Influenza vaccine preference and uptake among older people in nine countries
Bibliographic record
Abstract
AIM: This paper is a report of a study delineating factors that influence older people's preferences and uptake of the influenza vaccine in nine countries. BACKGROUND: Vaccination uptake for the aging population in many countries still remains below the World Health Organization recommended rate. Older people who perceive higher susceptibility to and severity of influenza, and more benefits from vaccination and action cues prompting vaccination, tend to accept the vaccine, but those with more perceived barriers to vaccination are less likely to accept it. METHOD: A total of 208 older people from China, Indonesia, Turkey, Korea, Greece, Canada, the United Kingdom, Brazil and Nigeria were recruited to 14 vaccinated and 12 unvaccinated focus groups. They shared their experiences of influenza, and influenza vaccination, and promotion of influenza vaccination in focus groups. The data were collected in 2007. FINDINGS: We identified five themes and generated a hypothetical framework for in-depth understanding of vaccination behaviour among older people. Participants' vaccine preferences were determined by their behavioural beliefs in vaccination, which were based on their probability calculation of susceptibility to and severity of influenza and vaccine effectiveness, and their utility calculation of vaccine, healthcare and social costs. Action cues prompting vaccination and vaccine access further affected the vaccine uptake of participants with vaccine preferences. Vaccination coverage was likely to be higher in the countries where normative beliefs in favour of vaccination had formed. CONCLUSION: The hypothetical framework can be used to guide healthcare providers in developing strategies to foster normative beliefs of older people in vaccination, provide effective action cues and promote vaccine access.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".