RSV Knowledge, Attitudes, and Vaccination Intentions: Evidence From Germany, Canada, and the UK
Bibliographic record
Abstract
Background: Respiratory Syncytial Virus (RSV) can cause severe illness in individuals over 75 years of age. After the European Medicines Agency and the Canadian health authorities approved two vaccines in 2024, the question now shifts to assessing public knowledge of RSV.Aims: To explore knowledge and misconceptions about RSV and identify socio-demographic determinants associated with higher vs. lower knowledge. Method: We collected data from three samples including individuals over 50 years of age (Germany: n = 565; UK: n = 556; Canada: n= 372). RSV awareness and knowledge about RSV using a pre-tested RSV knowledge scale as well as RSV-specific 5C vaccination antecedents (confidence, constraints, collective responsibility, complacency, calculation) and RSV vaccination intention were assessed. Results: Awareness was very different in the samples. In the German and UK data sets, around 50% stated that they had never heard of RSV, in the Canadian sample only 15% reported being completely unaware. Knowledge scores were lowest in the German cohort (M = .444, 95%-CI [.428;.460]), while the UK (MUK = .542, 95%-CI [.528;.556]) and Canadian cohorts were equally informed (MCA = .544, 95%-CI [.530;.557]). Intention and Knowledge were significantly correlated in all samples (GER: r(565)=.13[.05;.21]; UK: r(559) =.13[.05;.21]; CA: r (372) =.11[.01;.21]). 5C vaccination antecedents mediated the relation between knowledge and intentions in the German and UK based sample, but not in the Canadian sample. Limitations: Highly educated participants are over-represented in UK and Canadian samples. Conclusion: Educational interventions on RSV and respective vaccines are needed, as awareness, particularly about its infectiousness in children and vaccine availability, is low in the target group.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".