Creation of a Student-Run Medical Education Podcast: Tutorial
Bibliographic record
Abstract
BACKGROUND: Podcasting has become a popular medium for medical education content. Educators and trainees of all levels are turning to podcasts for high-quality, asynchronous content. Although numerous medical education podcasts have emerged in recent years, few student-run podcasts exist. Student-run podcasts are a novel approach to supporting medical students. Near-peer mentoring has been shown to promote medical students' personal and professional identity formation. Student-run podcasts offer a new medium for delivering near-peer advice to medical students in an enduring and accessible manner. OBJECTIVE: This paper describes the creation of the UnsCripted Medicine Podcast-a student-run medical education podcast produced at the University of Cincinnati College of Medicine. METHODS: The planning and preparatory phases spanned 6 months. Defining a target audience and establishing a podcast mission were key first steps. Efforts were directed toward securing funding; obtaining necessary equipment; and navigating the technical considerations of recording, editing, and publishing a podcast. In order to ensure that high professionalism standards were met, key partnerships were created with faculty from the College of Medicine. RESULTS: The UnsCripted Medicine Podcast published 53 episodes in its first 2 years. The number of episodes released per month ranges from 0 to 5, with a mean of 2.0 episodes. The podcast has a Twitter account with 217 followers. The number of listeners who subscribed to the podcast via Apple Podcasts grew to 86 in the first year and then to 218 in the second year. The show has an average rating of 4.8 (out of 5) on Apple Podcasts, which is based on 24 ratings. The podcast has hosted 70 unique guests, including medical students, resident physicians, attending physicians, nurses, physicians' family members, graduate medical education leadership, and educators. CONCLUSIONS: Medical student-run podcasts are a novel approach to supporting medical students and fostering professional identity formation. Podcasts are widely available and convenient for listeners. Additionally, podcast creators can publish content with lower barriers of entry compared to those of other forms of published content. Medical schools should consider supporting student podcast initiatives to allow for near-peer mentoring, augment the community, facilitate professional identity formation, and prepare the rising physician workforce for the technological frontier of medical education and practice.
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How this classification was reachedexpand
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 | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".