Distribution of core content coverage among three popular emergency medicine podcasts: A 10‐year analysis
Bibliographic record
Abstract
Objective: Podcasts are increasingly utilized as educational tools within emergency medicine (EM). As EM residency programs seek to incorporate asynchronous educational material, it is important to ensure we are covering the full breadth of EM core content. This study sought to describe the distribution of EM core content among three popular EM podcasts. Methods: We performed a retrospective study of the distribution of podcast topics among three popular EM podcasts from July 2011 to June 2021. We evaluated the podcast episode content and alignment with the EM core content, as defined by the Model of the Clinical Practice of Emergency Medicine (MCPEM) and American Board of Emergency Medicine (ABEM) examination distribution. Data are presented descriptively. Results: We identified 2759 podcast episodes, consisting of 7413 total topics and 2498.7 hours of content. The most frequently covered topics were "signs, symptoms, and presentations" (20.1% of total hours vs. 7.9% of MCPEM and 10.0% of ABEM exam) and "procedures and skills integral to the practice of emergency medicine" (14.8% of total hours vs. 8.1% of MCPEM and 8.0% of ABEM exam). The least frequently covered topics was were "immune system disorders"(0.5% of total hours vs. 2.0% of MCPEM and 2.0% of ABEM exam),"environmental disorders"(0.8% of total hours vs. 2.4% of MCPEM and 2.0% of ABEM exam), "obstetrics and gynecology" (1.0% of total hours vs. 5.4% of MCPEM and 3.0% of ABEM exam), and "cutaneous disorders" (0.9% of total hours vs. 4.3% of MCPEM and 3.0% of ABEM exam). Conclusions: Our findings suggest an imbalance of MCPEM core content in three popular EM 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".