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Enregistrement W4220695610 · doi:10.1101/2022.03.29.22273128

Faster, higher, stronger – together? A bibliometric analysis of author distribution in top medical education journals

2022· preprint· en· W4220695610 sur OpenAlexaffabout
Dawit Wondimagegn, Cynthia Whitehead, Carrie Cartmill, Elóy Rodrigues, Antónia Correia, Tiago Salessi Lins, Manuel João Costa

Notice bibliographique

RevuemedRxiv · 2022
Typepreprint
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensThe Wilson CentreWomen's College HospitalUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésPublishingDominance (genetics)Equity (law)Political scienceBibliometricsLibrary scienceSocial scienceSociologyLaw

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Medical education and medical education research are growing industries that have become increasingly globalized. Recognition of the colonial foundations of medical education has led to a growing focus on issues of equity, absence, and marginalization. One area of absence that has been under-explored is that of published voices from low- and middle-income countries. We undertook a bibliometric analysis of five top medical education journals to determine which countries were absent and which countries were represented in prestigious first and last authorship positions. Methods Web of Science was searched for all articles and reviews published between 2012 and 2018 within Academic Medicine , Medical Education , Advances in Health Sciences Education , Medical Teacher , and BMC Medical Education . Country of origin was identified for first and last author of each publication, and the number of publications originating from each country were counted. Results Our analysis revealed a dominance of first and last authors from five countries: USA, Canada, United Kingdom, Netherlands, and Australia. Authors from these five countries had first or last authored 74% of publications. Of the 195 countries in the world, 53% were not represented by a single publication. There was a slight increase in the percentage of publications from outside of these five countries from 22% in 2012 to 29% in 2018. Conclusion The dominance of wealthy nations within spaces that claim to be international is a finding that requires attention. We draw upon analogies from modern Olympic sport and our own collaborative research process to show how academic publishing continues to be a colonized space that advantages those from wealthy and English-speaking countries. Key messages What is already known on this topic -Authors from a small number of high income countries are over-represented in published journal articles on medical education. What this study adds -This study shows that almost three-quarters of first and last authorship positions in several prominent medical education journals are held by authors from only five countries: USA, Canada, UK, Netherlands, Australia. -Authors from low- and middle-income countries, and from countries where English is not the dominant language, are under-represented in prestigious first and last authorship positions within the medical education literature. -As a field that claims to be international in scope, perspectives from outside of these five dominant countries are under-represented, limiting the breadth of views that make up the field of medical education. How this study might affect research, practice or policy -This study provides support for academics, academic institutions, and academic publishers in establishing policies that prioritize the inclusion of authors from low- and middle-income countries and from countries in which English is not the dominant language. -Explicitly including descriptions of the ways research teams address potential power imbalances in research studies that involve collaboration between HIC and LMIC authors, as well as fluent English and less-fluent English speakers in English language publications may allow further development of more inclusive models of international research collaboration.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,006
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Bibliométrie, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesBibliométrie
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,140
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0750,157
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0180,000

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.

Tête enseignante Opus0,047
Tête enseignante GPT0,413
Écart entre enseignants0,365 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2022
Routes d'admission2
Résumé présentoui

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