3.1 General Situation
3.1.1 Demographics
A total of 210 participants, including 105 men and 105 women, were surveyed. Among them, 147 were nurses (70%), and 63 were physicians (30%). The average age of all participants was 30.47±4.53 years old. The average working years of all participants was 8.61±4.37 years. The distribution of their education levels was as follows: 14 workers graduated from secondary school (6.7%), 77 graduated from junior colleges (36.7%), 105 graduated from undergraduate programs (50%), and 14 graduated from graduate schools (6.7%). The distribution of marital status was as follows: 98 unmarried (46.7%); 112 married (53.3%). 3.1.2 Epidemic-related Information Among all the participants, 196 people had nursed/treated seriously ill patients in Hubei (93.3%); 140 people had experienced the death of patients during their nursing/treatment (66.7%); and 14 respondents reported that they had family members who were infected with COVID-19 (6.7%). Twenty-one people had a history of an anxiety disorder (10%). The self-reported physical health status of the participants was follows: 49 persons thought they were in excellent health (23.3%), 133 individuals considered themselves to be in good health (63.3%), and 28 individuals reported that they had average health (13.3%). 3.1.3 Views on the epidemic The participants reported that the most significant help they needed during the epidemic included psychological support (46.7%), improved medical conditions (33.3%) and communication with their families (10%).
The top three sources of support among the participants were friends (161 persons chose this item), other family members (133 persons chose this item), and colleagues (131 persons chose this item).
The top three most stressful situations faced by all the participants during the epidemic were as follows (the question was multiple choice): a shortage of protective materials (147 persons chose this item), the number of deaths (126 persons chose this item), and an increased number of newly confirmed cases (119 persons chose this item).
For all the participants, the top three situations that made them the most angry during the epidemic were as follows (the question was multiple choice): irresponsible rumours (147 persons chose this item), weak early control of the pandemic (105 persons chose this item), and misunderstanding/noncooperation from patients (98 persons chose this item).
For all the participants, the top three situations that made them the most distressed during the epidemic were as follows (the question was multiple choice): vulnerable medical staff (147 persons chose this item), a shortage of protective materials (126 persons chose this item), and vulnerable patients (105 persons chose this item).
3.2 The SRSS score and its influencing factors
3.2.1 The SRSS score
The average SRSS score of all participants was 25.13±6.41 points. The result was significantly different from the national average (t=6.765, p<0.001). This difference indicates that the sleep quality of medical workers in Hubei was worse than that of the general population.
3.2.2 Analysis of influencing factors related to sleep quality
The sleep quality scores of patients were compared based on different demographic characteristics and epidemic-related experiences. The results showed that there were statistically significant differences in sleep quality (P < 0.05) based on gender, whether the respondent had patients who died under their care, history of mental illness, whether their family members were infected with COVID-19 and self-perceived physical health. See Table 1 for details, which is located at the last page of this manuscript.
3.2.3 Multiple stepwise regression analysis of the factors related to the sleep quality among health care workers
Taking the total score of the SRSS as the dependent variable, factors that were statistically significant in the univariate analysis were used as independent variables in the multivariate analysis. In addition, factors such as age and working years, which possibly impact sleep quality, were also considered. The assignments of the factors are shown in Table 1. The results of the analysis indicated that gender, whether the respondent had patients who passed away under their care/treatment, whether participants had previously suffered from psychiatric-related diseases, and whether they had family members who were infected with COVID-19 during the outbreak (P <0. 05) are the primary factors that affect the patient's sleep quality. All the data are shown in Table 2. The F-value (8,201) is 25.235 (P <0.001), which indicates that the fitted multiple linear progressive regression equation was statistically significant. After evaluating the regression equation model, the multiple correlation coefficient (R = 0.708) and the coefficient of determination (R2= 0.481) were calculated. Thus, these four independent variables can effectively explain the 48.1% of the variance in the sleep quality of the participants.
3.3 The GAD-7 score and its influencing factors
3.3.1 GAD-7 score
The average GAD-7 score of all participants was 12.37±4.89, which indicated moderate anxiety. This result indicates that healthcare workers who provided aid in Hubei may have prominent anxiety.
3.3.2 Analysis of influencing factors related to anxiety
The GAD-7 scores were compared based on different demographic characteristics and epidemic-related experiences. The results showed that there were statistically significant differences among different groups (P < 0.05) in terms of gender, marital status, educational background, title, whether they had nursed/treated seriously ill patients, whether they had a history of psychosis, whether their family members were infected with COVID-19 and their self-perceived health status. See Table 1 for details.
3.3.3 The multiple stepwise regression analysis of the factors related to the anxiety of healthcare workers
Taking the total score of GAD-7 as the dependent variable, the factors that were statistically significant in the univariate analysis were analysed as independent variables in the multivariate analysis. In addition, factors such as age and working years, which possibly impact anxiety, were also considered. The assignments of the factors are shown in Table 1. The results of the analysis indicated that gender, working years, self-perceived health status, whether participants had previously suffered from psychiatric-related diseases, and whether their family members were infected with COVID-19 during the outbreak (P <0. 05) are the primary factors that affect the patient's level of anxiety. See table 3 for details. The F-value (8,201) was 21.395 (P< 0.001), which indicated that the fitted multilinear progressive regression equation is statistically significant. After evaluating the regression equation model, the multiple correlation coefficient (R = 0.678) and the coefficient of determination (R2= 0.438) were calculated. Thus, these five independent variables can adequately explain 43.8% of the variance in anxiety among the participants.
3.4 The PHQ-9 score and its influencing factors
3.4.1 The PHQ-9 score
The average PHQ-9 score of all participants was 8.90±5.42, which indicated mild depression. This suggests that healthcare workers who provided aid in Hubei may have a certain degree of depression.
3.4.2 Analysis of influencing factors related to depression
The PHQ-9 scores were compared based on different demographic characteristics and epidemic-related experiences. The results showed that there were statistically significant differences among different groups (P < 0.05) in terms of educational backgrounds, whether the respondent had nursed/treated seriously ill patients, and whether they had a history of psychosis. See Table 1 for details.
3.4.3 Multiple stepwise regression analysis of influencing factors related to depression among health care workers
Taking the total score of the PHQ-9 as the dependent variable, the factors that were statistically significant in the univariate analysis were analysed as independent variables in the multivariate analysis. In addition, factors such as age and working years, which possibly impact depression, were also considered. The assignments of the factors are shown in Table 1. The analysis results indicated whether participants had previously suffered from psychiatric-related diseases and whether they nursed/treated seriously ill patients during the outbreak (P <0. 05) are the primary factors that affect the participants’ degree of depression. See table 4 for details. The F-value (6,203) is 6.769 (P< 0.001), which indicates that the fitted multilinear progressive regression equation is statistically significant. The multiple correlation coefficient (R = 0.408) and the coefficient of determination (R2 = 0.142) were also calculated. Thus, these two independent variables can only explain 14.2% of the variance in depression.