Understanding COVID-19 booster information seeking in a collectivist context: the roles of social expectations, trust in experts, and uncertainty
Bibliographic record
Abstract
Background: Effective public health communication relies on understanding how individuals seek information during health emergencies. While previous work has investigated vaccine hesitancy and acceptance, little is known regarding the psychological and social motivations behind COVID-19 booster information-seeking in collectivist societies. Objective: This study extends the Risk Information Seeking and Processing (RISP) model to explore the impact of trust in experts, risk uncertainty, and subjective informational norms on the public's intention to seek information regarding COVID-19 booster shots in China. Methods: A national survey of 616 adults in China was undertaken. Structural equation modeling (SEM) examined hypothesized relationships among perceived advantages and disadvantages, affective responses, lack of information, trust in the expertise of others, uncertainty, perceived control over behavior, and social norms. Results: Informational subjective norms were the most significant predictor of intentions to seek information, indicating the influence of collectivist expectations on individual action. Trust in experts was positively associated with perceived risks and inversely related to perceived benefits-and decreased perceived information insufficiency. Uncertainty increased individuals' perceived ability to gather and interpret information, but affective responses had limited direct effects. Conclusion: Findings highlight the need to incorporate social norms, trust relationships, and uncertainty management into public health education campaigns to support vaccine promotion. This study offers empirical evidence for designing culturally adaptive communication interventions that promote booster uptake among collectivist societies and comparable environments.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".