Abstract 83: Barriers to Implementation of the PulsePoint Smartphone Application
Bibliographic record
Abstract
Background: PulsePoint is a smartphone application that alerts users in the vicinity of a cardiac arrest to the location of the emergency to facilitate bystander resuscitation. Identifying barriers to implementation provides an opportunity to increase the efficacy of the application. Objective: To identify potential barriers to bystander resuscitation facilitated by the PulsePoint application. Methods: A request for survey participation was sent to all alerted smartphones between June 28, 2012 and May 1, 2013. Only one request for participation was sent to potential respondents. The survey was structured with multiple choice and free text responses administered via Survey Monkey. PulsePoint was active in 10 US EMS agencies over the course of the survey (population served ~ 1.7 million). Results: Of 2790 PulsePoint users sent a CPR notification and request for survey completion, 23.8 % completed our survey (663/2790). Figure 1 details the responses to key questions in the survey and identifies potential barriers. Approximately 37% of survey respondents did not receive an alert, despite the system sending one. Seventy-eight percent of users who received an alert did not respond (328/419). Most cited being busy with a task they could not abandon or felt the location of the emergency was too far away. Approximately 78% of those who arrived on scene (54/69) did not find a victim in cardiac arrest. Of those users who arrived before EMS and found an unconscious victim who was not breathing normally, 6/7 (86%) performed CPR. Conclusion: Several potential barriers to implementation have been identified including specificity of the alerts for actual cardiac arrest and the radius of alert around a cardiac arrest episode. The very high proportion of respondents performing bystander CPR when arriving on scene prior to EMS (86%) suggests that optimized PulsePoint implementation, with identified barriers addressed, may improve community-level bystander CPR rates.
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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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".