Podcast Use in Undergraduate Medical Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it