SARS-CoV-2 vaccination intentions among mothers of children aged 9 to 12 years: a survey of the All Our Families cohort
Bibliographic record
Abstract
Background: Acceptance of a vaccine against SARS-CoV-2 is critical to achieving high levels of immunization. The objectives of this study were to understand mothers’ SARS-CoV-2 vaccine intentions to explore reasons for and against SARS-CoV-2 vaccination. Methods: Participants from the All Our Families pregnancy longitudinal cohort whose children had reached ages 9–12 years were invited in May–June 2020 to complete a survey on the impact of COVID-19. The survey covered topics about the impact of the pandemic and included 2 specific questions on mothers’ intentions to vaccinate their child against SARS-CoV-2. Current responses were linked to previously collected data, including infant vaccine uptake. Multinomial regression models were run to estimate associations between demographic factors, past vaccination status and vaccination intention. Qualitative responses regarding factors affecting decision-making were analyzed thematically. Results: The response rate was 53.8% (1321/2455). A minority of children of participants had partial or no vaccinations at age 2 (n = 200, 15.1%). A total of 60.4% of mothers (n = 798) intended to vaccinate their children with the SARS-CoV-2 vaccine, 8.6% (n = 113) did not intend to vaccinate and 31.0% (n = 410) were unsure. Participants with lower education, lower income and incomplete vaccination history were less likely to intend to vaccinate their children. Thematic analysis of qualitative responses showed 10 themes, including safety and efficacy, long-term effects and a rushed process. Interpretation: Within a cohort with historically high infant vaccination, a third of mothers remained unsure about vaccinating their children against SARS-CoV-2. Given the many uncertainties about future SARS-CoV-2 vaccines, clear communication regarding safety will be critical to ensuring vaccine uptake.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".