Gender and Publishing in Nursing: A secondary analysis of h‐index ranking tables
Bibliographic record
Abstract
AIMS: To analyse published ranking tables on academics' h-index scores to establish whether male nursing academics are disproportionately represented in these tables compared with their representation across the whole profession. BACKGROUND: Previous studies have identified a disproportionate representation of UK male nursing academics in publishing in comparison to their US counterparts. DESIGN: Secondary statistical analysis, which involved comparative correlation of proportions. METHODS: Four papers from the UK, Canada and Australia containing h-index ranking tables and published between 2010-2017, were re-analysed in June 2017 to identify authors' sex. Pearson's chi-squared test was applied to ascertain whether the number of men included in the tables was statistically proportionate to the number of men on the pertinent national professional register. FINDINGS: There was a disproportionate number of men with high h-index scores in the UK and Canadian data sets, compared with the proportion of men on the pertinent national registers. The number of men in the Australian data set was proportionate with the number of men on the nursing register. There was a disproportionate number of male professors in UK universities. CONCLUSION: The influence of men over nursing publishing in the UK and Canada outweighs their representation across the whole profession. Similarly, in the UK, men's representation in the professoriate is disproportionately great. However, the Australian results suggest that gender inequality is not inevitable and that it is possible to create more egalitarian nursing cultures. This article is protected by copyright. All rights reserved.
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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 | 0.028 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".