Using a Delphi process to establish consensus on emergency medicine clerkship competencies
Bibliographic record
Abstract
BACKGROUND: Currently, there is no consensus on the core competencies required for emergency medicine (EM) clerkships in Canada. Existing EM curricula have been developed through informal consensus or local efforts. The Delphi process has been used extensively as a means for establishing consensus. AIM: The purpose of this project was to define core competencies for EM clerkships in Canada, to validate a Delphi process in the context of national curriculum development, and to demonstrate the adoption of the CanMEDS physician competency paradigm in the undergraduate medical education realm. METHODS: Using a modified Delphi process, we developed a consensus amongst a panel of expert emergency physicians from across Canada utilizing the CanMEDS 2005 Physician Competency Framework. RESULTS: Thirty experts from nine different medical schools across Canada participated on the panel. The initial list consisted of 152 competencies organized in the seven domains of the CanMEDS 2005 Physician Competency Framework. After the second round of the Delphi process, the list of competencies was reduced to 62 (59% reduction). CONCLUSION: This study demonstrated that a modified Delphi process can result in a strong consensus around a realistic number of core competencies for EM clerkships. We propose that such a method could be used by other medical specialties and health professions to develop rotation-specific core competencies.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 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".