Gender disparity in critical care publications: a novel Female First Author Index
Bibliographic record
Abstract
BACKGROUND: Bibliometric analyses show gender bias against women in scientific publications and citations. We hypothesized that a metric of an individual senior author's inclusivity of women as first authors in critical care publications would predict gender inequality. METHODS: Using PubMed and Web of Science, we conducted a bibliometric analysis of original research publications in critical care from 2008 to 2018 in 11 specialty and general journals. Gender for first and senior authors was assigned by a gender determination application, and manually if needed. For all senior authors we defined the novel Female First Author Index (FFA-index) = #Female first authors in publications by an individual senior author/Total # publications by that senior author. We produced a novel interactive web-based application using the R package Shiny to increase potential utilization of the FFA-index. RESULTS: Of 7370 publications, 30.4% had female first authors and 15.5% had female senior authors. After adjustment for impact factor, journal, year of publication, number of authors, country, and gender determination accuracy, female senior authorship was associated with a 1.9-fold increase in female first authorship [OR = 1.85 (95% CI 1.62, 2.11); p < 0.001] compared with male senior authorship. The mean (SD) FFA-index for all individual senior authors was 30.5 (42.9); with a significant difference in FFA-index between male and female senior authors (27.6 versus 42.5, respectively; p < 0.001). The interactive web-based application (FFA-index App) produces the same FFA-index output as our study results. CONCLUSIONS: Female representation at prominent authorship positions in critical care publications is still far from achieving gender parity. By creating an authorship index score, we propose a frame of reference for the advancement of female first authorship.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".