YouTube as a Platform for Publishing Clinical Skills Training Videos
Bibliographic record
Abstract
The means to share educational materials have grown considerably over the years, especially with the multitude of Internet channels available to educators. This article describes an innovative use of YouTube as a publishing platform for clinical educational materials.The authors posted online a series of short videos for teaching clinical procedures anticipating that they would be widely used. The project Web site attracted little traffic, alternatives were considered, and YouTube was selected for exploration as a publication channel. YouTube's analytics tools were used to assess uptake, and viewer comments were reviewed for specific feedback in support of evaluating and improving the materials posted.The uptake was much increased with 1.75 million views logged in the first 33 months. Viewer feedback, although limited, proved useful. In addition to improving uptake, this approach also relinquishes control over how materials are presented and how the analytics are generated. Open and anonymous access also limits relationships with end users.In summary, YouTube was found to provide many advantages over self-publication, particularly in terms of technical simplification, increased audience, discoverability, and analytics. In contrast to the transitory interest seen in most YouTube content, the channel has seen sustained popularity. YouTube's broadcast model diffused aspects of the relationship between educators and their learners, thereby limiting its use for more focused activities, such as continuing medical education.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".