Influences for gender disparity in dermatology in North America
Bibliographic record
Abstract
BACKGROUND: Despite constituting half the population, women represent a minority of active physicians and hold a small proportion of faculty leadership positions in North America. However, dermatology is one of the few specialties where women comprise a substantial portion of the workforce. This study explores extent and contributors to gender disparity in academic dermatology faculty positions, leadership, and research. METHODS: We collected data on academic faculty including leadership from the websites of accredited U.S. and Canadian dermatology faculties. We used PubMed and SCOPUS to collect faculty research information including h-index, number of publications, citations, and years of active research. RESULTS: Although women constitute almost half of all dermatologists in the U.S. and Canada (47.9%), only one-fourth (26.1%) of all faculty heads are women. Furthermore, the proportion of women in higher faculty ranks (Assistant Professor, Associate Professors, and Professors) is much lower than males. Female dermatologists also have fewer publications, citations, and years of active research. Interestingly, having a female in a leadership position is associated with a higher proportion of female dermatologists in the faculty. CONCLUSIONS: Gender disparity exists in academic dermatology, and the current academics fail to account for the enormous social challenges that women face, which may put them at a disadvantage to career advancement. Among other factors, better representation of female leadership may encourage and inspire women joining academic faculties in the future.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".