Barriers and Facilitators to Accessing Digital Health Tools Faced by South Asian Canadians in Surrey, British Columbia: Community-Based Participatory Action Exploration Using Photovoice (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> South Asian community members in Canada experience a higher burden of chronic disease than the general population. Digital health innovations provide a significant opportunity to address various health care challenges such as supporting patients in their disease self-management. However, South Asian community members are less likely to use digital tools for their health and face significant barriers in accessing them because of language or cultural factors. </sec> <sec> <title>OBJECTIVE</title> The aim of this study is to understand the barriers to and facilitators of digital health tool uptake experienced by South Asian community members residing in Canada. </sec> <sec> <title>METHODS</title> This study used a qualitative community-based participatory action research approach. Residents from Surrey, British Columbia, Canada, who spoke 1 of 4 South Asian languages (Hindi, Punjabi, Urdu, or Tamil) were invited to participate in focus group discussions. A subsample of the participants were invited to use photovoice methods in greater depth to explore the research topics. </sec> <sec> <title>RESULTS</title> A total of 197 participants consented to the focus group discussions, with 12 (6.1%) participating in the photovoice phase. The findings revealed several key obstacles (older age, lack of education, and poor digital health literacy) and facilitators (social support from family or community members and positive attitudes toward technology) to using digital health tools. </sec> <sec> <title>CONCLUSIONS</title> The results support the value of using a community-based participatory action research approach and photovoice methods to engage the South Asian community in Canada to better understand digital health competencies and needs. There were several important implications for policy makers and future research, such as continued engagement of community leaders by health care providers and administrators to learn about attitudes and preferences. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".