UNDERSTANDING HOW RESIDENT-TO-RESIDENT AGGRESSION IN LONG-TERM CARE DEMENTIA UNITS UNFOLDS
Bibliographic record
Abstract
Resident-to-resident aggression (RRA) is the most common form of institutionally-based interpersonal violence that occurs in long-term care (LTC) facilities. RRA is associated with physical injury, poor psychosocial status, and an unpleasant or threatening day-to-day LTC living environment among residents. There is a knowledge gap regarding how RRA manifests in specific units within LTC facilities, most notably, dementia-specific units. Given that cognitive impairment level is associated with different forms of aggression, dementia-specific LTC units likely manifest a distinct representation of RRA. Informed by a social-ecological framework, this study sought to develop conceptual models to explain how RRA occurs in dementia-specific LTC units. We conducted in-depth, in-person individual interviews and focus groups with LTC staff (n = 37) representing several occupational groups (nurses, social workers, personal support workers, food service) who are directly exposed to everyday inter-resident relational dynamics in dementia-specific units of two large, urban LTC settings. A grounded theory approach was used to develop process models that explain how RRA unfolds between residents with dementia. Using an iterative, constant-comparison analytical approach, transcripts were analyzed by two independent raters. Findings support two distinct models characterized by multi-step, interactional resident processes and mediated by limitations in cognitive processing. Models provide insight into specific points of prevention/intervention along the RRA pathways. This study helps advance the RRA literature from research that categorizes the problem towards a process-oriented understanding of how it occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".