Navigating the Competency Revolution in Medical Education
Bibliographic record
Abstract
The Flexner Report in 1910 is considered as the first major milestone in medical education, where the quality of graduates and their training was given a priority; it also marked the beginning of a system-based medical education. Since 1910, there has been a slow update of the medical education system components till the recent change to Competency-Based Medical Education (CBME) in the last 10 years. For over 100 years, the system was mainly a time-based system that used time to meet certain goals and objectives, which were not necessarily outcome-based and relied mainly on summative assessments to assess competency. The CBME, with the introduction of entrustable professional activities (EPAs) and milestones, has clearly identified the major competencies that are required for all graduates to meet before graduating, regardless of time; nevertheless, time is still used as a resource to achieve them. The major implementation of CBME started in Canadian and American postgraduate training programs and was later adopted by their medical schools. The concept of CBME brought new ideas to the medical education system, such as more focus on the number and type of learner assessments in different contexts, a more thorough decision-making process through the creation of competence committees, and a shift toward more learner-driven training. The critical link between abstract competencies and clinical practice is provided by EPAs, which are specific, professional tasks that a trainee can be fully entrusted to perform unsupervised after demonstrating the necessary competence. National frameworks, such as the Association of American Medical Colleges (AAMC) Core EPAs, have been developed to standardize this approach and prepare graduates for residency. Despite the benefits, implementing CBME and EPAs faces several challenges. These include faculty resistance and resource intensity, as well as the risk of “assessment fatigue” and “conceptual dilution,” where the term EPA is misapplied. The current and future direction of medical education will mainly focus on overcoming these issues through focused faculty development, optimized assessment systems, and a commitment to standardized definitions, all of which are essential to fully realize the potential of CBME in producing competent, practice-ready physicians. In addition, the medical education society will continue to develop and work on fully implementing the concept of master adaptive learners.
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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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".