Gender disparity in dermatologic society leadership: A global perspective
Bibliographic record
Abstract
BACKGROUND: In the last half-century, there has been increased representation of women in medicine. Despite this increase, there continues to be underrepresentation of women in medical leadership positions. The objective of this study was to investigate the phenomenon of gender disparity in the leadership of professional societies of dermatology worldwide. METHODS: Online databases were used to extract the names of global dermatologic societies. Individual society websites were accessed to obtain information on executive members. Data not available on society websites were obtained through internet searches. Scopus was used to obtain H-indexes and other bibliometric outcomes. RESULTS: Our data collection spanned 92 countries, with 1733 society leaders identified and information available for 1710. In North America, Europe, Asia, Australia, and the Middle East, women were in a minority in dermatology professional society leadership. In South America, Central America, and Africa, women were in a slight majority. Across all professional societies, the role of president was more frequently held by men (n = 95) as opposed to women (n = 75). Female leaders were less likely to hold concurrent academic positions as deans/chairpersons/directors (83.33%) than their male counterparts (92.06%). The median H-index of female leaders (9) was lower than that of men (14). CONCLUSION: Gender disparity exists in leadership positions in professional dermatology societies. Cultural/continental specific factors should be explored further. Enhancement of institutional support, mentorship, and sponsorship for female dermatologists should be encouraged.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".