Challenges and strategies for promoting health equity in virtual care: findings and policy directions from a scoping review of reviews
Bibliographic record
Abstract
OBJECTIVE: We sought to understand and synthesize review-level evidence on the challenges associated with accessibility of virtual care among underserved population groups and to identify strategies that can improve access to, uptake of, and engagement with virtual care for these populations. MATERIALS AND METHODS: A scoping review of reviews was conducted (protocol available at doi: 10.2196/22847). A total of 14 028 records were retrieved from MEDLINE, EMBASE, CINAHL, Scopus, and Epistemonikos databases. Data were abstracted, and challenges and strategies were identified and summarized for each underserved population group and across population groups. RESULTS: A total of 37 reviews were included. Commonly occurring challenges and strategies were grouped into 6 key thematic areas based on similarities across communities: (1) the person's orientation toward health-related needs, (2) the person's orientation toward health-related technology, (3) the person's digital literacy, (4) technology design, (5) health system structure and organization, and (6) social and structural determinants of access to technology-enabled care. We suggest 4 important directions for policy development: (1) investment in digital health literacy education and training, (2) inclusive digital health technology design, (3) incentivizing inclusive digital health care, and (4) investment in affordable and accessible infrastructure. DISCUSSION AND CONCLUSION: Challenges associated with accessibility of virtual care among underserved population groups can occur at the individual, technological, health system, and social/structural determinant levels. Although the policy approaches suggested by our review are likely to be difficult to achieve in a given policy context, they are essential to a more equitable future for virtual care.
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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 | 0.083 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".