The learning impact of a virtual CPR webinar for seniors
Bibliographic record
Abstract
Aim: To assess the learning impact of a virtual interactive CPR webinar for seniors through mix-methods quantitative and qualitative survey analysis. Methods: We surveyed 350 webinar attendees. The webinar trained participants in hands-only CPR technique and AED use. Survey questions included multiple-choice selection and open-ended responses. Qualitative inductive thematic analysis was conducted on open-ended question responses. Knowledge of CPR was measured on a 3-point scale (very little knowledge, some knowledge, a lot of knowledge). Proportions were compared pre and post seminar using a z-test. Results: 231 respondents ≥ 65 years participated in the survey (response rate 66.0 %). There was a significant increase in self-reported knowledge of CPR pre and post webinar (very little knowledge 33.9 % to 1.8 % P < 0.00001, some knowledge 54.2 % to 12.1 % P < 0.0001, a lot of knowledge 11.9 % to 86.1 % P < 0.0001). We found 5 main themes on participant feedback: Positive affective comments, learning, constructive criticism, the desire to share information and comments on CPR ability. We identified 4 main themes related to further questions: Performing CPR in different circumstances, risks of CPR, information sharing, and prevention of death from myocardial infarction. Following the webinar, 89.9 % of respondents chose that they would be very likely to perform CPR on a friend, family member or colleague. Conclusion: This study highlights the success of virtual CPR webinars for senior citizens in improving self-reported CPR knowledge. This has potential to address barriers to online education for seniors and increase 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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".