Search

Background: With efforts increasing worldwide to understand and treat paranoia, there is a pressing need for cross-culturally valid assessments of paranoid beliefs. The recently developed Revised Green et al., Paranoid Thoughts Scale (R-GPTS) constitutes an easy to administer self-report assessment of mild ideas of reference and more severe persecutory thoughts. Moreover, it comes with clinical cut-offs for increased usability in research and clinical practice. With multiple translations of the R-GPTS already available and in use, a formal test of its measurement invariance is now needed., Methods: Using data from a multinational cross-sectional online survey in the UK, USA, Australia, Germany, and Hong Kong ( N = 2510), we performed confirmatory factory analyses on the R-GPTS and tested for measurement invariance across sites., Results: We found sufficient fit for the two-factor structure (ideas of reference, persecutory thoughts) of the R-GPTS across cultures. Measurement invariance was found for the persecutory thoughts subscale, indicating that it does measure the same construct across the tested samples in the same way. For ideas of reference, we found no scalar invariance, which was traced back to (mostly higher) item intercepts in the Hong Kong sample., Conclusion: We found sufficient invariance for the persecutory thoughts scale, which is of substantial practical importance, as it is used for the screening of clinical paranoia. A direct comparison of the ideas of reference sum-scores between cultures, however, may lead to an over-estimation of these milder forms of paranoia in some (non-western) cultures.

Understanding factors driving vaccine hesitancy is crucial to vaccination success. We surveyed adults (N = 2510) from February to March 2021 across five sites (Australia = 502, Germany = 516, Hong Kong = 445, UK = 512, USA = 535) using a cross-sectional design and stratified quota sampling for age, sex, and education. We assessed willingness to take a vaccine and a comprehensive set of putative predictors. Predictive power was analysed with a machine learning algorithm. Only 57.4% of the participants indicated that they would definitely or probably get vaccinated. A parsimonious machine learning model could identify vaccine hesitancy with high accuracy (i.e. 82% sensitivity and 79-82% specificity) using 12 variables only. The most relevant predictors were vaccination conspiracy beliefs, various paranoid concerns related to the pandemic, a general conspiracy mentality, COVID anxiety, high perceived risk of infection, low perceived social rank, lower age, lower income, and higher population density. Campaigns seeking to increase vaccine uptake need to take mistrust as the main driver of vaccine hesitancy into account., (© 2022. The Author(s).)