The turnover challenge in China’s Centers for Disease Control and Prevention: Role of salaries, job satisfaction and burnout

The turnover challenge in China’s Centers for Disease Control and Prevention: Role of salaries, job satisfaction and burnout | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The turnover challenge in China’s Centers for Disease Control and Prevention: Role of salaries, job satisfaction and burnout Hanlin Nie, Wanjin Yang, Jingting Zeng, Qian Bai, Elizabeth Maitland, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8385919/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. As a core pillar in China’s public health system, Centers for Disease Control and Prevention (CDCs) face significant workforce turnover. Our study addresses two gaps: the structural relationships and combined effect on CDC turnover of salary, job satisfaction, and burnout, and, second, the inter-relationship of marital status and these factors. Methods. A cross-sectional survey was conducted across all CDCs in Yunnan Province, China (March-April 2024), collecting data on salary, job satisfaction, burnout, turnover intention, and sociodemographic information. Chained mediation models analyzed the multiple mediating effects of job satisfaction and burnout between salary and turnover intention. Results. Lower salary directly correlated with higher turnover intention (β = -0.059, P < 0.05), accounting for 42.14% of the total effect. Job satisfaction and burnout demonstrated significant chained mediation between salary and turnover intention. Job satisfaction also exerted an independent mediating effect (β = -0.058, P < 0.01), while burnout did not. The total indirect effect coefficient was − 0.081, constituting 57.86% of the total effect. Married staff mirrored the full-sample pathways, though effect sizes differed. Unmarried staff reported significantly higher burnout than married staff (6.94 vs. 6.31, P < 0.01), but showed no significant associations between salary and job satisfaction, burnout, or turnover intention. Conclusions. Job satisfaction and burnout served as chained mediators between salaries and turnover intentions, with job satisfaction exerting an additional independent mediating effect. Internal heterogeneity was revealed within the marriage subgroups. Interventions should prioritize equitable compensation, enhance job satisfaction, reduce burnout, and implement differentiated measures to reduce workforce turnover. Public health workforce Salary Job satisfaction Burnout Turnover intention Figures Figure 1 Figure 2 Figure 3 Background Centers for Disease Control and Prevention (CDCs) are a core pillar in China’s public health system, (1) responsible for disease prevention and control, public health emergency responses, vaccination monitoring, and public health promotion. (1, 2) Since 2009, the continuous reforms in China’s health sector have seen CDCs make significant improvements both in their institutional scale and operational capabilities, (2, 3) including increasing the number of CDC staff to 250,000 by 2025. (4) Due to the sudden and contagious nature of disease prevention and control work, CDC staff frequently face significant on-the-job challenges, including heavy workloads, uncertainties in infectious disease risks, and emotional stress. (5) Numerous studies confirm that CDC employees receive compensation disproportionately lower than their counterparts in medical institutions at equivalent levels. (6, 7) Not surprisingly, China’s CDC is experiencing severe staff turnover, particularly among highly educated backbone staff and young professionals, (8-11) which is especially pronounced in China’s central and western provinces. (3, 12) Research on the attrition of CDC personnel generally agrees that low salary is the key factor constraining recruitment and explaining high turnover. (9) Studies also indicate the rise in mental health issues, with low job satisfaction and burnout serving as significant accelerators of staff turnover. (5, 13, 14) Across diverse countries and professions, empirical research demonstrates that declining job satisfaction triggers burnout, and higher levels of burnout correlate with stronger intentions to leave jobs, ultimately leading to high turnover rates. (15-17) Within the context of China’s health workforce, the job satisfaction-burnout-turnover intention relationship has mainly been studied among urban doctors and nurses (18, 19) and rural primary health care providers, (20) with fewer studies of public health professionals, particularly the largest group, CDC staff. Some studies on CDC staff have confirmed that low salary correlates with reduced job satisfaction or increased burnout, or a higher likelihood of resignation. (7, 11, 21) These studies have largely been limited to analyzing the binary relationships between job satisfaction and burnout, or burnout and turnover intention, or job satisfaction and salary. The structural relationships and combined effect among these variables, especially on turnover, have not been fully analyzed for CDC personnel. In addition, the well-established impact of marital status on employees’ turnover behavior (22) has been neglected in CDC turnover studies. Non-CDC studies indicate that married employees may be more likely to remain in an organization due to family responsibilities or the need for stable work than unmarried staff. (14, 23) A stable marital life can provide emotional support and alleviate work-related stress, reducing burnout and lowering turnover rates. (24-26) Differences in the impact of marital status on job satisfaction, burnout, and turnover intentions among CDC staff remain unexplored. Our study addresses two gaps in the existing literature: the structural relationships and combined effect on CDC turnover of salary, job satisfaction, and burnout, and the inter-relationship of marital status and turnover, salaries, job satisfaction, and burnout. Understanding these relationships is critical to developing targeted intervention measures to improve salaries and job satisfaction, alleviate burnout, and reduce turnover intentions among CDC staff. China’s CDC system faces exceptionally high workloads and pressure due to China’s complex terrain and climate, a vast population, diverse disease profiles, regional economic disparities, and linguistic-cultural heterogeneity. (27-29) A renowned tourist province along China’s southwestern border, Yunnan’s CDCs experience significant population mobility (30) and high-intensity disease prevention and control at the border and within the province. (31, 32) We conducted a cross-sectional survey at all CDCs in Yunnan province to explore the mediating role of job satisfaction and burnout between salary and turnover intention. Additionally, we conduct a subgroup analysis based on marital status to explore its heterogeneity. The empirical evidence suggests that salary significantly impacts job satisfaction, burnout, and turnover intention. (7, 11, 33) The setting of salary levels should align with the value of labor, fully meet employees’ living and development needs, and remain consistent with social equity. (11, 34) Before 2017, the CDC budget provided fee-based services, such as health screenings, preventive physical examinations, and epidemic prevention, with a portion of the additional revenue subsidizing employee salaries. (35) When the Chinese government eliminated the CDC health service fees in 2017, falling CDC income reduced the CDC budget and squeezed employee salaries. (35) Subsequent government fiscal support failed to fully compensate for this income shortfall, leading to job dissatisfaction and resignations among staff due to reduced salaries. (36) While current research generally agrees that CDC personnel salaries require improvement, (7, 9, 21) our study explores the complex relationship between salaries, job satisfaction, reduced burnout, and turnover rates among CDC staff. Job satisfaction refers to the degree to which employees are satisfied with their job roles, work environment, and organizational policies, and it is a primary factor influencing employees’ work motivation and initiative. (37) Job satisfaction among Chinese CDC personnel is relatively low, as studies from different regions and different CDC branches have consistently concluded. (38-40) Since job satisfaction is a multidimensional concept, the causes of low satisfaction are complex and have been primarily attributed to factors such as low salary, unclear career advancement prospects, high work pressure, and low job recognition. (5, 38) Low job satisfaction can also lead to derivative issues, including low work efficiency, frequent absenteeism, burnout, and high employee turnover. (41) Burnout is a psychological syndrome resulting from prolonged exposure to emotional and interpersonal stressors in the workplace. (42) Burnout comprises three dimensions: emotional exhaustion, depersonalization, and diminished personal accomplishment. (43) High levels of burnout are common among Chinese CDC staff. (7) Burnout among CDC personnel has been linked to work intensity, compensation and benefits, job satisfaction, family responsibilities, and responses to public health emergencies. (44) High burnout can lead to numerous health and psychological problems, such as emotional exhaustion, low sense of achievement, headaches and insomnia, workforce instability, and impaired work efficiency, including lack of concentration and frequent errors. (7) Turnover intention has been identified as a critical factor constraining the development of China’s CDC. (11) Turnover intention is typically defined as an employee’s psychological tendency to leave their current organization for other jobs. (45) A complex decision-making process, job resignation can be broadly divided into three main interacting categories: internal factors, such as work pressure, fairness sense, and promotion expectations (11, 46) ; external factors, for example, salary, employment opportunities, and family responsibilities (7) ; and psychological factors, including job satisfaction, burnout, and professional identity. (5) Reducing turnover intentions among CDC personnel has become a significant challenge. Among healthcare workers, (18, 20) increasing salary can enhance job satisfaction, (47) reduce the occurrence of burnout, (48) and decrease turnover intention. (49) Improved job satisfaction can lower turnover intention by reducing burnout. As shown in Figure 1, our study constructs a chain-mediated model of the salary-job satisfaction-burnout-turnover intention relationship, and proposes the following hypotheses: Hypothesis 1: Salary increases reduce CDC staff turnover intention. Hypothesis 2: Among CDC staff, job satisfaction mediates the relationship between salary and turnover intention. Hypothesis 3: Among CDC staff, burnout mediates the relationship between salary and turnover intention. Hypothesis 4: Among CDC staff, job satisfaction and burnout jointly mediate the relationship between salary and turnover intention in a chain-mediated manner. Methods 2.1 Participants Using whole-cluster sampling, we conducted a cross-sectional survey from March to April 2024 at all 149 CDCs in Yunnan Province. Due to the gradual reduction in the number of staff allocated to each provincial, prefectural, and county level CDC, 80 provincial level, 50 prefecture level, and 30 county level CDC staff were randomly selected from each CDC. A total of 7,929 respondents completed the survey, with 6,343 respondents passing the strict consistency test, with an effective participation rate of 80%. Our study relied on a web-based survey platform (wjx.com) to distribute the electronic anonymous questionnaire and set up strict quality control. The wording and layout of the questionnaire were adjusted through two rounds of discussions with experts and the project team. The questionnaire included one consistency check question and three logic check questions, with responses not meeting the checks excluded. At each CDC branch, a trained data enumerator assisted in completing data collection and providing online and telephone consultations. All participants responded anonymously and gave informed consent. The study was approved by the Ethics Committee of Beijing University of Traditional Chinese Medicine (NO.2024BZYLL0303). 2.2 Measures 2.2.1 Socio-demographic characteristics We collected data on gender, age, bianzhi (tenured employment position in China), ( 50 ) education, title, whether they had worked overtime in the last six months, number of chronic diseases they had, marital status, and CDC level. Marital status was categorized as married and unmarried (including single and divorced). 2.2.2 Salary Salary was the average monthly sum of the basic wage plus bonuses, allowances, overtime pay, and any additional remuneration. By comparing the salary data of the same post, the same position, the same education level, and the same unit level, fuzzy data and abnormal data were excluded, and the extreme data were truncated by 5%. 2.2.3 Job satisfaction Using a pairwise translation, the Minnesota Satisfaction Questionnaire (MSQ) was adopted. ( 51 ) Two experts specialized in health management translated the MSQ into Chinese; English professionals back-translated the Chinese questionnaire into English; and any anomalies in the Chinese version were adjusted by the team. The final Chinese version of the MSQ was finalized after two rounds of validation and one pilot survey. A total of 20 items were included, measured by a 5-point Likert scale (1-very dissatisfied to 5-very satisfied). Job satisfaction scores were averaged over all items, with higher scores indicating higher job satisfaction. The MSQ has been widely applied in job satisfaction studies across numerous occupations in China, such as bank staff, ( 52 ) civil aviation pilots, ( 53 ) police officers, ( 54 ) and anaesthesiologists, ( 55 ) demonstrating its well-established validity. In the present research, the Cronbach’s alpha coefficient for the reliability test was 0.926, and the Kaiser-Meyer-Olkin Measure (KMO) for the total validity test was 0.949 (P < 0.001). 2.2.4 Burnout The same procedures applied to the MSQ were used for the Maslach Burnout Inventory–General Survey (MBI-GS). ( 43 , 56 ) The 15 items were divided into three sub-dimensions, including five emotional exhaustion, four depersonalization, and six diminished personal accomplishment items. ( 43 ) Each item was measured by a 7-point Likert scale, with 0 ‘never’ to 6 ‘every day’. The score for each sub-dimension was the average score of the items, with reverse scoring applied to the diminished personal accomplishment. The score for burnout was the sum of the 3 sub-dimensions. A higher score indicates more severe burnout. The MBI-GS has been widely used in China to assess staff burnout levels, such as among social welfare workers, ( 57 ) nurses, ( 58 ) and grassroots officials, ( 59 ) demonstrating good validity. In the present research, the Cronbach’s alpha was 0.831, and the KMO value was 0.904 (P < 0.001). 2.2.5 Turnover intention Following the MSQ and MBI-GS procedure, the four-item Intent to Leave Scale (ILS) was adopted. ( 60 ) Each item was measured by a 5-point Likert scale (1- strongly disagree to 5- strongly agree), with the score averaged over 4 items. A higher score indicates a stronger turnover intention. The ILS has been applied in studies examining job turnover among kindergarten teachers ( 61 ) and nurses ( 62 ) in China, demonstrating good validity. In the present research, the Cronbach’s alpha was 0.806, and the KMO value was 0.775 (p < 0.001). 