The role of assessment in competency-based medical education
Bibliographic record
Abstract
Competency-based medical education (CBME), by definition, necessitates a robust and multifaceted assessment system. Assessment and the judgments or evaluations that arise from it are important at the level of the trainee, the program, and the public. When designing an assessment system for CBME, medical education leaders must attend to the context of the multiple settings where clinical training occurs. CBME further requires assessment processes that are more continuous and frequent, criterion-based, developmental, work-based where possible, use assessment methods and tools that meet minimum requirements for quality, use both quantitative and qualitative measures and methods, and involve the wisdom of group process in making judgments about trainee progress. Like all changes in medical education, CBME is a work in progress. Given the importance of assessment and evaluation for CBME, the medical education community will need more collaborative research to address several major challenges in assessment, including "best practices" in the context of systems and institutional culture and how to best to train faculty to be better evaluators. Finally, we must remember that expertise, not competence, is the ultimate goal. CBME does not end with graduation from a training program, but should represent a career that includes ongoing assessment.
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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 | 0.236 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".