A scoping review of virtual clinics in underserved regions: barriers and opportunities
Bibliographic record
Abstract
BACKGROUND: Universal access to quality healthcare remains a fundamental human right; however, underserved populations in rural and remote areas face persistent barriers to timely and appropriate care. Recent advancements in digital technology, especially virtual clinics, present promising opportunities to bridge these healthcare gaps by enhancing access and continuity of care. This review aims to systematically explore the barriers and facilitators influencing the implementation of virtual clinics in underserved regions to guide future policies and strategic development. METHODS: Employing the Joanna Briggs Institute Manual for Evidence Synthesis and adhering to PRISMA-ScR guidelines, comprehensive searches were conducted in PubMed, Web of Science, Scopus, Google, and Google Scholar databases, supplemented by gray literature from targeted Google searches and snowballing techniques. A three-phase screening process was independently carried out by multiple reviewers based on predetermined inclusion and exclusion criteria. Data extraction and thematic analysis were conducted through a six-stage process using MAXQDA software. RESULTS: Sixty-six articles were included from an initial 1590 records screened. The body of research has grown steadily since 2002, predominantly originating from Australia, the USA, and Canada. Major challenges identified include inadequate policy support, high startup costs, resistance from healthcare providers, limited internet infrastructure, privacy and security concerns regarding data, and restrictions in performing physical examinations remotely. Key facilitators encompass strong government policy backing, active stakeholder engagement, sustainable financial models, targeted workforce training (notably indigenous-focused), investment in infrastructure, and the adoption of hybrid care models combining in-person and virtual services. Notably, the nature and severity of these challenges and facilitators vary across countries of different income levels, underscoring the necessity of tailored, context-sensitive strategies. CONCLUSION: Implementing virtual clinics in underserved areas faces multifaceted challenges, but strategic facilitators can significantly advance their adoption and sustainability. Context-specific approaches considering economic capacities, infrastructural strengths, and workforce readiness are essential. Strengthening infrastructure, enhancing digital literacy, supporting human resource development, and formulating enabling policies form the cornerstone for equitable and enduring virtual healthcare access in marginalized regions. CLINICAL TRIAL: Not applicable.
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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 | 0.040 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".