Influenza and <scp>COVID</scp>‐19 vaccination in Canadian blood donors: A comparison across pre‐ and post‐pandemic periods
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Seasonal vaccinations reduce donor illness and appointment cancellations and ensure plasma products have antibodies to vaccine-directed strains. We aimed to describe donor influenza and COVID-19 vaccination history and compare this with the general population. MATERIALS AND METHODS: Two online donor surveys were carried out in 2021 and 2024. Donors were asked about demographics, influenza (2019/2020, 2020/2021 and 2023/2024 seasons) and COVID-19 (ever and 2023/2024 season) vaccination and reasons for vaccination choices. General population vaccination statistics were extracted from public reports. Percentages of donors receiving vaccination were calculated with 95% confidence intervals. Multiple logistic regression models were fitted with demographics as independent variables. RESULTS: In survey 1, 4582 (30.4% response rate) donors completed a questionnaire; in survey 2, 6376 (21% response rate). More donors under age 65 received the influenza vaccine compared with the general population under age 65 (58% vs. 30% in 2019/2020, 63% vs. 28% in 2023/2024, p < 0.0001) and aged 65+ (81% vs. 70% in 2019/2020, 90% vs. 73% in 2023/2024, p < 0.0001). Fewer donors and the general population received the COVID-19 vaccine in 2023/2024 (under 65 45% vs. 39%; 65+ 76% vs. 67%, p < 0.0001). Most said they were vaccinated to prevent infection and protect others. CONCLUSION: Seasonal vaccination rates are higher in older donors, consistent with public health recommendations. Blood donors are more likely to be vaccinated against seasonal influenza than the general population, but post-pandemic uptake of the COVID-19 booster vaccine was low, more similar to the general population.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".