How Balanced Is Gender Representation at ASH? a Cross-Sectional Analysis
Notice bibliographique
Résumé
Background: Gender disparities in medical leadership remain prevalent. In fact, one study reported only 30.1% of invited speakers at 9 different national medical conferences were women. Conferences such as the ASH (American Society of Hematology) annual meeting serve as crucial platforms for knowledge sharing and professional development, making it essential to examine gender representation in these settings. Here, we present a cross-sectional analysis of gender representation at the ASH 2023 annual meeting. Methods: We analyzed the ASH 2023 conference catalogue, focusing on sessions including Education program, General session, Friday Satellite Symposium, Health Equity Studio, Special Interest Sessions, Spotlight Sessions, Scientific symposia, How I treat, and Meet the Scientist. From these sessions, we analyzed the gender distribution of chairs and speakers. Gender was identified based on physical appearance from institutional websites as well as pronouns listed on the ASH website. Data was then stratified by topic (malignant vs classical) and geographic region. One-sample z proportion tests were used to analyze statistical significance of gender distribution, with a p value <0.05 considered statistically significant. Results: A total of 148 sessions from the ASH 2023 conference were analyzed, encompassing 466 speakers in total. Males constituted 242 (51.9%) of these speakers while females comprised 224 (48.1%). Specifically, there were a total of 64 male chairs (53.3%) and 178 male speakers (51.45%), while there were 56 female chairs (46.7%) and 168 female speakers (48.55%). There was no significant difference that was found in the overall gender distribution of speaker and chairs at ASH 2023 (p=0.56). When examining gender distribution by topic (malignant hematology vs classical hematology), males were significantly more represented in malignant hematology (62% vs 38%, p=0.003), while classical hematology had an equal distribution (50% each), showing no significant difference. When analyzing chairs specifically, we also found that there was higher representation of male chairs for malignant hematology (n=40, 67.8%, p=0.006). There was no significant difference in the distribution of chairs by gender in classical hematology (males=14, females=19, p=0.54). Further analysis by session type revealed that of the 152 speakers that were a part of the Friday Satellite Symposium, males were more represented (n=100, 65.7%, p =0.0001). Conversely, females were more represented in Special Interest sessions, with 83 females (66.9%, p=0.0002) out of 124 speakers. Distribution of speaker per region (USA vs Canada vs Europe vs Asia) showed no significant difference in gender distribution. Conclusion: Our analysis of ASH 2023 sessions reveals a significant gender disparity in malignant hematology, with a higher representation of male speakers and chairs. In contrast, classical hematology showed an equal gender distribution. Session-specific disparities were also observed. Although we applaud ASH for having a similar distribution of gender overall among speakers and chairs, our analysis does reveal an imbalance within sessions and topic. Concerted efforts are needed to ensure equitable opportunities for women in medical leadership roles at ASH in all sessions and topics.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».