Gender equity in medical publications: nurses have smashed the glass ceiling
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".