Temporal trends and determinants of COVID-19 vaccine series initiation after recent pregnancy
Bibliographic record
Abstract
During the rapid deployment of COVID-19 vaccines in 2021, safety concerns may have led some pregnant individuals to postpone vaccination until after giving birth. This study aimed to describe temporal patterns and factors associated with COVID-19 vaccine series initiation after recent pregnancy in Ontario, Canada. Using the provincial birth registry linked with the COVID-19 vaccine database, we identified all individuals who gave birth between January 1 and December 31, 2021, and had not yet been vaccinated by the end of pregnancy, and followed them to June 30, 2022 (follow-up ranged from 6 to 18 months). We used cumulative incidence curves to describe COVID-19 vaccine initiation after pregnancy and assessed associations with sociodemographic, pregnancy-related, and health behavioral factors using Cox proportional hazards regression to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (CI). Among 137,198 individuals who gave birth in 2021, 87,376 (63.7%) remained unvaccinated at the end of pregnancy; of these, 65.0% initiated COVID-19 vaccination by June 30, 2022. Lower maternal age (<25 vs. 30-34 y aHR: 0.73, 95%CI: 0.70-0.77), smoking during pregnancy (vs. nonsmoking aHR: 0.68, 95%CI: 0.65-0.72), lower neighborhood income (lowest quintile vs. highest aHR: 0.79, 95%CI: 0.76-0.83), higher material deprivation (highest quintile vs. lowest aHR: 0.74, 95%CI: 0.70-0.79), and exclusive breastfeeding (vs. other feeding aHR: 0.81, 95%CI: 0.79-0.84) were associated with lower likelihood of vaccine initiation. Among unvaccinated individuals who gave birth in 2021, COVID-19 vaccine initiation after pregnancy reached 65% by June 30, 2022, suggesting persistent issues with vaccine hesitancy and/or access to vaccination in this population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".