Do International Medical Graduates (IMGs) “Fill the Gap” in Rural Primary Care in the United States? A National Study
Bibliographic record
Abstract
CONTEXT: The contribution that international medical graduates (IMGs) make to reducing the rural-urban maldistribution of physicians in the United States is unclear. Quantifying the extent of such "gap filling" has significant implications for planning IMG workforce needs as well as other state and federal initiatives to increase the numbers of rural providers. PURPOSE: To compare the practice location of IMGs and US medical graduates (USMGs) practicing in primary care specialties. METHODS: We used the 2002 AMA physician file to determine the practice location of all 205,063 primary care physicians in the United States. Practice locations were linked to the Rural-Urban Commuting Areas, and aggregated into urban, large rural, small rural, and isolated small rural areas. We determined the difference between the percentage of IMGs and percentage of USMGs in each type of geographic area. This was repeated for each Census Division and state. FINDINGS: One quarter (24.8% or 50,804) of primary care physicians in the United States are IMGs. IMGs are significantly more likely to be female (31.9% vs 29.9%, P < .0001), older (mean ages 49.7 and 47.1 year, P < .0001), and less likely to practice family medicine (19.0% vs 38%, P < .0001) than USMGs. We found only two Census Divisions in which IMGs were relatively more likely than USMGs to practice in rural areas (East South Central and West North Central). However, we found 18 states in which IMGs were more likely, and 16 in which they were less likely to practice in rural areas than USMGs. CONCLUSIONS: IMGs fill gaps in the primary care workforce in many rural areas, but this varies widely between states. Policies aimed to redress the rural-urban physician maldistribution in the United States should take into account the vital role of IMGs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".