Toward Defining the Foundation of the MD Degree: Core Entrustable Professional Activities for Entering Residency
Bibliographic record
Abstract
Currently, no standard defines the clinical skills that medical students must demonstrate upon graduation. The Liaison Committee on Medical Education bases its standards on required subject matter and student experiences rather than on observable educational outcomes. The absence of such established outcomes for MD graduates contributes to the gap between program directors' expectations and new residents' performance.In response, in 2013, the Association of American Medical Colleges convened a panel of experts from undergraduate and graduate medical education to define the professional activities that every resident should be able to do without direct supervision on day one of residency, regardless of specialty. Using a conceptual framework of entrustable professional activities (EPAs), this Drafting Panel reviewed the literature and sought input from the health professions education community. The result of this process was the publication of 13 core EPAs for entering residency in 2014. Each EPA includes a description, a list of key functions, links to critical competencies and milestones, and narrative descriptions of expected behaviors and clinical vignettes for both novice learners and learners ready for entrustment.The medical education community has already begun to develop the curricula, assessment tools, faculty development resources, and pathways to entrustment for each of the 13 EPAs. Adoption of these core EPAs could significantly narrow the gap between program directors' expectations and new residents' performance, enhancing patient safety and increasing residents', educators', and patients' confidence in the care these learners provide in the first months of their residency training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".