International Medical Graduates in the US Physician Workforce and Graduate Medical Education: Current and Historical Trends
Bibliographic record
Abstract
ABSTRACT Background Data show that international medical graduates (IMGs), both US and foreign born, are more likely to enter primary care specialties and practice in underserved areas. Comprehensive assessments of representation trends for IMGs in the US physician workforce are limited. Objective We reported current and historical representation trends for IMGs in the graduate medical education (GME) training pool and US practicing physician workforce. Methods We compared representation for the total GME and active practicing physician pools with the 20 largest residency specialties. A 2-sided test was used for comparison, with P < .001 considered significant. To assess significant increases in IMG GME trainee representation for the total pool and each of the specialties from 1990–2015, the slope was estimated using simple linear regression. Results IMGs showed significantly greater representation among active practicing physicians in 4 specialties: internal medicine (39%), neurology (31%), psychiatry (30%), and pediatrics (25%). IMGs in GME showed significantly greater representation in 5 specialties: pathology (39%), internal medicine (39%), neurology (36%), family medicine (32%), and psychiatry (31%; all P < .001). Over the past quarter century, IMG representation in GME has increased by 0.2% per year in the total GME pool, and 1.1% per year for family medicine, 0.5% for obstetrics and gynecology and general surgery, and 0.3% for internal medicine. Conclusions IMGs make up nearly a quarter of the total GME pool and practicing physician workforce, with a disproportionate share, and larger increases over our study period in certain specialties.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".