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 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".