Overarching challenges to the implementation of competency-based medical education
Bibliographic record
Abstract
Medical education is under increasing pressure to more effectively prepare physicians to meet the needs of patients and populations. With its emphasis on individual, programmatic, and institutional outcomes, competency-based medical education (CBME) has the potential to realign medical education with this societal expectation. Implementing CBME, however, comes with significant challenges. This manuscript describes four overarching challenges that must be confronted by medical educators worldwide in the implementation of CBME: (1) the need to align all regulatory stakeholders in order to facilitate the optimization of training programs and learning environments so that they support competency-based progression; (2) the purposeful integration of efforts to redesign both medical education and the delivery of clinical care; (3) the need to establish expected outcomes for individuals, programs, training institutions, and health care systems so that performance can be measured; and (4) the need to establish a culture of mutual accountability for the achievement of these defined outcomes. In overcoming these challenges, medical educators, leaders, and policy-makers will need to seek collaborative approaches to common problems and to learn from innovators who have already successfully made the transition to CBME.
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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 | 0.136 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".