Becoming doctors again in the United States: An intersectional approach to understanding women refugee physicians
Bibliographic record
Abstract
Although International Medical Graduates (IMGs) make up close to one quarter of practicing physicians in the US, formal and informal barriers to gaining a US medical license are high. Previous research has identified a number of such obstacles including linguistic and cultural impediments, subtle and overt prejudice, bias, and discrimination, as well as formal and informal hurdles in the admission process for residency positions. For purposes of US medical licensure qualifications and record-keeping, all IMGs are lumped together. However, IMGs are not homogeneous. Studies of the licensure process typically distinguish between US citizens who go to medical school outside the US (USIMGs) and non-US citizens who prepare to complete their medical training in a US residency (non-USIMGs) but this distinction conceals significant differences among non-USIMGs. This paper contributes to the growing body of literature that explores differences among the trajectories of would-be physicians who are non-US citizens by focusing on women physicians who are both non-USIMGs and forced to flee from their homelands (Refugee Physicians). It applies an intersectional lens to understand ways in which gender, forced migration, and medical licensure in the US are interrelated factors constraining the decisions of non-USIMGs. Drawing upon a larger qualitative study of 18 men and 10 women Refugee Physicians in the United States this paper focuses on the experiences of the 10 women and asks: how does gender matter in Refugee Physicians’ navigation of the medical licensure system and migration?
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".