Prevalence and Motivators of Getting a COVID-19 Booster Vaccine in Canada: Results from the iCARE Study
Bibliographic record
Abstract
Studies have shown that the protection afforded by COVID-19 vaccines against hospitalization and death decreases slowly over time due to the emergence of new variants and waning immunity. Accordingly, booster doses remain critical to minimizing the health impacts of the pandemic. This study examined the prevalence rate, sociodemographic determinants, and motivators of getting a COVID-19 booster vaccine within the Canadian population. We recruited a representative sample of 3001 Canadians aged 18+ years as part of the iCARE study using an online polling form between 20 January and 2 February 2022. Participants self-reported their booster status and were dichotomized into two groups: those who did vs. did not receive at least one booster dose. A total of 67% of participants received a booster dose. Chi-square analyses revealed that older age (p < 0.001) and having a chronic disease diagnosis (p < 0.001) were associated with being more likely to get a booster. Boosted individuals reported motivators tied to efficacy and altruism, whereas non-boosted individuals reported motivators tied to efficacy and safety. Results suggest that messaging will require careful tailoring to address the identified behavioral motivators among non-boosted individuals who emphasize safety and efficacy of additional vaccine doses.
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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 | 0.001 |
| 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 | 0.001 | 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".