Human Factors in Drone Delivery of Automatic External Defibrillators for Out of Hospital Cardiac Arrest: Older Adult Considerations
Bibliographic record
Abstract
An Automatic External Defibrillator (AED) delivered by drone has the potential to improve survival rates of an Out of Hospital Cardiac Arrest (OHCA) due to earlier access to defibrillation.There is a lack of user-centred, and older adult-focused research in this area.The present study evaluated the perceptions and interactions of older adults with a drone delivered AED operation to identify human factors considerations that may enable the design development of a more inclusive and accessible drone delivered AED service.A Drone Bystander Centred Design Framework (DBCD) for drone delivery of AEDs was developed and informed design concepts for key service touchpoints, including the drone itself, the drop mechanism, AED packaging, and dispatch communication.Results from this study provide novel insight into older adult characteristics and how they may relate to this emerging service model, older adults' service experience of a simulated drone delivered AED, as well as cognitive, psychographic, sensory, perception, and movement control considerations.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".