North American Public Opinion Survey on the Acceptability of Crowdsourcing Basic Life Support for Out-of-Hospital Cardiac Arrest With the PulsePoint Mobile Phone App
Bibliographic record
Abstract
BACKGROUND: The PulsePoint Respond app is a novel system that can be implemented in emergency dispatch centers to crowdsource basic life support (BLS) for patients with cardiac arrest and facilitate bystander cardiopulmonary resuscitation (CPR) and automated external defibrillator use while first responders are en route. OBJECTIVE: The aim of this study was to conduct a North American survey to evaluate the public perception of the above-mentioned strategy, including acceptability and willingness to respond to alerts. METHODS: We designed a Web-based survey administered by IPSOS Reid, an established external polling vendor. Sampling was designed to ensure broad representation using recent census statistics. RESULTS: A total of 2415 survey responses were analyzed (1106 from Canada and 1309 from the United States). It was found that 98.37% (1088/1106) of Canadians and 96% (1259/1309) of Americans had no objections to PulsePoint being implemented in their community; 84.27% (932/1106) of Canadians and 55.61% (728/1309) of Americans said they would download the app to become a potential responder to cardiac arrest, respectively. Among Canadians, those who said they were likely to download PulsePoint were also more likely to have ever had CPR training (OR 1.7, 95% CI 1.2-2.4; P=.002); however, this was not true of American respondents (OR 1.0, 95% CI 0.79-1.3; P=.88). When asked to imagine themselves as a cardiac arrest victim, 95.39% (1055/1106) of Canadians and 92.44% (1210/1309) of Americans had no objections to receiving crowdsourced help in a public setting; 88.79% (982/1106) of Canadians and 84.87% (1111/1309) of Americans also had no objections to receiving help in a private setting, respectively. The most common concern identified with respect to PulsePoint implementation was a responder's lack of ability, training, or access to proper equipment in a public setting. CONCLUSIONS: The North American public finds the concept of crowdsourcing BLS for out-of-hospital cardiac arrest to be acceptable. It demonstrates willingness to respond to PulsePoint CPR notifications and to accept help from others alerted by the app if they themselves suffered a cardiac arrest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".