Assumptions About Competency-Based Medical Education and the State of the Underlying Evidence: A Critical Narrative Review
Bibliographic record
Abstract
PURPOSE: As educators have implemented competency-based medical education (CBME) as a framework for training and assessment, they have made decisions based on available evidence and on the medical education community's assumptions about CBME. This critical narrative review aimed to collect, synthesize, and judge the existing evidence underpinning assumptions the community has made about CBME. METHOD: The authors searched Ovid MEDLINE to identify empirical studies published January 2000 to February 2019 reporting on competence, competency, and CBME. The knowledge synthesis focused on "core" assumptions about CBME, selected via a survey of stakeholders who judged 31 previously identified assumptions. The authors judged, independently and in pairs, whether evidence from included studies supported, did not support, or was mixed related to each of the core assumptions. Assumptions were also analyzed to categorize their shared or contrasting purposes and foci. RESULTS: From 8,086 unique articles, the authors reviewed 709 full-text articles and included 189 studies reporting evidence related to 15 core assumptions. Most studies (80%; n = 152) used a quantitative design. Many focused on procedural skills (48%; n = 90) and assessed behavior in clinical settings (37%; n = 69). On aggregate, the studies produced a mixed evidence base, reporting 362 data points related to the core assumptions (169 supportive, 138 not supportive, and 55 mixed). The 31 assumptions were organized into 3 categories: aspirations, conceptualizations, and assessment practices. CONCLUSIONS: The reviewed evidence base is significant but mixed, with limited diversity in research designs and the types of competencies studied. This review pinpoints tensions to resolve (where evidence is mixed) and research questions to ask (where evidence is absent). The findings will help the community make explicit its assumptions about CBME, consider the value of those assumptions, and generate timely research questions to produce evidence about how and why CBME functions (or not).
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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 | 0.005 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".