Gender Differences in Authorship of Critical Care Literature
Bibliographic record
Abstract
Abstract Rationale Gender gaps exist in academic leadership positions in critical care. Peer-reviewed publications are crucial to career advancement, and yet little is known regarding gender differences in authorship of critical care research. Objectives To evaluate gender differences in authorship of critical care literature. Methods We used a validated database of author gender to analyze authorship of critical care articles indexed in PubMed between 2008 and 2018 in 40 frequently cited journals. High-impact journals were defined as those in the top 5% of all journals. We used mixed-effects logistic regression to evaluate the association of senior author gender with first and middle author gender, as well as association of first author gender with journal impact factor. Measurements and Main Results Among 18,483 studies, 30.8% had female first authors, and 19.5% had female senior authors. Female authorship rose slightly over the last decade (average annual increases of 0.44% [P < 0.01] and 0.51% [P < 0.01] for female first and senior authors, respectively). When the senior author was female, the odds of female coauthorship rose substantially (first author adjusted odds ratio [aOR], 1.93; 95% confidence interval [CI], 1.71–2.17; middle author aOR, 1.48; 95% CI, 1.29–1.69). Female first authors had higher odds than men of publishing in lower-impact journals (aOR, 1.30; 95% CI, 1.16–1.45). Conclusions Women comprise less than one-third of first authors and one-fourth of senior authors of critical care research, with minimal increase over the past decade. When the senior author was female, the odds of female coauthorship rose substantially. However, female first authors tend to publish in lower-impact journals. These findings may help explain the underrepresentation of women in critical care academic leadership positions and identify targets for improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".