Process Models to Understand Resident-to-Resident Aggression Among Residents With Dementia in Long-Term Care
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Resident-to-resident aggression (RRA) is a prevalent form of interpersonal violence in long-term care (LTC) settings. Research to guide preventive interventions is limited. Using social-ecological and need-driven dementia-compromised behavior perspectives, we sought to generate process models representing common RRA pathways in dementia-specific LTC units. RESEARCH METHODS: = 36) exposed to everyday resident interactions at two urban LTC facilities in Toronto, Canada. Semistructured interviews were audio-recorded and transcribed. Two independent raters coded the transcripts using iterative, constant comparison analytic processes. RESULTS: Two distinct RRA process models in dementia-specific LTC units were developed. Models reflect sequential pathways driven by residents' benign or responsive behaviors and cognitive processing limitations, with escalation points within resident dyads or groups. IMPLICATIONS: This study furthers RRA conceptualization as a process rather than an aggressive event. Models capture unique RRA manifestations in dementia-specific LTC units and entrypoints for prevention or management.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".