Trust in the scientific research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset

Abstract

In the present study, I explored the relationship between people's trust in different agents related to prevention of spread of COVID-19 and their compliance with pharmaceutical and non-pharmaceutical preventive measures. The COVIDiSTRESSII Global Survey dataset, which was collected from international samples, was analysed to examine the aforementioned relationship across different countries. For data-driven exploration, network analysis and Bayesian generalized linear model (GLM) analysis were performed. The result from network analysis demonstrated that trust in the scientific research community was most central in the network of trust and compliance. In addition, the outcome from Bayesian GLM analysis indicated that the same factor, trust in the scientific research community, was most fundamental in predicting participants' intent to comply with both pharmaceutical and non-pharmaceutical preventive measures. I briefly discussed the implications of the findings, the importance of trust in the scientific research community in explaining people's compliance with measure to prevent spread of COVID-19.

Validity testing of the conspiratorial thinking and anti-expert sentiment scales during the COVID-19 pandemic across 24 languages from a large-scale global dataset

“…Specifically, doubters and deniers of COVID-19 risk tended to believe conspiracy theories related to the pandemic, expressed anti-elitist sentiments and reported low compliance with measures to reduce the spread of the virus [12]. Low trust in institutions, including the scientific community, is also linked to vaccine hesitancy as well as compliance with preventive measures in general [15][16][17]. Finally, conspiracy theories and negative attitudes towards experts have other detrimental effects such as increasing uncertainty and discrimination against marginalised groups [9].…”

“…The presence of scalar invariance, which assumes the same factor loadings and intercepts across groups, is essential to assure the quality of cross-national research using the scales [29]. Measurement alignment was performed to address the potential issue of measurement non-invariance reported by the measurement invariance test as done in prior COVID-19-related international survey studies if needed [15]. The measurement alignment process was expected to address non-invariance so that researchers would be able to conduct cross-national comparison.…”