Educational Impact of a Podcast Covering Vitreoretinal Topics: 1-Year Survey Results
Bibliographic record
Abstract
Purpose: This article aims to determine preferences and demographics for listeners of an ophthalmology podcast, since podcasts are gaining favor for medical education. Methods: The present study used a cross-sectional, online survey as well as Podtrac, Inc and Squarespace, Inc platform analytics to examine perceived educational usefulness of podcasts for listeners. Results: Quarterly episode downloads increased from 684 in first-quarter 2017 to 16 016 in third-quarter 2018. A total of 102 participants completed the survey: 82 (80.4%) men and 68 (66.7%) retina attending physicians or fellows. Most respondents listened to “stay up to date” or “learn more about the field of retina” (67; 65.7% each). Most respondents agreed that podcasts are useful for medical education and result in changes in practice, but not that podcasts have surpassed traditional educational methods. For respondents, there was no difference in perceived usefulness between podcasts and peer-reviewed journals, textbooks, continuing medical education lectures, or national conferences; these did not differ with respondent listening histories. Conclusions: Podcasts are valuable adjuncts for distributing clinically relevant material.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".