Challenges faced by female oncologists in sub-Saharan Africa.
Notice bibliographique
Résumé
11001 Background: Recent articles by ASCO and ESMO have identified challenges facing female oncologists in western contexts. The challenges female oncologists face in sub-Saharan Africa (SSA) have yet to be explored. This study was launched by the AORTIC Education and Training Committee to determine the most common and substantial challenges faced by female oncologists in SSA and identify potential solutions. Methods: A diverse panel of 32 female oncologists from 20 countries in SSA was recruited through professional and personal networks. Following an initial meeting to review terminology, a modified three-round Delphi process took place. Participants iteratively reviewed a list of previously identified challenges facing women in oncology in SSA and identified new challenges. The survey was conducted via REDCap, a secure-web-based software platform. Participants reflected on personal experiences or those of colleagues, and were asked to indicate their agreement with each listed challenge, as well as propose solutions. Descriptive statistics identified the most common challenges. Following the third survey, a focus group was held to enrich study data. A thematic analysis is being conducted on the focus group transcript to identify key themes, and a subsequent modified Delphi process is being executed to build consensus around potential solutions to identified challenges. Results: Response rates for the 3 modified Delphi rounds were 66%, 66%, and 53%. The challenge with the greatest agreement was, “pressure to maintain a work-family life balance and meet social obligations”. These were felt to be unique to women in SSA due to an extended family network with several responsibilities beyond the nuclear family. The next two top-scored challenges were “lack of female support and networks”, and “micro-aggressions” (Table). Conclusions: Female oncologists in SSA experience many of the challenges that have been previously identified by similar studies in other regions, with different degrees of perceived importance. Some challenges have a different lived experience for female oncologists in SSA. The second part of this study will include thematic analysis of the recent focus group and explore potential solutions to mitigate these challenges, which will add insight and potential paths forward to optimizing a diverse workforce in SSA.[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 | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».