Surgery 101: Evaluating the use of podcasting in a general surgery clerkship
Bibliographic record
Abstract
BACKGROUND: Provision of learning resources online is rapidly becoming a feature of medical education. AIMS: This study set out to determine how medical students engaged in a 6-week clerkship in General Surgery would make use of a series of audio podcasts designed to meet their educational objectives. METHODS: Patterns of use and student learning styles were determined using an anonymous survey. RESULTS: Of the 112 students, 93 responded to the survey (83%); 68% of students reported listening to at least one podcast (average number: six). While students reported listening in a variety of time and places, the majority of students reported listening on a computer in dedicated study time. Of the listeners, 84% agreed the podcasts helped them learn core topics, and over 80% found the recordings interesting and engaging. CONCLUSIONS: This study demonstrates that podcasts are an acceptable learning resource for medical students engaged in a surgery clerkship, and can be integrated into existing study habits. We believe that podcasting can help us cater to busy students with a range of learning styles. We have also shown that a free online resource developed by one school can reach a global audience many times larger than its intended target: to date, the 'Surgery 101' podcast series has been downloaded more than 160,000 times worldwide.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".