Challenges of implementing competency-based medical education postgraduate training programs: the issue of context
Bibliographic record
Abstract
Introduction: Competency-based medical education (CBME) is being adopted worldwide. The aim of this paper is to discuss the evolution of CBME and address some perceived challenges in CBME curriculum development and implementation in postgraduate (residency) medical education. Methods: This is an opinion paper based on lived experiences and personal beliefs. The authors have professional training in medical education and are actively involved in CBME research, curriculum development and implementation around the world. Results: The issue of local and system-wide context seems to be of particular importance to individuals, programs, institutions, governing bodies and other stakeholders involved in the development and implementation of CBME programs. CBME has evolved differently at different places, and there are concerns regarding the fidelity of implementation. Stakeholders have been dealing with challenging questions in their CBME journeys, which reflect the varied, complex and dynamic nature of health and education systems. Recently, scholars have established core components of any CBME program. Discussion and conclusions: CBME design should benefit from ground-up strategies that consider the local context. It is essential to approach implementation with a quality improvement lens and pay special attention to the fidelity and integrity of the core CBME components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".