A Survey of Emergency Medicine Residentsâ Use of Educational Podcasts
Bibliographic record
Abstract
\n Introduction\n \n Emergency medicine (EM) educational podcasts have become increasingly popular. Residents spend a greater percentage of their time listening to podcasts than they do using other educational materials. Despite this popularity, research into podcasting in the EM context is sparse. We aimed to determine EM residents' consumption habits, optimal podcast preferences, and motivation for listening EM podcasts. \n \n \n Methods\n \n The authors created a survey and emailed it to EM residents at all levels of training at twelve residencies across the United States from September 2015 to June of 2016. In addition to demographics, the twenty-question voluntary survey asked questions exploring three domains: habits, attention, and motivation. The authors used descriptive statistics to analyze results. \n \n \n Results\n \n Of the 605 residents invited to participate, 356 (n= 60.3%) completed the survey. The vast majority listen to podcasts at least once a month (88.8%). Two podcasts were the most popular by a wide margin, with 77.8% and 62.1% regularly listening to Emergency Medicine: Reviews and Perspectives (EM:RAP) and the EMCrit Podcast, respectively. 84.6% reported the ideal length of a podcast was less than 30 minutes. Residents reported their motivation to listen to EM podcasts was to “Keep up with current literature” (88.5%) and “Learn EM core content” (70.2%). 72.2% of residents said podcasts change their clinical practice either “somewhat” or “very much”. \n \n \n Conclusion\n \n The results of this survey study suggest most residents listen podcasts at least once a month, prefer podcasts less than 30 minutes in length, have several motivations for choosing podcasts, and report that podcasts change their clinical practice. \n
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".