Prevalence and factors related to COVID‐19 vaccine hesitancy and unwillingness in Canada: A systematic review and meta‐analysis
Bibliographic record
Abstract
This systematic review and meta-analysis examined the prevalence and factors associated with vaccine hesitancy and vaccine unwillingness in Canada. Eleven databases were searched in March 2022. The pooled prevalence of coronavirus disease 2019 (COVID-19) vaccine hesitancy and unwillingness was estimated. Subgroup analyses and meta-regressions were performed. Out of 667 studies screened, 86 full-text articles were reviewed, and 30 were included in the systematic review. Twenty-four articles were included in the meta-analysis; 12 for the pooled prevalence of vaccine hesitancy (42.3% [95% CI, 33.7%-51.0%]) and 12 for vaccine unwillingness (20.1% [95% CI, 15.2%-24.9%]). Vaccine hesitancy was higher in females (18.3% [95% CI, 12.4%-24.2%]) than males (13.9% [95% CI, 9.0%-18.8%]), and in rural (16.3% [95% CI, 12.9%-19.7%]) versus urban areas (14.1% [95%CI, 9.9%-18.3%]). Vaccine unwillingness was higher in females (19.9% [95% CI, 11.0%-24.8%]) compared with males (13.6% [95% CI, 8.0%-19.2%]), non-White individuals (21.7% [95% CI, 16.2%-27.3%]) than White individuals (14.8% [95% CI, 11.0%-18.5%]), and secondary or less (24.2% [95% CI, 18.8%-29.6%]) versus postsecondary education (15.9% [95% CI, 11.6%-20.2%]). Factors related to racial disparities, gender, education level, and age are discussed.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".