Information overload regarding COVID-19: Adaptation and validation of the cancer information overload scale

Abstract

BACKGROUND: Access to excessive information from multiple sources relating to COVID-19 in a short span of time can have detrimental effects on individuals. AIM: The study aims to validate Corona Information Overload Scale (CoIOS) by adaptation of Cancer Information Overload scale (CIOS) on English speaking Indian citizens. MATERIALS AND METHODS: An online survey was carried out using Google Form on 300 individuals out of whom 183 responded. The CoIOS was to be filled up. It was an 8 item Likert type scale with responses ranging from "strongly agree" to "strongly disagree." RESULTS: Principal components analysis showed two components with an initial eigenvalue > unity (3.38 and 1.09), with 42.33% and 13.64% of variance, respectively, making a total of 55.97% variance. The composite reliability value was also found to be 0.789 and 0.815 for factors I and II, respectively, convergent validity and discriminant validity calculation also affirmed good construct reliability. CONCLUSION: CoIOS appears to be a valid and reliable scale for measuring health information overload in relation to COVID-19. However, it has a two factor component, namely "excessiveness of information" and "rejection of information."