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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".