Digital Health Interventions’ Impact on Health Literacy: A Systematic Review
Bibliographic record
Abstract
Abstract In the digital era, health literacy is crucial for informed health decisions and outcomes. This systematic review evaluates the effectiveness of digital health interventions (DHIs) in enhancing health literacy, as defined by the World Health Organization. We included a variety of studies, such as cross-sectional studies, surveys, and case reports, focusing on interventions like mobile health apps, online platforms, and telehealth services. Our search, adhering to PRISMA guidelines, spanned databases like PubMed, IEEE, and ACM, covering publications from 2013 to 2023. From 1,029 initial articles, 58 met our inclusion criteria after rigorous screening and duplicates removal. Our findings highlight that DHIs, including multimedia tools and remote sessions, significantly bolster health literacy across diverse populations. However, the impact varies due to the digital divide, influenced by factors like age and socioeconomic status. This review underscores the potential of DHIs in public health and the necessity to address accessibility to reduce health disparities. The full synthesis of data and methodological details will be discussed in the presentation, aiming to guide future digital health strategies and policies. Key messages • Digital Interventions Enhance Health Literacy Across Diverse Populations. • Addressing the Digital Divide to Ensure Equitable Access to Health Benefits.
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 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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".