Determinants of influenza non-vaccination among Canadian children: insights from a nationwide survey
Bibliographic record
Abstract
Background To identify determinants influencing Canadian parents’ decision not to vaccinate their children aged 6 months to 17 years against seasonal influenza. Methods Data from the 2022 Childhood COVID-19 Immunization Coverage Survey, a national survey of approximately 10,500 Canadian parents/guardians and their children, was analyzed. The survey examined influenza vaccine coverage, parental perspectives on vaccines, reasons for hesitancy, and factors influencing immunization. Socio-demographic characteristics, including ethnicity, household income, working sector, educational attainment, and prevalence of chronic medical conditions among children were considered. Historical vaccine uptake and the impact of the COVID-19 pandemic on immunization decisions were also reviewed. Key determinants of non-vaccination in the 2021–2022 influenza season were analyzed using multivariable logistic regression, with a statistical significance level set at p -value <0·05. Results 70% of children aged 6 months to 17 years did not receive the seasonal influenza vaccine. Key predictors for non-vaccination included: residing in rural settings (aOR 1·35, 95% CI 1·13–1·60), parental education attainment of less than high school (aOR 2·48, 95% CI 1·24–4·97), and the absence of chronic medical conditions in children (aOR 1.60, 95% CI 1.34-1.91)· Other strong predictors included lower household income; deterrence due to the COVID-19 pandemic; and parental hesitancy stemming from concerns about the vaccine’s safety, effectiveness, and by beliefs that their child was not at risk of contracting the influenza or severe consequences from the infection. Conclusion This research underscores pivotal determinants of parental decisions not to vaccinate their children against seasonal influenza and sheds light on the impact of the COVID-19 pandemic. The results highlight the importance of addressing safety concerns and providing clear information to alleviate hesitancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".