Factors associated with childhood non-vaccination against COVID-19 in Canada: A national survey analysis
Bibliographic record
Abstract
Background: COVID-19 vaccination efforts are critical in mitigating the impact of the virus, but despite proven safety and efficacy, vaccination rates among children in Canada are lower than in adults, prompting a need to explore determinants of childhood COVID-19 non-vaccination to improve uptake. Method: This study analyzed data from the Canadian COVID-19 Immunization Coverage Survey 2022. Using multivariable logistic regression, it examined the association between COVID-19 non-vaccination among children aged 5-17 and factors such as parental sociodemographic characteristics, vaccine knowledge, attitudes, and beliefs (KAB), and vaccination history. Results: The analysis revealed that negative KAB towards vaccines, reflected in higher KAB composite scores, significantly increased the likelihood of non-vaccination. Additionally, factors such as lower household incomes, rural residence, employment in sectors not at risk for vaccine-preventable diseases, and younger parental age were associated with higher non-vaccination. The study also highlighted ethnic disparities in vaccination odds and found that children with incomplete routine vaccinations or inconsistent flu vaccination histories were more likely to be unvaccinated against COVID-19. Surprisingly, children of parents who consistently received flu vaccinations were more likely to be unvaccinated against COVID-19. Furthermore, parental education levels showed a complex relationship with children's COVID-19 vaccination status, indicating nuanced influences on vaccination decisions. Conclusion: The findings offer vital insights into the factors influencing COVID-19 vaccination uptake among children in Canada, suggesting avenues for targeted strategies to improve vaccine coverage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.001 | 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 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".