Vaccine intention and hesitancy among Australian women who are currently pregnant or have recently given birth: the Birth in the Time of COVID-19 (BITTOC) national online survey
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of COVID-19 vaccination, and factors associated with vaccination intention and hesitancy in pregnant and postnatal women in Australia. DESIGN AND SETTING: A national online survey was conducted over 6 months between 31 August 2021 and 1 March 2022 and responses to vaccination status were categorised as: 'vaccinated', 'vaccine intended' and 'vaccine hesitant'. The data were weighted to reflect the proportion of women of reproductive age. Potential confounding variables were examined using multinomial logistic regression analyses, and all comparisons were made against vaccinated pregnant and postnatal women. PARTICIPANTS: 2140 women responded to the survey (838 pregnant; 1302 recently post partum). RESULTS: Amongst pregnant women, 586 (69.9%) were vaccinated, 166 (19.8%) indicated intention and 86 (10.3%) were hesitant. In postnatal women, this was 1060 (81.4%), 143 (11.0%) and 99 (7.6%), respectively. Only 52 (6.2%) of pregnant women stated never wanting a COVID-19 vaccine. Vaccine hesitancy increased over time, and for pregnant women was associated with: living in a state other than New South Wales (NSW) (Adjusted Relative Risk (ARR) 2.77, 95%CI: 1.68-4.56 for vaccine intention and ARR=3.31, 95%CI: 1.52-7.20 for vaccine hesitancy), younger age <30 years, not having a university education, income <80K AUD, gestation <28 weeks, having no pregnancy risk factors, and being less satisfied with life (ARR=2.20, 95%CI: 1.04-4.65 for vaccine intention and ARR=2.53, 95%CI: 1.02-6.25 for vaccine hesitancy) . For postnatal women: living in a state other than NSW or Victoria, income <80K AUD and having private obstetric care (ARR=2.06, 95%CI: 1.23-3.46) were significantly associated with vaccine hesitancy. CONCLUSIONS: Around 1 in 10 pregnant women and just over 1 in 13 postnatal women reported vaccine hesitancy in this Australian survey, and hesitancy was higher in the latter 3-month period. Tailored messages to younger mothers and those from lower-middle socioeconomic groups, alongside advice from midwives and obstetricians, could help to reduce hesitancy among pregnant and postnatal women. Financial incentives may help to facilitate COVID-19 vaccine uptake. A real-time surveillance system and additional pregnancy fields added to the Australian immunisation register would support the safety monitoring of multiple vaccines in pregnancy and may build confidence.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".