YouTube Videos as a Source of Information About Immunology for Medical Students: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The use of the internet as a source of information has grown exponentially in the last decade. YouTube is currently the second most visited website and a major Web-based educational resource for medical students. OBJECTIVE: The aim of this study was to evaluate the quality, accuracy, and attractiveness of the information acquired from YouTube videos about 2 central concepts in immunology. METHODS: YouTube videos posted before August 27, 2018 were searched using selected keywords related to either antigen presentation or immunoglobulin gene rearrangement. Video characteristics were recorded, and the Video Power Index (VPI) was calculated. Videos were assessed using 5 validated scoring systems: understandability and attractiveness, reliability, content and comprehensiveness, global quality score (GQS), and a subjective score. Videos were categorized by educational usefulness and by source. RESULTS: A total of 82 videos about antigen presentation and 70 about immunoglobulin gene rearrangement were analyzed. Videos had a mean understandability and attractiveness score of 6.57/8 and 5.84/8, content and comprehensiveness score of 9.84/20 and 5.84/20, reliability score of 1.65/4 and 1.53/4, GQS of 3.38/5 and 2.76/5, and subjective score of 2.00/3 and 2.00/3, respectively. The organized channels group tended to have the highest VPI and GQS. CONCLUSIONS: YouTube can provide medical students with some useful information about immunology, although content wise it cannot substitute textbooks and academic courses. Students and teachers should be aware of the educational quality of available videos if they intend to use them in the context of blended learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".