Quantifying COVID-19 vaccination hesitancy during early vaccination rollout in Canada
Bibliographic record
Abstract
Abstract Background Understanding vaccination hesitancy during early vaccination rollout in Canada can help the government’s vaccination efforts in education and outreach, which may help eventually achieving herd immunity. This study uses an online survey to assess vaccination hesitancy in population subgroups in Canada. Method Panel members from the nationally representative Angus Reid Forum were randomly invited to complete an online survey on their experiencing with COVID-19 symptoms and testing, as well as intention to vaccination against COVID-19. Respondents were asked “when a vaccine against the coronavirus becomes available to you, will you get vaccinated or not?” Vaccination hesitancy was defined as choosing “No – I will not get a coronavirus vaccination” as a response. Results 14,621 panel members (46% male and 53% female) completed the survey. Although the respondents overrepresent age 60+ and higher levels of education, other demographics, the prevalences of smoking, obesity, diabetes and hypertension were comparable to the Canadian national census and health surveys. COVID-19 vaccination hesitancy is relatively low overall (9%). Being a resident of Alberta (predicted probability = 15%), aged 40-59 (OR = 0.87, 0.78 – 0.97, predicted probability = 12%), identifying as a visible minority (OR = 0.56, 0.37 – 0.84, predicted probability = 15%), having some college level education or lower (predicted probability = 14%), or living in households of at least 5 are related to greater vaccination hesitancy (OR = 0.82, 0.76 – 0.88, predicted probability = 13%). Conclusion Our study enhances the understanding of COVID-19 vaccination hesitancy and identifies key population groups with higher vaccination hesitancy. As the Canadian COVID-19 vaccination effort continues, policymakers may focus outreach, education, and other efforts on these groups, which also represent groups with higher risks for contracting and dying from COVID-19. Furthermore, Canada would need to vaccinate virtually the entire population to reach herd immunity due to its relatively low infection level, and a high vaccination hesitancy would be a major hurdle to achieving that.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".