Transitioning towards senior medical resident: identification of the required competencies using consensus methodology
Bibliographic record
Abstract
BACKGROUND: Residency programs are facing significant restructuring through the "Competence by Design" (CBD) framework proposed by the Royal College of Physicians and Surgeons of Canada (RCPSC). Our goal was to establish the competencies to be acquired during the transition to a senior role within Internal Medicine (IM) training. METHODS: Using a modified Delphi technique, practicing IM physicians and recent graduates were polled to develop consensus on the required competencies to effectively transition from junior to senior medical resident. Participants rated each competency on a three-point Likert scale. Each competency was linked to an Entrustable Professional Activity (EPA) identified by the RCPSC IM Specialty Committee. RESULTS: A total of eighteen participants took part in item generation (16% response rate) and nineteen in the initial ranking with seventeen completing all three iterations (89% completion rate). Eighty-three competencies were identified during questionnaire development. A final list of seventy-seven competencies reached consensus after three rounds. Most competencies matched to core of discipline EPAs. CONCLUSION: This consensus-based list of competencies will help create a framework and tools for the assessment of junior residents as they prepare to transition to the role of senior in the new CBD curricula for IM trainees at our institution.
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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 | 0.101 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".