The (un)caring experienced by racialized and/or ethnoculturally diverse residents in supportive living: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Racialized and/or ethnocultural minority older adults in supportive living settings may not have access to appropriate services and activities. Most supportive living facilities are mainstream (not specific to one group); however, culturally specific facilities are purpose-built to accommodate older adults from a particular group. Our objective was to describe the perspectives of diverse participants about access to culturally appropriate care, accessible services, and social and recreation activities in culturally specific and mainstream (non-specific) supportive living facilities. METHODS: We conducted semi-structured interviews with 21 people (11 staff, 8 family members, 2 residents) from 7 supportive living homes (2 culturally specific and 5 mainstream) in Alberta, Canada. We used a rapid qualitative inquiry approach to structure the data collection and analysis. RESULTS: Staff and family members described challenges in accessing culturally appropriate care in mainstream facilities. Family members expressed guilt and shame when their relative moved to supportive living, and they specifically described long waitlists for beds in culturally specific homes. Once in the facility, language barriers contributed to quality of care issues (e.g., delayed assessments) and challenges accessing recreation and social activities in both mainstream and culturally specific homes. Mainstream facilities often did not have appropriate food options and had limited supports for religious practices. Residents who had better English language proficiency had an easier transition to supportive living. CONCLUSIONS: Racialized and/or ethnoculturally diverse residents in mainstream supportive living facilities did not receive culturally appropriate care. Creating standalone facilities for every cultural group is not feasible; therefore, we must improve the care in mainstream facilities, including recruiting more diverse staff and integrating a wider range of recreation and religious services and food options.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".