2.3 Statistical analysis Descriptive statistics, Chi-square test, and Kruskal-Wallis H test were used to analyze the scores of subgroup differences in job satisfaction, burnout, and turnover intention for different demographic characteristics. Harman’s one-way statistical control was used to check the effect of common method bias. ( 63 , 64 ) Correlations between salary, job satisfaction, burnout, and turnover intention were analyzed using the Pearson chi-square test. To check the chain mediation effect of job satisfaction and burnout between salaries and turnover intentions for the full sample, the model was constructed using the command gsem in STATA software version MP17.0, and Bootstrap (5000) was used to check the chain mediation effect. When the 95% confidence interval (CI) after bias correction does not include zero, it indicates that the pathway has a significant effect. Salary, job satisfaction, burnout, and turnover intention were utilized to construct a chain-mediated effects model, with gender, age, education, overtime, and chronic diseases included as control variables. Chain mediation models for both the married and unmarried subgroups were analyzed and tested following the main model. Salary was log-transformed, and all models were adjusted for covariates, including age, education, job title, and overtime. Using SPSS software version 26.0, reliability was analyzed using Cronbach’s alpha coefficients and validity tests of the scales used the KMO values. The rest of the statistical analyses were completed using STATA software version MP17.0, and statistical significance was defined as a two-tailed p-value of less than 0.05. Currency conversion was based on the average exchange rate for 2024: 1 USD ≈ 7.1217 CNY. Results Table 1 shows that our sample comprised 4,257 females (67.11%); the average age was 39.28; and 5,054 (79.68%) were married. There were 5,543 cases (87.39%) with the bianzhi; those with a bachelor’s degree accounted for 69.16% of the sample; and primary titles accounted for 37.35%, intermediate titles for 25.11% and senior titles for 18.51% of the sample. There were 1,240 cases (19.55%) of overtime work in the last six months; and 5.09% worked at the provincial level, 19.45% at prefecture, and 75.45% at the county level CDCs. 3.1 Descriptive statistics From Table 1 , the average monthly salary (RMB6147.67 RMB (USD863.23)) exhibits significant differences across various sociodemographic subgroups, excluding overtime. The mean scores of job satisfaction (3.52 ± 0.52) and turnover intention (2.36 ± 0.69) varied significantly across sociodemographic subgroups. Except for gender and bianzhi , all sociodemographic characteristics also showed significant differences in the burnout score (44 ± 2.36). (All P < 0.05) Table 1 Salary, Job Satisfaction, Burnout, and Turnover Intention Scores for Different Characteristics Characteristics Salary (RMB) Job satisfaction Burnout Turnover intention mean ± sd P-value mean ± sd P-value mean ± sd P-value mean ± sd P-value Gender female 6031.42 ± 2368.72 0.007 3.54 ± 0.50 0.002 6.40 ± 2.36 0.482 2.32 ± 0.67 < 0.001 male 6384.92 ± 2219.33 3.48 ± 0.55 6.51 ± 2.35 2.45 ± 0.73 Age group <=25 4135.18 ± 2698.34 < 0.001 3.67 ± 0.56 < 0.001 6.33 ± 2.56 < 0.001 2.61 ± 0.74 < 0.001 25 < x < = 40 5624.53 ± 2225.45 3.48 ± 0.53 6.78 ± 2.42 2.51 ± 0.72 40 < x =56 8150.73 ± 2383.08 3.63 ± 0.45 5.71 ± 1.91 2.06 ± 0.52 Bianzhi yes 6513.48 ± 1830.77 < 0.001 3.50 ± 0.51 < 0.001 6.50 ± 2.35 0.123 2.34 ± 0.69 < 0.001 no 3613.07 ± 3514.29 3.68 ± 0.53 6.03 ± 2.37 2.53 ± 0.69 Degree master or above 8137.42 ± 2856.19 < 0.001 3.39 ± 0.53 < 0.001 6.98 ± 2.53 < 0.001 2.35 ± 0.70 < 0.001 bachelor 6117.36 ± 2030.57 3.49 ± 0.52 6.59 ± 2.38 2.41 ± 0.72 below bachelor 5842.57 ± 2734.22 3.64 ± 0.50 5.93 ± 2.16 2.23 ± 0.60 Title senior 7946.78 ± 1966.91 < 0.001 3.53 ± 0.49 < 0.001 6.12 ± 2.18 < 0.001 2.14 ± 0.62 < 0.001 intermediate 6587.40 ± 1709.18 3.46 ± 0.51 6.51 ± 2.31 2.33 ± 0.67 primary 5630.14 ± 2091.23 3.52 ± 0.52 6.60 ± 2.39 2.45 ± 0.72 no 4833.17 ± 2567.74 3.61 ± 0.54 6.33 ± 2.47 2.45 ± 0.69 Overwork yes 6427.51 ± 2215.19 0.102 3.36 ± 0.56 < 0.001 7.09 ± 2.51 < 0.001 2.48 ± 0.74 < 0.001 no 6079.68 ± 2347.82 3.56 ± 0.50 6.28 ± 2.29 2.34 ± 0.68 Group of chronic diseases number 0 5883.13 ± 2397.74 < 0.001 3.57 ± 0.51 < 0.001 6.32 ± 2.31 < 0.001 2.38 ± 0.70 =2 6946.58 ± 1917.30 3.35 ± 0.56 6.97 ± 2.47 2.32 ± 0.70 Marital status married 6389.55 ± 2226.33 < 0.001 3.53 ± 0.51 0.016 6.31 ± 2.26 < 0.001 2.31 ± 0.66 < 0.001 unmarried 5199.32 ± 2464.64 3.51 ± 0.56 6.94 ± 2.64 2.59 ± 0.76 CDC level provincial 8506.38 ± 3401.54 < 0.001 3.46 ± 0.51 0.011 6.66 ± 2.54 0.037 2.23 ± 0.62 < 0.001 prefectural 6904.92 ± 2349.57 3.51 ± 0.51 6.31 ± 2.40 2.26 ± 0.70 county 5793.24 ± 2079.20 3.53 ± 0.52 6.46 ± 2.33 2.40 ± 0.69 *P-values are based on the Chi-square test and Kruskal-Wallis H test. sd: standard deviation. 3.2 Common method bias Since all variables were self-reported, Harman’s one-way statistical control method ( 63 , 64 ) was used to test for common method bias. The percentage of the variance explained by the first factor in the total variance was used as the basis for judgment, with the 26.84% variance not exceeding the critical criterion of 40%, indicating that there was no obvious common method bias. 3.3 Correlation of main variables Table 2 shows the results of the correlation analysis between the main variables. There was a significant positive correlation between salary and job satisfaction; salary was significantly negatively correlated with burnout and turnover intention; job satisfaction was significantly negatively correlated with burnout and turnover intention; and burnout was significantly positively correlated with turnover intention. The correlation results mirrored the relationship between the variables in the Fig. 1 hypothesized model. (All P < 0.05) Table 2 Correlations among main variables Salary Job satisfaction Burnout Turnover intention Salary 1 Job satisfaction 0.028* 1 Burnout -0.037** -0.554*** 1 Turnover intention -0.173*** -0.431*** 0.472*** 1 *P < 0.05 **P < 0.01 ***P < 0.001 3.4 The chain mediation model for the total sample Table 3 displays the chain mediation model results for the total sample. After fixing the confounding variables, Table 3 shows that for every 1% increase in respondents’ salary, the score of job satisfaction would increase by 0.00165 points (β = 0.165, P < 0.001), and the score of turnover intention would decrease by 0.00059 points (β=-0.059, P < 0.05). For every 1-point increase in respondents’ job satisfaction, the score of burnout would decrease by 2.396 points (β=-2.396, P < 0.001) and turnover intention would decrease by 0.349 points (β=-0.349, P < 0.001). For every 1-point increase in burnout, the score of turnover intention would increase by 0.088 points (β = 0.088, P < 0.001). These results support Hypothesis 1 . Table 3 Regression results for the chain mediation model of the total sample Job satisfaction Burnout Turnover intention β R.SE 95% CI β R.SE 95% CI β R.SE 95% CI Salary 0.165*** 0.026 0.113 ~ 0.217 0.126 0.091 -0.053 ~ 0.304 -0.059* 0.026 -0.110~-0.008 Job satisfaction -2.396*** 0.055 -2.504~-2.287 -0.349*** 0.019 -0.386~-0.312 Burnout 0.088*** 0.004 0.080 ~ 0.096 Control Variables Gender female 0.063*** 0.014 0.035 ~ 0.091 -0.031 0.054 -0.136 ~ 0.074 -0.166*** 0.016 -0.197~-0.135 Age 0.006*** 0.001 0.004 ~ 0.007 -0.041*** 0.004 -0.048~-0.033 -0.012*** 0.001 -0.014~-0.010 Bianzhi yes -0.201*** 0.028 -0.257~-0.145 0.183 0.102 -0.017 ~ 0.383 -0.180*** 0.030 -0.238~-0.121 Degree master or above -0.162*** 0.039 -0.238~-0.086 0.125 0.150 -0.168 ~ 0.419 0.049 0.041 -0.032 ~ 0.130 bachelor -0.089*** 0.017 -0.123~-0.054 -0.043 0.067 -0.175 ~ 0.089 0.034 0.019 -0.002 ~ 0.071 Title senior -0.103*** 0.026 -0.154~-0.051 -0.023 0.102 -0.224 ~ 0.178 -0.024 0.029 -0.080 ~ 0.033 intermediate -0.140*** 0.022 -0.184~-0.096 0.008 0.087 -0.163 ~ 0.179 0.021 0.024 -0.027 ~ 0.069 primary -0.058** 0.020 -0.096~-0.019 0.053 0.075 -0.094 ~ 0.200 0.026 0.021 -0.015 ~ 0.068 Overtime yes -0.162*** 0.017 -0.195~-0.129 0.293*** 0.064 0.168 ~ 0.419 0.012 0.019 -0.026 ~ 0.049 Number of chronic diseases -0.107*** 0.010 -0.126~-0.089 0.196*** 0.037 0.123 ~ 0.268 -0.017 0.011 -0.037 ~ 0.004 CDC level provincial -0.049 0.033 -0.112 ~ 0.015 -0.119 0.131 -0.377 ~ 0.138 -0.209*** 0.037 -0.281~-0.137 prefectural -0.006 0.016 -0.038 ~ 0.026 -0.227*** 0.065 -0.354~-0.100 -0.120*** 0.019 -0.158~-0.082 _cons 2.238*** 0.208 1.831 ~ 2.645 15.160*** 0.729 13.732 ~ 16.588 4.291*** 0.217 3.866 ~ 4.716 *P < 0.05, **P < 0.01, ***P < 0.001, β: Regression coefficient, R.SE: Robust standard error, CV: Control variable, 95% CI refer to lower and upper 95% confidence intervals of the indirect effects estimated by the bias-corrected percentile Bootstrap method, respectively. The baseline level was male workers in county-level CDCs with no bianzhi , below bachelor’s degree, no title, and no overtime. Table 4 and Fig. 2 provide the total, direct, and indirect effects of the model paths. The significance of all effects was checked using 95% bootstrap confidence interval estimates. Except for the indirect effect path 2, all other paths were significant, with the direct path accounting for 42.14% and the indirect effects accounting for 57.86% of the total effect. The model showed that respondents’ salary directly affected turnover intention (direct effect, β=-0.059). Job satisfaction played a separate mediating role between salary and turnover intention (Path 1, β=-0.058). This confirms Hypothesis 2 . Burnout played a separate mediating role between job satisfaction and turnover intention (β=-0.211=-2.396*0.088), which rejects Hypothesis 3 . Job satisfaction and burnout were found to play a chain mediating role between salary and turnover intention (Path 3, β=-0.035), which supports Hypothesis 4 . Table 4 Total, direct, and indirect effects of model paths for the total sample Model paths Observed β Bootstrap SE 95% CI Total effect -0.140 0.033 -0.209~-0.084 (P) -0.203~-0.082 (BC) Direct effect -0.059 0.026 -0.113~-0.013 (P) -0.107~-0.010 (BC) Indirect effect -0.081 0.018 -0.117~-0.047 (P) -0.118~-0.050 (BC) Path1 -0.058 0.010 -0.078~-0.038 (P) -0.079~-0.040 (BC) Path2 0.011 0.008 -0.005 ~ 0.027 (P) -0.005 ~ 0.026 (BC) Path3 -0.035 0.006 -0.046~-0.024 (P) -0.046~-0.024 (BC) Path1: Salary → Job satisfaction → Turnover intention, Path2: Salary → Burnout → Turnover intention, Path3: Salary → Job satisfaction → Burnout → Turnover intention. β: Regression coefficient, SE: Standard error, CI: Confidence intervals, P: Percentile confidence interval, BC: Bias-corrected confidence interval. 3.5 The chain mediation model of marriage subgroups Using the same method and control variables as the total sample, the chained mediated effects model was separately analyzed for the married and unmarried subgroup samples. The main results of the model are displayed in Table 5 . After fixing the confounding variables, the results of the model for married respondents showed that for every 1% increase in the respondent’s salary, the score of job satisfaction would increase by 0.00214 points (β = 0.214, P < 0.001), and the score of turnover intention decreased by 0.00065 points (β = -0.065, P < 0.05). For every 1-point increase in respondents’ job satisfaction, the score of burnout decreased by 2.230 points (β = -2.230, P < 0.001), and turnover intention decreased by 0.302 points (β = -0.302, P < 0.001). For every 1-point increase in burnout, the score of turnover intention increased by 0.085 points (β = 0.085, P < 0.001). The results of the model for unmarried respondents showed that for every 1-point increase in job satisfaction, the score of burnout decreased by 2.852 points (β = 2.852, P < 0.05) and turnover intention decreased by 0.508 points (β = -0.508, P < 0.001). For every 1-point increase in burnout, the score of turnover intention increased by 0.090 points (β = 0.090, P < 0.001). Table 5 Regression results for the chain mediation model among married subgroups Job satisfaction Burnout Turnover intention β.CV R.SE 95%CI β.CV R.SE 95%CI β.CV R.SE 95%CI Model-1: Married Salary 0.214*** 0.031 0.152 ~ 0.275 0.109 0.111 -0.110 ~ 0.327 -0.065* 0.031 -0.126~-0.004 Job satisfaction -2.230*** 0.061 -2.349~-2.111 -0.302*** 0.021 -0.344~-0.261 Burnout 0.085*** 0.004 0.076 ~ 0.094 Model-2: Unmarried Salary 0.091 0.048 -0.004 ~ 0.185 0.051 0.161 -0.265 ~ 0.368 -0.067 0.048 -0.161 ~ 0.026 Job satisfaction -2.852* 0.128 -3.103~-2.601 -0.508*** 0.041 -0.588~-0.427 Burnout 0.090*** 0.009 0.073 ~ 0.107 *P < 0.05, **P < 0.01, ***P < 0.001, β.CV: Regression coefficient with control variables, R.SE: Robust standard error, 95% CI refers to the lower and upper 95% confidence intervals of the indirect effects estimated by the bias-corrected percentile Bootstrap method, respectively. The baseline level was male workers in county-level CDCs with no bianzhi, below bachelor’s degree, no title, and no overtime. Table 6 and Fig. 3 provide the total, direct, and indirect effects of the paths for the married and unmarried subgroup samples. The significance of all effects was checked using 95% bootstrap confidence interval estimates. The significance of the model results for the married sample was similar to the path results for the total sample. Except for the indirect effect Path 2, all paths were significant, with direct effects accounting for 40.37% and indirect effects accounting for 59.63% of the total effect. Married respondents’ salary directly affected turnover intention (Direct effect, β = -0.065). Job satisfaction played an independent mediating role between salary and turnover intention (Path 1, β = -0.065). In Path 2, burnout played an independent mediating role between job satisfaction and turnover intention (β=-0.190=-2.230*0.085). Job satisfaction and burnout were also found to play a chain mediating role between salary and turnover intention (Path 3, β=-0.041). The results of the model for the married sample found that job satisfaction directly affected turnover intention (β=-0.508) and burnout played an independent mediating role between job satisfaction and turnover intention (β=-0.257=-2.852*0.090), but the direct and indirect effects between salary and turnover intention for the unmarried respondents were not significant (95% confidence intervals include 0). Table 6 Total, direct, and indirect effects of model paths for matrimony subgroups Model paths Model-1: Married Model-2: Unmarried Observed β Bootstrap SE 95% CI Observed β Bootstrap SE 95% CI Total effect -0.161 0.038 -0.237~-0.088 -0.132 0.067 -0.263 ~ 0.001 (P) -0.229~-0.082 -0.256 ~ 0.016 (BC) Direct effect -0.065 0.032 -0.129~-0.003 -0.067 0.049 -0.152 ~ 0.041 (P) -0.123 ~ 0.001 -0.152 ~ 0.045 (BC) Indirect effect -0.096 0.019 -0.133~-0.059 -0.065 0.041 -0.159 ~ 0.009 (P) -0.133~-0.059 -0.16 ~ 0.008 (BC) Path1 -0.065 0.011 -0.088~-0.045 -0.046 0.026 -0.101~-0.001 (P) -0.089~-0.046 -0.101~-0.001 (BC) Path2 0.009 0.009 -0.008 ~ 0.029 0.005 0.015 -0.026 ~ 0.032 (P) -0.008 ~ 0.029 -0.026 ~ 0.038 (BC) Path3 -0.041 0.006 -0.054~-0.028 -0.023 0.013 -0.052 ~ 0 (P) -0.054~-0.029 -0.052 ~ 0 (BC) Path1: Salary → Job satisfaction → Turnover intention, Path2: Salary → Burnout → Turnover intention, Path3: Salary → Job satisfaction → Burnout → Turnover intention. β: Regression coefficient, SE: Standard error, CI: Confidence intervals, P: Percentile confidence interval, BC: Bias-corrected confidence interval. Discussion China’s CDCs face high job turnover, especially in western and central regions. Our contribution was to investigate the complex inter-relationships between salary, job satisfaction, burnout, and turnover intention. We found a chain mediating effect of job satisfaction and burnout between salaries and turnover intentions among CDC staff in the western province of Yunnan. Previous studies have investigated single variables or only bilateral relationships between salary, job satisfaction, burnout, and turnover intention. For example, a study of local CDCs in Hainan Province reported a job satisfaction level of 3.35 out of 5 points, indicating a moderate degree of satisfaction. ( 38 ) A survey of Sichuan Province prefectural and county-level CDC staff found that 80.73% experienced burnout. ( 65 ) An investigation by CDC staff in Henan Province found that the overall job satisfaction score among was low (67.13/100), while their intention to leave was relatively high (7.31/10). (66) A survey of newly recruited CDC personnel in Lianyungang City, Jiangsu Province, revealed a moderate turnover intention level (57.20/100). (67) These findings are broadly consistent with our results, where the level of job satisfaction was moderate (3.52/5), the burnout rate was relatively high (80.83%), and turnover intention was at a moderate level (2.36/5). Our research revealed the independent mediating effect of job satisfaction between the direct effect of salary on turnover intention, and validated the chain mediating effect of job satisfaction and burnout between salary and turnover intention. Our study also found differences in the mediation path for the marital status subgroups suggest heterogeneity within the subsample. Analysis of the overall sample revealed that salary directly influences turnover intentions among CDC staff, with a direct effect of 42.14%, similar to reported results for Australian mental health workers, nurses in Philippine hospitals, and Indian dental hygienists. ( 15 – 17 ) The issue of low salary among CDC personnel has been extensively documented, ( 11 , 35 ) with salary the primary consideration for CDC staff in turnover decisions. ( 7 – 9 ) For example, a Beijing study found that 54.51% of CDC staff expressed salary dissatisfaction, and 44.48% had considered resigning, with low salary being the key driver. ( 21 ) Inadequate salaries play a key role in weakening the economic stickiness of job retention and reducing employees’ sense of identity and trust in the organization. ( 11 ) Our recommendation for higher salaries does not imply indiscriminately pursuing higher salaries as a stand-alone approach, but as part of a wider strategy optimizing compensation structures, aligning career development with performance incentives, addressing burnout and job satisfaction, and targeting an optimal salary incentive range. In 2023, the Chinese government proposed reforming the compensation scheme for CDC personnel as a measure to promote the high-quality development of the CDC system. ( 68 ) Given our mediation results, we suggest that a stand-alone compensation policy is unlikely to be successful. Our results show that both job satisfaction and burnout are associated with turnover intentions among CDC staff, and job satisfaction is negatively correlated with burnout. While these direct relationships have been noted in previous studies, ( 52 , 69 ) our mediated model’s full-sample results support two hypotheses. Job satisfaction and burnout exhibit a chained mediating effect between salary and turnover intentions among CDC workers, and job satisfaction demonstrates an independent mediating effect. The cumulative proportion of indirect effects reached 57.86%, with lower salaries leading to lower job satisfaction, triggering this mediation process. The negative effects of low job satisfaction were mainly concentrated on increasing turnover intention due to burnout (β=-2.396, P < 0.05). The mismatch between inadequate salary and individual value contributions creates a strong sense of unfairness among CDC staff. CDC staff perceive their work effort as failing to receive commensurate material rewards, eroding job satisfaction, and intrinsic motivation. This leads to their feelings that their hard work is underappreciated and their contributions undervalued. These feelings are exacerbated when excessive workloads and work pressure are compounded without significant salary increases, such as no significant salary difference between overtime and non-overtime subgroups, leading to psychological disappointment and emotional exhaustion. This is followed by a significant decline in work