Relationship-Based Care and Behaviours of Residents in Long-Term Care Facilities
Bibliographic record
Abstract
Introduction. In long-term care (LTC), person-centred approaches are encouraged. One such approach, relationship-based care (RBC), aims among other things to reduce residents' agitated behaviours. RBC has been used in numerous Quebec LTC facilities over the past decade but it has never been studied. Objective. Explore correlations between use of RBC by trained caregivers and the frequency of agitated and positive behaviours of residents with cognitive impairments. Methods. Two independent raters observed fourteen caregiver/resident dyads in two LTC facilities during assistance with hygiene and dressing. Checklists were used to quantify caregivers' RBC use and residents' agitated and positive behaviours. Results. Scores for RBC use were high, suggesting good application of the approach by caregivers. Correlation analyses showed that offering residents realistic choices and talking to them during care were associated with both positive and agitated behaviours (P from 0.03 to 0.003). However, many other components of RBC were not associated with residents' behaviours during care. Conclusions. There were only a few quantitative links between the RBC checklist items and the frequency of agitated or positive behaviours. Other studies with a more rigorous research design are needed to better understand the impact of relationship-based care on residents' behaviours.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".