Use of telemedicine to tackle health problems in South Asia during the COVID-19 era and beyond: a systematic review
Bibliographic record
Abstract
Introduction: Telemedicine (TM) and teleconsultation services flourished during coronavirus disease 2019 (COVID-19) transmission to avoid COVID-19 infection and physical contact. Many physicians switched to the virtual treatment mode and nearly all types of health disciplines were covered. Through this systematic review, the authors tried to explore the strengths and weaknesses of TM, identify the barriers to adopting TM by population, and explain the limitations of this healthcare delivery model. Methods and results: =7) were from Pakistan, Bangladesh, Sri Lanka, and Nepal. Advice related to cancer, autoimmune diseases, and neurological diseases were the most common among the health disciplines in which TM was used. A peak in teleconsultation was observed during the high transmission phase of COVID-19, although major queries were associated with existing health complications and comorbidities. Conclusion: Other than a few concerns regarding connectivity, privacy, and diagnosis, TM was in fact affordable, timesaving, feasible, and accurate, which ensured a highly satisfying experience among the participants (>80%).
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".