Podcasting in Medical Education: How Long Should an Educational Podcast Be?
Bibliographic record
Abstract
We were pleased to read guidance for the development of podcasts for graduate medical education (GME) in the July 2016 issue of the Journal of Graduate Medical Education. Ahn and colleagues1 recommended a 10- to 20-minute format for a podcast, and we would like to expand on that recommendation and share additional input on style and format concerns.How long will trainees listen to an educational podcast? What is the ideal length for learning? Primary evidence may be lacking, but there appears to be a consensus that learner attention in lecture settings wanes after 10 minutes.2 We recently shared our own 20-minute pilot podcast for telephone triage education with pediatrics residents to better evaluate format and length issues. We have done this subjectively using a survey and objectively using the YouTube (Google, San Bruno, CA) platform to record listening times.While most of the 27 responding residents described the length as “about right,” 22% (6 of 27) reported that it could be slightly shorter. Only 1 resident requested longer content. This fits well with a 2013 survey of Canadian anesthesiology residents reporting that most would prefer a 5- to 15-minute or a 15- to 30-minute format for educational podcasts, with 5 to 15 minutes being preferred for most topics.3 A preference for 5- to 15-minute running time was also expressed in another survey by learners outside of GME,4 and a study of medical students reported that 15 to 20 minutes was the “optimal” length.5 Our objective data showed that, of those who listen beyond 1 minute, 28% dropped off near the 10-minute mark (figure).Content may ultimately dictate length, but a good starting aim may be a total length of 10 to 15 minutes.Many other style and content issues receive a passing mention in the literature. Our survey finds support for dialog being preferred over monolog format (93%, 25 of 27); citation of current evidence (67%, 18 of 27); use of personal anecdotes (52%, 14 of 27); and humor (37%, 10 of 27). Multiple trainees requested summary points, either between sections or at the end. One trainee requested a platform where 1.25× or 1.5× speed was available, consistent with our own listening habits.As avid listeners and producers of content, we look forward to seeing further scholarship on best practices in podcasting in GME.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".