Predicting COVID-19 Preventive Behaviors based on Protection Motivation Theory in Hormozgan, Iran.

Abstract

Abstract Background: The present research aimed to predict the preventive behaviors of COVID-19 in the light of the Protection Motivation Theory (PMT).Methods: The present cross-sectional research was conducted in 2 months (March 2020-April 2020) in Hormozgan Province of Iran. The research population comprised all residents above 15 years of age. They received an online questionnaire survey in two parts, demographic information along with PMT constructs. To analyze the data, independent t-test and one-way anova test, Multiple linear regression and the statistical technique of path analysis and structural equation modeling was used. All statistical calculations and hypothesis testing were performed using spss21 and Amos21 software and a significant level of hypothesis testing was considered to be 0.05Results: A total number of 2,032 subjects participated in this research, the mean age of whom was 34.84±9.8 (R=15-98). The majority of participants were between 31 - 40 years old, female (60.4%), married (72%), urban residents (87.3%), holding a Bachelor’s degree or higher (58.8%) and employed (58.8%). Statistically significant positive correlation coefficients were estimated between preventive behaviors of COVID-19 and perceived vulnerability (r=0.192, p<0.001), perceived severity (r=0.092, p<0.001), response efficacy (r=0.398, p<0.001) ,self efficacy (r=0.497, p<0.001) and protection motivation (r=0.595, p<0.001). Statistically significant negative correlations were found between behavior and maladaptive behavior rewards (r=-0.243, p<0.001) and perceived costs (r=-0.121, p<0.001).Conclusions: The present findings showed that maladaptive behavior reward and fear predicted protective behaviors negatively; response efficacy and self-efficacy predicted protective behaviors positively. The effect of self-efficacy was the strongest among all. The information provided in this research can contribute to policy-making in the country.