Gender disparity between authors in leading medical journals during the COVID-19 pandemic: a cross-sectional review
Notice bibliographique
Résumé
OBJECTIVES: Evaluate gender differences in authorship of COVID-19 articles in high-impact medical journals compared with other topics. DESIGN: Cross-sectional review. DATA SOURCES: Medline database. ELIGIBILITY CRITERIA: Articles published from 1 January to 31 December 2020 in the seven leading general medical journals by impact factor. Article types included primary research, reviews, editorials and commentaries. DATA EXTRACTION: Key data elements were whether the study topic was related to COVID-19 and names of the principal and the senior authors. A hierarchical approach was used to determine the likely gender of authors. Logistic regression assessed the association of study characteristics, including COVID-19 status, with authors' likely gender; this was quantified using adjusted ORs (aORs). RESULTS: We included 2252 articles, of which 748 (33.2%) were COVID-19-related and 1504 (66.8%) covered other topics. A likely gender was determined for 2138 (94.9%) principal authors and 1890 (83.9%) senior authors. Men were significantly more likely to be both principal (1364 men; 63.8%) and senior (1332 men; 70.5%) authors. COVID-19-related articles were not associated with the odds of men being principal (aOR 0.99; 95% CI 0.81 to 1.21; p=0.89) or senior authors (aOR 0.96; 95% CI 0.78 to 1.19; p=0.71) relative to other topics. Articles with men as senior authors were more likely to have men as principal authors (aOR 1.49; 95% CI 1.21 to 1.83; p<0.001). Men were more likely to author articles reporting original research and those with corresponding authors based outside the USA and Europe. CONCLUSIONS: Women were substantially under-represented as authors among articles in leading medical journals; this was not significantly different for COVID-19-related articles. Study limitations include potential for misclassification bias due to the name-based analysis. Results suggest that barriers to women's authorship in high-impact journals during COVID-19 are not significantly larger than barriers that preceded the pandemic and that are likely to continue beyond it. PROSPERO REGISTRATION NUMBER: CRD42020186702.
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 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 | 0,038 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».