Author Diversity in Presentations at the Society of Gynecologic Surgeons Meetings 2023–2024
Bibliographic record
Abstract
INTRODUCTION: There is a disproportionately higher representation of White race and male gender in leadership and academic rank in OBGYN compared to the overall racial/ethnic and gender diversity of the field. While gender representation in publications and presentations has increased over time, racial/ethnic representation in the literature may still be lagging. Tracking and reporting author demographics can help highlight disparities in scientific presentation and publishing, motivating efforts to increase diversity and inclusion. OBJECTIVE: We aimed to investigate the racial/ethnic and gender makeup of those who present research abstracts at the annual scientific meeting of the Society of Gynecologic Surgeons (SGS). We hypothesized that authorship for accepted presentations would lack racial and ethnic diversity compared to the field of OBGYN and the subspecialties represented by the society. METHODS: Self-reported demographic data was collected for the submitting author for all submissions to the SGS annual scientific meetings in 2023 and 2024. After IRB approval, de-identified author data was distributed to the research team. Comparisons were made between years authors’ self-reported identities. Bivariate analysis was also to investigate the effect of these identities on presentation types (long oral, short oral, video podium presentation, non-podium video fest/video cafe, and non-discussed E-poster). Author demographics were also compared generally to available demographic data for national subspecialty societies, board-certified diplomats, and annual meeting attendees. RESULTS: In 2023, 277 abstracts were accepted for presentation. The majority of abstract authors were female and White. Over half of presenters in all categories were White with no statistical difference between categories of presentation related to gender or race (p=0.80, p=0.34, respectively). Twenty-three authors (8%) identified as Hispanic. In 2024, 253 abstracts were accepted for presentation. As in 2023, the majority of abstract authors were female and White. Twenty-two authors (9%) identified as Hispanic. Again, neither gender nor race was a significant predictor of the types of presentations (p=0.71, p=0.94, respectively). From 2023 to 2024, there was no statistically significant change in reported gender or race/ethnicity self-identification, apart from an increase in authors declining to report their racial identity in the 2024 submission (0% vs 7%). The racial diversity of authors between 2023 and 2024 closely mirrors the racial demographic data for ABOG diplomates in 2024 as well as AUGS members. There are more female SGS authors than expected when compared to ABOG diplomates, AUGS membership demographics, and SGS conference attendees in 2024. Neither SGS nor AAGL collect membership demographic data, so these comparisons could not be included (Figures 1 and 2). CONCLUSIONS: The majority of accepted abstracts at the SGS Annual Scientific Meeting are by female, White authors. We encourage SGS and other national organizations to continue to collect demographic data both at meetings and from general membership to identify opportunities to increase recruitment and engagement of under-represented groups (Table 1).
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".