Occupational burnout, job satisfaction and flourishing among healthcare workers in western China: a network analysis

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This study aimed to provide evidence of complex interrelations among occupational burnout, job satisfaction, and flourishing, and identify key variables from the perspective of network structure among healthcare workers. Methods A cross-sectional study was conducted between July and October 2021, and 922 healthcare workers were recruited to report their sociodemographic characteristics, occupational burnout, job satisfaction, and flourishing. Network analysis was conducted to investigate the interrelations of dimensions in occupational burnout, job satisfaction, and flourishing communities, and identify central variables and bridges connecting different dimensions with different bridge strength thresholds in the network structure. The Network Comparison Test (NCT) was conducted to examine the gender differences in networks. Results In the network, feeling exhausted at work (strength: 1.42) and feeling frustrated at work (1.27) in occupational burnout community, and interested in daily activities (1.32) in flourishing community were central variables. Bridges in the network were job reward satisfaction (bridge strength: 0.31), satisfaction with job itself (0.25), and job environment satisfaction (0.19) in job satisfaction community, as well as interested in daily activities (0.29) and feeling respectable (0.18) in flourishing community, with bridges selected with top 20% bridge strengths. Feeling frustrated at work (0.14) in occupational burnout community and leading a purposeful and meaningful life (0.11) in flourishing community became bridges when using thresholds of top 25% and 30% bridge strengths, respectively. We also observed higher network densities in females (network density: 0.37) than that in males (0.34) and gender differences in the distribution of partial correlation coefficients (M = 0.27, P = 0.017). Conclusions In the network structure of occupational burnout-job satisfaction-flourishing, feeling frustrated at work in occupational burnout community and interested in daily activities in flourishing community were both central variables and bridges, which may be targeted variables to intervene to alleviate the overall level of symptoms in the network and therefore prevent poor health outcomes in healthcare workers. Occupational burnout job satisfaction flourishing network analysis healthcare workers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Frontline healthcare workers are well-known to experience excessive workload. They provide not only medical services, such as disease therapy, health monitoring and follow-up, but also health education and guidance, etc. Healthcare workers are a significant part of the health system. Despite this, healthcare workers may have anxiety over safety practices because of the occupational exposure risk [ 1 ]. Furthermore, overloaded work, high work-related demand and unsupportive environments may induce healthcare workers’ occupational burnout [ 2 – 4 ]. A previous study on burnout syndrome in Brazilian healthcare workers showed that 26.4% were experiencing emotional exhaustion, 17.2% had an elevated level of depersonalization, and 10.5% had decreased personal accomplishment [ 5 ]. In Chinese healthcare workers, several studies showed a high prevalence of occupational burnout ranging from 75.5–80.0% [ 6 – 8 ]. Occupational burnout is one of the critical reasons for poor quality of medical services, medical accidents, low efficacy of job performance [ 9 – 14 ], high absenteeism and turnover and low well-being [ 15 – 19 ]. Thus, it is urgent to understand occupational burnout and its comprehensive influencing factors among healthcare workers. Occupational burnout of healthcare workers may be caused by negative psychological factors, such as anxiety, depression, negative coping strategies about events, and the poor aptitude for interpersonal relationships [ 20 , 21 ]. In contrast, positive psychological factors are beneficial in reducing occupational burnout [ 22 ]. Flourishing, which belongs to positive psychology research, goes beyond the confines of simple well-being and may better capture the complexity of positive symptoms. Recent studies suggested that flourishing, a state in which all aspects of one’s experience are well, can reflect the well-being and happiness of individuals and groups in a more comprehensive way and has more advantages in explaining complex relations and connotation compared with other tools [ 23 , 24 ]. Abundant studies found that employees with flourishing would exhibit positive individual and organizational outcomes, such as higher job satisfaction and lower risk of occupational burnout [ 25 – 30 ]. To date, evidence on flourishing and the association between flourishing and occupation burnout in healthcare