Impact of COVID-19 on longitudinal ophthalmology authorship gender trends
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic increased the gender gap in academic publishing. This study assesses COVID-19's impact on ophthalmology gender authorship distribution and compares the gender authorship proportion of COVID-19 ophthalmology-related articles to previous ophthalmology articles. METHODS: This cohort study includes authors listed in all publications related to ophthalmology in the COVID-19 Open Research Dataset and CDC COVID-19 research database. Articles from 65 ophthalmology journals from January to July 2020 were selected. All previous articles published in the same journals were extracted from PubMed. Gender-API determined authors' gender. RESULTS: Out of 119,457 COVID-19-related articles, we analyzed 528 ophthalmology-related articles written by 2518 authors. Women did not exceed 40% in any authorship positions and were most likely to be middle, first, and finally, last authors. The proportions of women in all authorship positions from the 2020 COVID-19 group (29.6% first, 31.5% middle, 22.1% last) are significantly lower compared to the predicted 2020 data points (37.4% first, 37.0% middle, 27.6% last) (p < .01). The gap between the proportion of female authors in COVID-19 ophthalmology research and the 2020 ophthalmology-predicted proportion (based on 2002-2019 data) is 6.1% for overall authors, 7.8% for first authors, and 5.5% for last and middle authors. The 2020 COVID-19 authorship group (1925 authors) was also compared to the 2019 group (33,049 authors) based on journal category (clinical/basic science research, general/subspecialty ophthalmology, journal impact factor). CONCLUSIONS: COVID-19 amplified the authorship gender gap in ophthalmology. When compared to previous years, there was a greater decrease in women's than men's academic productivity.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".