Dynamic Trends and Underlying Factors of COVID-19 Vaccine Booster Hesitancy in Adults: Cross-Sectional Observational Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 vaccine hesitancy reduces vaccination rates, which is detrimental to building herd immunity and halting the spread of COVID-19 and its variations. Most researches have simply identified the reasons affecting COVID-19 vaccination reluctance without delving into its dynamics, which makes forecasting future trends difficult. OBJECTIVE: This study aimed to examine the current COVID-19 vaccine booster hesitancy rate in Chinese adults as well as the dynamics of vaccine hesitancy and its influencing factors. The results of this study will have practical implications for policy responses in mainland China, and effective COVID-19 booster vaccination in specific populations. METHODS: The web-based survey was completed by creating questionnaires and using a stratified random sampling method to collect information from adults (≥18 years old) among 2556 households in 4 geographical regions of China. We collected sociodemographic information, health status, awareness of COVID-19 and its vaccine, self-perceptions, trust in medical staff and vaccine developers, and so on. The odds ratios and 95% CI for the statistical associations were estimated using logistic regression models. RESULTS: Overall, 6659 participants (females: n=3540, 53.2%; males: n=3119, 46.8%) responded. In total, 533 (8%; 95% CI 7.4%-8.7%) participants presented a clear hesitancy in receiving the COVID-19 booster vaccination, while 736 (11.1%; 95% CI 10.3%-11.8%) expressed hesitancy in regular booster vaccination. A higher prevalence of vaccine hesitancy in both booster vaccination and regular booster vaccination was observed among participants with a history of allergies, experiencing chronic disease, lower levels of public health prevention measures or susceptibility or benefits or self-efficiency, higher levels of severity or barriers, and lower trust in both medical staff and vaccine developers (P<.05). The females and participants with higher education levels, higher levels of barriers, lower levels of susceptibility, and lower trust in vaccine developers preferred to have attitudinal changes from acceptance to hesitancy, while people with higher education levels, lower self-report health conditions, experiencing chronic disease, history of allergies, and lower trust in medical staff and developers were all positively associated with constant COVID-19 booster hesitancy. CONCLUSIONS: The prevalence of COVID-19 vaccine booster hesitancy is not high in mainland China. However, there is a slight increment in hesitancy on regular booster vaccination. Conducting targeted information guidance for people with higher education levels and chronic diseases, as well as improving accessibility to booster vaccination and increasing trust in medical staff and vaccine producers may be highly effective in reducing vaccine hesitancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".