International Medical Graduates: Evaluating New Legislative Routes to Address U.S. Physician Gaps
Bibliographic record
Abstract
A growing physician shortage in the US could restrict access to care, especially in underserved and rural areas. While increasing medical school and residency programs are often viewed as a solution, this method is expensive and takes years before new physicians can start working.My research explored an alternative: integrating international medical graduates (IMGs) into the U.S. healthcare system.In order to better understand the economic impact of depending on IMGs as opposed to increasing residency slots, I spent the summer examining workforce data, policy reports, and international examples, particularly from Canada.The findings suggest that creating clearer and more efficient pathways for IMGs could provide a faster and more cost-effective way to address shortages while simultaneously broadening the physician workforce.I gained insight into how economics, policy, and healthcare are intertwined from this project, which also reaffirmed the necessity of innovative approaches to guarantee that patients receive timely, high-quality care. It will take multiple strategies to address the physician shortage, but it is both necessary and practical to include IMGs in the solution.
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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 | 0.057 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".