Pandemic Pregnancy Experiences and Risk Mitigation Behaviors: COVID-19 Vaccination Uptake in Canada
Bibliographic record
Abstract
Background: Pregnant people in Canada during the pandemic faced complex decision-making related to COVID-19 exposure risks and the safety of mitigation measures, including vaccines. To help inform future infectious disease–health promotion, we assessed pandemic pregnancy experiences and COVID-19 risk mitigation strategies. Methods: Respondents, pregnant at any time after January 2020 in Canada, completed an online, cross-sectional, descriptive survey from September 2021 to February 2022. Logistic regression was used to identify predictive factors associated with COVID-19 vaccine uptake and history of infection. Results: A purposive sample of predominantly non-racialized, high socioeconomic status women (n = 564), 58.2% primigravid during the pandemic, reported high COVID-19 vaccine uptake (87.4%). Educational attainment beyond high school predicted COVID-19 vaccination (college AOR: 2.72, CI: 1.24–5.94, p < 0.001; university AOR 4.01, CI: 1.91–8.40, p < 0.001; post-graduate university AOR: 7.31, CI: 2.84–18.81, p < 0.001). Immigrant status reduced the likelihood of COVID-19 vaccination (AOR: 0.20; CI: 0.09–0.49, p < 0.001). Racialized participants were 2.78-fold more likely to report infection (CI:1.19–6.50, p = 0.018). Conclusions: COVID-19 vaccination uptake was very high; however, vaccine hesitancy was evident among immigrants, with racialized participants more likely to report a history of COVID-19 infection. Tailored public health messaging using a health equity lens may yield more robust vaccine uptake for future infectious respiratory disease outbreaks.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".