The use of drones for the delivery of diagnostic test kits and medical supplies to remote First Nations communities during Covid-19
Bibliographic record
Abstract
BACKGROUND: Health care inequity in remote and rural Indigenous communities often involves difficulty accessing health care services and supplies. Remotely Piloted Aircraft Systems, or drones, offer a potentially cost-effective method for reducing inequity by removing geographic barriers, increasing timeliness, and improving accessibility of supplies, equipment, and remote care. METHODS: We assessed the feasibility of drones for delivery of supplies, medical equipment, and medical treatment across multiple platforms, including drone fleet development and testing; payload system integration (custom fixed-mount, winch, and parachute); and medical delivery simulations (COVID-19 test kit delivery and return, delivery of personal protective equipment, and remote ultrasound delivery and testing). RESULTS: Drone operational development has led to a finalized, scalable fleet of small to large drones with functional standard operating procedures across a range of scenarios, and custom payload systems including a fixed-mount, winch-based and parachute-based system. Simulation scenarios were successful, with COVID-19 test swabs returned to the lab with no signal degradation and a remote ultrasound successfully delivered and remotely guided in the field. DISCUSSION/CONCLUSIONS: Drone-based medical delivery models offer an innovative approach to addressing longstanding issues of health care access and equity and are particularly relevant in the context of SARS-CoV-2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".