Iso-lating optimal automated external defibrillator signage: An international survey
Bibliographic record
Abstract
Introduction: This study investigated the public's preference to a recognisable and meaningful signage for Automated External Defibrillators (AEDs) in alignment with ISO 7010 standards, aiming to identify improvements for better public awareness and response during out-of-hospital cardiac arrests (OHCA). Methods: A survey was administered via SurveyMonkey® and Heart of the Nation's social media. The survey evaluated recognition of ISO signage colors and AED symbols, and preferences for alternative AED signs. Baseline data including geographic location, industry employment, and first aid training were collected. Results: A total of 935 responses were received (Heart of the Nation's social media (n = 244) Survey Monkey's (paid, and independent of Heart of the Nation, n = 691). There were 511 from the US and Canada (54.65 %), 222 from the UK and Europe (23.76 %), 133 from the Asia Pacific (14.22 %), 6 from South America (0.64 %), 2 from the Middle East (0.21 %), and 61 from other territories (6.53 %). Among participants, 455 (48.66 %) were first aid trained. The healthcare sector was the most common employment (n = 155, 16.58 %). Only 187 (20 %) participants correctly identified the ISO AED sign. The preferred sign was a yellow sign with a red heart and blue font, chosen by 252 (27 %) participants. Conclusion: Current ISO 7010 AED signage is not widely recognised, and is only correctly interpreted by a small percentage of the public. The study suggests a need for more intuitive and visually distinct signage, such as the preferred yellow sign, to improve visibility and understanding, thereby enhancing AED accessibility and usage in OHCA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".