Abstract 19542: A North American Survey of Public Opinion on the Acceptability of Crowdsourcing Basic Life Support for Out-of-hospital Cardiac Arrest
Bibliographic record
Abstract
Introduction: Performance of bystander CPR and early defibrillation following out-of-hospital cardiac arrest (OHCA) have been shown to increase the odds of survival to hospital discharge more than 3-fold. The PulsePoint Respond™ Application (App) is a novel system that can be implemented by EMS to crowdsource basic life support for victims of OHCA. The system sends cardiac arrest notifications to a user’s mobile device which includes the location of the emergency and nearby public access defibrillators to facilitate bystander CPR and AED use while EMS personnel are en route. We conducted a North American survey to evaluate public perceptions of such an application, including acceptability and willingness to respond to alerts. Methods: The web-based survey was conducted in Canada and the USA by an established external polling vendor, Ipsos Reid. Sampling was designed to ensure broad representation of gender, age, geography, and spoken language following recent census statistics. Respondents were presented with a short concept description of cardiac arrest and the Pulsepoint app in text format followed by 6 closed-ended and 4 open-ended questions. Results: A random sample of 2,415 total surveys were collected (1106 from Canada and 1309 from the US). 70% of Canadian respondents but only 47% of US respondents had been trained in CPR at some point. On average, 79.5% of respondents agreed that Pulsepoint is something they would like to see in their community and 59.5% said they would download the App. 80% of Canadians and 77% of Americans were comfortable with receiving help in a public setting (street, office, etc) and 72 and 68% respectively, indicated they would be comfortable with receiving help in a private setting (home). Less than 40% of respondents identified concerns; as expected those identified included training concerns and trust issues. An average of 89% of the sample from both countries felt it was important that responders have up-to-date CPR certification. Conclusions: Overall, the North American public find the concept of the Pulsepoint application and crowdsourcing basic life support for OHCA acceptable and would be willing to respond. This is encouraging insight to support the use of social media to increasing bystander CPR rates in North America.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".