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Volatility and heterogeneity of vaccine sentiments means continuous monitoring is needed when measuring message effectiveness (preprint)
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2590646.v1
ABSTRACT
Background The success of vaccination programs often depends on the effectiveness of the vaccine messages, particularly during emergencies such as the COVID-19 pandemic. The current suboptimal uptake of COVID-19 vaccines across many parts of the world highlights the tremendous challenges in overcoming vaccine hesitancy and refusal even in the context of a world-devastating pandemic. Methods We conducted a randomized controlled trial in Hong Kong to evaluate the impact of seven vaccine messages on COVID-19 vaccine uptake (with the government slogan as the control). The participants included 127,000 individuals who googled COVID-19-related information during July-October 2021. Results The impact of vaccine messages on uptake varied substantially over time and among different groups of users. For example, the message that emphasized the indirect protection of vaccination on family members (i) increased overall uptake by 30% (6-59%) in July but had no effect afterwards for English language users; and (ii) had no effect on overall uptake for Chinese language users throughout the study. Such volatility and heterogeneity in message effectiveness highlight the limitations of one-size-fits-all and static vaccine communication. Conclusions Epidemic nowcasting should include real-time monitoring of vaccine hesitancy and message effectiveness, in order to adapt messaging appropriately. This dynamic dimension of surveillance has so far been underinvested. Trial registration The study was registered at ClinicalTrials.gov (NCT05499299).
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Full text: Available Collection: Preprints Database: PREPRINT-RESEARCHSQUARE Main subject: COVID-19 Language: English Year: 2023 Document Type: Preprint

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Full text: Available Collection: Preprints Database: PREPRINT-RESEARCHSQUARE Main subject: COVID-19 Language: English Year: 2023 Document Type: Preprint