COVID-19 vaccination in pregnancy: How discrepant public health discourses shape responsibility for fetal health
Bibliographic record
Abstract
Early in COVID-19 vaccine rollout, expert recommendations about vaccination while pregnant and breastfeeding changed rapidly. This paper addresses the (re)production of gendered power relations in these expert discourses and recommendations in Canada. We collected texts about COVID-19 vaccine use in pregnancy (N = 52) that Canadian health organizations (e.g., professional societies, advisory groups, health authorities) and vaccine manufacturers made publicly available online. A discourse analysis was undertaken to investigate intertextuality (relations between texts), social construction (incorporation of assumptions about gender), and contradictions between and within texts. National expert recommendations varied in stating COVID-19 vaccines are recommended, should be offered, or may be offered, while manufacturer texts consistently stated there was no evidence. Provincial and territorial texts reproduced discrepancies between the Society of Obstetricians and Gynaecologists of Canada and National Advisory Committee on Immunization recommendations, including that COVID-19 vaccines should be versus may be offered in pregnancy. Our findings suggest gaps in data and discrepant COVID-19 vaccine recommendations, eligibility, and messaging limit guidance regarding vaccination in pregnancy. We argue that these discrepancies magnified the already common practice of deferring responsibility for the uncertainties of vaccination in pregnancy onto parents and healthcare providers. The deferral of responsibility could be reduced by harmonizing recommendations, regularly updating texts that describe evidence and recommendations, and prioritizing research into disease burden, vaccine safety, and efficacy before vaccine rollout.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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 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".