Study design and study population
The Chinese Sixth National Health and Services Survey (NHSS) in Sichuan Province was conducted from August 2018 to October 2018. All healthcare workers in the public medical institutions of Sichuan Province were eligible to participate in the NHSS. The study sample was selected using multistage stratified random sampling, which was the same as The Chinese Fifth National Health and Services Survey (NHSS) in Sichuan Province [27]. In the first stage, 14 cities were randomly selected from 21 cities, and a county /district was selected from each of the 14 cities. In the second stage, all third-class hospitals and some second-class hospitals were randomly selected in the 14 counties /districts. At the same time, 5 streets /towns were randomly selected from each county (district), and all community health service centres and township hospitals in each selected street and township were included in the survey medical institutions-a total of 70 community health service centers and township hospitals. In the third stage, 20 physicians and 10 nurses were randomly selected from each second-class and above hospital. At the same time, 5 physicians, 3 nurses and 2 public health professionals were randomly selected from each community health service centre and township hospital. Respondents were asked to complete the questionnaire anonymously. Informed consent was obtained from each healthcare worker following a detailed explanation about the purpose of the study. Overall, 1685 healthcare workers were investigated, of which 1327 provided valid responses (for an effective response rate of 78.80%).
Measures
The questionnaire was developed and designed by an expert panel from the National Health Commission of the People’s Republic of China for this study.
Sociodemographic characteristics of healthcare workers
The sociodemographic characteristics examined included the following: gender, age (< 30, 30–39, 40–49, or ≥ 50 years), marital status (single, divorced, widowed, or married), education level (junior college or below, bachelor’s, master’s, or above), specialty (physician, nurse, or public health professional), technical title (no title, primary title, middle title, vice-senior title,or above), service years (< 5, 5–9, 10–19, 20–29, or ≥ 30 years), weekly hours at work (≤ 40, > 40), night shifts per month (none, 1–7, or ≥ 8), and grade of medical institutions (community health service centres and township hospitals, second-class hospitals, or above).
Effort-reward imbalance
The Effort-Reward Imbalance Scale assesses three dimensions: extrinsic effort (3 items), reward (7 items), and overcommitment (6 items). Participants responded to the items on a four-point Likert scale (1 = strongly disagree, 4 = strongly agree). To assess the degree of imbalance between high cost and low gain at work, an ERR was calculated as E/(R*C), where E was the total score of the effort dimension, R was the total score of the reward dimension, and C was the correction coefficient based on the difference in the number of numerators and denominators [28]. Here, C = 3/7 = 0.4286. An ERR value of > 1.0 indicates that the amount of effort is not rewarded adequately [19]. Higher scores represented higher overcommitment to work. Cronbach’s alpha coefficient of the scale in this study was 0.786.
Job Satisfaction
Work Engagement
Work engagement was measured by the Chinese version of the Utrecht Work Engagement Scale [29]. It comprises 17 items measuring three aspects of work engagement: work vigour (6 items), work dedication (5 items), and work absorption (6 items). Items were responded to using a seven-point Likert scale ranging from 0 (never) to 6 (every day) and were combined into summary scores. Higher scores indicated higher work engagement. Cronbach’s alpha coefficient of the scale in this study was 0.941.
Outcome variable
Self-rated health status was assigned scores of 5 (good), 4 (relatively good), 3 (fair), 2 (relatively poor) and 1 (poor) by asking the participants ‘How do you feel about your health?’ Higher scores indicated better self-rated health.
Statistical analysis
We first used descriptive statistics to examine the sociodemographic characteristics, ERR, overcommitment, job satisfaction, work engagement, and self-rated health status. Second, Pearson’s correlation coefficients were used to analyse the correlations among ERR, overcommitment, work engagement, job satisfaction, and self-rated health. Third, we used self-rated health as the dependent variable and the sociodemographic variables, ERR, overcommitment, job satisfaction, and work engagement as independent variables in a linear regression model. Fourth, a structural equation model (SEM) was employed to further test the hypothesized relationships among the study variables.
Several indicators were used to assess the fit between the current data and the hypothesized model. The goodness of fit index (GFI) > 0.9, norm fit index (NFI) > 0.9, relative fit index (RFI) > 0.9, comparative fit index (CFI) > 0.9, incremental fit index (IFI) > 0.9, and Tucker-Lewis Index (TLI) > 0.9 indicate whether the model fit was acceptable. All statistical analyses were performed using IBM SPSS version 23.0 (SPSS Inc., Chicago, IL, USA) and Analysis of Moment Structures (AMOS) version 22.0 (IBM, New York, NY, USA). Statistical significance was set at P < 0.05.