engagement, when emotionally exhausted staff subconsciously reduce extra effort, performing only the bare minimum required to avoid the feeling of being “exploited”, fostering and intensifying burnout. When this psychological burnout persists without salary increases or other recognitions, CDC staff seek external alternative opportunities, viewing resignation as the ultimate path to restore psychological equity and gain value recognition. Ultimately, this chain reaction—from ‘perceived mismatch’ to ‘psychological imbalance’ and then to ‘behavioral withdrawal’ —led to resignations. Other studies have indicated that factors such as limited career advancement opportunities, inadequate benefits, and suboptimal working conditions contribute to turnover among CDC staff. ( 7 , 70 ) But improvements in these factors are likely to have only a limited impact. We found that salary stood at the head of the chain effect, which requires CDC administrators to optimize performance incentives and financial management mechanisms. As discussed, salary adjustments alone are unlikely to solve CDC’s personnel retention problem without addressing employees’ multifaceted developmental needs, job dissatisfaction, burnout, and job satisfaction. Significantly, our subgroup marital status analysis revealed heterogeneous pathways linking salary, job satisfaction, burnout, and turnover intention. In the married group, the direct and indirect effects as well as pathways linking salary, job satisfaction, burnout, and turnover intention closely mirrored those in the full sample. In the unmarried group, job satisfaction directly or indirectly predicted turnover intention through burnout; no significant associations were observed between salary and job satisfaction, burnout, or turnover intention. This indicates that the economic factors were less significant in the married subgroup. We posit that this inertia operates bidirectionally. ( 24 , 71 ) Married individuals are compelled to remain employed due to family financial obligations, but their salary level failed to balance household expenditures and their perceived labor value, triggering a negative feedback cycle between the work and family domains, which accelerated turnover. Further, job dissatisfaction induced by low salaries heightened the likelihood of employee turnover. While our data confirmed prior studies ( 14 ) that lower turnover intentions among married staff (2.31 ± 0.66) versus unmarried (2.59 ± 0.76) (P < 0.01), our explanation of the process is more nuanced. While married workers’ heightened economic dependence drove more stable married CDC employment, our marital subgroup analysis provides empirical substantiation for this heterogeneity based on the complex inter-play between salaries, perceived work worth, job satisfaction, family-work domain, and turnover. These results reinforce our recommendation to link CDC salary rises with a range of work and family arrangements. Although unmarried individuals did not exhibit economic retention inertia, the burnout score among unmarried CDC staff (6.94 ± 2.64) was significantly higher than that of married staff (6.31 ± 2.26) (P < 0.01). Existing studies indicate that unmarried—typically younger—workers demonstrate lower marginal utility sensitivity to salary. ( 72 ) Professional development opportunities, not salary, were prioritized by most unmarried workers in their early career stages. ( 50 ) Ambiguous career trajectories and unfavourable advancement prospects constitute critical contributors to burnout among young CDC employees. Besides addressing unmarried individuals’ salary levels, we recommend the design and clear communication of well-defined career development paths, providing growth-oriented incentives and supportive training to enhance young CDC staff’s confidence in their professional prospects. Like Chinese doctors and nurses, ( 18 , 23 ) our analysis showed that work and sociodemographic characteristics, such as gender, age, job title, educational background, overtime work, number of chronic diseases, and unit level, impacted job satisfaction and burnout of CDC staff and directly or indirectly influenced their turnover intentions. To tackle turnover intention, CDC administrators must address the psychological well-being of CDC staff, ( 5 ) particularly among highly educated individuals holding master’s degrees or higher and young workers frequently engaged in night shifts and overtime commitments. When refining retention strategies for CDC personnel, policymakers must shape retention policies for the heterogeneous characteristics of employee subgroups. Besides income-related policies, such as fair overtime compensation, we recommend that CDC policymakers provide preferential access to research resources for highly educated research-oriented personnel; develop burnout intervention programs for “at-risk” workers; and conduct regular mental health screenings for staff. We collected a rich research dataset on salaries, job satisfaction, burnout, and turnover intentions on all CDCs in Yunnan Province. Generalising our Yunnan findings to other regions and provinces should consider the specific circumstances in Yunnan and the different conditions in other provinces and regions. Our cross-sectional survey data preclude definitive causal interpretations among key variables. Variables were measured via self-administered questionnaires and self-rated scales. Despite high reliability and validity and rigorous quality control, inherent subjectivity and measurement errors are unavoidable. Future studies should collect further decision-making variables, such as professional identity, social support, and familial stressors. Finally, future research should implement longitudinal tracking to enable more robust causal inference. Conclusions Our study revealed that job satisfaction and burnout demonstrated a chained mediating effect between salary and turnover intention, with job satisfaction additionally exhibiting an independent mediating effect. No comparable mediating role was observed for burnout. The unmarried subgroup was found to lack the turnover stickiness observed in the married subgroup, and exhibited a higher burnout score. Given our mediation results, a stand-alone compensation policy is unlikely to be successful in addressing CDC turnover. We recommend a multifaceted marital-specific approach to CDC personnel management that optimizes compensation structures, aligns career development with performance incentives, addresses burnout, and improves job satisfaction. Abbreviations CDC Center for Disease Control and Prevention MSQ Minnesota Satisfaction Questionnaire KMO Kaiser-Meyer-Olkin Measure MBI-GS Maslach Burnout Inventory–General Survey ILS Intent to Leave Scale CI confidence interval Declarations Ethics approval and consent to participate All respondents included in the analyses responded anonymously after reading and agreeing to the informed consent information for this study. The Ethics Committee of Beijing University of Chinese Medicine reviewed and approved this study (NO.2024BZYLL0303). The entire research process strictly complied with the Helsinki Declaration. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The data and materials used in the article can be obtained from the subject team if required for reasonable purposes. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the Chinese Centre for Disease Control and Prevention (NO:90020671720092). Authors’ contributions HLN and XFS designed the research and formulated the research objectives; HLN, WJY, and QB conducted data sorting, data cleaning, and statistical analysis; HLN and JTZ drafted the manuscript; EM and SN made important revisions to the manuscript and provided language support; HLN and YJW participated in scale revisions; XFS collected data, helped develop ideas, revised and edited the manuscript. All authors reviewed the manuscript. Acknowledgments Thanks are due to China CDC and the 39 sample CDCs for their assistance in completing the data collection. References Cai WQ, Li CY, Sun M, Hao M. Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China. International journal for equity in health. 2019;18(1). Chen H, Cui Y, Liu KX, Lai CY, Wang Y, Chen QF, et al. China's Initiatives and Achievements in Enhancing the Professional Capabilities of CDCs. China CDC Wkly. 2024;6(38):979-81. Li CY, Sun M, Wang Y, Luo L, Yu MZ, Zhang Y, et al. The Centers for Disease Control and Prevention System in China: Trends From 2002-2012. Am J Public Health. 2016;106(12):2093-102. China NHCotPsRo. "14th Five-Year" Health Talent Development Plan Issued by the National Health Commission: Government of China; 2022 [cited 2025 2025-9-16]. Available from: https://www.gov.cn/zhengce/zhengceku/2022-08/18/content_5705867.htm. Cui Q, Liu L, Hao ZJ, Li MY, Liu CL, Yang CX, et al. Research on the influencing factors of fatigue and professional identity among CDC workers in China: an online cross-sectional study. BMJ Open. 2022;12(4). Hong-yi C, Li-na Y, Guang-peng Z, Lin W, Hao C, Xue-ning W. Comparative analysis on comprehensive quality of mobile personnel in CDCs. Practical Preventive Medicine. 2017;24(07):841-3. Shan Y, Liu GW, Zhou CQ, Li SX. The Relationship Between CDC Personnel Subjective Socioeconomic Status and Turnover Intention: A Combined Model of Moderation and Mediation. Front Psychiatry. 2022;13. Meng QY. Strengthening public health systems in China. Lancet Public Health. 2022;7(12):E987-E8. Na-na L, Lie-yu H, Yang L, Zhen-jun L. Analysis of the Personnel Outflow in Chinese Center For Disease Control and Prevention From 2014 To 2021. Chinese Health Resources Management. 2023;40(04):289-91+96. Liming L, Hua W, Xiaofeng L, Zhenqiang B, Jun R, Lan W. Recommendation on the modernization of disease control and prevention. Chinese Journal of Epidemiology. 2020;41(04):453-60. Zhuge RQ, Wang YP, Gao YR, Wang QK, Wang YX, Meng N, et al. Factors influencing the turnover intention for disease control and prevention workers in Northeast China: an empirical analysis based on logistic-ISM model. BMC Health Serv Res. 2024;24(1). Qi X, Wang Y, Xia L, Meng Y, Li Y, Yu S, et al. Cross-sectional survey on public health informatics workforce in China: issues, developments and the future. Public Health. 2015;129(11):1459-64. Zhang XW, Zhang WJ, Xue L, Xu ZY, Tian Z, Wei C, et al. The influence of professional identity, job satisfaction, burnout on turnover intention among village public health service providers in China in the context of COVID-19: A cross-sectional study. Front Public Health. 2022;10. Xu Z, Zhang L, Yang Z, Yang G. Burnout and turnover intention of primary health care providers during the COVID-19 pandemic in China. Public Health. 2023;225:191-7. Scanlan JN, Still M. Relationships between burnout, turnover intention, job satisfaction, job demands and job resources for mental health personnel in an Australian mental health service. BMC Health Serv Res. 2019;19. Labrague LJ, de los Santos JAA, Falguera CC, Nwafor CE, Galabay JR, Rosales RA, et al. Predictors of nurses' turnover intention at one and five years' time. Int Nurs Rev. 2020;67(2):191-8. Patel BM, Boyd LD, Vineyard J, LaSpina L. Job Satisfaction, Burnout, and Intention to Leave among Dental Hygienists in Clinical Practice. J Dent Hyg. 2021;95(2):28-35. Zhang YM, Feng XS. The relationship between job satisfaction, burnout, and turnover intention among physicians from urban state-owned medical institutions in Hubei, China: a cross-sectional study. BMC Health Serv Res. 2011;11. Shuangshuang L. The impact of training load on turnover intention of junior nurses: a chain-mediation study of burnout and job satisfaction [硕士]: Shandong University; 2023. Wang HP, Jin YZ, Wang D, Zhao SC, Sang XG, Yuan BB. Job satisfaction, burnout, and turnover intention among primary care providers in rural China: results from structural equation modeling. BMC Fam Pract. 2020;21(1). Yu-jie Y, Shuai D, Yue-li M, Pei D, Min-jie Z, Kun W, et al. Investigation and analysis of work situation of professional and technical personnel in Beijing Centers for Disease Prevention and Control during the COVlD-19 epidemic. Chinese Journal of Disease Control and Prevention. 2022;26(06):696-702. Croes R, Padrón-Avila H, Rivera M, Renduchintala C. A triadic model of job retention and turnover dynamics in the hospitality industry. International Journal of Contemporary Hospitality Management. 2025;37(3):700-21. Ma Y, Chen F, Xing D, Meng Q, Zhang Y. Study on the associated factors of turnover intention among emergency nurses in China and the relationship between major factors. Int Emerg Nurs. 2022;60:101106. Zhang Y. Disclosing the relationship between public service motivation and job satisfaction in the Chinese public sector: A moderated mediation model. Front Psychol. 2023;14. Ouyang YQ, Zhou WB, Xiong ZF, Wang R, Redding SR. A Web-based Survey of Marital Quality and Job Satisfaction among Chinese Nurses. Asian Nurs Res (Korean Soc Nurs Sci). 2019;13(3):216-20. Denson N, Szelényi K. Faculty perceptions of work-life balance: the role of marital/relationship and family status. High Educ (Dordr). 2022;83(2):261-78. Zhang Z. Analysis report on trends in public infectious disease control in China. Front Public Health. 2024;12:1423191. Tong MX, Hansen A, Hanson-Easey S, Xiang J, Cameron S, Liu Q, et al. Public health professionals' perceptions of the capacity of China's CDCs to address emerging and re-emerging infectious diseases. J Public Health (Oxf). 2021;43(1):209-16. Ji JS, Xia Y, Liu L, Zhou W, Chen R, Dong G, et al. China's public health initiatives for climate change adaptation. Lancet Reg Health West Pac. 2023;40:100965. Liu T, Zhang Y, Zhang H, Yang XP. A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China. Sustainability. 2021;13(9). Li N, Feng Y, Vrancken B, Chen Y, Dong L, Yang Q, et al. Assessing the impact of COVID-19 border restrictions on dengue transmission in Yunnan Province, China: an observational epidemiological and phylogenetic analysis. Lancet Reg Health West Pac. 2021;14:100259. Zeng XC, Sun XD, Li JX, Chen MN, Deng DW, Zhang CL, et al. Assessment of malaria control consultation and service posts in Yunnan, P. R. China. Infect Dis Poverty. 2016;5(1):102. Zhang F, Liu Y, Wei TQ. Psychological Capital and Job Satisfaction Among Chinese Residents: A Moderated Mediation of Organizational Identification and Income Level. Front Psychol. 2021;12. Xu XM, Cropanzano R, McWha-Hermann I, Lu CQ. Multiple Salary Comparisons, Distributive Justice, and Employee Withdrawal. J Appl Psychol. 2024;109(10):1533-54. Qian W, Jian W, Yue Z. Analysis on the impact and countermeasures after cancelling "Three Charges" in the Centers for Disease Control and Prevention.Chinese Health Economics. Chinese Health Economics. 2018;37(08):26-8. Chan W, Zhiqiang L, Zhendong G. Challenges faced by china's CDCs and their countermeasures: ananalysis based on the survey data from six provinces. Chinese Journal of Medical Management Sciences. 2020;10(01):10-4. Judge TA, Weiss HM, Kammeyer-Mueller JD, Hulin CL. Job Attitudes, Job Satisfaction, and Job Affect: A Century of Continuity and of Change. J Appl Psychol. 2017;102(3):356-74. Li N, Wang YW, Yu DE, Xiao S, Liu YR. Job satisfaction of staff in agencies for disease prevention and control in Hainan Province, China. J Pak Med Assoc. 2020;70(3):523-5. Hanlin N. Research on job satisfaction and its influencing factors among disease prevention and control teams [硕士]: Beijing University of Chinese Medicine; 2024. Wanyin X, Fen Z, Rui M. Study on Job Satisfaction and Influencing Factors of Staff in Center of Disease Control and Prevention at County/District-level. Chinese Journal of Social Medicine. 2023;40(02):225-9. Chen GH, Lin CQ, Chen YH, Li L, Luo ST, Liu XY, et al. Job Satisfaction Among Methadone Maintenance Treatment Clinic Service Providers in Jiangsu, China: A Cross-sectional Survey. J Addict Med. 2020;14(1):12-7. Christina Maslach SEJ. The measurement of experienced burnout. J Organ Behav. 1981;2(2):99-113. pin LC, kan S. The influence of distributive justice and procedural justice on job burnout. Acta Psychologica Sinica. 2003(05):677-84. Wang G, Yuan Q, Feng X, Zhang T, Wang Q, Huang Q, et al. The job burnout of tuberculosis healthcare workers and associated factors under integrated tuberculosis control model: a mixed-method study based on the two-factor theory. BMC Health Serv Res. 2024;24(1):984. Mobley WH. Intermediate linkages in the relationship between job satisfaction and employee turnover. J Appl Psychol. 1977;62(2):237-40. Wang EJ, Hu HW, Mao S, Liu HT. Intrinsic motivation and turnover intention among geriatric nurses employed in nursing homes: the roles of job burnout and pay satisfaction. Contemp Nurse. 2019;55(2-3):195-210. Deriba BK, Sinke SO, Ereso BM, Badacho AS. Health professionals' job satisfaction and associated factors at public health centers in West Ethiopia. Hum Resour Health. 2017;15. Mao YQ, Hu Y, Feng ZC, Wang RX, Chen XY, Zhang WP, et al. Job burnout and correlated factors of three-tiered public health workers: A cross-sectional study in China. Health Soc Care Community. 2020;28(4):1241-51. Li X, Wang JL, He L, Hu Y, Li CW, Xie YM, et al. Turnover intention and influential factors among primary healthcare workers in Guangdong province, China: a cross-sectional study. BMJ Open. 2024;14(11). Guo Y, Nie HL, Chen H, Nicholas S, Maitland E, Chen SS, et al. Job Preferences of Centers for Disease Control and Prevention Workers: A Discrete Choice Experiment in China. Biomed Environ Sci. 2025;38(6):740-50. Weiss DJ, Dawis RV, England GW. Manual for the Minnesota Satisfaction Questionnaire. Minnesota Studies in Vocational Rehabilitation. 