Trade-offs between Accessibility and Practicality in Global Telemedicine: A Systematic Review
Bibliographic record
Abstract
Background: Telemedicine is increasingly vital in healthcare, offering remote consultations via message-based and video call-based platforms. These methods improve healthcare accessibility, particularly when in-person visits are limited. Telemedicine is increasingly vital in healthcare, offering remote consultations via message-based and video call-based platforms. These methods improve healthcare accessibility, particularly when in-person visits are limited. Methods: A systematic search was conducted in ScienceDirect, PubMed, and up to August 2024. Studies evaluating the accessibility and practicality of global telemedicine were evaluated. From initial 439 records, 19 studies were finally in this systematic review. Studies were reviewed thoroughly with quality appraisal using the Newcastle Ottawa Scale, in which those rated for high quality studies were included. Results: Most studies (74%) were from high-income countries, notably the United States (26%) and Denmark (11%). Observational studies dominated (95%), focusing on follow-up consultations (47%) and diagnostic services (32%), particularly in general care (32%), neurology (11%), and surgery (5%). Research peaked in 2022 (32%) during the COVID-19 pandemic. Common platforms included WhatsApp, AnyDesk, and QliqSOFT. Accessibility was the primary focus in 53% of studies, while 47% addressed feasibility. Challenges like technological barriers and privacy concerns were particularly noted in lower-income regions. Conclusion: Telemedicine has enhanced healthcare accessibility and demonstrated feasibility. However, technological limitations and remote consultation challenges persist, particularly in lower-income regions. Continued research is needed to optimize telemedicine and equitable access.
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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 | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".