Competency-Based Medical Education in the Internal Medicine Clerkship: A Report From the Alliance for Academic Internal Medicine Undergraduate Medical Education Task Force
Bibliographic record
Abstract
As medical educators continue to redefine learning and assessment across the continuum, implementation of competency-based medical education in the undergraduate setting has become a focus of many medical schools. While standards of competency have been defined for the graduating student, there is no uniform approach for defining competency expectations for students during their core clerkship year. The authors describe the process by which an Alliance for Academic Internal Medicine task force developed a paradigm for competency-based assessment of students during their inpatient internal medicine (IM) clerkship. Building on work at the resident and fellowship levels, the task force focused on the development of key learning outcomes as defined by entrustable professional activities (EPAs) that were specific to educational experiences on the IM clerkship, as well as identification of high-priority assessment domains. The work was informed by a national survey of clerkship directors.Six key EPAs emerged: generating a differential diagnosis, obtaining a complete and accurate history and physical exam, obtaining focused histories and clinically relevant physical exams, preparing an oral presentation, interpreting the results of basic diagnostic studies, and providing well-organized clinical documentation. A model for assessment was proposed, with descriptors aligned to the scale of supervision and mapped to Accreditation Council for Graduate Medical Education domains of competence. The proposed paradigm offers a standardized template that may be used across IM clerkships, and which would effectively bridge competency evaluation in the clerkship to fourth-year assessment as well as eventual postgraduate training.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".