Gender inequity and COVID-19 vaccination policies for pregnant women in the Americas
Bibliographic record
Abstract
The region of the Americas has been the epicenter of the COVID-19 pandemic’s worst outcome in terms of number of deaths due to COVID-19. SARS-CoV-2 infection during pregnancy and the postpartum period has been found to be associated with increased risk of mortality and severe disease. Several Latin American and Caribbean countries have disproportionally high maternal mortality rates due to COVID-19. Although this region achieved relatively high vaccination rates among the general adult population, there were differing restrictions regarding the vaccination of those who were pregnant. In a pandemic, policies reflect political priorities in responses to the threats posed to populations and play an important role in promoting gender equity. This paper reports the results of an ethical analysis of 45 national COVID-19 vaccination public policies from seven countries – Argentina, Brazil, Canada, Colombia, Mexico, Peru, and United States. The analysis drew on reproductive justice and feminist bioethics frameworks, paying close attention to whether and how gender and social and economic inequities were addressed. It found that exclusionary approaches in immunization policies which restricted access to vaccination during pregnancy were often justified on the basis of a lack of evidence about the effects of immunization of pregnant persons, and on the grounds of medical expertise, to the detriment of women’s autonomy and agency. As such these policies reiterate patriarchal moral understandings of women, pregnancy and motherhood. In practice, they counter human rights gender equity and equality principles, and became lethal, particularly to racialized women in Latin America. During an emerging lethal disease, policies and policy development must consider the intersection of oppressive structures to protect and guarantee rights of women, girls, and pregnant persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".