Use of mind genomics for public health and wellbeing: Lessons from COVID 19 pandemic
Description
Background: Machine Learning (ML) tools can analyze human mindset and forecast behavior patterns. ML can be used to comprehend the psychological processes and behavioral principles underlying public decision-making patterns. The aim of the study was to explore participant mindsets using ML, accordingly, build messages for each mindset to enhance compliance to a public health policy, specifically physical distancing during COVID19. Methods An online questionnaire was conducted, using systematically varied combinations of elements using the science of mind genomics. The questions focused on the perceived risk level related to COVID-19, strategies to enhance physical distancing compliance, the appropriate communicator of the policy and different physical distancing practices. Snowball sampling was used to recruit participants until sample saturation was achieved among residents of United Arab Emirates (UAE), aged 18-80 years. Results: A total of 117 completed the study. In the total panel, the strongest performing elements were those communicated by the government (p<0.01) and the clergy (p<0.05); with no differences between gender and age groups. Three mindset segments were identified: 1) followers of the general strategies of physical distancing, 2) those interested in novel ways of practicing physical distancing, and 3) fascinated onlookers of the pandemic. Conclusion: Our results revealed that COVID-19 health-related messages are best communicated by the government and clergy in UAE. These strategies may help in the implementation and adoption of other public health policies.