Mapping COVID-19 vaccine acceptance and uptake amongst Chinese residents: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objective Controlling the COVID-19 pandemic depends on the widespread acceptance of vaccination. Vaccine hesitancy is a growing area of concern in China. The aim of the study is to map the overall acceptance and uptake rates of COVID-19 vaccines across different groups. Methods Five peer-reviewed databases bases were searched (PubMed, EMBASE, Web of Science, EBSCO, and Scopus). Studies that conducted cross-sectional surveys in China to understand the acceptance/willingness to receive COVID-19 vaccines were included. Results Among 2420 identified studies, 47 studies with 327,046 participants were eligible for data extraction. Males had a higher uptake of COVID-19 vaccines (OR=1.17; 95% CI:1.08 - 1.27) along with Chinese residents with ≥ 5000 RMB monthly income (OR=1.08; 95% CI:1.02 - 1.14). Conclusion COVID-19 vaccination uptake rates in China need to be improved. To inform public health decisions, continuous vaccination uptake monitoring is required.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".