Predicting COVID-19 vaccination intentions: the roles of threat appraisal, coping appraisal, subjective norms, and negative affect
Bibliographic record
Abstract
BACKGROUND: As a new disease, communities possess little natural immunity to COVID-19 and vaccines are considered critical to preventing and reducing the incidence of severe illness. This study, inspired by Protection Motivation Theory (PMT), examines the relationship between citizens' threat appraisal, coping appraisal, subjective norms, negative affect, and their COVID-19 vaccination intentions. METHODS: A sample of 340 citizens from two main cities in Mainland China, Xi'an and Wuxi, was used for data analysis. Structural Equation Modeling (SEM) was employed with latent and observed variables to test hypotheses. Data were analyzed using AMOS 24.0. RESULTS: Several findings extend current understanding. Firstly, our proposed model explains 73% of the variance in vaccination intentions. Secondly, perceived severity only indirectly shapes COVID-19 vaccination intentions through negative affect. Thirdly, negative affect and response costs are negatively related to COVID-19 vaccination intentions. Finally, Perceived probability, subjective norms, response efficacy and self-efficacy are positively related to COVID-19 vaccination intentions; among them, self-efficacy contributes the most, followed by response efficacy and subjective norms, and lastly perceived probability. CONCLUSION: Theoretically, this study increases current understanding about subjective norms and affective responses. We provoke a certain amount of thought about the role of affect response in relation to threat appraisal and vaccination intentions. Specifically, governments must be vigilant that citizens' negative affect, such as fear, may cause vaccine hesitation.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".