Reducing Barriers to Optimal Automated External Defibrillator Use: An Elementary School Intervention Study
Bibliographic record
Abstract
Background Timely use of an automated external defibrillator (AED) improves outcomes in sudden cardiopulmonary arrest (SCA). Our project aims were to: 1) identify the barriers to optimal AED use in the Québec City area elementary schools; 2) create targeted educational material regarding AEDs; and 3) measure the impact of the teaching module. Methods Using a quality improvement in health-care framework, a survey exploring the barriers to AED use was sent to 139 elementary schools. We then developed a video teaching module on using AEDs to address these barriers. A convenience sample of 92 elementary school professionals participated in a mock scenario. Metrics related to AED use were assessed at baseline and after completing the post-teaching module. The primary outcome was the time to first shock and secondary outcomes consisted of evaluating the completion of each step required for safe and effective AED use. Results The barrier analysis survey received a response rate of 52.5%. Most schools reported having an AED (95%), but 48.6% indicated that no formal training was offered. After the teaching module, the appropriate use of the AED in an SCA simulation improved from 53% to 92% ( P < 0.001). The average time elapsed before first shock was 66 (95% confidence interval [CI], 63-70) seconds at baseline compared with 47 (95% CI, 45-49) seconds post-teaching module ( P < 0.001). Conclusions Lack of training, the main barrier to optimal use of AEDs in elementary schools, can be addressed through a brief video teaching module, thus improving the ability to deliver timely and effective defibrillation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".