Working Towards Gender and Racial Diversity in Pediatric Residency Programs in the United States
Bibliographic record
Abstract
Introduction The gender and racial profile of the pediatric population in the United States (US) is more diverse than that of the pediatricians that cater to their healthcare needs. Gender and racial diversity remains limited among pediatric residents and fellows, faculty, and leadership. Our study objectives were to explore the gender and racial disparity among pediatric residents in the US. Methods This was a retrospective analysis of the Accreditation Council for Graduate Medical Education (ACGME) database. The database encompassed all residents in US pediatrics residency programs from 2007 to 2021, categorizing them into White (non-Hispanic), Asian/Pacific Islander, Hispanic, African American/Black (non-Hispanic), Native American/Alaskan, others (races not included in the mentioned categories), and unknown. Gender was grouped into male, female, and not reported. Results From 2011 to 2021, the greatest increase in relative change (%) was seen for Asian or Pacific Islander (+58.42%), followed by Black (non-Hispanic) (+45.24%), White (non-Hispanic) (+43.37%), and Hispanic (+42.18%) races. The Native American/Alaskan relatively decreased 50%. The representation of female residents relatively increased by 13.27% as compared to the relative increase of male residents (+14.77%) from 2007 to 2021. Conclusion It is imperative to have a healthcare workforce that is representative of the existing communities in the US in terms of race, ethnicity, and gender to provide culturally sensitive care to the diverse patient population of the US.
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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 | 0.002 | 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 | 0.001 | 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 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".