Developing ClerkCast: An Emergency Medicine Clerkship Needs Assessment Project
Bibliographic record
Abstract
Introduction and objectives: For Canadian medical students completing their emergency medicine (EM) clerkship rotation, developing an approach to undifferentiated patients can be difficult. Open educational resources (OERs) are a convenient solution, but faculty authored materials may not meet students' needs. There is a lack of EM OERs that deconstruct these undifferentiated EM presentations for medical students. The objective of this study was to identify EM topics poorly understood by medical students to inform a novel Free Open Access Medical Education podcast curriculum for approaching undifferentiated EM patients for medical students. Methods: An online survey-based needs assessment was distributed to key stakeholders through direct email, social media, and the blog CanadiEM. The survey included 32 EM topics graded on a five-point Likert scale according to how much participants believe medical students require further teaching. Results: Over six weeks, a total of 74 participants completed the needs assessment survey, and 58 participants met the criteria for inclusion into our study: medical students (n=23) and EM educators (inclusive of resident physicians (n=19), and staff EM physicians (n=16)). A number of presentations (n=23) were prioritized by both students and EM educators to be of the greatest need for medical students. No presentations identified as high priority by students were not also identified as high priority by EM educators. Conclusions: The greatest mean topic scores in both EM educators and medical student responses included critical care and acute medicine topics. Of the 32 topics in the survey, 23 topics were determined to be high priority for the development of future online educational resources. Analysis of free-text responses revealed nine topics not previously listed in our survey. Our findings will be used to inform the development of our new open access podcast and can be useful for developing medical student curricula in EM.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".