COVID-19 vaccination attitudes and intention among Quebecers during the first and second waves of the pandemic: findings from repeated cross-sectional surveys
Bibliographic record
Abstract
The availability of safe and effective vaccines is a major breakthrough in controlling the COVID-19 pandemic. However, the success of the COVID-19 vaccination campaign relies on high uptake by the public. We monitored Quebecers' attitudes and intention toward COVID-19 vaccination during the first and second waves of the pandemic. Since March 2020, online surveys are conducted every week in Quebec (Canada) to assess Quebecers' adherence to recommended public health measures (3,300 respondents are surveyed every week through an online panel; respondents are not invited to answer the survey for 21 days after responding). Ten items measured respondents' attitudes and intentions regarding COVID-19 vaccination. Logistic regression models were used to identify determinants of intention to be vaccinated against COVID-19. Intention to be vaccinated against COVID-19 ranged from 76%-66% between the first and second waves. The proportion of undecided adults remained stable (12%). Being a man; being 60 years of age and over; having a university education level; having or living with someone with chronic medical conditions and increased risk perceptions of COVID-19 were the strongest predictors of COVID-19 vaccine acceptance in multivariate analysis. During data collection, COVID-19 vaccine supply was very limited. It was reassuring to note that intention to be vaccinated is the highest among older age groups that are prioritized to be vaccinated first. As more doses and vaccines will be available it will be important to enhance vaccine acceptance and uptake, especially among adults younger than 60 years of age and Quebecers with lower risk perceptions of COVID-19.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".