Co-Administration of Influenza and COVID-19 Vaccines: A Cross-Sectional Survey of Canadian Adults’ Knowledge, Attitudes, and Beliefs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it