1967;22:120-. Wu FY, Ren Z, Wang Q, He MF, Xiong WJ, Ma GD, et al. The relationship between job stress and job burnout: the mediating effects of perceived social support and job satisfaction. Psychol Health Med. 2021;26(2):204-11. Zhao YZ, Wang YL, Guo W, Cheng L, Tong JL, Ji RP, et al. Studies on the Relationship between Occupational Stress and Mental Health, Performance, and Job Satisfaction of Chinese Civil Aviation Pilots. Aerospace. 2023;10(10). Zeng XQ, Zhang XX, Chen MR, Liu JP, Wu CM. The Influence of Perceived Organizational Support on Police Job Burnout: A Moderated Mediation Model. Front Psychol. 2020;11. Li HG, Zuo MZ, Gelb AW, Zhang B, Zhao XH, Yao DD, et al. Chinese Anesthesiologists Have High Burnout and Low Job Satisfaction: A Cross-Sectional Survey. Anesth Analg. 2018;126(3):1004-12. Maslach C, Jackson SE. Evaluating Stress: a Book of Resources1997. Wang Y, Zhang H, Lei J, Yu YH. Burnout in Chinese social work: Differential predictability of the components of the Maslach Burnout Inventory. Int J Soc Welf. 2019;28(2):217-28. Wu SY, Zhu W, Wang ZM, Wang MZ, Lan YJ. Relationship between burnout and occupational stress among nurses in China. J Adv Nurs. 2007;59(3):233-9. Huang Q, Liu HX, Chu CY. Effects of Paternalistic Leadership on Quality of Life of Grassroots Officials in China: Mediation Effects of Burnout. Appl Res Qual Life. 2021;16(5):2113-30. Scott CR, Connaughton, S. L., Diaz-Saenz, H. R., Maguire, K., Ramirez, R., Richardson, B., Shaw, S. P., & Morgan, D. The Impacts of Communication and Multiple Identifications on Intent to Leave: A Multimethodological Exploration. Manag Commun Q. 1999;12(3):400-35. Feng BA, Dou GJ, Zhan XQ. Negative workplace gossip and turnover intention among kindergarten teachers: psychological safety as a mediator and organizational identification as a moderator. Front Psychol. 2025;16. Wu XX, Hayter M, Lee AJ, Yuan Y, Li S, Bi YX, et al. Positive spiritual climate supports transformational leadership as means to reduce nursing burnout and intent to leave. J Nurs Manag. 2020;28(4):804-13. hao z, lirong l. Statistical Remedies for Common Method Biases. Advances in Psychological Science. 2004(06):942-50. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. Si-mou W, Gui-gian S, Wei Q, En-ging Z, Yan T. Status of job burnout among disease control and prevention technicians at county and city levels in Sichuan Province in 2017. Occupation and Health. 2019;35(12):1592-5. Jincheng C, Shucun M. Investigation on job satisfaction and turnover intention among employees of CDC institutions in Henan Province. Jiangsu Journal of Preventive Medicine. 2022;33(01):115-7. Axiu L, Zhengjun Q, Fan G, Yumeng Z, Jincan C, Ting S. Analysis of Turnover Intention and Related Factors Among Newly Recruited Staff in the Center for Disease Control and Prevention System of Lianyungang City from 2020 to 2022. Jiangsu Journal of Health Care. 2024;26(05):453-5. China SCotPsRo. Guidelines on promoting the high-quality development of disease prevention and control: Government of China; 2023 [cited 2025 2025-9-16]. Available from: http://www.gov.cn/zhengce/content/202312/content_6922483.htm. Song X, Xiang M, Liu Y, Yu C. Relationship Between Job Satisfaction and Burnout Based on a Structural Equation Model. J Occup Environ Med. 2020;62(12):e725-e31. Li XY, Yang CX, Liu LB, Ding YL, Xue JC, He JN, et al. Configurational paths to turnover intention among primary public health workers in Liaoning Province, China: a fuzzy-set qualitative comparative analysis. BMC Public Health. 2024;24(1). Wang HQ, Xu X, Yang YL, Li L. The effects of organization and community embeddedness on public health professionals' intention to stay during the COVID-19 pandemic: a cross-sectional study. Hum Resour Health. 2025;23(1). Ze-gui T, Si-si C, Yi-xuan C, Hao Y, Xue-feng S. A study on job preferences of CDC staffs at the prefectural-levels in Shandong province:Based on a discrete choice experiment. Chinese Journal of Health Policy. 2024;17(01):60-7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8385919","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573855828,"identity":"915d751c-6ca6-46e4-a2c9-c9d640f5cc21","order_by":0,"name":"Hanlin Nie","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hanlin","middleName":"","lastName":"Nie","suffix":""},{"id":573855829,"identity":"ec24a14e-a894-45d9-9346-1e185991dae4","order_by":1,"name":"Wanjin Yang","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wanjin","middleName":"","lastName":"Yang","suffix":""},{"id":573855830,"identity":"5a022858-7258-4a65-b87a-306733aa51c1","order_by":2,"name":"Jingting Zeng","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingting","middleName":"","lastName":"Zeng","suffix":""},{"id":573855831,"identity":"4ff0d18c-0427-4f1a-9b9e-414a3190d66c","order_by":3,"name":"Qian Bai","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Bai","suffix":""},{"id":573855834,"identity":"92611d4d-c4a1-473f-aaab-2b93448692c6","order_by":4,"name":"Elizabeth Maitland","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Maitland","suffix":""},{"id":573855836,"identity":"09c46370-b5ba-499c-b9cd-2996b6df8b5a","order_by":5,"name":"Stephen Nicholas","email":"","orcid":"","institution":"University of Newcastle Australia","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Nicholas","suffix":""},{"id":573855848,"identity":"47769609-151e-462e-bf5e-91dc6569c616","order_by":6,"name":"Yijie Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijie","middleName":"","lastName":"Wang","suffix":""},{"id":573855850,"identity":"10cb546e-1ab4-4a0b-9ddf-2b138fdfdb11","order_by":7,"name":"Xuefeng Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCcYGICkHxMzHoEIJRGkxBmK2NGK1gEmQFh4z4rTIz25uk+apMJAz51/z7TFPzTYGfvYcA4afO3BrMbhzsNmY54yBseWMt9uNeY7dZpDseWPA2HsGjxaJxMbHvG1/EjfcOLtNmrfhNoPBjRwDZsY2PA6bkdhwmPefQf2GG2eegbXYE9LCcANkS4NBgsH5HjaILRIEtBjcSGw2nHPMwHDDDTZzIOM2j8SZZwUHe/E6LP2ZxJsaA3mD84efPXhTc1uOvz1544Of+BwGBxIJYIoHRBwgRgMDAz+R6kbBKBgFo2DkAQCV3VK5HF1bdAAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xuefeng","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-12-17 12:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8385919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8385919/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100546228,"identity":"8a79879a-f3a9-422f-b4e4-8016df819d6f","added_by":"auto","created_at":"2026-01-19 08:02:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":258909,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptv2.0OriginalArticle.docx","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/91dc03700772062f79e97816.docx"},{"id":100546324,"identity":"d5f21234-c3e0-42f8-8a9d-0a44bb2c04cf","added_by":"auto","created_at":"2026-01-19 08:05:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102606,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/69ac801e40b0950f091e22bc.docx"},{"id":100546438,"identity":"70894d3b-4d89-4abd-9411-df9c8daeb752","added_by":"auto","created_at":"2026-01-19 08:08:41","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9596,"visible":true,"origin":"","legend":"","description":"","filename":"35fff9134d1148fe9b12f9b0c707b3ca.json","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/0dc1ab910ea122e4e19ced51.json"},{"id":100546898,"identity":"27859f04-533a-4159-84d3-8955763810e3","added_by":"auto","created_at":"2026-01-19 08:13:07","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":224884,"visible":true,"origin":"","legend":"","description":"","filename":"35fff9134d1148fe9b12f9b0c707b3ca1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/f52da71cde8e1ad84cc0fcc8.xml"},{"id":100546135,"identity":"6863d88b-233f-4f11-a9fe-996cc083c044","added_by":"auto","created_at":"2026-01-19 07:59:26","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17899,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/dcd8f356a8edaa40c52a7f94.png"},{"id":100546261,"identity":"f446bbe2-6dd4-4867-8706-250956151750","added_by":"auto","created_at":"2026-01-19 08:03:30","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22696,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/584a956648d60f47e092494f.png"},{"id":100547224,"identity":"9055f7d1-a938-4ed0-9e92-8ca6e4c61de2","added_by":"auto","created_at":"2026-01-19 08:14:55","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45545,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/f877ad0edfb28a8518480236.png"},{"id":100546263,"identity":"fd739d8a-1637-4f67-8f8c-6d30dadd2b3f","added_by":"auto","created_at":"2026-01-19 08:03:39","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17899,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/e37b4d8b8103d01911391ade.png"},{"id":100546780,"identity":"45ab792d-45f3-432e-b9da-b5c241db7549","added_by":"auto","created_at":"2026-01-19 08:12:32","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22696,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/da697a1f255b3427bba1ae4a.png"},{"id":100430476,"identity":"9a4c0d5b-9317-40a2-905a-63d4f8e53f2c","added_by":"auto","created_at":"2026-01-16 14:41:44","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45545,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/bdb0fb683d2cc50a7a6d630e.png"},{"id":100546354,"identity":"1d8b1efd-1747-47db-bcf9-ef91bfc17a9d","added_by":"auto","created_at":"2026-01-19 08:06:42","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9767,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/240eae0db9df9455fcc68207.png"},{"id":100546409,"identity":"96e5b05d-0cb6-4994-b6e1-e1e71b3740ff","added_by":"auto","created_at":"2026-01-19 08:08:17","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11448,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/d90de60ec7217b77955bed07.png"},{"id":100546803,"identity":"d31e2eb6-20f2-4132-a3c1-0fde2a4d16b4","added_by":"auto","created_at":"2026-01-19 08:12:39","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21944,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/cd07d165c576b33b55db4dce.png"},{"id":100546418,"identity":"d21a0ddf-391c-4b3d-bf84-f04d98c796c9","added_by":"auto","created_at":"2026-01-19 08:08:23","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9767,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/323883a9abf1dc2aa644f4f0.png"},{"id":100546690,"identity":"72cc3ead-fbc5-4f07-a1aa-6c1883a3524c","added_by":"auto","created_at":"2026-01-19 08:11:54","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11448,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/dddc505e59cc786ad6c2459f.png"},{"id":100546688,"identity":"21a7c00b-cf52-4723-8bc4-049d2e7e951e","added_by":"auto","created_at":"2026-01-19 08:11:53","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21944,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/779b4ba98def34c9801b3db8.png"},{"id":100546191,"identity":"c23d3af1-5c6c-4e6c-adc0-1655cba192ae","added_by":"auto","created_at":"2026-01-19 08:00:25","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221146,"visible":true,"origin":"","legend":"","description":"","filename":"35fff9134d1148fe9b12f9b0c707b3ca1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/2007dbfbf98fd23b40ff4354.xml"},{"id":100546338,"identity":"7497007b-25f0-418b-864f-f317745fc3dc","added_by":"auto","created_at":"2026-01-19 08:06:18","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":238321,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/37e2a6e3680fd7b6eea27935.html"},{"id":100430466,"identity":"b13b4fe7-60a3-4aa4-9b92-986dd8971555","added_by":"auto","created_at":"2026-01-16 14:41:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48722,"visible":true,"origin":"","legend":"\u003cp\u003eModel of the hypothesized mediating role (+: Positive correlation, -: Negative correlation)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/a393d8754f24811b00fe633c.png"},{"id":100547155,"identity":"f1362b67-0d23-4ee7-a7e4-c80a0605076d","added_by":"auto","created_at":"2026-01-19 08:14:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":114267,"visible":true,"origin":"","legend":"\u003cp\u003eModel of the hypothesized mediating role for the total sample (*P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/509c90eb31f632ee0708288a.png"},{"id":100430467,"identity":"909463ac-61a6-4c00-a42d-b77e4944b9c3","added_by":"auto","created_at":"2026-01-16 14:41:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60997,"visible":true,"origin":"","legend":"\u003cp\u003eModel of the hypothesized mediating role for matrimony subgroups (*P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/779fa0267a454940c55ab129.png"},{"id":105904274,"identity":"6fb85907-9899-4fda-a44f-1f3f698932c9","added_by":"auto","created_at":"2026-04-01 10:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1616145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8385919/v1/4d330611-cf44-442b-adb0-1959c52e7c2f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The turnover challenge in China’s Centers for Disease Control and Prevention: Role of salaries, job satisfaction and burnout","fulltext":[{"header":"Background","content":"\u003cp\u003eCenters for Disease Control and Prevention (CDCs) are a core pillar in China\u0026rsquo;s\u0026nbsp;public health system,\u003csup\u003e(1)\u003c/sup\u003e responsible for disease prevention and control, public health emergency responses, vaccination monitoring, and public health promotion.\u003csup\u003e(1, 2)\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eSince 2009, the continuous reforms in China\u0026rsquo;s health sector have seen CDCs make significant improvements both in their institutional scale and operational capabilities,\u003csup\u003e(2, 3)\u003c/sup\u003e including increasing the number of CDC staff to 250,000 by 2025.\u003csup\u003e(4)\u003c/sup\u003e Due to the sudden and contagious nature of disease prevention and control work, CDC staff frequently face significant on-the-job challenges, including heavy workloads, uncertainties in infectious disease risks, and emotional stress.\u003csup\u003e(5)\u003c/sup\u003e Numerous studies confirm that CDC employees receive compensation disproportionately lower than their counterparts in medical institutions at equivalent levels.\u003csup\u003e(6, 7)\u003c/sup\u003e Not surprisingly, China\u0026rsquo;s CDC is experiencing severe staff turnover, particularly among highly educated backbone staff and young professionals,\u003csup\u003e(8-11)\u003c/sup\u003e which is especially pronounced in China\u0026rsquo;s central and western provinces.\u003csup\u003e(3, 12)\u003c/sup\u003e Research on the attrition of CDC personnel generally agrees that low salary is the key factor constraining recruitment and explaining high turnover.\u003csup\u003e(9)\u003c/sup\u003e Studies also indicate the rise in mental health issues, with low job satisfaction and burnout serving as significant accelerators of staff turnover.\u003csup\u003e(5, 13, 14)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAcross diverse countries and professions, empirical research demonstrates that declining job satisfaction triggers burnout, and higher levels of burnout correlate with stronger intentions to leave jobs, ultimately leading to high turnover rates.\u003csup\u003e(15-17)\u003c/sup\u003e Within the context of China\u0026rsquo;s health workforce, the job satisfaction-burnout-turnover intention relationship has mainly been studied among urban doctors and nurses\u003csup\u003e(18, 19)\u003c/sup\u003e and rural primary health care providers,\u003csup\u003e(20)\u003c/sup\u003e with fewer studies of public health professionals, particularly the largest group, CDC staff. Some studies on CDC staff have confirmed that low salary correlates with reduced job satisfaction or increased burnout, or a higher likelihood of resignation.\u003csup\u003e(7, 11, 21)\u003c/sup\u003e These studies have largely been limited to analyzing the binary relationships between job satisfaction and burnout, or burnout and turnover intention, or job satisfaction and salary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe structural relationships and combined effect among these variables, especially on turnover,\u0026nbsp;have not been fully analyzed for CDC personnel. In addition, the well-established impact of marital status on employees\u0026rsquo; turnover behavior\u003csup\u003e(22)\u003c/sup\u003e has been neglected in CDC turnover studies. Non-CDC studies indicate that married employees may be more likely to remain in an organization due to family responsibilities or the need for stable work than unmarried staff.\u003csup\u003e(14, 23)\u003c/sup\u003e A stable marital life can provide emotional support and alleviate work-related stress, reducing burnout and lowering turnover rates.\u003csup\u003e(24-26)\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDifferences in the impact of marital status on job satisfaction, burnout, and turnover intentions among CDC staff remain unexplored. Our study addresses two gaps in the existing literature: the structural relationships and combined effect on CDC turnover of salary, job satisfaction, and burnout, and the inter-relationship of marital status and turnover, salaries, job satisfaction, and burnout. Understanding these relationships is critical to developing targeted intervention measures to improve salaries and job satisfaction, alleviate burnout, and reduce turnover intentions among CDC staff.\u003c/p\u003e\n\u003cp\u003eChina\u0026rsquo;s CDC system faces exceptionally high workloads and pressure due to China\u0026rsquo;s complex terrain and climate, a vast population, diverse disease profiles, regional economic disparities, and linguistic-cultural heterogeneity.\u003csup\u003e(27-29)\u003c/sup\u003e A renowned tourist province along China\u0026rsquo;s southwestern border, Yunnan\u0026rsquo;s CDCs experience significant population mobility\u003csup\u003e(30)\u003c/sup\u003e and high-intensity disease prevention and control at the border and within the province.