workers is scarce. Another factor that reflects the emotions of healthcare workers is job satisfaction, defined as people's feelings about their jobs, such as whether they like or dislike their jobs [ 31 ]. It is reported that job satisfaction would greatly increase enthusiasm and reduce the absenteeism of workers, even in those with high occupational burnout [ 32 – 37 ]. Besides, job satisfaction was related to positive psychological factors (e.g., empathy and psychological capital) [ 38 , 39 ], and as described in the spillover model, higher levels of job satisfaction lead to higher levels of well-being [ 40 ]. For example, the dimensions of job satisfaction, such as satisfaction with job and job reward satisfaction, were both associated with well-being. If the job were not a good fit for the worker and do not fulfill the basic material needs, their well-being would be hampered [ 41 ]. However, current studies only estimated the relationships between occupational burnout, flourishing, and job satisfaction based on the sum-scores of scales rather than individual items in a scale. Thus, the complex association among the dimensions of job satisfaction and flourishing on occupational burnout among healthcare workers remains to be elucidated more precisely. Existing studies using ordinary least squares (OLS) regression cannot estimate the relationships among multi-dimensional variables at a time. In recent years, network analysis is increasingly being used in psychological research to quantify the relationships among individual psychological factors’ dimensions and identify the most interconnected dimensions. Meanwhile, in a network, it is plausible that a group of nodes (variables) belong to a community (i.e., the same rating scale) where they are closely related, and within the set of variables there may be central variables that are closely related to other variables [ 42 ]. Moreover, network models describe an interacting web of symptoms and could help to understand the transdiagnostic symptom relationships that underlie the well-established association of occupational burnout, job satisfaction and flourishing. Network analysis can be a useful method to examine the bridges that are represented by one or more key variables bridging two or more communities [ 43 ]. These bridges may be used to develop targeted interventions, and deactivating bridges may constitute an effective strategy to block connections between items efficiently. For example, previous study identified bridge symptoms (i.e., “irritability”, “feeling afraid” and “sad mood”) that could be targeted in specific treatment and preventive measures for comorbid depressive and anxiety symptoms [ 44 ]. However, few study, to our knowledge, used this method in occupational population who have a high probability of occupational burnout and other negative emotions. In the light of the above gaps, we used network analysis to (1) explore the associations of occupational burnout, job satisfaction, and flourishing in a constructed network; (2) identify central variables in network structure of occupational burnout - job satisfaction - flourishing; and (3) seek bridges linking occupational burnout, job satisfaction and flourishing. Considering the diversity of medical services of health workers and limitations of implementation and data collection in real-world data, this study used a survey dataset of HIV/AIDS healthcare workers. The findings of this study would help obtain a straightforward view of variables’ interactions to clarify critical issues and guide more effective interventions to improve the health and professional happiness of healthcare workers in China. More broadly, our finding would help policy-making for improving the psychological health of other healthcare workers. Material And Methods Study design and participants This study is an observational and cross-sectional design in the Sichuan province of China, where is one of the most HIV-affected province in China [45 , 46]. In such a scenario, HIV/AIDS healthcare workers in Sichuan province may undertake overloaded work and high work-related demands. The current study used a stratified cluster sampling method to obtain a sample. First, we randomly selected three cities based on the prevalence of HIV as our study sites. Second, all related healthcare workers in selected cities were recruited for investigation (e.g., from the Centers for Disease Control and Prevention (CDC) and Designated Hospitals for HIV treatment) ( Figure S1 ). Finally, 922 HIV/AIDS healthcare workers were enrolled to complete the electronic survey. All the participants gave informed assent forms before data collection; it has been stated on the cover page that the participants cannot proceed with the online survey without consent. Ethics approval was obtained from the Ethics Committee West China School of Public Health and West China Fourth Hospital, Sichuan University (Gwll2021059). Data collection Data for the analysis was collected between July 2021and October 2021 through an online questionnaire designed by an expert panel. The panel consisted of two epidemiologists, one health psychologist, and two HIV/AIDS healthcare workers. A pretest was conducted in 10 HIV/AIDS healthcare workers to test the questionnaire's comprehension and the feasibility of investigation process. Their feedback was used to revise and finalize the questionnaire. Then, the online questionnaire link was released by the HIV/AIDS management department in the Health Commission of the three cities through WeChat and other social media platforms and forwarded to all the HIV/AIDS healthcare workers. The questionnaire took about 20 minutes to complete on average. Measurement Occupational burnout scale The widely used scale to measure occupational burnout was the version of the Maslach Burnout Inventory-General Survey (MBI-GS) by Maslach and Jackson [ 47 ], which had good reliability and validity in China [ 48 ]. Our study used a self-designed scale developed based on the MBI-GS to better adapt the local culture of HIV/AIDS healthcare workers, and to ensure the high-quality results, that is, within the endurance limits to be interviewed. The scale used 11 items to assess the occurrence of occupational burnout, including emotional exhaustion (3 items), work fatigue (1 item), emotional numbness (3 items), work frustration (2 items), and work pressure (2 items). All items had five degrees to choose, from strongly disagree (1-point) to strongly agree (5-point). A sum score ranging from 11 to 55 was calculated, and higher scores indicated more severe occupational burnout. Cronbach’s alpha for the occupational burnout scale was 0.895 in this study. Flourishing scale The flourishing scale (FS) was used to assess the flourishing of HIV/AIDS healthcare workers. The flourishing scale consists of 8 items describing essential aspects of human functioning and human needs, including the meaning of life, interpersonal relationship, life and work engagement, helpful to others, competence, self-esteem, optimism and respect from others [ 49 ]. Each item was answered on a 7-item scale ranging from strong disagreement (1-point) to strong agreement (7-point). The total score of all items ranged from 8 (strong disagreement) to 56 (strong agreement). A higher total score indicated a greater sense of thriving, which showed a person had a positive attitude toward life and owed many psychological resources and advantages. The Chinese version of flourishing scale had good validity and reliability [ 50 , 51 ]. Cronbach’s alpha for the flourishing scale was 0.932 in this study. Job satisfaction scale Job satisfaction was measured by a self-developed scale and was revised and finalized after the pilot study. Three dimensions of job satisfaction, including satisfaction with job itself (10 items), job environment satisfaction (6 items) and job reward satisfaction (8 items), were recorded. All items had five degrees ranging from disagree (1-point) to agree (5-point). The total score of each dimension was calculated, and higher scores indicated higher job satisfaction in the corresponding dimension. The Cronbach’s alpha coefficients for the three dimensions of job satisfaction were 0.853, 0.834 and 0.808 in this study, respectively. Socio-demographic and work-related characteristics The collected socio-demographic characteristics included age, sex, race, marital status, educational level, personal monthly income and living situation. Besides, work-related factors included professional status, job tenure, years working in HIV/AIDS units, and types and levels of institutions. All relevant descriptions and explanations were detailed in Table S1 . Statistical analysis Descriptive statistics Data were summarized by descriptive statistics, with frequency and percentage for categorical variables, and median and interquartile range (IQR) for continuous variables ( Table S2 ). We used R version 4.0.3 for data management and all statistical analyses. Network estimation We assessed the associations among occupational burnout, job satisfaction and flourishing based on the network of Graphical Gaussian Model (GGM). Partial correlation analysis was conducted to indicate the association of each pairwise variable and form networks; nodes in the network represented variables, and edges represented partial correlation coefficients between two variables. Stronger correlations were shown in thicker and more saturated edges. Positive and negative correlations were shown in green and red, respectively. In this network estimation, an Extended Bayesian Information Criterion (EBIC) model with the least absolute shrinkage and selection operator (LASSO) were used to get a sparse and intelligible network [ 52 , 53 ]. We also estimated the predictability, the upper bound of variance (measured in R 2 ) of a given variable