Differences in HRQOL of pilots related to demographic variables

Using the demographic variables number of years of employment, marital status, only child status, level of education and census register as factors, the HRQOL of the pilots was compared. Table 2 shows the results of these comparisons. Significant differences were detected in physical function related to education level (F= 13.853, pLSD-t= 17.675, pLSD-t= 21.630, p= 0.001). However, no significant differences were found between undergraduate and master’s degrees or above (LSD-t= 3.955, p= 0.481). In addition, the general health of urban pilots was better than that of rural pilots (F= 5.426, p= 0.021). The differences between the HRQOL indicators of the other pilots related to demographic variables were not significant.

Table 2 Physical health of pilots in demographic variables (NOT= 220)

With years of employment, marital and only child status, education level, and census record as factors, the pilots’ mental health related to demographic variables was compared. Table 3 shows that there were significant differences in somatization related to education level (F= 3.133, p= 0.046). Then, post-hoc tests were performed. The results of the pairwise comparison showed that the somatization of pilots with less than five years of professional experience was less severe than that of pilots employed between 5 and 10 years and more than 10 years (LSD-t= 0.116, p= 0.047; LSD-t= 0.145, p= 0.013). However, there was no significant difference between 5-10 years and over 10 years (LSD-t= 0.029, p= 0.396). In addition, the levels of somatization, anxiety, and terror of unique child pilots were lower than those of unique non-child pilots (F= 4.900, p= 0.028; F= 4.754, p= 0.030; F= 4.460, p= 0.036). The anxiety level of urban pilots was better than that of rural pilots (F= 4.795, p= 0.030). Differences in mental health indicators of other pilots related to demographic variables were not significant.

Table 3 Mental health of pilots in demographic variables (NOT= 220)

Factors influencing pilot HRQoL

The relationship between HRQoL, resilience, social support and personality was examined using correlational analysis. Table 4 shows the analysis results. Resilience (strength, tenacity, and optimism), social support (family, friends, and other supports), and personality (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience) were significantly correlated with HRQoL (physical function, role physical, bodily pain and general health) (p

Table 4 Correlation analysis of physical health, resilience, social support and personality (NOT= 220)

SF-36 total score was taken as dependent variable and personality, resilience and social support were taken as independent variables for hierarchical regression analysis. The first layer was the three dimensions of social support, the second layer was the three dimensions of resilience, and the five dimensions of personality were included in the third layer. Table 5 shows the results. The regression equation is statistically significant and explains 33.6% of the total variation in physical health. The standardized regression coefficient of the dimension of strength (resilience) with respect to physical health was β= 0.519, pβ= 0.186, p

Table 5 Hierarchical regression analysis of physical health (NOT= 220)

To further explore the relationship between resilience, personality, and physical health of pilots, a structural equation model was constructed based on the above results. Figure 1 shows the model fit parameters χ2/df= 2.319, p 1.96. Thus, personality is fully a mediator of resilience and HRQoL. Resilience affects the HRQOL of pilots through personality factors.

Fig. 1

Model of factors influencing the physical health of pilots (NOT= 220)

The relationship between mental health, resilience, social support and personality was described using correlation analysis. Table 6 shows the results. Resilience (strength, tenacity, and optimism), social support (family, friends, and other supports), and personality (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience) were significantly correlated with mental health (somatization, symptoms obsessions, interpersonal sensitivity, depression, anxiety, hostility, terror, paranoia and psychosis) (p

Table 6 Correlation analysis between mental health, resilience, social support and personality (NOT= 220)

Total mental health score was taken as dependent variable, and personality, resilience and social support were taken as independent variables for hierarchical regression analysis. The first layer was the three dimensions of resilience, the second layer was the three dimensions of social support, and the five dimensions of personality were included in the third layer. Table 7 displays the results. The regression equation is statistically significant and explains 29.7% of the total variation in mental health. The standardized regression coefficient of the friendship dimension of social support to mental health was β= -1.948, pβ= 3.945, p

Table 7 Hierarchical regression analysis of mental health (NOT= 220)

To further explore the relationship between social support, personality, and mental health of pilots, a structural equation model was constructed based on the above results. Figure 2 shows the model fit parameters χ2/df= 2.675, pz= 3.87 > 1.96. Therefore, personality shows a full mediating effect between resilience and mental health. Social support affects the mental health of pilots through personality factors.

Figure 2
Figure 2

Model of factors influencing the mental health of pilots (NOT= 220)

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