eHealth Literacy and Health-Related Internet Use Among Swedish Primary Health Care Visitors: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: Digitalization has profoundly transformed health care delivery, especially within primary health care, as a crucial avenue for providing accessible, cost-effective care. While eHealth services are frequently highlighted for improving health care availability and promoting equality, it is essential to recognize that digitalization can inadvertently exclude individuals who lack the prerequisites to use eHealth services, that is, those with low eHealth literacy. Previous research has identified lower eHealth literacy among older individuals, those with lower educational levels, and those who use the internet less frequently. However, in a Swedish context, only a few studies have investigated eHealth literacy. Objective: This study investigated eHealth literacy and its association with health-related internet use and sociodemographic characteristics among primary health care visitors. Methods: This cross-sectional study used a quantitative, descriptive approach. Swedish-speaking patients visiting a primary health care center participated by answering the multidimensional eHealth Literacy Questionnaire (eHLQ) and questions regarding sociodemographic characteristics and internet usage. The study compared mean scores using the Mann-Whitney U test and the Kruskal-Wallis test. A logistic regression analysis also explored the associations between eHealth literacy and significant independent variables identified in the univariate analyses. Results: As a group, the 172 participants rated highest in understanding and engagement with their health (median eHLQ score 3, IQR 2.8-3.4), as well as in feeling secure about the confidentiality of eHealth services (median eHLQ score 3, IQR 2-3), while they rated lower in motivation to use eHealth (median eHLQ score 2.6, IQR 2-3), the suitability of eHealth services to their personal needs (median eHLQ score 2.75, IQR 2-3), and their perceived ability to understand and use health-related internet information (median eHLQ score 2.6, IQR 2-3). The logistic regression analysis identified that lower eHealth literacy was associated with older age, particularly in domains related to finding, understanding, and using health-related internet information (odds ratio [OR] 1.02, 95% CI 1-1.05; P=.03); digital technology use (OR 1.05, 95% CI 1.02-1.08; P<.001); and accessing well-functioning eHealth services (OR 1.02, 95% CI 1-1.05; P=.03). Additionally, in the logistic regression analysis, perceiving health-related internet information as not useful was linked to lower literacy in all eHLQ domains except one. Conclusions: Our findings regarding the primary challenges within our sample underscore the importance of developing and tailoring eHealth services to accommodate users' individual needs better, enhancing motivation for eHealth use, and continuing efforts to improve overall health literacy. These measures, which both eHealth developers and health care professionals should consider, are crucial for addressing the digital divide and expanding access to eHealth services for as many people as possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".