International medical graduates representation in pathology academic workforce, departmental leadership and society leadership
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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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".