Podcast Use in Undergraduate Medical Education
Bibliographic record
Abstract
Introduction Podcasts have become increasingly popular as a medium for free open access medical education (FOAM). However, little research has examined the use of these extracurricular audio podcasts as tools in undergraduate medical education. We aimed to examine knowledge retention, usage conditions, and preferences of undergraduate medical students at a Canadian university interacting with extracurricular podcasts. Methods Students enrolled in the undergraduate medical program at McMaster University volunteered to participate in this study. Two podcasts were created specifically for the purposes of this study, and online tests and surveys were sent to participants to gather data regarding user preferences of podcasts. In addition, we recorded changes in topic test scores before and after podcast exposure. Results Forty-two students were recruited to this study. Participants who completed the assessments demonstrated an effect of learning. Podcasts of 30 minutes or less were preferred in the majority of participants who had a preference in duration. The top three activities participants were engaged in while listening to the podcasts were driving (46%), completing chores (26%), and exercising (23%). A large number of participants who did not complete the study in its entirety cited a lack of time and podcast length to be the top two barriers to completion. Conclusion This is one of the first studies to examine extracurricular podcast-usage data and preferences in a Canadian undergraduate medical student population. This information may help educators and FOAM producers to optimize educational tools for 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".