\u003csup\u003e(31, 32)\u003c/sup\u003e We conducted a cross-sectional survey at all CDCs in Yunnan province to explore the mediating role of job satisfaction and burnout between salary and turnover intention. Additionally, we conduct a subgroup analysis based on marital status to explore its heterogeneity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe empirical evidence suggests that salary significantly impacts job satisfaction, burnout, and turnover intention.\u003csup\u003e(7, 11, 33)\u003c/sup\u003e The setting of salary levels should align with the value of labor, fully meet employees\u0026rsquo; living and development needs, and remain consistent with social equity.\u003csup\u003e(11, 34)\u003c/sup\u003e Before 2017, the CDC budget provided fee-based services, such as health screenings, preventive physical examinations, and epidemic prevention, with a portion of the additional revenue subsidizing employee salaries.\u003csup\u003e(35)\u003c/sup\u003e When the Chinese government eliminated the CDC health service fees in 2017, falling CDC income reduced the CDC budget and squeezed employee salaries.\u003csup\u003e(35)\u003c/sup\u003e Subsequent government fiscal support failed to fully compensate for this income shortfall, leading to job dissatisfaction and resignations among staff due to reduced salaries.\u003csup\u003e(36)\u003c/sup\u003e While current research generally agrees that CDC personnel salaries require improvement,\u003csup\u003e(7, 9, 21)\u003c/sup\u003e our study explores the complex relationship between salaries, job satisfaction, reduced burnout, and turnover rates among CDC staff.\u003c/p\u003e\n\u003cp\u003eJob satisfaction refers to the degree to which employees are satisfied with their job roles, work environment, and organizational policies, and it is a primary factor influencing employees\u0026rsquo; work motivation and initiative.\u003csup\u003e(37)\u003c/sup\u003e Job satisfaction among Chinese CDC personnel is relatively low, as studies from different regions and different CDC branches have consistently concluded.\u003csup\u003e(38-40)\u003c/sup\u003e Since job satisfaction is a multidimensional concept, the causes of low satisfaction are complex and have been primarily attributed to factors such as low salary, unclear career advancement prospects, high work pressure, and low job recognition.\u003csup\u003e(5, 38)\u003c/sup\u003e Low job satisfaction can also lead to derivative issues, including low work efficiency, frequent absenteeism, burnout, and high employee turnover.\u003csup\u003e(41)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eBurnout is a psychological syndrome resulting from prolonged exposure to emotional and interpersonal stressors in the workplace.\u003csup\u003e(42)\u003c/sup\u003e Burnout comprises three dimensions: emotional exhaustion, depersonalization, and diminished personal accomplishment.\u003csup\u003e(43)\u003c/sup\u003e High levels of burnout are common among Chinese CDC staff.\u003csup\u003e(7)\u003c/sup\u003e Burnout among CDC personnel has been linked to work intensity, compensation and benefits, job satisfaction, family responsibilities, and responses to public health emergencies.\u003csup\u003e(44)\u003c/sup\u003e High burnout can lead to numerous health and psychological problems, such as emotional exhaustion, low sense of achievement, headaches and insomnia, workforce instability, and impaired work efficiency, including lack of concentration and frequent errors.\u003csup\u003e(7)\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eTurnover intention has been identified as a critical factor constraining the development of China\u0026rsquo;s CDC.\u003csup\u003e(11)\u003c/sup\u003e Turnover intention is typically defined as an employee\u0026rsquo;s psychological tendency to leave their current organization for other jobs.\u003csup\u003e(45)\u003c/sup\u003e A complex decision-making process, job resignation can be broadly divided into three main interacting categories: internal factors, such as work pressure, fairness sense, and promotion expectations\u003csup\u003e(11, 46)\u003c/sup\u003e; external factors, for example, salary, employment opportunities, and family responsibilities\u003csup\u003e(7)\u003c/sup\u003e; and psychological factors, including job satisfaction, burnout, and professional identity.\u003csup\u003e(5)\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eReducing turnover intentions among CDC personnel has become a significant challenge.\u003c/p\u003e\n\u003cp\u003eAmong healthcare workers,\u003csup\u003e(18, 20)\u003c/sup\u003e increasing salary can enhance job satisfaction,\u003csup\u003e(47)\u003c/sup\u003e reduce the occurrence of burnout,\u003csup\u003e(48)\u003c/sup\u003e and decrease turnover intention.\u003csup\u003e(49)\u003c/sup\u003e Improved job satisfaction can lower turnover intention by reducing burnout. As shown in Figure 1, our study constructs a chain-mediated model of the salary-job satisfaction-burnout-turnover intention relationship, and proposes the following hypotheses:\u003c/p\u003e\n\u003cul start=\"5\"\u003e\n \u003cli\u003eHypothesis 1: Salary increases reduce CDC staff turnover intention.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHypothesis 2: Among CDC staff, job satisfaction mediates the relationship between salary and turnover intention.\u003c/li\u003e\n \u003cli\u003eHypothesis 3: Among CDC staff, burnout mediates the relationship between salary and turnover intention.\u003c/li\u003e\n \u003cli\u003eHypothesis 4: Among CDC staff, job satisfaction and burnout jointly mediate the relationship between salary and turnover intention in a chain-mediated manner.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eUsing whole-cluster sampling, we conducted a cross-sectional survey from March to April 2024 at all 149 CDCs in Yunnan Province. Due to the gradual reduction in the number of staff allocated to each provincial, prefectural, and county level CDC, 80 provincial level, 50 prefecture level, and 30 county level CDC staff were randomly selected from each CDC. A total of 7,929 respondents completed the survey, with 6,343 respondents passing the strict consistency test, with an effective participation rate of 80%. Our study relied on a web-based survey platform (wjx.com) to distribute the electronic anonymous questionnaire and set up strict quality control. The wording and layout of the questionnaire were adjusted through two rounds of discussions with experts and the project team. The questionnaire included one consistency check question and three logic check questions, with responses not meeting the checks excluded. At each CDC branch, a trained data enumerator assisted in completing data collection and providing online and telephone consultations. All participants responded anonymously and gave informed consent. The study was approved by the Ethics Committee of Beijing University of Traditional Chinese Medicine (NO.2024BZYLL0303).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Socio-demographic characteristics\u003c/h2\u003e \u003cp\u003eWe collected data on gender, age, \u003cem\u003ebianzhi\u003c/em\u003e (tenured employment position in China),\u003csup\u003e(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e education, title, whether they had worked overtime in the last six months, number of chronic diseases they had, marital status, and CDC level. Marital status was categorized as married and unmarried (including single and divorced).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Salary\u003c/h2\u003e \u003cp\u003eSalary was the average monthly sum of the basic wage plus bonuses, allowances, overtime pay, and any additional remuneration. By comparing the salary data of the same post, the same position, the same education level, and the same unit level, fuzzy data and abnormal data were excluded, and the extreme data were truncated by 5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Job satisfaction\u003c/h2\u003e \u003cp\u003eUsing a pairwise translation, the \u003cem\u003eMinnesota Satisfaction Questionnaire\u003c/em\u003e (MSQ) was adopted.\u003csup\u003e(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/sup\u003e Two experts specialized in health management translated the MSQ into Chinese; English professionals back-translated the Chinese questionnaire into English; and any anomalies in the Chinese version were adjusted by the team. The final Chinese version of the MSQ was finalized after two rounds of validation and one pilot survey. A total of 20 items were included, measured by a 5-point Likert scale (1-very dissatisfied to 5-very satisfied). Job satisfaction scores were averaged over all items, with higher scores indicating higher job satisfaction. The MSQ has been widely applied in job satisfaction studies across numerous occupations in China, such as bank staff,\u003csup\u003e(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/sup\u003e civil aviation pilots,\u003csup\u003e(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e police officers,\u003csup\u003e(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)\u003c/sup\u003e and anaesthesiologists,\u003csup\u003e(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)\u003c/sup\u003e demonstrating its well-established validity. In the present research, the Cronbach\u0026rsquo;s alpha coefficient for the reliability test was 0.926, and the Kaiser-Meyer-Olkin Measure (KMO) for the total validity test was 0.949 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Burnout\u003c/h2\u003e \u003cp\u003eThe same procedures applied to the MSQ were used for the \u003cem\u003eMaslach Burnout Inventory\u0026ndash;General Survey\u003c/em\u003e (MBI-GS).\u003csup\u003e(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)\u003c/sup\u003e The 15 items were divided into three sub-dimensions, including five emotional exhaustion, four depersonalization, and six diminished personal accomplishment items.\u003csup\u003e(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/sup\u003e Each item was measured by a 7-point Likert scale, with 0 \u0026lsquo;never\u0026rsquo; to 6 \u0026lsquo;every day\u0026rsquo;. The score for each sub-dimension was the average score of the items, with reverse scoring applied to the diminished personal accomplishment. The score for burnout was the sum of the 3 sub-dimensions. A higher score indicates more severe burnout. The MBI-GS has been widely used in China to assess staff burnout levels, such as among social welfare workers,\u003csup\u003e(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/sup\u003e nurses,\u003csup\u003e(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)\u003c/sup\u003e and grassroots officials,\u003csup\u003e(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e)\u003c/sup\u003e demonstrating good validity. In the present research, the Cronbach\u0026rsquo;s alpha was 0.831, and the KMO value was 0.904 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Turnover intention\u003c/h2\u003e \u003cp\u003eFollowing the MSQ and MBI-GS procedure, the four-item \u003cem\u003eIntent to Leave Scale\u003c/em\u003e (ILS) was adopted.\u003csup\u003e(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/sup\u003e Each item was measured by a 5-point Likert scale (1- strongly disagree to 5- strongly agree), with the score averaged over 4 items. A higher score indicates a stronger turnover intention. The ILS has been applied in studies examining job turnover among kindergarten teachers\u003csup\u003e(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e)\u003c/sup\u003e and nurses\u003csup\u003e(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/sup\u003e in China, demonstrating good validity. In the present research, the Cronbach\u0026rsquo;s alpha was 0.806, and the KMO value was 0.775 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, Chi-square test, and Kruskal-Wallis H test were used to analyze the scores of subgroup differences in job satisfaction, burnout, and turnover intention for different demographic characteristics. Harman\u0026rsquo;s one-way statistical control was used to check the effect of common method bias.\u003csup\u003e(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)\u003c/sup\u003e Correlations between salary, job satisfaction, burnout, and turnover intention were analyzed using the Pearson chi-square test. To check the chain mediation effect of job satisfaction and burnout between salaries and turnover intentions for the full sample, the model was constructed using the command \u003cem\u003egsem\u003c/em\u003e in STATA software version MP17.0, and Bootstrap (5000) was used to check the chain mediation effect. When the 95% confidence interval (CI) after bias correction does not include zero, it indicates that the pathway has a significant effect. Salary, job satisfaction, burnout, and turnover intention were utilized to construct a chain-mediated effects model, with gender, age, education, overtime, and chronic diseases included as control variables. Chain mediation models for both the married and unmarried subgroups were analyzed and tested following the main model. Salary was log-transformed, and all models were adjusted for covariates, including age, education, job title, and overtime. Using SPSS software version 26.0, reliability was analyzed using Cronbach\u0026rsquo;s alpha coefficients and validity tests of the scales used the KMO values. The rest of the statistical analyses were completed using STATA software version MP17.0, and statistical significance was defined as a two-tailed p-value of less than 0.05. Currency conversion was based on the average exchange rate for 2024: 1 USD\u0026thinsp;\u0026asymp;\u0026thinsp;7.1217 CNY.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that our sample comprised 4,257 females (67.11%); the average age was 39.28; and 5,054 (79.68%) were married. There were 5,543 cases (87.39%) with the \u003cem\u003ebianzhi;\u003c/em\u003e those with a bachelor\u0026rsquo;s degree accounted for 69.16% of the sample; and primary titles accounted for 37.35%, intermediate titles for 25.11% and senior titles for 18.51% of the sample. There were 1,240 cases (19.55%) of overtime work in the last six months; and 5.09% worked at the provincial level, 19.45% at prefecture, and 75.45% at the county level CDCs.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the average monthly salary (RMB6147.67 RMB (USD863.23)) exhibits significant differences across various sociodemographic subgroups, excluding overtime. The mean scores of job satisfaction (3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52) and turnover intention (2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69) varied significantly across sociodemographic subgroups. Except for gender and \u003cem\u003ebianzhi\u003c/em\u003e, all sociodemographic characteristics also showed significant differences in the burnout score (44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36). (All P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSalary, Job Satisfaction, Burnout, and Turnover Intention Scores for Different Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSalary (RMB)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6031.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2368.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6384.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2219.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4135.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2698.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026thinsp;\u0026lt;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5624.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2225.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026thinsp;\u0026lt;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6838.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2018.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8150.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2383.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBianzhi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6513.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1830.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3613.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3514.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emaster or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8137.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2856.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6117.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2030.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebelow bachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5842.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2734.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7946.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1966.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6587.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1709.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5630.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2091.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4833.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2567.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6427.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2215.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e7.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6079.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2347.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGroup of chronic diseases number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5883.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2397.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6689.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2058.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6946.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1917.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6389.