A Foot in the Door: Foreign International Medical Students’ Obstacles to Hands-On Clinical Electives in the United States
Bibliographic record
Abstract
To the Editor: With an impending shortage of physicians in the United States, international medical graduates (IMGs) can help fill the void, especially in underserved communities. In the recent 2019 match, non-U.S. citizens that graduated from international medical schools (non-U.S. IMGs) comprised 13% of the matched postgraduate year 1 positions, filling almost a third of the matched preliminary surgical spots and just above a quarter of the internal medicine categorical positions.1 Coming from different cultures and educational backgrounds, lacking U.S. mentors, and encountering language barriers and biases, non-U.S. IMGs face specific challenges. An important factor for them to get into a residency in the United States is clinical hands-on experience in U.S. institutions. Clinical rotations at teaching hospitals benefit both non-U.S. IMGs and their hosts. Non-U.S. IMGs can fill elective spots at the times U.S. medical students are less interested in pursuing. Furthermore, they bring diversity to the teaching environment, and their training in the United States can make them emissaries of U.S. values back home. Dedicated programs aimed at integrating non-U.S. IMGs through electives exist but are few in number and are often unknown to foreign medical students. Once found, the application process can prove daunting. Not all U.S. teaching hospitals accept foreign international medical students, and those that do often include stringent application requirements, a long application process, and high application fees (in some cases up to $1,000). The Association of American Medical Colleges Visiting Student Learning Opportunities program was established to help concentrate away rotations for medical students, offering an application service that enables access to electives in 244 participating medical institutions. Notwithstanding, the program is open to only 59 international medical schools, and a mere 14% of the participating U.S. institutions agreed to take part in the Global Network Initiative, opening their programs to students from outside the United States.2 The result is that most opportunities and vast areas in the United States are left beyond the reach of students from around the world. In order for such programs to be successful in helping non-U.S. IMGs gain access to U.S. clinical electives, medical educators have to open their doors to give them a chance. Roi AntebyFourth-year medical student, Tel Aviv Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; [email protected]
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".