Diversity, Equity, and Inclusion in the Pediatric Pulmonary Workforce: An Official American Thoracic Society Workshop Report
Bibliographic record
Abstract
Despite growing recognition of the need for increased diversity among students, trainees, and faculty in health care, the medical workforce still lacks adequate representation from groups historically underrepresented in medicine (URiM). The subspecialty field of pediatric pulmonology is no exception. Although there have been efforts to address issues of diversity, equity, and inclusion (DEI) in our own field, gaps persist. To address these gaps, the members of the Diversity, Equity, and Inclusion Advisory Group (DEI-AG) of the American Thoracic Society Pediatrics Assembly created and distributed a Needs Assessment Survey in the United States and Canada to better understand the racial and ethnic demographics of the pediatric pulmonary workforce and to learn more about successes, gaps, and opportunities to enhance how we recruit, train, and retain a diverse workforce. The DEI-AG leadership cochairs convened a workshop to review the findings of the DEI Needs Assessment Survey and to develop strategies to improve the recruitment and retention of URiM fellows and faculty. This Official ATS Workshop Report aims to identify barriers and opportunities for recruitment, training, and career development within the field of pediatric pulmonology. Additionally, we offer useful strategies and resources to improve the recruitment of URiM residents, the mentorship of trainees and junior faculty, and the career development of URiM faculty in academic centers. This Workshop Report is an important first deliverable by the DEI-AG. We hope that this work, originating from within the Pediatrics Assembly, will serve as a model for other Assemblies, disciplines across the ATS, and other fields in 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 | 0.036 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".