Relationship Between Socio-Economic Background, USMLE Step Scores, and Demographics of International Medical Graduates and Residency Match Results
Bibliographic record
Abstract
Abstract Purpose Twenty five percent of practicing physicians in the US are International Medical Graduates (IMGs) – physicians who completed their medical school training outside of the United States and Canada. There are multiple studies demonstrating higher socio-economic background is associated with medical school matriculation in the US. However, despite a substantial prevalence of IMGs in the American healthcare system, studies of the association between demographics, socio-economic background, and securing a residency position in the match are lacking. Methods We created a survey with questions on residency match-related data and information on personal socio-economic background. An invitation to participate in the study was sent to all IMGs that applied to the included residency programs after the conclusion of the 2022 residency match. We used multivariable logistic regression to compare survey responses to the odds of securing a residency match. Results The total number of survey respondents was 744 (response rate 15.1%). We found that younger age, higher United States Medical License Examination (USMLE) scores, higher-income country of origin (including the United States), fewer match attempts, applying to fewer specialties, having parents with college degree or higher, and coming from higher-than-average or lower-than-average family income were associated with increased odds of matching. Gender, personal income, and visa status did not demonstrate significant associations with residency match. Conclusions Residency match is a significant expense for IMGs, especially for those from lower-income countries. International applicants from higher socio-economic backgrounds might have advantages in securing medical residency positions in the United States when controlling for other variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".