Telemedicine and Pediatric Care in Rural and Remote Areas of Middle-and-Low-Income Countries: Narrative Review
Bibliographic record
Abstract
OBJECTIVES: Caring for children in low- and middle-income countries (LMIC) can be challenging. This review article aims to explore role of telemedicine in supporting pediatric care in LMIC. METHODOLOGY: A narrative review of existing English and Spanish literature was conducted to assess role of telemedicine to support pediatric care in LMIC. RESULTS: Beside medical education and direct pediatric care, telemedicine can provide sub-specialties consultations without extra burden on families. Additionally, telemedicine can help in lowering under-5 mortality by supporting neonatal care, infectious illnesses, and non-communicable diseases (NCDs). Telemedicine can be a gate for universal coverage for all children at a lower cost. For over a decade, it has been implemented successfully and sustained in a few LMIC. However, challenges in implementing telemedicine are enormous. Still, opportunities arise by using simpler technology, low-width band internet, smartphones, instant messaging applications and solar energy. COVID-19 pandemic facilitated acceptance and applicability of telemedicine worldwide including LMIC. Nevertheless, governments must regulate telemedicine by issuing policies and ensuring employment of local experts when possible to meet local resources and cultural competency. CONCLUSION: Telemedicine has proven successful in improving pediatrics care. Many LMIC should take advantage of this innovation to promote equity and access to high quality pediatric 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".