Advancing Competency-Based Medical Education: A Charter for Clinician–Educators
Bibliographic record
Abstract
The International Competency-Based Medical Education (ICBME) Collaborators have been working since 2009 to promote understanding of competency-based medical education (CBME) and accelerate its uptake worldwide. This article presents a charter, supported by a literature-based rationale, which is meant to provide a shared mental model of CBME that will serve as a path forward in its widespread implementation.At a 2013 summit, the ICBME Collaborators laid the groundwork for this charter. Here, the fundamental principles of CBME and professional responsibilities of medical educators in its implementation process are described. The authors outline three fundamental principles: (1) Medical education must be based on the health needs of the populations served; (2) the primary focus of education and training should be the desired outcomes for learners rather than the structure and process of the educational system; and (3) the formation of a physician should be seamless across the continuum of education, training, and practice.Building on these principles, medical educators must demonstrate commitment to teaching, assessing, and role modeling the range of identified competencies. In the clinical setting, they must provide supervision that balances patient safety with the professional development of learners, being transparent with stakeholders about level of supervision needed. They must use effective and efficient assessment strategies and tools for basing transition decisions on competence rather than time in training, empowering learners to be active participants in their learning and assessment. Finally, advancing CBME requires program evaluation and research, faculty development, and a collaborative approach to realize its full potential.
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 | 0.172 | 0.123 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.037 | 0.042 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.022 | 0.060 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".