Choosing Our Own Pathway to Competency-Based Undergraduate Medical Education
Bibliographic record
Abstract
After many years in the making, an increasing number of postgraduate medical education (PGME) training programs in North America are now adopting a competency-based medical education (CBME) framework based on entrustable professional activities (EPAs) that, in turn, encompass a larger number of competencies and training milestones. Following the lead of PGME, CBME is now being incorporated into undergraduate medical education (UME) in an attempt to improve integration across the medical education continuum and to facilitate a smooth transition from clerkship to residency by ensuring that all graduates are ready for indirect supervision of required EPAs on day one of residency training. The Association of Faculties of Medicine of Canada recently finalized its list of 12 EPAs, which closely parallels the list of 13 EPAs published earlier by the Association of American Medical Colleges, and defines the "core" EPAs that are an expectation of all medical school graduates.In this article, the authors focus on important, practical considerations for the transition to CBME that they feel have not been adequately addressed in the existing literature. They suggest that the transition to CBME should not threaten diversity in UME or require a major curricular upheaval. However, each UME program must make important decisions that will define its version of CBME, including which terminology to use when describing the construct being evaluated, which rating tools and raters to include in the assessment program, and how to make promotion decisions based on all of the available data on EPAs.
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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.033 | 0.023 |
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".