Older adults' perceptions of government handling of COVID-19: Predictors of protective behaviors from lockdown to post-lockdown - SIEW, Savannah Kiah Hui (Institutional Knowledge at Singapore Management University, 2022)
Background: Distrust, and more broadly, public perception of government's handling of a crisis, has been a widely studied topic within health crisis research and suggests that these perceptions are significantly associated with the behavior of its citizens. Purpose: To understand which aspects of the public's perception of government handling of the COVID-19 pandemic predicted engagement of protective behaviors among older adults, who are the most vulnerable to COVID-19.Methods: Participants were recruited from an ongoing biopsychosocial study on aging amongst community-dwelling older adults. There were two rounds of data collection, during the national lockdown and post-lockdown. The average length of follow-up was 5.88 months. N = 421 completed the first round of data collection and N = 318 subsequently completed the second round of questionnaires. Results: During the lockdown, perceptions that pandemic-related measures in place were sufficient, effective, timely, provided a sense of safety, important information was easily accessible, and government handling of the pandemic could be trusted, were found to significantly predict engagement in protective behaviors. During post-lockdown, only perceptions that measures in place were sufficient, provided a sense of safety, and important information was easily accessible, remained significant predictors. The perception that COVID-19 measures were clear and easy to understand now became a significant predictor. Conclusions: Public perceptions of government handling of the pandemic predicted engagement in protective behaviors but were less important during post-lockdown. To effectively engage older adults in protective behavior, our findings suggest for pandemic-related information to be accessible, introducing timely safety measures, and having easy-to-understand instructions for nuanced measures.