The Impact of Person-Centered Care Indicators on Care Strain Among Care Aides in Long-Term Care Homes in New Brunswick: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Person-centered care (PCC) approaches are widely recognized for improving the quality of life of residents living with dementia in long-term care (LTC). However, residents are only one part of the care dyad, and it remains unclear whether PCC also impacts nursing care aides in similarly adventitious ways. Care aides in this context experience significant care strain, which refers to the physical, emotional, and psychological burden experienced by caregivers. While PCC approaches are promoted as the best approach for supporting residents living with dementia, there is limited research on whether their implementation also impacts care aides. This study examined potential associations between organization-level PCC indicators and care strain among nursing care aides who work with residents living with dementia in LTC homes in New Brunswick, Canada. Methods: A cross-sectional survey design was used to explore the relationship between PCC approaches and care strain. Care strain was measured using the strain in dementia care scale, including the daily emotions subscale. A modified version of the Dementia Policy Questionnaire assessed the extent to which PCC approaches were implemented in participants’ workplaces. Descriptive statistics characterized the sample, and multivariable regression analyses examined associations between PCC indicators and care strain, adjusting for demographic factors. Results: Twenty-eight participants completed both measures. Overall, participants reported high levels of care strain but also high levels of positive daily emotions. Findings partially supported the hypothesis that PCC indicators were associated with lower care strain and more positive daily emotions. Certain PCC indicators, such as structured education and ethical support, appeared particularly beneficial. Implications for Practice: Strengthening PCC practices—especially through hands-on training and ethical support—may help reduce care strain and enhance care aides’ emotional well-being. LTC facilities that prioritize these strategies over policy implementation alone may improve both staff well-being and quality of care for residents.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".