Co-Administration of Influenza and COVID-19 Vaccines: A Cross-Sectional Survey of Canadian Adults’ Knowledge, Attitudes, and Beliefs
Bibliographic record
Abstract
Vaccination rates against both influenza and COVID-19 fall short of targets, especially among persons at risk of influenza complications. To gain insights into strategies to boost influenza vaccine coverage, we surveyed 3000 Canadian residents aged ≥ 18 years and examined their knowledge and receipt of co-administered influenza and COVID-19 vaccines. During the 2022-2023 influenza season, 70% of respondents reported being aware the influenza and COVID-19 vaccines could be co-administered, but only 26.2% (95% CI, 23.6% to 28.8%) of respondents received them together. The most common reason for not getting the vaccines together was receipt of the COVID-19 vaccine before the annual influenza vaccine was available (reported by 34.5% [31.2% to 37.7%]). Lack of interest in co-administration was reported by 22.6% (20.8% to 24.3%); of this group, 20.8% (17.1% to 24.5%) reported seeing no benefit in receiving the two vaccines together and 17.2% (13.5% to 20.9%) were concerned about compounded adverse effects from the two vaccines. These results support the willingness of most Canadians to receive COVID-19 and influenza vaccines at the same time. Co-administration is a viable strategy to improve uptake of influenza vaccines, especially if health professionals proactively offer education and co-administration of influenza and COVID-19 (or other) vaccines as appropriate to clinical need.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".