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Record W4415236184 · doi:10.2196/79675

Hospital-at-Home for South Asian Communities in British Columbia, Canada: Qualitative Interview Study

2025· article· en· W4415236184 on OpenAlexaffvenueabout
Emma Wong, Mahabhir Kandola, Kamal Arora, Harroop Sharda, Roman Deol, Mary E. Jung, Robert Paquin, Megan MacPherson

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaBritish Columbia Institute of TechnologyFraser Health
Fundersnot available
KeywordsSouth asiaCulturally appropriateQualitative researchHealth careCultural diversityCulturally sensitiveEthnic groupQualitative analysis

Abstract

fetched live from OpenAlex

Background: South Asian communities in Canada face significant disparities in access to health care and experience higher rates of chronic conditions such as cardiovascular disease, diabetes, and hypertension. Hospital-at-Home services have the potential to improve access and outcomes, yet little is known about how these services are perceived and experienced by South Asian patients and caregivers. Understanding both barriers and facilitators is critical for culturally responsive implementation. Objective: This study aimed to explore the experiences of South Asian community members with in-person hospital care and their perceptions, attitudes, and expectations regarding virtual Hospital-at-Home services, with the goal of identifying culturally tailored strategies to improve access, quality, and satisfaction. Methods: A qualitative study using semistructured interviews was conducted with 20 South Asian community members in the Fraser Health region in British Columbia, Canada. Interviews explored experiences with in-person hospital care, perceptions of a virtual hospital service (also known as Hospital-at-Home), and recommendations for enhancing awareness and accessibility. Interviews were audio-recorded, transcribed, and analyzed thematically to identify key patterns in perceptions, experiences, and needs. Results: Participants described multiple systemic barriers to in-person hospital care, including long wait times, overcrowding, transportation challenges, and difficulty navigating the health system. Cultural and religious needs, such as gender-concordant care and culturally appropriate food, were frequently unmet, while language-concordant care and family involvement were critical to positive experiences. Discrimination and assumptions based on ethnicity or age further shaped perceptions of care. Virtual hospital services were valued for convenience, comfort, reduced exposure to hospital-acquired infections, and support for family involvement. However, participants raised concerns about clinical quality, the absence of physical examinations, digital literacy, privacy, and home-based responsibilities. Acceptance varied by age, immigration status, and familiarity with technology. Participants emphasized the importance of culturally tailored outreach, leveraging community leaders, ethnic media, and peer testimonials to increase awareness and trust. Conclusions: South Asian patients and caregivers recognize both challenges in traditional hospital care and potential benefits of Hospital-at-Home services. Implementation strategies that address systemic barriers, integrate cultural and linguistic considerations, and engage trusted community networks are essential to improving equity, access, and satisfaction. Findings highlight the need for culturally responsive, patient-centered approaches in the design and delivery of virtual health services for racialized populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0180.004
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.058
GPT teacher head0.383
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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