“Drones are a great idea! What is an AED?” novel insights from a qualitative study on public perception of using drones to deliver automatic external defibrillators
Bibliographic record
Abstract
BACKGROUND: The quickest way to ensure survival in an out-of-hospital cardiac arrest (OHCA) is for a bystander to provide immediate cardiopulmonary resuscitation (CPR) and apply an automated external defibrillator (AED). The urgency of OHCA treatment has led to the proposal of alternative avenues for better access to AEDs, particularly in rural settings. More recently, using unmanned aerial vehicles (or drones) to deliver AEDs to rural OHCA sites has proven promising in improving survival rates. OBJECTIVE: A pilot drone AED delivery program is currently being piloted in the community of Caledon, Ontario. The purpose of this study was to develop an understanding of public perception and acceptance of the use of drones for this purpose and to identify tailored community engagement strategies to ensure successful uptake. METHODS: In-depth qualitative descriptive study using interviews and focus group data collection and inductive thematic analysis. Purposive sampling was used to recruit 67 community members (40 interviews; 2 focus groups of 15) at existing community events in the project area. Interview guides were used to ensure consistency across data collection events. Detailed field notes were recorded when audio-recording was not possible. RESULTS: The central message seen throughout the data was quickly identified as the potential impact of low levels of CPR and AED literacy in the community over anything else including concerns about the drone. The impact of the community's existing relationship with the EMS; the need for bystander CPR & AED promotion prior to the program launch; and the value the community places on transparency and accountability related to the research and the drones were also key findings. In general, the drone concept was found to be acceptable but concerns about providing CPR and using the AED was what created anxieties in the lay public that we underestimated. CONCLUSION: Drone-delivered AEDs may be feasible and effective but successful uptake in smaller communities will require a deep understanding of a community's cardiac arrest literacy levels, information needs and readiness for innovation. This work will inform a robust community engagement plan that will be scalable to other locations considering a drone AED program.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".