Racial and Gender Disparities in Obstetrics and Gynecology Applicants and Professionals
Bibliographic record
Abstract
Background: Equity is a driving force in healthcare, with the goal of creating a diversified workforce, particularly for medically underserved populations. The aim of this study was to measure demographic shifts in the Obstetrics and Gynecology (Ob/Gyn) trainee and physician workforce, including attrition and promotion rates. Methods: This study included Ob/Gyn residency applicants, matriculants, and early-career faculty recorded by the Association of American Colleges (AAMC) from 2005 to 2020. Gender, race, publications, faculty promotion/attrition rates were analyzed. The χ 2 test and two-sample t -test were used as appropriate. A P value < 0.05 was considered statistically significant, and Prism 9.0 (GraphPad Software Inc., CA, US) was used for analyses. Results: By 2020, Ob/Gyn residency applicants were 20% male and 80% female, compared to 35% and 65% in 2005 (P < 0.001). By 2021, 66% of attendings, and 85% of residents were female, with an increase in White females of 8%, Black females 3%, and no increase in Hispanic and Asian females. Males declined across all races. White female faculty increased by 8%, while minority female faculty rose by 1-2%. Male faculty representation fell by 15%. Promotion rates were higher for White females (44%) than Black females (28%), with Black and Asian females leaving academia early than White and Hispanic females (44% vs. 38%). Male applicants published more than females (3.41 vs. 2.75, P < 0.001) but comprised only 20% of 2020 applicants. Asian males had the highest average publications (P = 0.001). Conclusions: Over the last 20 years, Ob/Gyn applicants and faculty have seen large increases in White female faculty with minimal to no increase in minority and male representation. Understanding the reasons for this disparity will help promote more diverse representation in the field of Ob/Gyn. J Clin Gynecol Obstet. 2024;13(3):75-82 doi: https://doi.org/10.14740/jcgo997
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".