Covid-19 vaccination intentions among Canadian parents of 9-12 year old children: results from the All Our Families longitudinal cohort
Bibliographic record
Abstract
Abstract Background Acceptance of a COVID-19 vaccine is critical to achieving high levels of immunization. The objective of this study is to understand factors associated with COVID-19 vaccine intentions among parents and explore reasons underlying decision making. Methods Participants from a longitudinal cohort were invited to participate in a COVID-19 impact survey in May-June 2020 (n=1321). Parents were asked about the impact of the pandemic and their intention to vaccinate their child against COVID-19 should a vaccine be approved. Past infant vaccination status was validated against public health records. Multinomial regression models were run to estimate associations between demographic factors, past vaccination status, and vaccine intention. Qualitative responses regarding factors impacting decision making were analyzed thematically. Results Sixty percent of parents (n=798) intended to vaccinate their children, but 9% (n=113) said they did not intend to vaccinate and 31% (n=410) were unsure. Lower education and income were inversely associated with intention to vaccinate. Incomplete vaccination history was associated with intention not to vaccinate but not uncertainty. Qualitative responses revealed concerns over vaccine safety and efficacy, long term effects and a rushed vaccination process. Interpretation Almost a third of parents remain unsure about vaccinating their children against COVID-19, even within a group with historically high uptake of infant vaccines. Given the many uncertainties about future COVID-19 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".