Women Representation on Editorial Boards in Latin America Journals: Promoting Gender Equity in Academic Surgery, Anesthesia, and Obstetrics
Bibliographic record
Abstract
BACKGROUND: Inequitable representation in journal editorial boards may impact women's career progression across surgical, anesthesia, and obstetric (SAO) specialties. However, data from Latin America are lacking. We evaluated women's representation on editorial boards of Latin America SAO journals in 2021. METHODS: We conducted a cross-sectional analysis, retrieving journals through Scimago Journal and Country Rank 2020. Journals were included if active, focused on SAO topics, and publicly provided information on editorial board staff. Editorial board member names and positions were extracted from journals' websites. Members were classified into senior (e.g., editor-in-chief), academic (e.g., reviewer), and non-academic roles (e.g., administrative office). Women's representation was predicted from first names using Genderize.io. The number of women SAO physicians per country was obtained from articles and governmental reports. RESULTS: We included 19 of 25 identified journals and analyzed 1,318 names. Three anesthesiology, seven obstetric, and nine surgical journals represented five Latin American countries. Women held 17% (224/1,318) of board positions [p < 0.0001; 95% CI(0.14, 0.19)]. Women held fewer academic roles (14.3%, 155/1,084) compared to senior [28.9%, 64/221 (p < 0.001)] and non-academic roles [38.4%, 5/13 (p = 0.042)]. Surgical journals had fewer women (7.7%, 58/752) compared to anesthesia [25.5%, 52/204 (p = 0.006)] and obstetrics [31.5%, 114/362 (p < 0.001)]. Women's proportion on editorial boards increased according to the number of women SAO physicians per country (p < 0.001). CONCLUSIONS: Our study assessed the composition of editorial boards from Latin America SAO journals and demonstrated that women remain underrepresented. Our findings highlight the need for regional strategies to advance women's careers across SAO specialties.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".