Navigating Digital Demands in Virtual Healthcare: A Systematic Review of the Impact of Digital Literacy on Patient Healthcare Access and Utilization
Bibliographic record
Abstract
Background: Digital literacy is increasingly recognized as a determinant of access to virtual healthcare services. As healthcare delivery shifts toward digital platforms, disparities in digital literacy risk deepening existing health inequities. Objective: This systematic review aimed to examine how digital literacy influences access to and utilization of virtual healthcare services. Methods: Five databases were searched (Web of Science, Medline, Scopus, CINAHL, and IEEE Xplore) for peer-reviewed studies published between 2014 and 2024. Studies were eligible if they assessed digital literacy—defined to include technical skills, awareness, attitudes, confidence, or support—in relation to patients’ use of virtual care. Results: From 31 studies identified, limited skills, unawareness of available services, low confidence, and lack of support were common barriers. Conversely, greater eHealth literacy, digital confidence, and assistance from family or care teams were linked to increased use of digital tools. Conclusion: Digital literacy is a modifiable, multi-level determinant of virtual care access and utilization. As virtual healthcare expands, prioritizing digital literacy is essential to ensuring equitable access and preventing the widening of existing disparities. Future policy strategies should include embedding digital literacy support into primary-care reimbursement models and funding community-based digital navigator programs to bridge gaps for underserved populations. Keywords: digital literacy; virtual care; telehealth; eHealth; health access; digital inclusion; patient portals
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".