Change in intention and hesitancy regarding COVID-19 vaccines in a cohort of adults in Quebec during the pandemic
Bibliographic record
Abstract
Although COVID-19 vaccine uptake was high in Quebec for the primary series, vaccine acceptance decreased for the subsequent booster doses. This article presents the evolution of vaccine intention, self-reported vaccination behaviors, and vaccine hesitancy over 2 years. A series of cross-sectional surveys were conducted in Quebec between March 2020 and March 2023, with a representative sample of 3,330 adults recruited biweekly via a Web panel. Panelists could have answered multiple times over the course of the project. A cohort of respondents was created to assess how attitudes and behaviors about COVID-19 vaccines evolved. Descriptive statistics and multivariate logistic regressions were performed. Among the 1,914 individuals with no or low intention of getting vaccinated in Fall 2021 (Period 1), 1,476 (77%) reported having received at least two doses in the Winter 2023 (Period 2). Not believing in conspiracy theory (OR = 2.08, 95% CI: 1.65-2.64), being worried about catching COVID-19 (OR = 2.12, 95% CI: 1.65-2.73) and not living in a rural area (ORs of other areas are 2.27, 95% CI: 1.58-3.28; 1.66, 95% CI: 1.23-2.26; 1.82 95% CI: 1.23-2.73) were the three main factors associated with being vaccinated at Period 2. Among the 11,117 individuals not hesitant at Period 1, 1,335 (12%) became hesitant at Period 2. The three main factors significantly associated with becoming vaccine hesitant were the adherence to conspiracy theories (OR = 2.28, 95% CI: 1.95-2.66), being a female (OR = 1.67, 95% CI: 1.48-1.90) and being younger than 65 years old (the ORs for 18-34, 35-49, and 50-64 compared with 65 and over are 2.82, 95% CI: 2.32-3.44; 2.39, 95% CI: 2.00-2.86 and 1.82, 95% CI: 1.55-2.15 respectively). As the pandemic is over, monitoring the evolution of vaccine attitudes and uptake will be important.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".