Bi-directional drones to strengthen healthcare provision: experiences and lessons from Madagascar, Malawi and Senegal
Bibliographic record
Abstract
Drones are increasingly being used globally for the support of healthcare programmes. Madagascar, Malawi and Senegal are among a group of early adopters piloting the use of bi-directional transport drones for health systems in sub-Saharan Africa. This article presents the experiences as well as the strengths, weaknesses, opportunities and threats (SWOT analysis) of these country projects. Methods for addressing regulatory, feasibility, acceptability, and monitoring and evaluation issues are presented to guide future implementations. Main recommendations for governments, implementers, drone providers and funders include (1) developing more reliable technologies, (2) thorough vetting of drone providers' capabilities during the selection process, (3) using and strengthening local capacity, (4) building in-country markets and businesses to maintain drone operations locally, (5) coordinating efforts among all stakeholders under government leadership, (6) implementing and identifying funding for long-term projects beyond pilots, and (7) evaluating impacts via standardised indicators. Sharing experiences and evidence from ongoing projects is needed to advance the use of drones for healthcare.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".