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2226.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5199.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2464.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprovincial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8506.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3401.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprefectural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6904.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2349.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecounty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5793.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2079.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e \u003cp\u003e6.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*P-values are based on the Chi-square test and Kruskal-Wallis H test. sd: standard deviation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Common method bias\u003c/h2\u003e \u003cp\u003eSince all variables were self-reported, Harman\u0026rsquo;s one-way statistical control method\u003csup\u003e(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e)\u003c/sup\u003e was used to test for common method bias. The percentage of the variance explained by the first factor in the total variance was used as the basis for judgment, with the 26.84% variance not exceeding the critical criterion of 40%, indicating that there was no obvious common method bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation of main variables\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the correlation analysis between the main variables. There was a significant positive correlation between salary and job satisfaction; salary was significantly negatively correlated with burnout and turnover intention; job satisfaction was significantly negatively correlated with burnout and turnover intention; and burnout was significantly positively correlated with turnover intention. The correlation results mirrored the relationship between the variables in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e hypothesized model. (All P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations among main variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.037**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.554***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.173***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.431***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.472***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The chain mediation model for the total sample\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the chain mediation model results for the total sample. After fixing the confounding variables, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that for every 1% increase in respondents\u0026rsquo; salary, the score of job satisfaction would increase by 0.00165 points (β\u0026thinsp;=\u0026thinsp;0.165, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the score of turnover intention would decrease by 0.00059 points (β=-0.059, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For every 1-point increase in respondents\u0026rsquo; job satisfaction, the score of burnout would decrease by 2.396 points (β=-2.396, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and turnover intention would decrease by 0.349 points (β=-0.349, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For every 1-point increase in burnout, the score of turnover intention would increase by 0.088 points (β\u0026thinsp;=\u0026thinsp;0.088, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results support Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for the chain mediation model of the total sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR.SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR.SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eR.SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.165***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113\u0026thinsp;~\u0026thinsp;0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.053\u0026thinsp;~\u0026thinsp;0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.059*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.110~-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.396***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-2.504~-2.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.349***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.386~-0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.080\u0026thinsp;~\u0026thinsp;0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u0026thinsp;~\u0026thinsp;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.136\u0026thinsp;~\u0026thinsp;0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.166***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.197~-0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u0026thinsp;~\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.048~-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.014~-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBianzhi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.201***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.257~-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.017\u0026thinsp;~\u0026thinsp;0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.238~-0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emaster or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.162***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.238~-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.168\u0026thinsp;~\u0026thinsp;0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.032\u0026thinsp;~\u0026thinsp;0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.089***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.123~-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.175\u0026thinsp;~\u0026thinsp;0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.002\u0026thinsp;~\u0026thinsp;0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.103***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.154~-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.224\u0026thinsp;~\u0026thinsp;0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.080\u0026thinsp;~\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eintermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.140***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.184~-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.163\u0026thinsp;~\u0026thinsp;0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.027\u0026thinsp;~\u0026thinsp;0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.058**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.096~-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.094\u0026thinsp;~\u0026thinsp;0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.015\u0026thinsp;~\u0026thinsp;0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvertime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.162***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.195~-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.168\u0026thinsp;~\u0026thinsp;0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.026\u0026thinsp;~\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.126~-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.196***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.123\u0026thinsp;~\u0026thinsp;0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.037\u0026thinsp;~\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprovincial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.112\u0026thinsp;~\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.377\u0026thinsp;~\u0026thinsp;0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.209***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.281~-0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprefectural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.038\u0026thinsp;~\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.227***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.354~-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.158~-0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.238***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.831\u0026thinsp;~\u0026thinsp;2.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.160***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.732\u0026thinsp;~\u0026thinsp;16.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.291***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.866\u0026thinsp;~\u0026thinsp;4.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, β: Regression coefficient, R.SE: Robust standard error, CV: Control variable, 95% CI refer to lower and upper 95% confidence intervals of the indirect effects estimated by the bias-corrected percentile Bootstrap method, respectively. The baseline level was male workers in county-level CDCs with no \u003cem\u003ebianzhi\u003c/em\u003e, below bachelor\u0026rsquo;s degree, no title, and no overtime.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide the total, direct, and indirect effects of the model paths. The significance of all effects was checked using 95% bootstrap confidence interval estimates. Except for the indirect effect path 2, all other paths were significant, with the direct path accounting for 42.14% and the indirect effects accounting for 57.86% of the total effect. The model showed that respondents\u0026rsquo; salary directly affected turnover intention (direct effect, β=-0.059). Job satisfaction played a separate mediating role between salary and turnover intention (Path 1, β=-0.058). This confirms Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Burnout played a separate mediating role between job satisfaction and turnover intention (β=-0.211=-2.396*0.088), which rejects Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Job satisfaction and burnout were found to play a chain mediating role between salary and turnover intention (Path 3, β=-0.035), which supports Hypothesis \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal, direct, and indirect effects of model paths for the total sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel paths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBootstrap SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.209~-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.203~-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.113~-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.107~-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.117~-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.118~-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.078~-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.079~-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u0026thinsp;~\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005\u0026thinsp;~\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.046~-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.046~-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePath1: Salary \u0026rarr; Job satisfaction \u0026rarr; Turnover intention, Path2: Salary \u0026rarr; Burnout \u0026rarr; Turnover intention, Path3: Salary \u0026rarr; Job satisfaction \u0026rarr; Burnout \u0026rarr; Turnover intention.\u003c/p\u003e \u003cp\u003eβ: Regression coefficient, SE: Standard error, CI: Confidence intervals, P: Percentile confidence interval, BC: Bias-corrected confidence interval.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The chain mediation model of marriage subgroups\u003c/h2\u003e \u003cp\u003eUsing the same method and control variables as the total sample, the chained mediated effects model was separately analyzed for the married and unmarried subgroup samples. The main results of the model are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. After fixing the confounding variables, the results of the model for married respondents showed that for every 1% increase in the respondent\u0026rsquo;s salary, the score of job satisfaction would increase by 0.00214 points (β\u0026thinsp;=\u0026thinsp;0.214, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the score of turnover intention decreased by 0.00065 points (β = -0.065, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For every 1-point increase in respondents\u0026rsquo; job satisfaction, the score of burnout decreased by 2.230 points (β = -2.230, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and turnover intention decreased by 0.302 points (β = -0.302, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For every 1-point increase in burnout, the score of turnover intention increased by 0.085 points (β\u0026thinsp;=\u0026thinsp;0.085, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of the model for unmarried respondents showed that for every 1-point increase in job satisfaction, the score of burnout decreased by 2.852 points (β\u0026thinsp;=\u0026thinsp;2.852, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and turnover intention decreased by 0.508 points (β = -0.508, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For every 1-point increase in burnout, the score of turnover intention increased by 0.090 points (β\u0026thinsp;=\u0026thinsp;0.090, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression results for the chain mediation model among married subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eTurnover intention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ.CV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR.SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ.CV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR.SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eβ.CV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR.SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel-1: Married\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.214***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.152\u0026thinsp;~\u0026thinsp;0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.110\u0026thinsp;~\u0026thinsp;0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.065*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.126~-0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.230***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-2.349~-2.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.302***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.344~-0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.085***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.076\u0026thinsp;~\u0026thinsp;0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel-2: Unmarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.004\u0026thinsp;~\u0026thinsp;0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-0.265\u0026thinsp;~\u0026thinsp;0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.161\u0026thinsp;~\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.852*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-3.103~-2.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.508***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.588~-0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.090***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.073\u0026thinsp;~\u0026thinsp;0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, β.CV: Regression coefficient with control variables, R.SE: Robust standard error, 95% CI refers to the lower and upper 95% confidence intervals of the indirect effects estimated by the bias-corrected percentile Bootstrap method, respectively. The baseline level was male workers in county-level CDCs with no bianzhi, below bachelor\u0026rsquo;s degree, no title, and no overtime.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provide the total, direct, and indirect effects of the paths for the married and unmarried subgroup samples. The significance of all effects was checked using 95% bootstrap confidence interval estimates. The significance of the model results for the married sample was similar to the path results for the total sample. Except for the indirect effect Path 2, all paths were significant, with direct effects accounting for 40.37% and indirect effects accounting for 59.63% of the total effect. Married respondents\u0026rsquo; salary directly affected turnover intention (Direct effect, β = -0.065). Job satisfaction played an independent mediating role between salary and turnover intention (Path 1, β = -0.065). In Path 2, burnout played an independent mediating role between job satisfaction and turnover intention (β=-0.190=-2.230*0.085). Job satisfaction and burnout were also found to play a chain mediating role between salary and turnover intention (Path 3, β=-0.041). The results of the model for the married sample found that job satisfaction directly affected turnover intention (β=-0.508) and burnout played an independent mediating role between job satisfaction and turnover intention (β=-0.257=-2.852*0.090), but the direct and indirect effects between salary and turnover intention for the unmarried respondents were not significant (95% confidence intervals include 0).