Rethinking the Standards for State Licensure of Physicians
Bibliographic record
Abstract
The US faces a shortage of physicians that is going unmet by the current US medical education system. One option to address this shortfall is to increase the number of international medical graduates (IMGs) practicing medicine in the US. In April of 2023, Tennessee enacted a law that would afford IMGs provisional licensure to practice medicine in the state without undertaking graduate medical education. Passage of this law was followed soon after by passage of the “Physician Workforce Act” in Alabama, which reduced the requirement for domestic graduate education for IMGs from 3 to 2 years. The Alabama law also established a medical “bridge year” program aimed at US and Canadian medical graduates who went unmatched in the National Residency Matching Program. The past year has seen a total of at least 15 states enacting or considering measures that reduce licensing barriers for IMGs. In some cases, provisional licensing of IMGs has replaced requirements for graduate medical education. All these moves, aimed at relieving physician shortages, have the potential to degrade the standards to which physicians are held for licensing and entry into the practice of medicine. It is incumbent on states to assure that IMGs and others who forego extant graduate medical education requirements are fully qualified for licensure and the practice of medicine.
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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 | 0.085 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".