South Asians for Equitable (SAFE) Virtual Health: Understanding South Asian’s Experiences with Virtual Hospitals-at-Home (Preprint)
Bibliographic record
Abstract
BACKGROUND Hospital-at-Home and virtual care models have the potential to improve healthcare delivery but may present unique challenges for ethnocultural communities. OBJECTIVE This study explores barriers and facilitators to Hospital-at-Home adoption among South Asian patients and caregivers. METHODS A qualitative study using semi-structured interviews was conducted with South Asian community members and healthcare providers in the Fraser Health region in British Columbia, Canada. Thematic analysis was used to identify key patterns in perceptions, experiences, and needs. RESULTS Participants expressed mixed views about Hospital-at-Home care, with concerns centering on digital literacy, language barriers, caregiver readiness, and home environment suitability. Facilitators included comfort at home, trust in providers, and alignment with cultural and religious values. Many participants were unaware of healthcare alternatives beyond emergency departments. CONCLUSIONS Equitable implementation of Hospital-at-Home services for South Asian communities requires culturally tailored strategies that address digital access, health system awareness, and family caregiver roles.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".