Transforming long-term care: Understanding relationship-centered care practices in ethno-specific context
Bibliographic record
Abstract
Culturally responsive care is essential to meaningful relationship-building in long-term care (LTC) settings, yet there is limited understanding of how Relationship-Centered Care (RCC) is interpreted and enacted in contexts shaped by shared cultural frameworks. This critical ethnographic study examines how RCC practices unfolded within an ethno-specific LTC home serving predominantly Chinese older adults in Vancouver, drawing on document review, participant observation, and interviews with residents, families, and staff. Four key themes were identified: (1) Caring like family (i.e., building trust through kinship-based language and emotional familiarity); (2) Honoring roots (i.e., integrating residents’ cultural identities into care); (3) Recreation and celebrations (i.e., fostering belonging through culturally meaningful activities); and (4) Collaboration as the heart of care (i.e., reinforcing reciprocity through resident-family-staff partnerships). These findings illustrate how culturally grounded practices shape the interpretation and enactment of RCC in everyday care. Future research should explore how RCC operates in more culturally heterogeneous settings and use inclusive methods to amplify the voices of diverse residents. • Grounding RCC in cultural context supports identity, belonging, and emotional well-being in LTC. • Care practices rooted in shared language, culture, and embodied communication strengthens trust and connection. • Culturally grounded recreation and celebrations serve as vital spaces for inclusion, joy, and community-making. • Relational care is co-constructed through collaboration among residents, families, and culturally attuned staff. • RCC is a dynamic and culturally situated practice with implications for LTC policy and practice.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".