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal, direct, and indirect effects of model paths for matrimony subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel paths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel-1: Married\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel-2: Unmarried\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBootstrap SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eObserved β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBootstrap SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.237~-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.263\u0026thinsp;~\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.229~-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.256\u0026thinsp;~\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.129~-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.152\u0026thinsp;~\u0026thinsp;0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.123\u0026thinsp;~\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.152\u0026thinsp;~\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.133~-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.159\u0026thinsp;~\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.133~-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.16\u0026thinsp;~\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.088~-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101~-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.089~-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.101~-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u0026thinsp;~\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.026\u0026thinsp;~\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u0026thinsp;~\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.026\u0026thinsp;~\u0026thinsp;0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.054~-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.052\u0026thinsp;~\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(P)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e-0.054~-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.052\u0026thinsp;~\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(BC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePath1: Salary \u0026rarr; Job satisfaction \u0026rarr; Turnover intention, Path2: Salary \u0026rarr; Burnout \u0026rarr; Turnover intention, Path3: Salary \u0026rarr; Job satisfaction \u0026rarr; Burnout \u0026rarr; Turnover intention.\u003c/p\u003e \u003cp\u003eβ: Regression coefficient, SE: Standard error, CI: Confidence intervals, P: Percentile confidence interval, BC: Bias-corrected confidence interval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eChina\u0026rsquo;s CDCs face high job turnover, especially in western and central regions. Our contribution was to investigate the complex inter-relationships between salary, job satisfaction, burnout, and turnover intention. We found a chain mediating effect of job satisfaction and burnout between salaries and turnover intentions among CDC staff in the western province of Yunnan. Previous studies have investigated single variables or only bilateral relationships between salary, job satisfaction, burnout, and turnover intention. For example, a study of local CDCs in Hainan Province reported a job satisfaction level of 3.35 out of 5 points, indicating a moderate degree of satisfaction.\u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e A survey of Sichuan Province prefectural and county-level CDC staff found that 80.73% experienced burnout.\u003csup\u003e(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e)\u003c/sup\u003e An investigation by CDC staff in Henan Province found that the overall job satisfaction score among was low (67.13/100), while their intention to leave was relatively high (7.31/10).\u003csup\u003e(66)\u003c/sup\u003e A survey of newly recruited CDC personnel in Lianyungang City, Jiangsu Province, revealed a moderate turnover intention level (57.20/100).\u003csup\u003e(67)\u003c/sup\u003e These findings are broadly consistent with our results, where the level of job satisfaction was moderate (3.52/5), the burnout rate was relatively high (80.83%), and turnover intention was at a moderate level (2.36/5). Our research revealed the independent mediating effect of job satisfaction between the direct effect of salary on turnover intention, and validated the chain mediating effect of job satisfaction and burnout between salary and turnover intention. Our study also found differences in the mediation path for the marital status subgroups suggest heterogeneity within the subsample.\u003c/p\u003e \u003cp\u003eAnalysis of the overall sample revealed that salary directly influences turnover intentions among CDC staff, with a direct effect of 42.14%, similar to reported results for Australian mental health workers, nurses in Philippine hospitals, and Indian dental hygienists.\u003csup\u003e(\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e The issue of low salary among CDC personnel has been extensively documented,\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e with salary the primary consideration for CDC staff in turnover decisions.\u003csup\u003e(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e For example, a Beijing study found that 54.51% of CDC staff expressed salary dissatisfaction, and 44.48% had considered resigning, with low salary being the key driver.\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e Inadequate salaries play a key role in weakening the economic stickiness of job retention and reducing employees\u0026rsquo; sense of identity and trust in the organization.\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e Our recommendation for higher salaries does not imply indiscriminately pursuing higher salaries as a stand-alone approach, but as part of a wider strategy optimizing compensation structures, aligning career development with performance incentives, addressing burnout and job satisfaction, and targeting an optimal salary incentive range. In 2023, the Chinese government proposed reforming the compensation scheme for CDC personnel as a measure to promote the high-quality development of the CDC system.\u003csup\u003e(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e)\u003c/sup\u003e Given our mediation results, we suggest that a stand-alone compensation policy is unlikely to be successful.\u003c/p\u003e \u003cp\u003eOur results show that both job satisfaction and burnout are associated with turnover intentions among CDC staff, and job satisfaction is negatively correlated with burnout. While these direct relationships have been noted in previous studies,\u003csup\u003e(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/sup\u003e our mediated model\u0026rsquo;s full-sample results support two hypotheses. Job satisfaction and burnout exhibit a chained mediating effect between salary and turnover intentions among CDC workers, and job satisfaction demonstrates an independent mediating effect. The cumulative proportion of indirect effects reached 57.86%, with lower salaries leading to lower job satisfaction, triggering this mediation process. The negative effects of low job satisfaction were mainly concentrated on increasing turnover intention due to burnout (β=-2.396, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The mismatch between inadequate salary and individual value contributions creates a strong sense of unfairness among CDC staff. CDC staff perceive their work effort as failing to receive commensurate material rewards, eroding job satisfaction, and intrinsic motivation. This leads to their feelings that their hard work is underappreciated and their contributions undervalued. These feelings are exacerbated when excessive workloads and work pressure are compounded without significant salary increases, such as no significant salary difference between overtime and non-overtime subgroups, leading to psychological disappointment and emotional exhaustion. This is followed by a significant decline in work engagement, when emotionally exhausted staff subconsciously reduce extra effort, performing only the bare minimum required to avoid the feeling of being \u0026ldquo;exploited\u0026rdquo;, fostering and intensifying burnout. When this psychological burnout persists without salary increases or other recognitions, CDC staff seek external alternative opportunities, viewing resignation as the ultimate path to restore psychological equity and gain value recognition. Ultimately, this chain reaction\u0026mdash;from \u0026lsquo;perceived mismatch\u0026rsquo; to \u0026lsquo;psychological imbalance\u0026rsquo; and then to \u0026lsquo;behavioral withdrawal\u0026rsquo; \u0026mdash;led to resignations.\u003c/p\u003e \u003cp\u003eOther studies have indicated that factors such as limited career advancement opportunities, inadequate benefits, and suboptimal working conditions contribute to turnover among CDC staff.\u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e)\u003c/sup\u003e But improvements in these factors are likely to have only a limited impact. We found that salary stood at the head of the chain effect, which requires CDC administrators to optimize performance incentives and financial management mechanisms. As discussed, salary adjustments alone are unlikely to solve CDC\u0026rsquo;s personnel retention problem without addressing employees\u0026rsquo; multifaceted developmental needs, job dissatisfaction, burnout, and job satisfaction.\u003c/p\u003e \u003cp\u003eSignificantly, our subgroup marital status analysis revealed heterogeneous pathways linking salary, job satisfaction, burnout, and turnover intention. In the married group, the direct and indirect effects as well as pathways linking salary, job satisfaction, burnout, and turnover intention closely mirrored those in the full sample. In the unmarried group, job satisfaction directly or indirectly predicted turnover intention through burnout; no significant associations were observed between salary and job satisfaction, burnout, or turnover intention. This indicates that the economic factors were less significant in the married subgroup. We posit that this inertia operates bidirectionally.\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e)\u003c/sup\u003e Married individuals are compelled to remain employed due to family financial obligations, but their salary level failed to balance household expenditures and their perceived labor value, triggering a negative feedback cycle between the work and family domains, which accelerated turnover. Further, job dissatisfaction induced by low salaries heightened the likelihood of employee turnover. While our data confirmed prior studies\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e that lower turnover intentions among married staff (2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66) versus unmarried (2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), our explanation of the process is more nuanced. While married workers\u0026rsquo; heightened economic dependence drove more stable married CDC employment, our marital subgroup analysis provides empirical substantiation for this heterogeneity based on the complex inter-play between salaries, perceived work worth, job satisfaction, family-work domain, and turnover. These results reinforce our recommendation to link CDC salary rises with a range of work and family arrangements.\u003c/p\u003e \u003cp\u003eAlthough unmarried individuals did not exhibit economic retention inertia, the burnout score among unmarried CDC staff (6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64) was significantly higher than that of married staff (6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Existing studies indicate that unmarried\u0026mdash;typically younger\u0026mdash;workers demonstrate lower marginal utility sensitivity to salary.\u003csup\u003e(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e)\u003c/sup\u003e Professional development opportunities, not salary, were prioritized by most unmarried workers in their early career stages.\u003csup\u003e(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e Ambiguous career trajectories and unfavourable advancement prospects constitute critical contributors to burnout among young CDC employees. Besides addressing unmarried individuals\u0026rsquo; salary levels, we recommend the design and clear communication of well-defined career development paths, providing growth-oriented incentives and supportive training to enhance young CDC staff\u0026rsquo;s confidence in their professional prospects.\u003c/p\u003e \u003cp\u003eLike Chinese doctors and nurses,\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e our analysis showed that work and sociodemographic characteristics, such as gender, age, job title, educational background, overtime work, number of chronic diseases, and unit level, impacted job satisfaction and burnout of CDC staff and directly or indirectly influenced their turnover intentions. To tackle turnover intention, CDC administrators must address the psychological well-being of CDC staff,\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e particularly among highly educated individuals holding master\u0026rsquo;s degrees or higher and young workers frequently engaged in night shifts and overtime commitments. When refining retention strategies for CDC personnel, policymakers must shape retention policies for the heterogeneous characteristics of employee subgroups. Besides income-related policies, such as fair overtime compensation, we recommend that CDC policymakers provide preferential access to research resources for highly educated research-oriented personnel; develop burnout intervention programs for \u0026ldquo;at-risk\u0026rdquo; workers; and conduct regular mental health screenings for staff.\u003c/p\u003e \u003cp\u003eWe collected a rich research dataset on salaries, job satisfaction, burnout, and turnover intentions on all CDCs in Yunnan Province. Generalising our Yunnan findings to other regions and provinces should consider the specific circumstances in Yunnan and the different conditions in other provinces and regions. Our cross-sectional survey data preclude definitive causal interpretations among key variables. Variables were measured via self-administered questionnaires and self-rated scales. Despite high reliability and validity and rigorous quality control, inherent subjectivity and measurement errors are unavoidable. Future studies should collect further decision-making variables, such as professional identity, social support, and familial stressors. Finally, future research should implement longitudinal tracking to enable more robust causal inference.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study revealed that job satisfaction and burnout demonstrated a chained mediating effect between salary and turnover intention, with job satisfaction additionally exhibiting an independent mediating effect. No comparable mediating role was observed for burnout. The unmarried subgroup was found to lack the turnover stickiness observed in the married subgroup, and exhibited a higher burnout score. Given our mediation results, a stand-alone compensation policy is unlikely to be successful in addressing CDC turnover. We recommend a multifaceted marital-specific approach to CDC personnel management that optimizes compensation structures, aligns career development with performance incentives, addresses burnout, and improves job satisfaction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCDC \u0026nbsp; \u0026nbsp;Center for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eMSQ \u0026nbsp; \u0026nbsp;Minnesota Satisfaction Questionnaire\u003c/p\u003e\n\u003cp\u003eKMO \u0026nbsp; \u0026nbsp;Kaiser-Meyer-Olkin Measure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMBI-GS \u0026nbsp; \u0026nbsp;Maslach Burnout Inventory\u0026ndash;General Survey\u003c/p\u003e\n\u003cp\u003eILS \u0026nbsp; \u0026nbsp;Intent to Leave Scale\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; confidence interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll respondents included in the analyses responded anonymously after reading and agreeing to the informed consent information for this study. The Ethics Committee of Beijing University of Chinese Medicine reviewed and approved this study (NO.2024BZYLL0303).\u0026nbsp;The entire research process strictly complied with the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials used in the article can be obtained from the subject team if required for reasonable purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Chinese Centre for Disease Control and Prevention (NO:90020671720092).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHLN and XFS designed the research and formulated the research objectives; HLN, WJY, and QB conducted data sorting, data cleaning, and statistical analysis; HLN and JTZ drafted the manuscript; EM and SN made important revisions to the manuscript and provided language support; HLN and YJW participated in scale revisions; XFS collected data, helped develop ideas, revised and edited the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks are due to China CDC and the 39 sample CDCs for their assistance in completing the data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCai WQ, Li CY, Sun M, Hao M. Measuring inequalities in the public health workforce at county-level Centers for Disease Control and Prevention in China. International journal for equity in health. 2019;18(1).\u003c/li\u003e\n\u003cli\u003eChen H, Cui Y, Liu KX, Lai CY, Wang Y, Chen QF, et al. China\u0026apos;s Initiatives and Achievements in Enhancing the Professional Capabilities of CDCs. China CDC Wkly. 2024;6(38):979-81.\u003c/li\u003e\n\u003cli\u003eLi CY, Sun M, Wang Y, Luo L, Yu MZ, Zhang Y, et al. The Centers for Disease Control and Prevention System in China: Trends From 2002-2012. Am J Public Health. 2016;106(12):2093-102.\u003c/li\u003e\n\u003cli\u003eChina NHCotPsRo. \u0026quot;14th Five-Year\u0026quot; Health Talent Development Plan Issued by the National Health Commission: Government of China; 2022 [cited 2025 2025-9-16]. Available from: https://www.gov.cn/zhengce/zhengceku/2022-08/18/content_5705867.htm.\u003c/li\u003e\n\u003cli\u003eCui Q, Liu L, Hao ZJ, Li MY, Liu CL, Yang CX, et al. Research on the influencing factors of fatigue and professional identity among CDC workers in China: an online cross-sectional study. BMJ Open. 2022;12(4).