Time for action: towards an intersectional gender approach to COVID-19 vaccine development and deployment that leaves no one behind
Bibliographic record
Abstract
### Summary box The COVID-19 pandemic has exposed once again how gender and other inequalities are inter-related with and worsen health disparities. Gender shapes risk of infection, vulnerability to disease and experience of ill health, and socioeconomic disparities.1 Important interplays between biological sex and gender, as a social construct, and other variables such as age, race and ethnicity, and other health conditions, have demonstrated differential risks of COVID-19 exposure, acquisition and outcomes.2 3 Sex-based differences in vaccine-induced immune response and adverse events are well documented, and may influence vaccine acceptance, access and uptake, which are also highly gendered.4 Hence, it is imperative that sex and gender be meaningfully considered alongside other intersecting dimensions when developing and deploying COVID-19 vaccines.5 Inherent in this is the need for meaningful engagement of the expertise and leadership of women in all scientific research, policymaking and programmatic decision-making processes at global, national and local levels. This article …
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".