Gender leadership in gynecologic oncology professional societies worldwide
Bibliographic record
Abstract
OBJECTIVE: We aim to study gender representation among the different national gynecologic oncology (GO) societies' presidents over the past decade. METHODS: A cross-sectional study examining the period 2013-2022. The leadership positions in 11 GO societies in the USA (SGO), internationally (IGCS), Europe (ESGO), Australia (ASGO), Israel (ISGO), Japan (JSGO), Asia-Oceania (AOGIN), India (INSGO), Latin America (SLAGO), South Africa (SASGO), and Turkey (TRSGO) was studied. The proportion of leadership positions held by women was calculated and trends were evaluated. RESULTS: Overall, the average rate of women's representation during the study period was 26.4%, and for the different organizations was: SASGO 70.0%, SGO 50.0%, ESGO 40.0%, ASGO 30.0%, INSGO 30.0%, and IGCS, ISGO, and SLAGO 20.0% each, TRSGO 10%, and no representation of women in JSGO and AOGIN. There was a significant decrease from 2013 to 2016 (63.6% to 9.1%) (P = 0.009) in women's representation as presidents of societies. From 2017 to 2022, there was no difference in women's representation (ranging from 9.1% to 36.4%) (P = 0.13). CONCLUSIONS: This study demonstrates that women are significantly under-represented in leadership positions in GO professional societies; however, in South Africa and the USA their representation in the last decade was close to equality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".