Assessing YouTube as an Educational Tool for Shingles: Cross-Sectional Study
Bibliographic record
Abstract
Background YouTube is a popular platform with many videos, which have potential educational value for medical students. Due to the lack of peer review, other surrogates are necessary to determine the content quality of such educational videos. Few studies have analyzed the research background or academic affiliation of the physicians associated with the production of YouTube videos for medical education. The research background or academic affiliations of those physicians may be a reflection of the content quality of these educational videos. Objective This study identifies physicians associated with the production of educational YouTube videos about shingles and analyzes those physicians based on their research background or academic affiliation, which may be good surrogates for video content quality. Methods Using the YouTube search engine with default settings, the term “shingles” was searched on May 8, 2020. A cross-sectional study was performed using the first 50 search results. A search on Scopus for each identified physician was performed, and data regarding their research background and academic affiliation were recorded. Results Of the 50 YouTube videos, 35 (70%) were categorized as academic. Of the 35 academic videos, 24 (71%) videos featured physicians, totaling 25 physicians overall. Out of these 25 physicians, 5 (20%) had at least 1 shingles-related publication and 8 (32%) had an h-index >10. A total of 21 (84%) physicians held an academic affiliation. Conclusions These results ensure to a certain degree the quality of the content in academic videos on YouTube for medical education. However, further evaluation is needed for this growing platform.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".