Outcomes of competency-based medical education: A taxonomy for shared language
Bibliographic record
Abstract
As the global transformation of postgraduate medical training continues, there are persistent calls for program evaluation efforts to understand the impact and outcomes of competency-based medical education (CBME) implementation. The measurement of a complex educational intervention such as CBME is challenging because of the multifaceted nature of activities and outcomes. What is needed, therefore, is an organizational taxonomy to both conceptualize and categorize multiple outcomes. In this manuscript we propose a taxonomy that builds on preceding works to organize CBME outcomes across three domains: focus (educational, clinical), level (micro, meso, macro), and timeline (training, transition to practice, practice). We also provide examples of how to conceptualize outcomes of educational interventions across medical specialties using this taxonomy. By proposing a shared language for outcomes of CBME, we hope that this taxonomy will help organize ongoing evaluation work and catalyze those seeking to engage in the evaluation effort to help understand the impact and outcomes of 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 | 0.071 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".