Questioning medical competence: Should the Covid-19 crisis affect the goals of medical education?
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted many societal institutions, including health care and education. Although the pandemic’s impact was initially assumed to be temporary, there is growing conviction that medical education might change more permanently. The International Competency-based Medical Education (ICBME) collaborators, scholars devoted to improving physician training, deliberated how the pandemic raises questions about medical competence. We formulated 12 broad-reaching issues for discussion, grouped into micro-, meso-, and macro-level questions. At the individual micro level, we ask questions about adaptability, coping with uncertainty, and the value and limitations of clinical courage. At the institutional meso level, we question whether curricula could include more than core entrustable professional activities (EPAs) and focus on individualized, dynamic, and adaptable portfolios of EPAs that, at any moment, reflect current competence and preparedness for disasters. At the regulatory and societal macro level, should conditions for licensing be reconsidered? Should rules of liability be adapted to match the need for rapid redeployment? We do not propose a blueprint for the future of medical training but rather aim to provoke discussions needed to build a workforce that is competent to cope with future health care crises.
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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 | 0.047 | 0.177 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.035 | 0.045 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".