COVID-19 vaccination intention during early vaccine rollout in Canada: a nationwide online survey
Bibliographic record
Abstract
BACKGROUND: Understanding vaccination intention during early vaccination rollout in Canada can help the government's efforts in vaccination education and outreach. METHOD: Panel members age 18 and over from the nationally representative Angus Reid Forum were invited to complete an online survey about their experience with COVID-19, including their intention to get vaccinated. Respondents were asked "When a vaccine against the coronavirus becomes available to you, will you get vaccinated or not?" Having no intention to vaccinate was defined as choosing "No - I will not get a coronavirus vaccination" as a response. Odds ratios and predicted probabilities are reported for no vaccine intentionality in demographic groups. FINDINGS: 14,621 panel members completed the survey. Having no intention to vaccinate against COVID-19 is relatively low overall (9%) with substantial variation among demographic groups. Being a resident of Alberta (predicted probability = 15%; OR 0.58 [95%CI 0.14-2.24]), aged 40-59 (predicted probability = 12%; OR 0.87 [0.78-0.97]), identifying as a visible minority (predicted probability = 15%; OR 0.56 [0.37-0.84]), having some college level education or lower (predicted probability = 14%) and living in households of at least five members (predicted probability = 13%; OR 0.82 [0.76-0.88]) are related to lower vaccination intention. INTERPRETATION: The study identifies population groups with greater and lesser intention to vaccinate in Canada. As the Canadian COVID-19 vaccination effort continues, policymakers may use this information to focus outreach, education, and other efforts on the latter groups, which also have had higher risks for contracting and dying from COVID-19. FUNDING: Pfizer Global Medical, Unity Health Foundation, Canadian COVID-19 Immunity Task Force.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".