RRA in Canadian nursing homes and identify risk factors from different levels of social-ecological influence. Specifically, this study sought to replicate a gold standard RRA prevalence study in the U.S. by selecting a random sample of nursing homes (n = 10) across the province of Ontario and collecting RRA prevalence data using multiple sources/methods, including staff interviews, resident interviews, and direct observation from staff and research assistants. As this study is in early stages of design/implementation, the current presentation will focus on various issues requiring consideration and the challenges faced when setting up a large-scale RRA prevalence study. Specifically, the presentation will focus on the adaptation of existing measurement tools to a new context with unique local language, legal and legislative frameworks, and the development of a sampling strategy requiring considerations related to sample inclusion/exclusion criteria, temporal and geographical logistical concerns, and sample size. We will also discuss new realities and heightened sensitivities involved in conducting LTC-based research and recruiting nursing homes, residents and staff during the COVID era that includes staffing instabilities/shortages and outbreaks. This presentation will assist other researchers around the world in planning/implementing RRA prevalence study designs.
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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 | 0.056 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".