Editorial: Inequalities in COVID-19 healthcare and research affecting women
Bibliographic record
Abstract
Editorial on the Research Topic Inequalities in COVID-19 healthcare and research affecting womenThis Research Topic, Inequalities in COVID-19 Healthcare and Research Affecting Women, presents a rich collection of articles from all over, including Africa, Asia, Australia, North America, and Europe.The Topic is a compilation of various articles addressing diverse topics such as gender gaps, violence against women, gender bias in research, postpartum care, accessibility to services, and so on, with a basket of policy options and recommendations.The current COVID-19 pandemic is no exception, as women are disproportionately affected by the global crisis.Therefore, building fair, sustainable, and healthy societies requires understanding and attention to the impacts of both sex and gender on health outcomes.Although the lockdowns and stay-at-home orders are critical in limiting and preventing COVID-19 spread, they devastate vulnerable groups such as women and girls for gender-based violence (GBV).Besides, preventive confinement practices exacerbate many causes of or contributors to violence against women and girls (1).Additionally, COVID-19 has revealed that women are underrepresented in ongoing COVID-19 research publications, in the governance of epidemic management, and as authors of COVID research (2).In the same context, it was also highlighted that women are generally underrepresented as research participants in COVID research, and pregnant women were frequently excluded from research (3).Despite the World Health Organization's (WHO) Executive Board recognizing the need of including women in decision-making for pandemic planning and response, women's representation is inadequate in COVID-19 policy domains at both national and global platforms.COVID-19 policy domains (4).The COVID-19 pandemic has disrupted
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.036 | 0.017 |
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".