Medical education videos – comparative analysis of sonography vs. clinical examination videos: user perception and educational value
Bibliographic record
Abstract
Abstract Background Video content has become an increasingly valuable tool in medical education, particularly for teaching hands-on skills like sonography and clinical examination. This study evaluates the satisfaction and content of sonography and clinical examination videos on the AMBOSS platform, a prominent medical education resource in Germany. Objective The goal of this study was to compare how effective and well-received sonography and clinical examination videos are on the AMBOSS platform. The study looked at aspects such as the medical and technical quality of the videos, their usefulness for learning, and overall user satisfaction. Methods Eighteen instructional videos were chosen and made accessible on the AMBOSS platform, grouped into sonography (n = 9) and clinical examination (n = 9) categories. Users were asked to voluntarily and anonymously fill out a questionnaire evaluating the videos. Over 49.5 months, data from 1,643,274 video views and 936 completed questionnaires were gathered. Results Clinical examination videos were watched significantly more often than sonography videos (86 vs. 14%). Both video types were highly rated in terms of medical and technical quality. However, sonography videos were judged superior in technical quality and clarity, whereas clinical examination videos were preferred for their medical quality and practical application. Feedback from users indicated a desire for more detailed annotations and clearer explanations. Conclusion The findings underline the crucial role of video resources in medical education, particularly in teaching practical skills. To improve educational outcomes, it is important to tailor content to the specific needs of medical students and professionals, incorporate user feedback, and take advantage of technological advancements.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".