International medical graduates representation in pathology academic workforce, departmental leadership and society leadership
Why this work is in the frame
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Bibliographic record
Abstract
Compared with the overall physician workforce, pathologist workforce in the United States has significant representation of international medical graduates (IMGs). IMG representation in the academic pathology workforce, as well as in departmental and pathology societal leadership, has not been documented. In this cross-sectional study, we surveyed a sample of 20 North American academic pathology departmental publicly available websites. Each faculty was recorded according to the location of their medical school training as either US or Canadian medical graduateor IMG (country of medical school graduation any other than US or Canada). Past and present presidents of four major North American pathology societies [American Society for Clinical Pathology (ASCP), Association for Academic Pathology (AAPath), College of American Pathologists (CAP), United States and Canadian Academy of Pathology (USCAP)] were also recorded. A total of 1455 pathologists were retrieved in our search: 924 (63.5 %) were USCMGs and 531 (36.5 %) IMGs. Likewise, 65 % of pathology chairs were USCMGs and 35 % IMGs. These data mirror the 2022 Association of American Medical Colleges distribution in the pathology workforce (65.6 % USCMGs and 34.4 % IMGs). In contrast, historic data from 1993 to 2024 show that only 8 (8 %) past or current presidents of the major US pathology societies were IMGs (USCAP = 6, ASCP = 1, AAPath = 1, CAP = none). While the academic pathology community has proportional representation of physicians based on location of their medical school training, there is historical underrepresentation of IMGs in societal leadership. Unveiling the causes of this disparity and identifying any potential obstacles for faculty engagement is paramount.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it