Understanding the Use and Perceived Impact of a Medical Podcast: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Although podcasts are increasingly being produced for medical education, their use and perceived impact in informal educational settings are understudied. OBJECTIVE: This study aimed to explore how and why physicians and medical learners listen to The Rounds Table (TRT), a medical podcast, as well as to determine the podcast's perceived impact on learning and practice. METHODS: Web-based podcast analytics were used to collect TRT usage statistics. A total of 17 medical TRT listeners were then identified and interviewed through purposive and convenience sampling, using a semistructured guide and a thematic analysis, until theoretical sufficiency was achieved. RESULTS: The following four themes related to podcast listenership were identified: (1) participants thought that TRT increased efficiency, allowing them to multitask, predominantly using mobile listening platforms; (2) participants listened to the podcast for both education and entertainment, or "edutainment"; (3) participants thought that the podcast helped them keep up to date with medical literature; and (4) participants considered TRT to have an indirect effect on learning and clinical practice by increasing overall knowledge. CONCLUSIONS: Our results highlight how a medical podcast, designed for continuing professional development, is often used informally to promote learning. These findings enhance our understanding of how and why listeners engage with a medical podcast, which may be used to inform the development and evaluation of other podcasts.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".