Evaluating the Impact of Digital Health Literacy on the Adoption of Preventive Health Measures in Socioeconomically Vulnerable Communities: A Narrative Review
Bibliographic record
Abstract
As digital platforms become central to healthcare delivery, digital health literacy (DHL), the ability to seek, understand, evaluate, and apply health information using digital technologies, has emerged as a vital determinant of preventive health behavior. This review addresses how limited DHL hinders the adoption of preventive measures such as vaccination, screening, hygiene, and lifestyle modification in socioeconomically vulnerable populations. The objective is to synthesize quantitative evidence linking DHL to preventive behavior while analyzing the influence of socioeconomic status, digital access, trust in technology, and health self-efficacy. Through a structured review of quantitative studies published between 2015 and 2025, the analysis focuses on validated DHL instruments, including the eHealth Literacy Scale and the Digital Health Literacy Instrument, as well as diverse intervention formats and population-specific findings. Results indicate that improved DHL correlates with up to a 25% increase in preventive health uptake. However, disparities in digital access, foundational literacy, and cultural alignment limit these benefits in underserved settings. Methodological inconsistencies and gaps in region-specific data also constrain generalizability. The findings underscore the need to embed DHL within national health literacy strategies, public health infrastructure, and community-led programs. The key takeaway is that without DHL, digital innovation may widen rather than reduce existing health inequities.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".