Gender Disparity in Persian Gulf Urological Conferences Over the Past Three Years
Bibliographic record
Abstract
Background/Objectives: Gender disparity is prevalent in urology and other surgical specialties, with under-representation of females in both academic and professional settings, including in the Persian Gulf region. To investigate female participation in Persian Gulf urological conferences over the past three years, focusing on abstract presenters, faculty, speakers, and moderators. Methods: Data were collected from three major conferences: the 34th Saudi Urological Conference (SUA), the Urological Asian Association and Emirates Urological Conference (UAA-EUSC), and the 11th Emirates Urological Conference and 18th Pan Arab Continence Society Conference (EUSC-PACSC). The gender of the presenters and faculty was identified using genderize.io, faculty images, and Google searches. Statistical analyses, including chi-square and Fisher’s exact tests, were conducted to assess gender disparities. Results: Out of 536 abstracts, 13.25% were presented by females, with significant variation across conferences (p = 0.018). Female representation was lowest in the basic sciences category (3.13%) and highest in the other category (35.29%) (p = 0.01). Abstract to publication rates did not differ significantly between genders. Male dominance was noted among faculty members (94.21% male), speakers (96.44% male), and moderators (98.98% male), with no significant gender distribution differences across roles (p = 0.1762). Conclusions: This study highlights significant gender disparities at Persian Gulf urological conferences, particularly in leadership roles and research presentations. Recommendations include promoting female leadership, supporting mentorship programs, and ensuring gender diversity in conference management and speaker line-ups to foster a more inclusive environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".