Diversity in pediatric dermatology: A report from the Pediatric Dermatology Research Alliance and a call to action
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: The Pediatric Dermatology Research Alliance (PeDRA) connects pediatric dermatologists, trainees, basic scientists, allied health professionals, and patient advocates to improve the lives of children with skin disease through research. As a training pipeline for future pediatric dermatologists and steward of research in the field, PeDRA has a responsibility to examine its history and take actionable steps to diversify its membership, grant recipients, study leads, research priorities, and leadership. METHODS: In 2020, PeDRA formed an Equity, Diversity, and Inclusion Task Force to address this need. In an effort to assess PeDRA's past and plan for PeDRA's future, a review of PeDRA's membership, leadership, grant awardees, and research topics was conducted. RESULTS/CONCLUSIONS: Results demonstrated gaps in PeDRA's current operational efforts to diversify the pediatric dermatology workforce and identified areas for improvement. Recommendations are proposed as a call to action for the community.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".