eHealth Literacy in a Sample of South Asian Adults in Edmonton, Alberta, Canada: Subanalysis of a 2014 Community-Based Survey
Bibliographic record
Abstract
BACKGROUND: Digital health interventions are efficient and flexible methods for enhancing the prevention and management of cardiovascular disease and type 2 diabetes. However, little is known about the characteristics associated with eHealth literacy in the Canadian South Asian population. OBJECTIVE: The aim of this study is to describe perceived eHealth literacy and explore the extent to which it is associated with sociodemographic, health status, and technology use variables in a subset of South Asian Canadians. METHODS: We analyzed data from the e-Patient Project survey, a mixed-mode cross-sectional survey that occurred in 2014. The eHealth Literacy Scale (eHEALS) was used to measure eHealth literacy in a convenience sample of 511 English- or Punjabi-speaking South Asian adults recruited from a community pharmacy, a family physician office, and community events in Edmonton, Alberta. Multivariable quantile regression was used to explore variables associated with eHealth literacy. RESULTS: The analysis was restricted to 301 internet users (mean age 39.9, SD 14.8 years; 166/301, 55.1% female) who provided responses to all 8 eHEALS questions and complete demographic information. The mean overall eHEALS score was 29.3 (SD 6.8) out of 40, and 71.4% (215/301) agreed to at least 5 out of the 8 eHEALS items. The eHEALS item with the lowest level of agreement was "I can tell high-quality health resources from low-quality health resources on the internet" (182/301, 60.5%). Although there were statistically significant differences in eHEALS scores according to age, educational achievement, language preference, and the presence of chronic medical conditions, multivariable regression analysis indicated that language preference was the only variable independently associated with eHealth literacy (coefficient -6.0, 95% CI -9.61 to -2.39). CONCLUSIONS: In our sample of South Asian Canadian internet users, preference for written health information in languages other than English was associated with lower eHealth literacy. Opportunities exist to improve eHealth literacy using culturally and linguistically tailored interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".