Academic surgery: Faculty gender and racial trends through an intersectional lens
Bibliographic record
Abstract
The healthcare workforce in the United States (US) has an inequitable representation of women and Underrepresented in Medicine (URIM) groups, including Black or African Americans, American Indians, Alaska Natives, Pacific Islanders, Hispanic or Latinos, and Asians. Despite almost three decades of equal representation of women students in medical school, the gender disparity persists throughout leadership ranks. Studies have shown that residency recruitment is a limiting factor in diversity in surgery, and systemic changes are needed to increase the representation of minorities and women in the medical and surgical disciplines.Our study used data from the AAMC (Association of American Medical Colleges) to analyze the demographic distribution of surgical faculty at medical schools from 1971 to 2021. Data was analyzed using Microsoft Excel and JupyterLab programs, and a t-test was used to determine significant changes over time. The categories with significant changes were reported, and proportion bar graphs were created. Data was classified into multiple categories.URIM Surgeons have seen an increased representation in Surgical faculty during our study over the past five decades. Still, these trends have not brought them in line with their proportion among the US population. Significant trends in surgical chair positions included a 21% decrease in Whites, a 15% increase in Asians, a 2.4% increase in Blacks, and a 3.8% increase in Hispanics. There were no significant trends for Natives. Significant trends in the academic rank of Professor included a 14% increase in Asians, a 1.1% increase in Blacks, a 1.5% increase in Hispanics, and an 18.4% decrease in Whites. A slight increase among URIM Surgeons is concerning when considering that these trends span from 1971 to 2021.The data showed that White and Asian Surgeons were overrepresented in surgical discipline compared to their proportions among the US population, while Black, Hispanic, and Native Surgeons were underrepresented. This was especially true in higher academic ranks and chair positions. Women were also underrepresented in surgery, with the slowest growth in higher academic ranks and leadership positions.Al.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".