Factors affecting hesitancy toward COVID-19 vaccine booster doses in Canada: a cross-national survey
Bibliographic record
Abstract
OBJECTIVE: COVID-19 transmission, emergence of variants of concern, and weakened immunity have led to recommended vaccine booster doses for COVID-19. Vaccine hesitancy challenges broad immunization coverage. We deployed a cross-national survey to investigate knowledge, beliefs, and behaviours toward continued COVID-19 vaccination. METHODS: We administered a national, cross-sectional online survey among adults in Canada between March 16 and March 26, 2022. We utilized descriptive statistics to summarize our sample, and tested for demographic differences, perceptions of vaccine effectiveness, recommended doses, and trust in decisions, using the Rao-Scott correction for weighted chi-squared tests. Multivariable logistic regression was adjusted for relevant covariates to identify sociodemographic factors and beliefs associated with vaccine hesitancy. RESULTS: We collected 2202 completed questionnaires. Lower education status (high school: odds ratio (OR) 1.90, 95% confidence interval (CI) 1.29, 2.81) and having children (OR 1.89, CI 1.39, 2.57) were associated with increased odds of experiencing hesitancy toward a booster dose, while higher income ($100,000-$149,999: OR 0.60, CI 0.39, 0.91; $150,000 or more: OR 0.49, CI 0.29, 0.82) was associated with decreased odds. Disbelief in vaccine effectiveness (against infection: OR 3.69, CI 1.98, 6.90; serious illness: OR 3.15, CI 1.69, 5.86), disagreeing with government decision-making (somewhat disagree: OR 2.70, CI 1.38, 5.29; strongly disagree: OR 4.62, CI 2.20, 9.7), and beliefs in over-vaccinating (OR 2.07, CI 1.53, 2.80) were found associated with booster dose hesitancy. CONCLUSION: COVID-19 vaccine hesitancy may develop or increase regarding subsequent vaccines. Our findings indicate factors to consider when targeting vaccine-hesitant populations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".