\u003c/li\u003e\n\u003cli\u003eHong-yi C, Li-na Y, Guang-peng Z, Lin W, Hao C, Xue-ning W. Comparative analysis on comprehensive quality of mobile personnel in CDCs. Practical Preventive Medicine. 2017;24(07):841-3.\u003c/li\u003e\n\u003cli\u003eShan Y, Liu GW, Zhou CQ, Li SX. The Relationship Between CDC Personnel Subjective Socioeconomic Status and Turnover Intention: A Combined Model of Moderation and Mediation. Front Psychiatry. 2022;13.\u003c/li\u003e\n\u003cli\u003eMeng QY. Strengthening public health systems in China. Lancet Public Health. 2022;7(12):E987-E8.\u003c/li\u003e\n\u003cli\u003eNa-na L, Lie-yu H, Yang L, Zhen-jun L. Analysis of the Personnel Outflow in Chinese Center For Disease Control and Prevention From 2014 To 2021. Chinese Health Resources Management. 2023;40(04):289-91+96.\u003c/li\u003e\n\u003cli\u003eLiming L, Hua W, Xiaofeng L, Zhenqiang B, Jun R, Lan W. Recommendation on the modernization of disease control and prevention. Chinese Journal of Epidemiology. 2020;41(04):453-60.\u003c/li\u003e\n\u003cli\u003eZhuge RQ, Wang YP, Gao YR, Wang QK, Wang YX, Meng N, et al. Factors influencing the turnover intention for disease control and prevention workers in Northeast China: an empirical analysis based on logistic-ISM model. BMC Health Serv Res. 2024;24(1).\u003c/li\u003e\n\u003cli\u003eQi X, Wang Y, Xia L, Meng Y, Li Y, Yu S, et al. Cross-sectional survey on public health informatics workforce in China: issues, developments and the future. Public Health. 2015;129(11):1459-64.\u003c/li\u003e\n\u003cli\u003eZhang XW, Zhang WJ, Xue L, Xu ZY, Tian Z, Wei C, et al. The influence of professional identity, job satisfaction, burnout on turnover intention among village public health service providers in China in the context of COVID-19: A cross-sectional study. Front Public Health. 2022;10.\u003c/li\u003e\n\u003cli\u003eXu Z, Zhang L, Yang Z, Yang G. Burnout and turnover intention of primary health care providers during the COVID-19 pandemic in China. Public Health. 2023;225:191-7.\u003c/li\u003e\n\u003cli\u003eScanlan JN, Still M. Relationships between burnout, turnover intention, job satisfaction, job demands and job resources for mental health personnel in an Australian mental health service. BMC Health Serv Res. 2019;19.\u003c/li\u003e\n\u003cli\u003eLabrague LJ, de los Santos JAA, Falguera CC, Nwafor CE, Galabay JR, Rosales RA, et al. Predictors of nurses\u0026apos; turnover intention at one and five years\u0026apos; time. Int Nurs Rev. 2020;67(2):191-8.\u003c/li\u003e\n\u003cli\u003ePatel BM, Boyd LD, Vineyard J, LaSpina L. Job Satisfaction, Burnout, and Intention to Leave among Dental Hygienists in Clinical Practice. J Dent Hyg. 2021;95(2):28-35.\u003c/li\u003e\n\u003cli\u003eZhang YM, Feng XS. The relationship between job satisfaction, burnout, and turnover intention among physicians from urban state-owned medical institutions in Hubei, China: a cross-sectional study. BMC Health Serv Res. 2011;11.\u003c/li\u003e\n\u003cli\u003eShuangshuang L. The impact of training load on turnover intention of junior nurses: a chain-mediation study of burnout and job satisfaction [硕士]: Shandong University; 2023.\u003c/li\u003e\n\u003cli\u003eWang HP, Jin YZ, Wang D, Zhao SC, Sang XG, Yuan BB. Job satisfaction, burnout, and turnover intention among primary care providers in rural China: results from structural equation modeling. BMC Fam Pract. 2020;21(1).\u003c/li\u003e\n\u003cli\u003eYu-jie Y, Shuai D, Yue-li M, Pei D, Min-jie Z, Kun W, et al. Investigation and analysis of work situation of professional and technical personnel in Beijing Centers for Disease Prevention and Control during the COVlD-19 epidemic. Chinese Journal of Disease Control and Prevention. 2022;26(06):696-702.\u003c/li\u003e\n\u003cli\u003eCroes R, Padr\u0026oacute;n-Avila H, Rivera M, Renduchintala C. A triadic model of job retention and turnover dynamics in the hospitality industry. International Journal of Contemporary Hospitality Management. 2025;37(3):700-21.\u003c/li\u003e\n\u003cli\u003eMa Y, Chen F, Xing D, Meng Q, Zhang Y. Study on the associated factors of turnover intention among emergency nurses in China and the relationship between major factors. Int Emerg Nurs. 2022;60:101106.\u003c/li\u003e\n\u003cli\u003eZhang Y. Disclosing the relationship between public service motivation and job satisfaction in the Chinese public sector: A moderated mediation model. Front Psychol. 2023;14.\u003c/li\u003e\n\u003cli\u003eOuyang YQ, Zhou WB, Xiong ZF, Wang R, Redding SR. A Web-based Survey of Marital Quality and Job Satisfaction among Chinese Nurses. Asian Nurs Res (Korean Soc Nurs Sci). 2019;13(3):216-20.\u003c/li\u003e\n\u003cli\u003eDenson N, Szel\u0026eacute;nyi K. Faculty perceptions of work-life balance: the role of marital/relationship and family status. High Educ (Dordr). 2022;83(2):261-78.\u003c/li\u003e\n\u003cli\u003eZhang Z. Analysis report on trends in public infectious disease control in China. Front Public Health. 2024;12:1423191.\u003c/li\u003e\n\u003cli\u003eTong MX, Hansen A, Hanson-Easey S, Xiang J, Cameron S, Liu Q, et al. Public health professionals\u0026apos; perceptions of the capacity of China\u0026apos;s CDCs to address emerging and re-emerging infectious diseases. J Public Health (Oxf). 2021;43(1):209-16.\u003c/li\u003e\n\u003cli\u003eJi JS, Xia Y, Liu L, Zhou W, Chen R, Dong G, et al. China\u0026apos;s public health initiatives for climate change adaptation. Lancet Reg Health West Pac. 2023;40:100965.\u003c/li\u003e\n\u003cli\u003eLiu T, Zhang Y, Zhang H, Yang XP. A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China. Sustainability. 2021;13(9).\u003c/li\u003e\n\u003cli\u003eLi N, Feng Y, Vrancken B, Chen Y, Dong L, Yang Q, et al. Assessing the impact of COVID-19 border restrictions on dengue transmission in Yunnan Province, China: an observational epidemiological and phylogenetic analysis. Lancet Reg Health West Pac. 2021;14:100259.\u003c/li\u003e\n\u003cli\u003eZeng XC, Sun XD, Li JX, Chen MN, Deng DW, Zhang CL, et al. Assessment of malaria control consultation and service posts in Yunnan, P. R. China. Infect Dis Poverty. 2016;5(1):102.\u003c/li\u003e\n\u003cli\u003eZhang F, Liu Y, Wei TQ. Psychological Capital and Job Satisfaction Among Chinese Residents: A Moderated Mediation of Organizational Identification and Income Level. Front Psychol. 2021;12.\u003c/li\u003e\n\u003cli\u003eXu XM, Cropanzano R, McWha-Hermann I, Lu CQ. Multiple Salary Comparisons, Distributive Justice, and Employee Withdrawal. J Appl Psychol. 2024;109(10):1533-54.\u003c/li\u003e\n\u003cli\u003eQian W, Jian W, Yue Z. Analysis on the impact and countermeasures after cancelling \u0026quot;Three Charges\u0026quot; in the Centers for Disease Control and Prevention.Chinese Health Economics. Chinese Health Economics. 2018;37(08):26-8.\u003c/li\u003e\n\u003cli\u003eChan W, Zhiqiang L, Zhendong G. Challenges faced by china\u0026apos;s CDCs and their countermeasures: ananalysis based on the survey data from six provinces. Chinese Journal of Medical Management Sciences. 2020;10(01):10-4.\u003c/li\u003e\n\u003cli\u003eJudge TA, Weiss HM, Kammeyer-Mueller JD, Hulin CL. Job Attitudes, Job Satisfaction, and Job Affect: A Century of Continuity and of Change. J Appl Psychol. 2017;102(3):356-74.\u003c/li\u003e\n\u003cli\u003eLi N, Wang YW, Yu DE, Xiao S, Liu YR. Job satisfaction of staff in agencies for disease prevention and control in Hainan Province, China. J Pak Med Assoc. 2020;70(3):523-5.\u003c/li\u003e\n\u003cli\u003eHanlin N. Research on job satisfaction and its influencing factors among disease prevention and control teams [硕士]: Beijing University of Chinese Medicine; 2024.\u003c/li\u003e\n\u003cli\u003eWanyin X, Fen Z, Rui M. Study on Job Satisfaction and Influencing Factors of Staff in Center of Disease Control and Prevention at County/District-level. Chinese Journal of Social Medicine. 2023;40(02):225-9.\u003c/li\u003e\n\u003cli\u003eChen GH, Lin CQ, Chen YH, Li L, Luo ST, Liu XY, et al. Job Satisfaction Among Methadone Maintenance Treatment Clinic Service Providers in Jiangsu, China: A Cross-sectional Survey. J Addict Med. 2020;14(1):12-7.\u003c/li\u003e\n\u003cli\u003eChristina Maslach SEJ. The measurement of experienced burnout. J Organ Behav. 1981;2(2):99-113.\u003c/li\u003e\n\u003cli\u003epin LC, kan S. The influence of distributive justice and procedural justice on job burnout. Acta Psychologica Sinica. 2003(05):677-84.\u003c/li\u003e\n\u003cli\u003eWang G, Yuan Q, Feng X, Zhang T, Wang Q, Huang Q, et al. The job burnout of tuberculosis healthcare workers and associated factors under integrated tuberculosis control model: a mixed-method study based on the two-factor theory. BMC Health Serv Res. 2024;24(1):984.\u003c/li\u003e\n\u003cli\u003eMobley WH. Intermediate linkages in the relationship between job satisfaction and employee turnover. J Appl Psychol. 1977;62(2):237-40.\u003c/li\u003e\n\u003cli\u003eWang EJ, Hu HW, Mao S, Liu HT. Intrinsic motivation and turnover intention among geriatric nurses employed in nursing homes: the roles of job burnout and pay satisfaction. Contemp Nurse. 2019;55(2-3):195-210.\u003c/li\u003e\n\u003cli\u003eDeriba BK, Sinke SO, Ereso BM, Badacho AS. Health professionals\u0026apos; job satisfaction and associated factors at public health centers in West Ethiopia. Hum Resour Health. 2017;15.\u003c/li\u003e\n\u003cli\u003eMao YQ, Hu Y, Feng ZC, Wang RX, Chen XY, Zhang WP, et al. Job burnout and correlated factors of three-tiered public health workers: A cross-sectional study in China. Health Soc Care Community. 2020;28(4):1241-51.\u003c/li\u003e\n\u003cli\u003eLi X, Wang JL, He L, Hu Y, Li CW, Xie YM, et al. Turnover intention and influential factors among primary healthcare workers in Guangdong province, China: a cross-sectional study. BMJ Open. 2024;14(11).\u003c/li\u003e\n\u003cli\u003eGuo Y, Nie HL, Chen H, Nicholas S, Maitland E, Chen SS, et al. Job Preferences of Centers for Disease Control and Prevention Workers: A Discrete Choice Experiment in China. Biomed Environ Sci. 2025;38(6):740-50.\u003c/li\u003e\n\u003cli\u003eWeiss DJ, Dawis RV, England GW. Manual for the Minnesota Satisfaction Questionnaire. Minnesota Studies in Vocational Rehabilitation. 1967;22:120-.\u003c/li\u003e\n\u003cli\u003eWu FY, Ren Z, Wang Q, He MF, Xiong WJ, Ma GD, et al. The relationship between job stress and job burnout: the mediating effects of perceived social support and job satisfaction. Psychol Health Med. 2021;26(2):204-11.\u003c/li\u003e\n\u003cli\u003eZhao YZ, Wang YL, Guo W, Cheng L, Tong JL, Ji RP, et al. Studies on the Relationship between Occupational Stress and Mental Health, Performance, and Job Satisfaction of Chinese Civil Aviation Pilots. Aerospace. 2023;10(10).\u003c/li\u003e\n\u003cli\u003eZeng XQ, Zhang XX, Chen MR, Liu JP, Wu CM. The Influence of Perceived Organizational Support on Police Job Burnout: A Moderated Mediation Model. Front Psychol. 2020;11.\u003c/li\u003e\n\u003cli\u003eLi HG, Zuo MZ, Gelb AW, Zhang B, Zhao XH, Yao DD, et al. Chinese Anesthesiologists Have High Burnout and Low Job Satisfaction: A Cross-Sectional Survey. Anesth Analg. 2018;126(3):1004-12.\u003c/li\u003e\n\u003cli\u003eMaslach C, Jackson SE. Evaluating Stress: a Book of Resources1997.\u003c/li\u003e\n\u003cli\u003eWang Y, Zhang H, Lei J, Yu YH. Burnout in Chinese social work: Differential predictability of the components of the Maslach Burnout Inventory. Int J Soc Welf. 2019;28(2):217-28.\u003c/li\u003e\n\u003cli\u003eWu SY, Zhu W, Wang ZM, Wang MZ, Lan YJ. Relationship between burnout and occupational stress among nurses in China. J Adv Nurs. 2007;59(3):233-9.\u003c/li\u003e\n\u003cli\u003eHuang Q, Liu HX, Chu CY. Effects of Paternalistic Leadership on Quality of Life of Grassroots Officials in China: Mediation Effects of Burnout. Appl Res Qual Life. 2021;16(5):2113-30.\u003c/li\u003e\n\u003cli\u003eScott CR, Connaughton, S. L., Diaz-Saenz, H. R., Maguire, K., Ramirez, R., Richardson, B., Shaw, S. P., \u0026amp; Morgan, D. The Impacts of Communication and Multiple Identifications on Intent to Leave: A Multimethodological Exploration. Manag Commun Q. 1999;12(3):400-35.\u003c/li\u003e\n\u003cli\u003eFeng BA, Dou GJ, Zhan XQ. Negative workplace gossip and turnover intention among kindergarten teachers: psychological safety as a mediator and organizational identification as a moderator. Front Psychol. 2025;16.\u003c/li\u003e\n\u003cli\u003eWu XX, Hayter M, Lee AJ, Yuan Y, Li S, Bi YX, et al. Positive spiritual climate supports transformational leadership as means to reduce nursing burnout and intent to leave. J Nurs Manag. 2020;28(4):804-13.\u003c/li\u003e\n\u003cli\u003ehao z, lirong l. Statistical Remedies for Common Method Biases. Advances in Psychological Science. 2004(06):942-50.\u003c/li\u003e\n\u003cli\u003ePodsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903.\u003c/li\u003e\n\u003cli\u003eSi-mou W, Gui-gian S, Wei Q, En-ging Z, Yan T. Status of job burnout among disease control and prevention technicians at county and city levels in Sichuan Province in 2017. Occupation and Health. 2019;35(12):1592-5.\u003c/li\u003e\n\u003cli\u003eJincheng C, Shucun M. Investigation on job satisfaction and turnover intention among employees of CDC institutions in Henan Province. Jiangsu Journal of Preventive Medicine. 2022;33(01):115-7.\u003c/li\u003e\n\u003cli\u003eAxiu L, Zhengjun Q, Fan G, Yumeng Z, Jincan C, Ting S. Analysis of Turnover Intention and Related Factors Among Newly Recruited Staff in the Center for Disease Control and Prevention System of Lianyungang City from 2020 to 2022. Jiangsu Journal of Health Care. 2024;26(05):453-5.\u003c/li\u003e\n\u003cli\u003eChina SCotPsRo. Guidelines on promoting the high-quality development of disease prevention and control: Government of China; 2023 [cited 2025 2025-9-16]. Available from: http://www.gov.cn/zhengce/content/202312/content_6922483.htm.\u003c/li\u003e\n\u003cli\u003eSong X, Xiang M, Liu Y, Yu C. Relationship Between Job Satisfaction and Burnout Based on a Structural Equation Model. J Occup Environ Med. 2020;62(12):e725-e31.\u003c/li\u003e\n\u003cli\u003eLi XY, Yang CX, Liu LB, Ding YL, Xue JC, He JN, et al. Configurational paths to turnover intention among primary public health workers in Liaoning Province, China: a fuzzy-set qualitative comparative analysis. BMC Public Health. 2024;24(1).\u003c/li\u003e\n\u003cli\u003eWang HQ, Xu X, Yang YL, Li L. The effects of organization and community embeddedness on public health professionals\u0026apos; intention to stay during the COVID-19 pandemic: a cross-sectional study. Hum Resour Health. 2025;23(1).\u003c/li\u003e\n\u003cli\u003eZe-gui T, Si-si C, Yi-xuan C, Hao Y, Xue-feng S. A study on job preferences of CDC staffs at the prefectural-levels in Shandong province:Based on a discrete choice experiment. Chinese Journal of Health Policy. 2024;17(01):60-7. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Public health workforce, Salary, Job satisfaction, Burnout, Turnover intention","lastPublishedDoi":"10.21203/rs.3.rs-8385919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8385919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eAs a core pillar in China\u0026rsquo;s public health system, Centers for Disease Control and Prevention (CDCs) face significant workforce turnover. Our study addresses two gaps: the structural relationships and combined effect on CDC turnover of salary, job satisfaction, and burnout, and, second, the inter-relationship of marital status and these factors.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted across all CDCs in Yunnan Province, China (March-April 2024), collecting data on salary, job satisfaction, burnout, turnover intention, and sociodemographic information. Chained mediation models analyzed the multiple mediating effects of job satisfaction and burnout between salary and turnover intention.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eLower salary directly correlated with higher turnover intention (β = -0.059, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accounting for 42.14% of the total effect. Job satisfaction and burnout demonstrated significant chained mediation between salary and turnover intention. Job satisfaction also exerted an independent mediating effect (β = -0.058, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while burnout did not. The total indirect effect coefficient was \u0026minus;\u0026thinsp;0.081, constituting 57.86% of the total effect. Married staff mirrored the full-sample pathways, though effect sizes differed. Unmarried staff reported significantly higher burnout than married staff (6.94 vs. 6.31, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but showed no significant associations between salary and job satisfaction, burnout, or turnover intention.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eJob satisfaction and burnout served as chained mediators between salaries and turnover intentions, with job satisfaction exerting an additional independent mediating effect. Internal heterogeneity was revealed within the marriage subgroups. Interventions should prioritize equitable compensation, enhance job satisfaction, reduce burnout, and implement differentiated measures to reduce workforce turnover.\u003c/p\u003e","manuscriptTitle":"The turnover challenge in China’s Centers for Disease Control and Prevention: Role of salaries, job satisfaction and burnout","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 14:41:39","doi":"10.21203/rs.3.rs-8385919/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf4fd425-882e-472b-b98f-aa61e5a8163f","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T19:54:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 14:41:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8385919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8385919","identity":"rs-8385919","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}