Didactic and Content Quality of Basic Life Support Videos on YouTube: Cross-Sectional Study
Bibliographic record
Abstract
Background: Cardiopulmonary resuscitation (CPR) is vital for improving patient outcomes in medical emergencies. Both laypersons and health care professionals often seek guidance on performing CPR. In today's digital age, many turn to easily accessible platforms such as YouTube for practical skills. Objective: This study evaluates the didactic and content quality of CPR videos on YouTube using comprehensive checklists and investigates the association between the assigned quality scores and type of publisher, view count, and video rankings. Methods: Videos were included based on defined search terms and exclusion criteria. Two emergency physicians rated each video independently using validated checklists concerning content and didactic quality. Linear regression analysis was performed to assess the relationships between video quality scores and view counts, as well as video rankings. Results: Of the 250 videos identified, 74 (29.6%) met the inclusion criteria. On the content checklist, videos scored an average of 56.5% (SD 19.2%), and on the didactic checklist, they scored 66.6% (SD 14.3%); none achieved the maximum score. Videos from official medical institutions scored significantly higher in content quality compared to nonofficial sources (P=.04). Video quality scores were not associated with video rankings or view counts. Conclusions: The study highlights substantial variability in the didactic and content quality of CPR-related videos on YouTube. For medical educators, this underlines the need to curate and recommend reliable online resources or to develop new high-quality content aligned with established checklists. For the general public, the findings caution against relying on popularity metrics as indicators of accuracy and emphasize the importance of guidance from trusted institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".