Rethinking the Standards for State Licensure of Physicians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 “<i>Physician Workforce Act</i>” 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 <i>National Residency Matching Program</i>. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it