An evaluation of the ‘5 Minute Medicine’ video podcast series compared to conventional medical resources for the internal medicine clerkship
Bibliographic record
Abstract
BACKGROUND: '5 Minute Medicine' (5MM) is a series of video podcasts, that in approximately 5 min, each explain a core objective of the internal medicine clerkship that all clinical clerks should understand. Video podcasts are accessible at www.5minutemedicine.com AIM: The aim of this study was to investigate how well received 5MM video podcasts are as an educational tool for clinical clerks to use while on call. METHODS: Clinical clerks rotating through their internal medicine clerkship rotation were asked to use the 5MM video podcasts or conventional resources to prepare themselves prior to seeing patients. Questionnaires were distributed to students to determine effectiveness, appropriateness and time-efficiency of the resources students used. RESULTS: Students almost unanimously strongly agreed or agreed that the 5MM video podcasts were effective learning tools, appropriate for clinical clerks and time-efficient, more so than conventionally used resources. The vast majority of clerks selected the 5MM videos as their preferred resource of all resources available to them. Most clerks felt the 5MM videos were better than textbooks and conventional online resources. CONCLUSION: Video podcasts such as the 5MM videos are welcomed as educational tools and may have a role in the future of undergraduate medical education.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".