Female representation in oncology leadership in Africa: Analysis of trends in the African organization for cancer research and training in cancer (AORTIC).
Notice bibliographique
Résumé
11052 Background: Board membership in oncology organizations and invitation to speak at major oncology conferences is an established indicator of gender disparities in oncology leadership. Recent studies note an upward trend in female board membership and female speakers at oncology conferences in European and American contexts. However, little is known about trends of female representation in board membership and at oncology conferences in Africa. AORTIC is the premier African cancer organization and hosts a biennial conference, during which its council members are elected for a two-year term. The conference hosts over 1000 participants and is highly regarded internationally. As the largest gathering of African oncologists, the conference presents a unique opportunity for insight into workforce disparities and trends. This study presents an analysis of female council membership and speaker participation at the 2015, 2017, 2019 and 2021 AORTIC conferences. Methods: AORTIC conference programmes and AORTIC elections for council membership in 2015, 2017, 2019 and 2021 were analyzed for gender representation. Speakers were divided into categories including keynote speakers, facilitators, session speakers, panelists, and oral abstract presenters. For each AORTIC session, data was collected on speaker name, gender, and affiliated country. Participants who presented multiple times were counted multiple times, as their role often varied between sessions. Personal data and country affiliation was determined through online biographies of speakers and/or visual confirmation of professional photographs. A simple descriptive analysis of the data and trends is presented here. Results: Since 2015, the proportion of female speakers and council members at AORTIC has increased. In 2015, females accounted for 44% of speakers, in contrast to 55% in 2021. In 2021, females made up over 50% of all speaker categories except keynote. The proportion of female council members also increased, from 42% in 2015 to 69% in 2021. Trends in geographic representation of speakers were also analyzed for gender, revealing an increase in female speakers residing in Africa. (Table) Conclusions: The proportion of female speakers and council members at Africa’s largest cancer conference has increased significantly since 2015. Female speaker participation was higher than in North America and Europe in 2017 and surpassed 50 % in 2021. In addition, female council membership in AORTIC has surpassed 50%, up to 69% in 2021. These trends suggest a rise in female leadership in cancer research and training on the continent, indicating that strengthening local platforms in LMIC may be associated with improved gender assimilation. [Table: see text]
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».