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Enregistrement W4375857256 · doi:10.1093/bjs/znad117

Authorship diversity in general surgery-related Cochrane systematic reviews: a bibliometric study

2023· article· en· W4375857256 sur OpenAlexaboutno aff
Roger B Rathna, Jyotirmoy Biswas, Christopher D’Souza, Jethin Mathew Joseph, Vincent Kipkorir, Arkadeep Dhali

Notice bibliographique

RevueBritish journal of surgery · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDiversity and Career in Medicine
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineSystematic reviewBibliometricsDiversity (politics)MEDLINELibrary scienceAnthropology

Résumé

récupéré en direct d'OpenAlex

Dear Editor Despite efforts to address fundamental inequities, surgical residencies lag behind their non-surgical counterparts in attracting women1. Equity of representation in authorship is an important aspect of evidence-based medicine. This study analysed representation in authorship of general surgery-related Cochrane systematic reviews with respect to gender and country. Data were collected from the Cochrane Library on 3 September 2022, using the keyword ‘general surgery’ in an advanced search under the subheading ‘All Text’. An online search was used to confirm the gender and country of an author, by discovering a minimum of two web pages (such as LinkedIn, institutional websites, Loop profile, junior editorial profile, and ResearchGate) demonstrating them. The corresponding authors were contacted when deemed necessary. Some 250 publications that included 1420 authors were included. Four authors had affiliations to two countries. The leading five nations represented in authorship were the UK (562, 39.4 per cent), China (163, 11.5 per cent), Italy (144, 10.1 per cent), Canada (91, 6.4 per cent), and the USA (89, 6.2 per cent) (Fig. 1a). Authorship diversity in general surgery-related Cochrane systematic reviews a Choropleth map showing nationwide author contributions in general surgery-related Cochrane systematic reviews, b gender representation in authorship, and c female authorship trend in general surgery-related Cochrane reviews over time. Syria was the only low-income country that had representation and constituted 0.3 per cent (5 authors). India (8, 0.6 per cent) and Nigeria (2, 0.1 per cent) were the only countries from lower–middle-income groups that had representation. The male to female ratio in this study was 2.11 : 1 (957 : 453) (Fig. 1b). Gender data for 10 authors could not be retrieved and these were categorized as ‘unknown’. There were 169 male (67.3 per cent) and 82 female (32.6 per cent) first authors (gender ratio 2.06 : 1). One study had designated two authors as co-first authors. Eighty-one women constituted 32.4 per cent of all the corresponding authors (male to female gender ratio 2.06 : 1). One article had no corresponding author. One hundred and fifty studies (60 per cent) did not have a female in a lead author (first or corresponding author) position. Fifty-eight studies (23.2 per cent) did not have any female authors, whereas only eight (3.2 per cent) did not have any male authors. In low-income countries, 1 in 5 authors were female. Similarly, in low–middle-income countries, 2 of 10 authors were female. There were no lead female authors from the low- or low–middle-income countries. Among the high-income countries, 450 of 1395 authors (32.2 per cent) were female. A similar gender gap was present in lead author positions in high-income countries. Among 114 first authors from the UK, only 30 (26.3 per cent) were female. Similarly, there were no women among 21 first and corresponding authors from Italy. Among first authors from the USA, 4 of 14 were female. The temporal trend in female authors is shown in Fig. 1c. Cochrane reviews are recognized around the world as having among the highest standards in evidence-based medicine. The main reason for this is that Cochrane reviews adopt a common and specific methodology to reduce bias and random error. The main aim of the Cochrane Collaboration is to help healthcare providers, policymakers, and patients and their advocates and carers make well informed decisions about healthcare. There have been articles detailing authorship diversity in Cochrane reviews in various fields2,3, but not much from general surgery. The present analysis showed that around one-quarter of the articles had no female authors (58, 23.2 per cent), compared with 8 (3.2 per cent) without male authors. This may partly be explained by discrimination through something called disparate impact. This practice, although seemingly very fair from the outside, leads to inequality4. Although surgical residencies exhibit a gender gap in comparison to non-surgical residencies, the number of authorship positions taken up by females is small. Increased diversity in leadership can reap benefits, such as improved productivity and clinical outcomes5. The authors have no funding to declare. R.B.R. and J.B. are joint first authors of this article. Roger Rathna (Writing—original draft), Jyotirmoy Biswas (Conceptualization, Methodology), Christopher D’Souza (Data curation), Jethin Joseph (Formal analysis), Vincent Kipkorir (Methodology), and Arkadeep Dhali (Conceptualization, Writing—review & editing). Data used in this article will be made available on reasonable request to the corresponding author.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,080
score de la tête « metaresearch » (Gemma)0,405
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Bibliométrie
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,920
Score d'incertitude au seuil0,422

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0800,405
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0070,008
Bibliométrie0,1930,266
Études des sciences et des technologies0,0030,003
Communication savante0,0070,008
Science ouverte0,0020,010
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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.

Tête enseignante Opus0,208
Tête enseignante GPT0,362
Écart entre enseignants0,154 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
DomaineÉvaluation
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

Citations8
Publié2023
Routes d'admission1
Résumé présentoui

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