Outcomes are what matter: Competency-based medical education gets us to our goal
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Health professions education is undergoing a major paradigm shift to competency-based medical education. When shifts in thinking are profound and result in transformations of existing paradigms, there is often an accompanying criticism. While competency-based medical education is an evidence guided change in approach to curriculum and assessment, it is not immune to critique and concerns. Some criticisms are valid, and must be addressed as competency-based medical education is implemented; other concerns raised about competency-based medical education highlight the importance of clarity in language and purpose when discussing new paradigms. In this commentary, we aim to offer a balanced view of competency-based medical education by presenting an overview of the origins and conceptual assumptions of competency-based medical education and acknowledging valid criticisms of the approach.
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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 | 0.022 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 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".