Factors Influencing the Implementation of Remote Delivery Strategies for Non-Communicable Disease Care in Low- and Middle-Income Countries: A Narrative Review
Bibliographic record
Abstract
Objectives: The COVID-19 pandemic has disrupted health care for non-communicable diseases (NCDs) and necessitated strategies to minimize contact with facilities. We aimed to examine factors influencing implementation of remote (non-facility-based) delivery approaches for people with hypertension and/or diabetes in low- and middle-income countries (LMICs), to inform NCD care delivery during health service disruption, including humanitarian crises. Methods: Our narrative review used a hermeneutic and purposive approach, including primary studies conducted in LMICs, which assessed implementation factors influencing remote NCD care delivery. Results were analyzed using the Consolidated Framework for Implementation Research. Results: Twenty-eight included studies revealed the strong influence of both internal organizational and broader contextual factors, such as community health worker policies or technological environment. Addressing patients’ specific characteristics, needs and resources was important for implementation success. Conclusion: This review highlighted the multiple, complex, interdependent factors influencing implementation of remote NCD care in LMICs. Our findings may inform actors designing NCD care delivery in contexts where facility-based access is challenging. Implementation research is needed to evaluate context-adapted e-Health, community-based, and simplified clinical management strategies to facilitate remote NCD 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".