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Table 10 Logistic regression results (n = 1320)

From: RETRACTED ARTICLE: Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China

 

F1

F2

F3

F4

Beta

OR

(95% CI)

Beta

OR

(95% CI)

Beta

OR

(95% CI)

Beta

OR

(95% CI)

Gender

−0.294a

0.745

(0.596, 0.931)

      

Age

    

−0.128a

0.880

(0.778, 0.994)

  

Area of China (0)

        

Area of China (1)

        

Area of China (2)

        

Area of China (3)

        

Area of China (4)

        

Area of China (5)

        

Area of China (6)

        

Employment (0)

—c

 

—c

     

Employment (1)

0.594c

1.811

(1.328, 2.472)

−0.467b

0.627

(0.459, 0.857)

   

Employment (2)

0.585

1.796

(0.978, 3.296)

−0.360

0.698

(0.377, 1.291)

    

Employment (3)

0.855c

2.352

(1.654, 3.345)

−0.963c

0.382

(0.267, 0.546)

    

Employment (4)

0.580b

1.786

(1.237, 2.579)

−0.526b

0.591

(0.401, 0.871)

    

Employment (5)

0.591b

1.806

(1.209, 2.697)

−0.468a

0.626

(0.405, 0.967)

    

Education Degree

  

0.430c

1.537

(1.294, 1.826)

    
  1. aP < 0.05, bP < 0.01, and cP < 0.001. The variables followed by a number in a bracket are the dummy variables. Considering the student group, we coded Employment (0) to Employment (5) into 00000 to 00001, varying from student to others, respectively. We did not observe any significant result in Employment (2) in F1 and F2. The dummy variable of Staff in institutions showed a lesser significant result than the dummy variable of Student