First steps towards international competency goals for residency training: a qualitative comparison of 3 regional standards in anesthesiology
Bibliographic record
Abstract
BACKGROUND: Competency-based medical education (CBME) has revolutionized approaches to training by making expectations more concrete, visible, and relevant for trainees. Designing, applying, and updating CBME requirements challenges residency programs, which must address many aspects of training simultaneously. This challenge also exists for educational regulatory bodies in creating and adjusting national competencies to standardize training expectations. We propose that an international approach for mapping residency training requirements may provide a baseline for assessing commonalities and differences. This approach allows us to take our first steps towards creating international competency goals to enhance sharing of best practices in education and clinical work. METHODS: We chose anesthesiology residency training as our example discipline. Using two rounds of content analysis, we qualitatively compared published anesthesiology residency competencies for the European Union (The European Training Requirement), United States (ACGME Milestones), and Canada (CanMEDS Competence By Design), focusing on similarities and differences in representation (round one) and emphasis (round two) to generate hypotheses on practical solutions regarding international educational standards. RESULTS: We mapped the similarities and discrepancies between the three repositories. Round one revealed that 93% of competencies were common between the three repositories. Major differences between European Training Requirement, US Milestones, and Competence by Design competencies involved critical emergency medicine. Round two showed that over 30% of competencies were emphasized equally, with notable exceptions that European Training Requirement emphasized Anaesthesia Non-Technical Skills, Competence by Design highlighted more granular competencies within specific anesthesiology situations, and US Milestones emphasized professionalism and behavioral practices. CONCLUSIONS: This qualitative comparison has identified commonalities and differences in anesthesiology training which may facilitate sharing broader perspectives on diverse high-quality educational, clinical, and research practices to enhance innovative approaches. Determining these overlaps in residency training can prompt international educational societies responsible for creating competencies to collaborate to design future training programs. This approach may be considered as a feasible method to build an international core of residency competency requirements for other disciplines.
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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 | 0.053 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".