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Record W4210765916 · doi:10.1177/10775587211066994

Foreign-Trained Physicians in the United States: A Descriptive Profile

2022· article· en· W4210765916 on OpenAlexaboutno aff
Neeraj Kaushal, Robert Kaestner, Tsewang Rigzin

Bibliographic record

VenueMedical Care Research and Review · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
FundersCharles Koch Foundation
KeywordsWorkforceQuarter (Canadian coin)Work (physics)StatisticPrimary careDescriptive statisticsFamily medicineMedicineDemographic economicsMedical educationPolitical scienceGeographyStatistics

Abstract

fetched live from OpenAlex

More than a quarter of physicians in the United States are international medical graduates (IMGs). This statistic, although large, does not fully capture the importance of IMGs in certain specialties and locations. We provide a comprehensive profile of IMGs documenting where and in what specialties they work and how these distributions have changed over time. Estimates show that IMGs disproportionately work in densely populated, low-income communities with sicker residents and low physician density. IMGs are overrepresented in primary care and the lowest paying specialties, and their concentration in these specialties is growing. Calculations show that U.S. medical graduates exit the workforce at 2.5 times the exit rate of IMGs suggesting that in the near future IMGs will likely provide care for an increasingly larger share of Americans.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.175
GPT teacher head0.533
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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