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Record 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 on OpenAlexaffabout
Dawit Wondimagegn, Cynthia Whitehead, Carrie Cartmill, Elóy Rodrigues, Antónia Correia, Tiago Salessi Lins, Manuel João Costa

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThe Wilson CentreWomen's College HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsPublishingDominance (genetics)Equity (law)Political scienceBibliometricsLibrary scienceSocial scienceSociologyLaw

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0750.157
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.413
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2022
Admission routes2
Has abstractyes

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