Using Contribution Analysis to Evaluate Competency-Based Medical Education Programs: It’s All About Rigor in Thinking
Bibliographic record
Abstract
Competency-based medical education (CBME) aims to bring about the sequential acquisition of competencies required for practice. Although it is being adopted in centers of medical education around the globe, there is little evidence concerning whether, in comparison with traditional methods, CBME produces physicians who are better prepared for the practice environment and contributes to improved patient outcomes. Consequently, the authors, an international group of collaborators, wrote this article to provide guidance regarding the evaluation of CBME programs.CBME is a complex service intervention consisting of multiple activities that contribute to the achievement of a variety of outcomes over time. For this reason, it is difficult to apply traditional methods of program evaluation, which require conditions of control and predictability, to CBME. To address this challenge, the authors describe an approach that makes explicit the multiple potential linkages between program activities and outcomes. Referred to as contribution analysis (CA), this theory-based approach to program evaluation provides a systematic way to make credible causal claims under conditions of complexity. Although CA has yet to be applied to medical education, the authors describe how a six-step model and a postulated theory of change could be used to examine the link between CBME, physicians' preparation for practice, and patient care outcomes.The authors argue that adopting the methods of CA, particularly the rigor in thinking required to link program activities, outcomes, and theory, will serve to strengthen understanding of the impact of CBME over time.
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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 | 0.334 | 0.571 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".