PMC:7075079 / 5856-10975 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7075079","sourcedb":"PMC","sourceid":"7075079","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7075079","text":"Material and Methods\n\nEthical approval\nThis study was conducted in accordance with the Declaration of Helsinki. All participants signed the consent form. The study was approved by the Wuhan University School of Medicine Ethics Committee (Approval number: 20180928).\n\nStudy participants\nThis study included 180 medical staff from several provinces who treated patients with COVID-19 infection in January and February 2020. All the study participants were either doctors or nurses who worked in departments of respiratory medicine, fever clinics, or the intensive care unit (ICU). All study participants volunteered to participate in the study.\n\nStudy design\nAn observational and cross-sectional clinical study was conducted that included the use of self-reported questionnaires. Demographic and social data from the medical staff were obtained. Levels of anxiety, self-efficacy, stress, sleep quality, and social support were measured using validated clinical questionnaires and scoring systems. All questionnaires were completed anonymously by the 180 participating medical staff.\n\nDemographic and social data\nDemographic and social data from the study participants included age, gender, education, and marital status. Professional and work information included their title, income, role, department, and work experience, or seniority.\n\nThe Social Support Rate Scale (SSRS) [16]\nThe SSRS was used to measure the type and levels of social support received by the medical staff [16]. The SSRS contained ten items consisting of three grades, with an aggregate score that ranged from 7–56. A higher score indicated higher levels of social support [16]. The Cronbach’s alpha for internal consistency for the use of the SSRS was 0.808.\n\nThe Self-Rating Anxiety Scale (SAS) [17]\nThe SAS was used to measure the levels of anxiety the mood of the medical staff [17]. The SAS questionnaire contained 20 items consisting of four grades, with questions based on feelings of anxiety and mood in the previous seven days. An aggregate score of 20 was then multiplied by 1.25, with higher scores indicating more severe levels of anxiety [17]. The Cronbach’s alpha for internal consistency for the use of the SAS was 0.821.\n\nThe General Self-Efficacy Scale (GSES) [18]\nThe Chinese version of the GSES was used to measure the feelings of self-efficacy by the medical staff [18]. The scale consisted of ten items, with a score for each item of between 1–4, and the total score of 10–40, with the final points were divided by 10. Higher scores indicated higher self-efficacy [18]. The Chinese version of the GSES has previously been shown to have high sensitivity and validity [18]. The Cronbach’s alpha for internal consistency for the use of the GSES was 0.805.\n\nThe Stanford Acute Stress Reaction (SASR) [19]\nThe SASR questionnaire was used to measure self-reported stress by the medical staff [19]. The SASR is a six-point Likert scale consisting of 30 items. Each item has a score of between 0–5, with a combined score ranging from 0–150, and a higher score indicating higher levels of self-reported stress [19]. The Cronbach’s alpha for internal consistency for the use of the SASR was 0.837.\n\nThe Pittsburgh Sleep Quality Index (PSQI) [20]\nThe PSQI questionnaire was used to measure sleep quality using an 18-item scale containing seven items that included sleep quality, sleep duration, sleep latency, habitual sleep efficiency, sleep disturbance, use of sleeping medications, and daytime dysfunction [20]. Each dimension scored between 0–3, with a total score ranging from 0–21, and a higher score indicating lower sleep quality [20]. The Cronbach’s alpha for internal consistency for the use of the PSQI was 0.811.\n\nStatistical analysis\nData were presented as the mean ± standard deviation (SD). The chi-squared (χ2) test, Pearson’s correlation analysis, and multivariate analysis using the structural equation model (SEM) with path analysis were used to determine the structural relationship between the measured variables. Data were analyzed using EpiData version 3.1 software (EpiData, Buenos Aires, Argentina) and SAS version 9.4, software (SAS Institute, Cary, NC, USA), which were used for data entry and analysis. The intermediary effects of the variables were analyzed using IBM SPSS AMOS version 21.0 (IBM Corp., Armonk, NY, USA). The bootstrap number was set as 5,000. The significance of the specific intermediary was determined using the nonparametric percentile bootstrap method with deviation correction. Path analysis by the structural equation model (SEM) was performed to measure the associations and their importance. Path analysis included the use of the goodness-of-fit index (GFI), the adjusted goodness-of fit-index (AGFI) the incremental fit index (IFI), the comparative fit index (CFI), the Tucker-Lewis index (TLI), the normed fit index (NFI), and the root mean square error of approximation (RMSEA). The structural equation showed and ideal fit (GFI=0.995, CFI=0.995, TLI=0.953, IFI=0.996, NFI=0.991, AGFI=0.931, RMSEA=0.077, χ2/df=2.073). 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