What Regulatory Requirements and Existing Structures Must Change If Competency-Based, Time-Variable Training Is Introduced Into the Continuum of Medical Education in the United States?
Bibliographic record
Abstract
As competency-based medical education is adopted across the training continuum, discussions regarding time-variable medical education have gained momentum, raising important issues that challenge the current regulatory environment and infrastructure of both undergraduate and graduate medical education in the United States. Implementing time-variable medical training will require recognizing, revising, and potentially reworking the multiple existing structures and regulations both internal and external to medical education that are not currently aligned with this type of system. In this article, the authors explore the impact of university financial structures, hospital infrastructures, national accrediting body standards and regulations, licensure and certification requirements, government funding, and clinical workforce models in the United States that are all intimately tied to discussions about flexible training times in undergraduate and graduate medical education. They also explore the implications of time-variable training to learners' transitions between medical school and residency, residency and fellowship, and ultimately graduate training and independent practice. Recommendations to realign existing structures to support and enhance competency-based, time-variable training across the continuum and suggestions for additional experimentation/demonstration projects to explore new training models are provided.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".