Gender and Racial Profile of the Academic Pediatric Faculty Workforce in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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