International Medical Graduates in the Pediatric Workforce in the United States
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To describe the supply, distribution, and characteristics of international medical graduates (IMGs) in pediatrics who provide patient care in the United States. METHODS: Cross-sectional study, combining data from the 2019 Physician Masterfile of the American Medical Association and the Educational Commission for Foreign Medical Graduates database. RESULTS: In total, 92 806 pediatric physicians were identified, comprising 9.4% of the entire US physician workforce. Over half are general pediatricians. IMGs account for 23.2% of all general pediatricians and pediatric subspecialists. Of all IMGs in pediatrics, 22.1% or 4775 are US citizens who obtained their medical degree outside the United States or Canada, and 15.4% (3246) attended medical school in the Caribbean. Fifteen non-US medical schools account for 29.9% of IMGs currently in active practice in pediatrics in the United States. IMGs are less likely to work in group practice or hospital-based practice and are more likely to be employed in solo practice (compared with US medical school graduates). CONCLUSIONS: With this study, we provide an overview of the pediatric workforce, quantifying the contribution of IMGs. Many IMGs are US citizens who attend medical school abroad and return to the United States for postgraduate training. Several factors, including the number of residency training positions, could affect future numbers of IMGs entering the United States. Longitudinal studies are needed to better understand the implications that workforce composition and distribution may have for the care of pediatric patients.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".