eHealth Literacy Interventions: Scoping Review
Bibliographic record
Abstract
Background: Electronic resources have become a predominant modality for health information dissemination in recent years. eHealth literacy (eHL) means individuals' competencies to effectively acquire and use health information from electronic sources. Enhancing eHL is thus essential to facilitate individuals' effective engagement with electronic resources and promote improved health management. Objective: This scoping review aimed to synthesize the characteristics of eHL interventions, thereby providing a reference for future intervention strategies. Methods: A comprehensive search of PubMed, Embase, Cochrane Library, Web of Science, ProQuest, CINAHL, CNKI, VIP, Wan Fang Data, and Sino Med limited to Chinese and English-language studies published before August 2024 was conducted. The interventional studies included had the explicit primary objective of enhancing eHL. We also incorporated studies that assessed eHL as a secondary outcome or mediator influencing health behaviors or clinical outcomes. All publications were required to provide publicly accessible complete datasets. We excluded conference abstracts and protocols. Academic theses and dissertations were included if they underwent institutional quality assurance through rigorous academic review processes and met predefined eligibility criteria. Results: A total of 35 studies were included in this review. The most prevalent eHL interventions (12/35, 34%) were delivered via mobile apps and devices in various settings, including educational institutions, public spaces, health care facilities, and community centers. These interventions predominantly focused on enhancing information literacy, health literacy, and computer literacy across the 6 domains of eHL: traditional, health, information, scientific, media, and computer literacy. A majority of the interventions were conducted on a weekly basis (6/13, 46%) and had a duration of 24 weeks (6/35, 17%). However, 77% (27/35) of interventions did not assess long-term effects. The primary outcomes of eHL interventions encompassed perceived eHL, actual eHealth knowledge and skills, health literacy, health behavior, and clinical outcomes, with 86% (30/35) indicating positive effects. The eHealth Literacy Scale was the most frequently used assessment tool. Conclusions: This study synthesizes the characteristics of eHL interventions. Current eHL interventions exhibit limitations in theoretical grounding, longitudinal tracking, and traditional or media literacy components. Overreliance on self-reported metrics constrains validity assessment. Future work should strengthen theoretical frameworks, integrate objective metrics, and enhance longitudinal designs.
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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 | 0.085 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 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; both teacher heads agree on what is shown here.
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".