Government Communication, Perceptions of COVID-19, and Vaccination Intention: A Multi-Group Comparison in China
Bibliographic record
Abstract
Government communication has been playing an important role in mass vaccination to conduct the largest vaccination campaign of the world for COVID-19 and to counter vaccine hesitancy. This study employs the health belief model to examine the association between government communication and the COVID-19 vaccination intention. A survey of Chinese adults (N = 557) was conducted in March 2021, and partial least squares structural equation modeling was employed to estimate the multi-construct relationships. The findings indicate that government communication has both direct positive association with vaccination intention and indirect association with vaccination intention through the mediation of perceived severity, benefits, and barriers. Multi-group comparisons suggest that individuals from private sectors are more easily mobilized to receive COVID-19 vaccination by government communication than those from public sectors. Similarly, the correlation between government communication and the vaccination intention of individuals with a good health status was stronger than that of those with a poor health status. The theoretical and practical implications of these findings are further discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".