First Nations perspectives on the ethical use of drones in Indigenous health care
Bibliographic record
Abstract
Introducing drones into the health care sector is a recent advancement with minimal investigation of the context-specific factors related to their ethical deployment in First Nation environments. This qualitative study aimed to gain First Nations’ insights into the ethical use of drones within health care settings, responding to calls for drone perspectives in global health. In the summer of 2024, we held eight semi-structured interviews with First Nations Peoples working in drone technology in Canada. We employed thematic analysis, generating 18 inductive codes, which led to the construction of six themes: cultural sensitivity and inclusion, health care delivery and accessibility, ethical and legal considerations, education and community engagement, challenges and limitations, and future potential and recommendations. Our findings enhance understanding of the context-specific concerns and challenges that may arise when deploying drones within First Nations communities, specifically for health care use. Our recommendations stress engaging First Nations communities as essential partners. By addressing cultural, ethical, and practical considerations, stakeholders may create more effective and inclusive drone projects that improve health care delivery and empower First Nations communities.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".