Toward a research agenda for competency-based medical education
Bibliographic record
Abstract
Competency-based medical education (CBME) is both an educational philosophy and an approach to educational design. CBME has already had a broad impact on medical schools, residency programs, and continuing professional development in health professions around the world. As the CBME movement evolves and CBME programs are implemented, a wide range of emerging research questions will warrant scholarly examination. In this paper, we describe a proposed CBME research agenda developed by the International CBME Collaborators. The resulting framework includes questions about the meaning of key concepts of CBME and their implications for learners, faculty members, and institutional structures. Other research questions relate to the learning process, the meaning of entrustment decisions, fundamental measurement issues, and the nature and definition of standards. The exploration of these questions will help to solidify the theoretical foundation of CBME, but many issues related to implementation also need to be addressed. These pertain to, among other things, nurturing independent learning, assembling and using assessment results to make decisions about competence, structuring feedback, supporting remediation, and how best to evaluate the longer-term outcomes of CBME. High-quality research on these questions will require rigorous outcome measures with strong validity evidence. The complexity of CBME necessitates theoretical and methodological diversity. It also requires multi-institutional studies that examine effects at multiple levels, from the learner to the team, the institution, and the health care system. Such a framework of research questions can guide and facilitate scholarly discourse on the theoretical and practical body of knowledge related to competency-based health professions education.
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 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.035 | 0.052 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".