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Table 4 Logistic regression coefficients predicting RH service utilization in three Cities in China

From: RH knowledge and service utilization among unmarried rural-to-urban migrants in three major cities, China

Variables (obtained = 1, not = 0) RH consult services STD/AIDS health education RH checkup services
Gender (ref: female) 0.67 (0.59, 0.75)** 1.81 (1.59, 2.05) ** /
City(ref: Wuhan)    
   Shenzhen 0.64 (0.55, 0.75)*** 1.58 (1.32, 1.91)** 0.15 (0.12, 0.19)***
   Guangzhou 0.53 (0.46, 0.61)*** 1.24 (1.06, 1.48)** 0.26 (0.21, 0.32)***
Education (ref: Illiteracy)    
   Elementary school 1.36 (1.18, 1.63)*** 1.29 (1.23, 1.34)** 1.96(1.68, 2.28)***
   Junior school 2.36 (1.95, 2.94)*** 1.47 (1.14, 1.89)** 2.76 (2.37, 3.82)**
   high school 3.48 (2.90, 4.31)** 0.97 (0.87, 1.33) 3.67 (2.88, 4.65)**
   college and above 4.01 (2.63, 6.25)*** 0.99 (0.79, 1.24) 3.70 (2.33, 5.88)**
Age (ref:15-19)    
   20-24 1.65 (1.50, 1.78)** 1.22 (1.06, 1.42)* 1.84 (1.53, 2.06)**
   25-29 3.74 (2.23, 4.03)*** 1.31 (1.14, 1.56)** 2.22 (1.78, 2.57)***
   >30 2.94 (2.27, 3.70)*** 1.18 (0.94, 1.47) 3.92 (2.76, 5.11)***
Work experience (ref:<1)    
   1~ 1.17 (0.92, 1.49) 0.97 (0.85, 1.23) 0.63 (0.46, 0.87) ***
   2~ 1.01 (0.83, 1.21) 1.04 (0.86, 1.25) 0.73 (0.57, 0.93)**
   3~ 0.90 (0.73, 1.11) 1.28 (1.06, 1.55)** 0.94 (0.72, 1.21)
   5~ 1.03 (0.87, 1.23) 1.11 (0.94, 1.32) 0.93 (0.75, 1.16)
   >10 1.12 (0.95, 1.32) 1.14 (0.97, 1.34) 0.84 (0.68, 1.04)
Monthly income (ref: <600)    
   600~ 1.03 (0.78, 1.34) 1.09 (0.80, 1.65) 0.90 (0.60, 1.41)
   1000~ 0.97 (0.79, 1.25) 1.12 (0.90, 1.54) 0.84 (0.62, 1.16)
   1500~ 1.08 (0.96, 1.27) 1.07 (0.86, 1.39) 0.85 (0.62, 1.20)
   > = 2000 1.45 (1.02, 1.79)* 0.99 (0.80, 1.28) 0.91 (0.65, 1.22)
Occupation b (ref: Blue-collar)    
   white-collar 1.33 (1.11,1.61)*** 0.67 (0.54, 0.83)** 1.47 (1.14, 1.89)**
   Self-employed laborers 0.99 (0.98,1.03) 0.52 (0.45, 0.57)** 1.29 (1.23, 1.34)**
Constant 1.077** 3.38** 19.36***
  1. Notes: *** p ≤ 0.001; ** p ≤ 0.01; * p ≤ 0.05;
  2. b. we have regrouped “construction, traffic and storage, trade and food, manufacturing enterprise “ to blue-collar and “communication, real estate, finance” to white collar.