“Vaccines protect both you and your newborn:” A discourse analysis of risk and uncertainty in information about vaccination in pregnancy
Bibliographic record
Abstract
Health governance during pregnancy is grounded in cultural norms about good mothering, which centre around self-sacrifice and perceived conflicts between maternal and fetal bodies. Nonetheless, many health choices in pregnancy should have mutual benefits and risks for maternal and fetal bodies, including vaccination during pregnancy. This manuscript presents results from a discourse analysis of 440 texts about vaccines that are recommended in pregnancy in Canada, including inactivated influenza, tetanus-diphtheria-acellular-pertussis, and COVID-19 vaccines. Texts include publicly available online information (e.g., webpages, printouts, posters, videos, and other materials) developed by various authoritative institutions (e.g., public health services, professional organizations, vaccine manufacturers). This study contributes to feminist and risk theorist critiques of public health discourse by exploring how texts deploy emotionally laden technical discourses that govern pregnant individuals towards divergent goals. Specifically, they are governed towards socially desirable health decisions (e.g., vaccination acceptance) and towards agonizing over decision-making as “good mothers.” I analyze how texts pursue this divergent governance in three ways, by deploying discourses that reify conflict between maternal and fetal bodies, organizing the work of making choices around gendered power relations, and activating emotions around mothering responsibilities. My analysis compares how such governance differs across products by available evidence and by the intended purpose of vaccination to protect maternal and/or fetal bodies. I conclude by discussing the value of combining governmentality and cultural approaches to risk theory to understand how the interface of public health and mothering discourses reproduce power relations, and suggest recommendations on how to lessen that reproduction. • Feminist qualitative discourse analysis of texts about vaccination in pregnancy. • Application of governmentality and cultural approaches to risk theory. • Texts reproduce adversarial relationships between maternal and fetal bodies. • Texts differently organize the work of decision-making for each vaccine product. • Suggestions on how to improve information to reduce unequal gender relations.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".