Evidence brief on facilitators, barriers and hesitancy of COVID-19 booster doses in Canada
Bibliographic record
Abstract
Background: Understanding the facilitators, barriers and hesitancy to accepting COVID-19 booster doses is important for encouraging recommended vaccination. This evidence brief summarizes literature on the intention to accept or reject COVID-19 vaccine booster doses and the factors associated with intention/uptake among individuals in Canada. Methods: A database of COVID-19 literature established at the Public Health Agency of Canada was searched for articles referencing vaccination and knowledge, attitudes and behaviours towards COVID-19 boosters. A grey literature search of Canadian governmental and academic institutions was also conducted. Primary research conducted in Canada (n=21) and relevant systematic reviews of the global literature (n=8) were included in this evidence brief. Results: Intentions to get a booster dose in the general population have decreased between 2021-2023, with intentions varying across subpopulations. In Canada and within the global systematic reviews, facilitators, barriers and hesitancy were similar. Older age was the most common factor positively associated with intention/uptake of a booster, and the most common motivators were government/healthcare provider recommendations and helping to protect others. The main reasons for hesitancy were concerns about vaccine side effects and a lack of belief in the vaccine's efficacy. Conclusion: Intentions to get a booster dose have decreased in Canada. Understanding the reasons for vaccine hesitancy and motivators for obtaining a booster can help guide future public health COVID-19 booster vaccination programs.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".