Residents’ Perceptions of Effective Features of Educational Podcasts
Bibliographic record
Abstract
INTRODUCTION: Educational podcasts are used by emergency medicine (EM) trainees to supplement clinical learning and to foster a sense of connection to broader physician communities. Yet residents report difficulties remembering what they learned from listening, and the features of podcasts that residents find most effective for learning remain poorly understood. Therefore, we sought to explore residents' perceptions of the design features of educational podcasts that they felt most effectively promoted learning. METHODS: We used a qualitative approach to explore EM trainees' experiences with educational podcasts, focusing on design features that they found beneficial to their learning. We conducted 16 semi-structured interviews with residents from three institutions from March 2016-August 2017. Interview transcripts were analyzed line-by-line using constant comparison and organized into focused codes, conceptual categories, and then key themes. RESULTS: The five canons of classical rhetoric provided a framework for thematically grouping the disparate features of podcasts that residents reported enhanced their learning. Specifically, they reported valuing the following: 1) Invention: clinically relevant material presented from multiple perspectives with explicit learning points; 2) Arrangement: efficient communication; 3) Style: narrative incorporating humor and storytelling; 4) Memory: repetition of key content; and 5) Delivery: short episodes with good production quality. CONCLUSION: This exploratory study describes features that residents perceived as effective for learning from educational podcasts and provides foundational guidance for ongoing research into the most effective ways to structure medical education podcasts.
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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 | 0.001 | 0.011 |
| 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 | 0.000 | 0.000 |
| 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 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".