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Record W3170563654 · doi:10.1093/eurjcn/zvab025

Gender equity in medical publications: nurses have smashed the glass ceiling

2021· article· en· W3170563654 on OpenAlexaff
Anne‐Laure Féral‐Pierssens, Aurélie Avondo, Carla De Stefano, S. Deltour, Frédéric Lapostolle

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsGender equityMedicineGlass ceilingScholarshipPopulationFamily medicineNursingGender studiesPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Gender equity has become a major concern in many professional fields. The rate of women as authors has to be interpreted according to the rate of women in the related professions. In this perspective, studying nurses' population should be of particular interest since, worldwide, nurses are mostly women. Then, our aim was to study gender disparity in nurses' publications. METHODS: We selected the three main journals dedicated to nurse publications: International Journal of Nursing Studies, Journal of Nursing Scholarship, and European Journal of Cardiovascular Nursing. We included 20 recent consecutive papers from each journal. For each paper, the number of authors, their gender, and rank were recorded. Primary endpoint: overall rate of women as authors. Secondary endpoints: rate of women as first, last, second, and third authors. RESULTS: Sixty papers including 322 authors were analysed. Overall rate of women authors: 74%. Overall rate of women as first author: 82%. Overall rate of women as last author: 72%. Overall rate of women as second and third authors: respectively, 80% and 70%. CONCLUSION: Almost three-quarters of the authors in these main scientific journals of nursing studies were female. This rate is lower than the gender rate in the nursing profession.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.122
GPT teacher head0.391
Teacher spread0.269 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2021
Admission routes1
Has abstractyes

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