Gender and Racial Profile of the Academic Pediatric Faculty Workforce in the United States
Bibliographic record
Abstract
Background Equity, diversity, and inclusion remain a challenge in the healthcare workforce. This study explored the current gender and racial/ethnic trends in academic pediatric positions across the United States. Methodology The pediatric faculty self-reported data by the American Association of Medical Colleges (AAMC) Faculty Roster from 2007 to 2020 were analyzed. The races were classified as White (non-Hispanic), Asian, Hispanic, Black (non-Hispanic), Multiple races (including both non-Hispanic and Hispanic), Others, and Unknown. Gender was categorized as male and female. Results The results showed that Asian, Black (non-Hispanic), and Hispanic academic pediatricians increased in full professor, associate professor, and assistant professor positions and decreased in instructor positions from 2007 to 2020. Black (non-Hispanic) academic pediatricians relatively decreased 5.5% in chairperson positions. Women increased in full professor, associate professor, instructor, and chairperson positions; however, relatively decreased 1.8% in assistant professor positions. Men and White (non-Hispanic) academic pediatricians relatively decreased 10.5% and 16%, respectively, in all academic ranks. Women, Asian, Black (non-Hispanic), Hispanic, and Other races were underrepresented in tenured, on-track (tenure-eligible), and not-on-track (tenure-eligible) positions. Conclusions Women and underrepresented minorities in medicine (URiM) physicians continue to remain significantly underrepresented in academic pediatric faculty positions and tenured track positions. There is a dire need to adapt multifaceted strategies to increase the engagement of women and URiM in academic pediatrics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".