Author Diversity in Presentations at the Society of Gynecologic Surgeons Meetings 2023–2024
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».