Intrinsic CanMEDS Competencies Expected of Medical Students During Emergency Medicine Core Rotation: A Needs Assessment
Bibliographic record
Abstract
Objectives There are few high-quality free open-access medical (FOAM) education resources to guide medical students in the development of key non-medical expert skills and competencies during their emergency medicine (EM) clerkship core rotation. In our endeavor to develop a novel online educational EM curriculum for medical students, a needs assessment is required to effectively address needs specifically focused on aptitudes that are deemed to be imperative by educators in the EM academia. Methods An online needs assessment survey was developed and shared with residents, staff, nurses, and program/clerkship directors of Canadian emergency medicine programs by email correspondence and embedding the form on CanadiEM.org. The survey consisted of twelve proposed topics for a potential EM curriculum, which were graded on a five-point Likert scale. Free-typed responses for additional topics were also solicited from participants. Results Over the course of four weeks, 84 participants responded to the survey. Participants outside of North American were excluded (n=10). Most participants were North American staff physicians (n=52), which included residency program directors (n=10) and clerkship directors (n=6), followed by residents (n=14), and nurses (n=8). All 12 topics proposed by the authors were considered important for inclusion in an EM curriculum. Nine additional topics were identified from typed free-text responses. Top ranking topics included: how to present a case to an EM staff or resident, how to chart patient encounters, and how to effectively communicate with nurses and other healthcare professionals. Conclusions This online needs assessment analysis revealed a total of 21 topics that were deemed to be relevant to the development of an online curriculum to foster the development of core competencies of medical students during their EM core rotation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".