COVID-19’s Impact on Willingness to Be Vaccinated against Influenza and COVID-19 during the 2020/2021 Season: Results from an Online Survey of Canadian Adults 50 Years and Older
Bibliographic record
Abstract
There is considerable overlap in age-related risk factors for influenza and COVID-19. We explored the impact of the pandemic on anticipated influenza and COVID-19 vaccination behaviour in the 2020/2021 season. In May 2020, we conducted online surveys of Canadian adults 50 years and older via a market research panel platform, as part of a series of annual surveys to understand experiences with influenza. Given the current pandemic, respondents were also asked about COVID-19's impact on their vaccination decision-making for the 2020/2021 season. Of 1001 respondents aged 50-64 years, 470 (47.0%) originally intended on receiving the influenza vaccine and still planned to do so, while 200 (20%) respondents who had planned to abstain now reported willingness to receive the vaccine due to COVID-19. In the 65+ age group, 2525 (72.1%) reported that they had planned to be vaccinated and that COVID-19 had not changed their mind, while 285 individuals (8.1%) reported that they had initially planned to forgo the vaccine but now intended to receive it. Reasons for this change included COVID-19's demonstration of the devastating potential of viruses; and to protect against influenza, and decrease vulnerability to COVID-19. If the COVID-19 vaccine was available, 69.1% of 50-64 year olds and 79.5% of those 65 years and older reported they would opt to receive it. The COVID-19 pandemic has been a sobering demonstration of the dangers of infectious disease, and the value of vaccines, with implications for influenza and COVID-19 immunization programs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".