The Application of Drones in Healthcare and Health-Related Services in North America: A Scoping Review
Bibliographic record
Abstract
Using drone aircraft to deliver healthcare and other health-related services is a relatively new application of this technology in North America. For health service providers, drones represent a feasible means to increase their efficiency and ability to provide services to individuals, especially those in difficult to reach locations. This paper presents the results of a scoping review of the research literature to determine how drones are used for healthcare and health-related services in North America, and how such applications account for human operating and machine design factors. Data were collected from PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore using a block search protocol that combined 13 synonyms for “drone” and eight broad terms capturing healthcare and health-related services. Four-thousand-six-hundred-and-sixty-five documents were retrieved, and following a title, abstract, and full-text screening procedure completed by all authors, 29 documents were retained for analysis through an inductive coding process. Overall, findings indicate that drones may represent a financially feasible means to promote healthcare and health-related service accessibility for those in difficult-to-reach areas; however, further work is required to fully understand the costs to healthcare organizations and the communities they serve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".