Capturing outcomes of competency-based medical education: The call and the challenge
Bibliographic record
Abstract
There is an urgent need to capture the outcomes of the ongoing global implementation of competency-based medical education (CBME). However, the measurement of downstream outcomes following educational innovations, such as CBME is fraught with challenges stemming from the complexities of medical training, the breadth and variability of inputs, and the difficulties attributing outcomes to specific educational elements. In this article, we present a logic model for CBME to conceptualize an impact pathway relating to CBME and facilitate outcomes evaluation. We further identify six strategies to mitigate the challenges of outcomes measurement: (1) clearly identify the outcome of interest, (2) distinguish between outputs and outcomes, (3) carefully consider attribution versus contribution, (4) connect outcomes to the fidelity and integrity of implementation, (5) pay attention to unanticipated outcomes, and (6) embrace methodological pluralism. Embracing these challenges, we argue that careful and thoughtful evaluation strategies will move us forward in answering the all-important question: Are the desired outcomes of CBME being achieved?
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.007 | 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".