Gender Disparities in Authorships and Citations in Transplantation Research
Bibliographic record
Abstract
Background. Over the past decades, there has been a rapid change in the gender ratio of medical doctors, whereas gender differences in academia remain apparent. In transplantation research, a field already understaffed with female doctors and researchers, there is little published data on the development in proportion, citations, and funding of female researchers over the past years. Methods. To evaluate the academic impact of female doctors in transplantation research, we conducted a bibliometric analysis (01 January 1999 to 31 December 2018) of high-impact scientific publications, subsequent citations, and funding in this field. Web of Science data was used in combination with software R-Package “Gender,” to predict gender by first names. Results. For this study, 15 498 (36.2% female; 63.8% male) first and 13 345 (30.2% female; 69.8% male) last author gender matches were identified. An increase in the percentage of female first and last authors is seen in the period 1999–2018, with clear differences between countries (55.1% female authors in The Netherlands versus 13.1% in Japan, for example). When stratifying publications based on the number of citations, a decline was seen in the percentage of female authors, from 34.6%–30.7% in the first group (≤10 citations) to 20.8%–23.2% in the fifth group (>200 citations), for first (P < 0.001) and last (P = 0.014) authors, respectively. From all first author name-gender matches, 6574 (41.6% female; 58.4% male, P < 0.001) publications reported external funding, with 823 (35.5% female; 64.5% male, P = 0.701) reported funding by pharmaceutical companies and 1266 (36.6% female; 63.4% male, P < 0.001) reporting funding by the National Institutes of Health. Conclusions. This is the first analysis of gender bias in scientific publications, subsequent citations, and funding in transplantation research. We show ongoing differences between male and female authors in citation rates and rewarded funding in this field. This requires an active approach to increase female representation in research reporting and funding rewarding.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".