Impact of a Free Influenza Vaccination Policy on Older Adults in Zhejiang, China: Cross-Sectional Survey of Vaccination Willingness and Determinants
Bibliographic record
Abstract
Background: In 2024, Zhejiang Province introduced a new policy offering free influenza vaccinations to individuals aged 60 years and older. However, the vaccination willingness among the newly eligible 60-69 years age group remains ambiguous in comparison to those aged 70 years and older. Objective: This study aimed to evaluate the willingness of individuals aged ≥60 years in Zhejiang Province, China, to receive free influenza vaccines under a newly implemented policy. It further explored their sources of influenza-related health information and identified key determinants of vaccination hesitancy across age subgroups. Methods: A cross-sectional survey was conducted using multistage convenience sampling via on-site questionnaires. Structured questionnaires were administered to 7162 eligible participants aged ≥60 years from March to May 2024. Valid responses (n=7103; response rate: 99.18%) were analyzed via logistic regression and Kruskal-Wallis tests. Results: Overall vaccination willingness was 73.15% (5196/7103), with 11.71% (832/7103) refusal and 15.14% (1075/7103) hesitancy. Key predictors of hesitancy included male gender (odds ratio [OR] 1.27, 95% CI 1.05-1.54), ages 60-69 years (OR 1.46, 95% CI 1.06-2.02), corporate employment (OR 0.75, 95% CI 0.58-0.98), and absence of chronic diseases (OR 2.06, 95% CI 1.44-2.96). The 60-69 year age group demonstrated lower awareness of the free policy (61.9% vs 73.72% in the ≥70 years group; H=61.25, P<.001) but higher engagement with social media (WeChat [Tencent Holdings Limited]: H=345.44; TikTok [ByteDance Ltd]: H=294.66; P<.001) for health information. Conclusions: Despite high willingness, knowledge gaps persist, particularly among adults aged 60-69 years. Targeted dissemination of policy information via social media platforms (eg, WeChat and TikTok) and community-driven campaigns is recommended to enhance vaccination uptake. This approach may serve as a model for regions implementing similar policies. Future studies should track actual vaccination uptake postpolicy and explore artificial intelligence-driven social media interventions to boost engagement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".