COVID-19: a magnifying glass for gender inequalities in medical research
Bibliographic record
Abstract
The authorship gender gap has been observed in most scientific disciplines, including medicine.For example, the proportion of female first authorship was only 37% in 2014 in six high-impact general medical journals, 1 and 34% in 2006-2008 in five US primary care medical journals.2 The situation appeared, however, to improve in recent years with some disciplines such as pediatrics and primary care demonstrating a reversal in the male/female ratio of first authorship.3,4 The under-representation of women as last authors in biomedical research instead remains, and may be symptomatic of their minority presence among senior faculty members.The aforementioned imbalance appears to apply to the growing field of COVID-19 research as well.Anderson et al demonstrated that female first and last authorship for COVID-19-related publications was respectively 23% and 16% lower than the average female authorship representation in 2019.5 The number of women who authored preprints submitted to arXiv (an online archive for preprints of scientific papers) rose only by 2.7% between 2019 and 2020, compared to a 6.4% rise for men.6 Women represent only a quarter of COVID-19 experts in the media and a quarter of the members of national task forces.7 The situation is likely to worsen in the near future as most of the published studies and recently submitted preprints were planned long before the onset of the pandemic.Although primary care has not received the media attention that intensive care has, primary care physicians have experienced immense increases in workload and changes to workplace practices, even in countries where the pandemic has been well controlled, leaving them little time to pursue research.Both men and women have been affected by COVID-19; however, it is likely that the impact on female primary care physicians is, and will be, more significant.
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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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.030 | 0.030 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".