Independent and Interwoven: A Qualitative Exploration of Residents’ Experiences With Educational Podcasts
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
PURPOSE: Educational podcasts are an increasingly popular platform for teaching and learning in health professions education. Yet it remains unclear why residents are drawn to podcasts for educational purposes, how they integrate podcasts into their broader learning experiences, and what challenges they face when using podcasts to learn. METHOD: The authors used a constructivist grounded theory approach to explore residents' motivations and listening behaviors. They conducted 16 semistructured interviews with residents from 2 U.S. and 1 Canadian institution from March 2016 to August 2017. Interviews were recorded and transcribed. The transcripts were analyzed using constant comparison, and themes were identified iteratively, working toward an explanatory framework that illuminated relationships among themes. RESULTS: Participants described podcasts as easy to use and engaging, enabling both broad exposure to content and targeted learning. They reported often listening to podcasts while doing other activities, being motivated by an ever-present desire to use their time productively; this practice led to challenges retaining and applying the content they learned from the podcasts to their clinical work. Listening to podcasts also fostered participants' sense of connection to their peers, supervisors, and the larger professional community, yet it created tensions in their local relationships. CONCLUSIONS: Despite the challenges of distracted, contextually constrained listening and difficulties translating their learning into clinical practice, residents found podcasts to be an accessible and engaging learning platform that offered them broad exposure to core content and personalized learning, concurrently fostering their sense of connection to local and national professional communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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