Independent and Interwoven: A Qualitative Exploration of Residents’ Experiences With Educational Podcasts
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.
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.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".