Comparing Learning Outcomes in Cardiopulmonary Resuscitation (CPR) and/or Automated External Defibrillator (AED) Training for Laypeople in Face-to-Face, Online, and Mixed Training Methods: An Integrative Literature Review
Bibliographic record
Abstract
Cardiovascular diseases and cardiac arrest (CA) are the main causes of death worldwide. This review aims to identify publications on the learning outcomes for the use of an automated external defibrillator (AED) and/or cardiopulmonary resuscitation (CPR) to train laypeople (LP), the method of training used, the year of publication and their recommendations. We employed Miller's assessment pyramid to describe learning outcomes as knowledge, skills, and confidence. The methods of training are face-to-face, online, and mixed. The evidence found in this study will be used to support the development and validation of a simulation-based training program to teach LP to operate AEDs delivered by drones in rural and remote (R&R) locations. This article is an integrative literature review with a quantitative and qualitative research design and is composed of seven steps: research question, inclusion and exclusion criteria, search and selection of studies, the role of a second reviewer of the findings, data analysis, interpretation and discussion of the results, and finally knowledge synthesis. The results of this review demonstrate that there are no significant differences in the learning outcomes of the different training methods. Since these findings suggest good results in all methods, the development of a training program based on face-to-face, online, and mixed, especially for places with few resources such as R&R places, indicates all methods can be used as good practices to develop training programs.
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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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".