ReviewVaccines2024
Message Effectiveness of Fear Appeals in Vaccination Communication Campaigns: A Systematic Review.
Review in Vaccines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Childhood immunization barriers and interventions in low- and middle-income countries: A scoping review of access, delivery systems, and health literacy.Human vaccines & immunotherapeutics · 2026Article
- Vaccine Perceptions Outweigh Emotional Flow in Predicting HPV Vaccination Intentions Among Gen Z College Students.Vaccines · 2026Article
- The mediating role of attitudes to aging in the relationship between information overload, cyberchondria, and influenza vaccine hesitancy among Chinese older adults.Frontiers in public health · 2026Article
- A cluster randomised trial of digital messaging nudges to improve influenza vaccination uptake in China.NPJ digital medicine · 2025Article
- Strategies to correct vaccine misinformation on social media for pregnant women and the impact of vaccine skepticism.Scientific reports · 2025Article
- Examining the use of different message categories to communicate AMR: a content analysis of instagram posts.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This systematic review of 54 cross-disciplinary peer-reviewed causal empirical studies helps public health officials, researchers, and healthcare professionals better comprehend the effects of fear appeals in vaccine promotional campaigns on message processing, persuasion, vaccination attitudes, and vaccination intentions. This review documents inconsistent findings across studies, which it attempts to clarify by considering differences in research designs, sample populations, and outcomes measured. In general, we find that fear appeals increase risk perceptions, message involvement, and vaccination attitudes. However, fear appeals have less influence on vaccination intentions, especially among female and general adult populations or populations from the U.S. and other Western cultures. On the other hand, the effect of fear appeals on vaccination intentions is stronger among student populations and those from China (People's Republic of China and Hong Kong) and other non-Western cultures. Also, fear appeals are less persuasive when promoting COVID-19 vaccines and boosters than they are for other vaccines (e.g., HPV, influenza, MMR). Future research should compare fear appeal effectiveness in messages across vaccines or when combined with other executional elements, such as the endorser or type of evidence provided. Finally, future studies should explore other methodological approaches and measure underexplored message outcomes, such as vaccine uptake behavior, in more naturalistic settings.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.