Prediction of Coronavirus Anxiety Based on Anxiety Tolerance and Cognitive Emotion Regulation in Employed Women

Abstract

Aim: This study aimed to predict coronavirus anxiety based on anxiety tolerance and cognitive emotion regulation in employed women. Method:  the research method is descriptive-correlational and the statistical population of this study consisted of 220 women working in health centers in Yasouj in 2020. Using Morgan table, 140 employed women were selected using random sampling method and responded to the Corona Anxiety Scale of Alipour, Ghadami, Alipour, and Abdollahzadeh (2019), Garnefski et al. (2002) Cognitive Emotion Regulation Questionnaire, and Simmons and Gahr (2005) Distress Tolerance Questionnaire; Pearson correlation coefficient and stepwise regression analysis were used to analyze the data. Results: The results showed that there was a negative correlation between adaptive emotion regulation strategy and distress tolerance with coronary anxiety and a negative correlation between non-adaptive emotional regulation strategy and coronary anxiety (P = 0.01); Stepwise regression coefficients showed that adaptive emotional regulation (P = 0.001, β = -0.28), non-adaptive emotional regulation (P = 0.001, β = 0.18), and distress tolerance (P = 0.007, β=-0.38) were able to predict coronary anxiety. Conclusion: According to the results, to reduce the coronary anxiety of employed women, programs can be designed to increase positive cognitive emotion regulation strategies and their anxiety tolerance and reduce negative cognitive emotion regulation strategies and implemented through electronic and virtual workshops.