Impact of COVID-19 on longitudinal ophthalmology authorship gender trends
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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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it