explained by all the other variables in the network [ 54 ]. Centrality and bridge symptoms For the constructed networks, we calculated strength centrality (i.e., the sum of the absolute value of all partial correlation coefficients for a given variable) to identify the most central variables, using the centralityplot function in the qgraph package in R [ 55 ]. In addition, we conducted a stability and reliability analysis of our results using a commonly applied bootstrapping procedure in R with bootnet package, which showed whether the networks remained stable when dropping 75% of the sample, and whether the results of bootstrap 95% CI for edges were narrow, indicating the trustworthy of edges [ 56 ]. We used the term community to designate a group of items or dimensions, which were supposed to be related according to scale classification, independent of the actual network structure [ 42 ]. We also estimated variables that acted as bridges connecting communities, which allowed us to identify which items were the most interconnected across occupational burnout, job satisfaction and flourishing communities. The bridges were estimated with different scoring variable thresholds (i.e., variables that are most strongly connected to all the variables of different communities) using the bridge function in the networktools package in R [ 57 ]. Network comparison test Network Comparison Test (NCT) is a permutation-based hypothesis test for invariance of network structure (i.e., how the connections between variables within a network differ across samples) and global strength of connections (i.e., how the density of the network differs across samples – the sum of all edge strengths). To examine sex differences in the structure of networks, we ran the NCT using the Network Comparison Test package in R [ 58 ]. We compared the distribution of partial correlation coefficients in each network to characterize the network structure. Then, we compared differences in strength for each edge of networks between females and males, after controlling for multiple tests using a Holm-Bonferroni correction. Additionally, the network densities (the actual number of variables in the network / the theoretical number of variables in the network[ 59 ]) between females and males were also compared. Results Sociodemographic and work characteristics A total of 907 (98.37%) HIV/AIDS healthcare workers were included in the final analysis, with a mean age of 38.0 ± 9.4 years. Among them, 69.1% (n = 627) were women, 49.5% (n = 449) graduated from junior colleges, 77.8% (n = 706) were married or had a spouse, 87.3% lived with their parents (n = 792), and 27.9% (n = 253) had a personal income below 3,000 Renminbi (RMB) per month. There were 318 (35.1%) participants working for 2–5 years, and 464 (51.2%) working in county-level medical institutions. Further details were provided in Table 1 . Table 1 Baseline characteristics of the participants Variables N % Age (years) 18–29 224 24.7 30–39 300 33.1 40–49 253 27.9 ≥ 50 130 14.3 Sex Female 627 69.1 Male 280 30.9 Marital status Unmarried 118 13.0 Married/Living together 706 77.8 Divorce/Widowed/Living separately 83 9.2 Educational level High school or below 123 13.6 Junior college 449 49.5 Undergraduate or above 335 36.9 Personal monthly income (RMB) < 3000 253 27.9 3000–4000 304 33.5 4000–5000 176 19.4 ≥5000 174 19.2 Living situation Alone 74 8.2 With friends or others 41 4.5 With parents 792 87.3 Professional status Physicians 180 19.8 HIV/AIDS care workers 334 36.8 Nurses 103 11.4 Public health physicians 104 11.5 Others 1 186 20.5 Job tenure (years) <2 29 3.2 2– 5 85 9.4 5–10 181 20.0 10–20 268 29.5 ≥ 20 344 37.9 Years working in HIV/AIDS units < 2 170 18.7 2–5 318 35.1 5–10 174 19.2 ≥10 245 27.0 Types of institution CDC 91 10.0 Designated Hospital for treatment 105 11.6 County Governments 95 10.5 Community Health Service Centers 591 65.2 Others 1 25 2.8 Levels of institution Municipal-level 136 15.0 District-level 307 33.8 County-level 464 51.2 Abbreviations: CDC, Centers for Disease Control and Prevention. 1 Others is a self-selected designation that indicates the institution is not listed. Network structure and centrality measures The network structure showed a high degree of interrelations between variables from occupational burnout, job satisfaction and flourishing communities, and the variables in the same communities tended to cluster together. Most of the associations between variables of different communities were negative (Fig. 1). The predictabilities of variables were shown as ring shaped pie charts, and variables with the highest centrality were E2 (feeling exhausted at work in occupational burnout community; strength: 1.42), H3 (interested in daily activities in flourishing community; strength: 1.32) and E10 (feeling frustrated at work in occupational burnout community; strength: 1.27). We also found the stability of the network remained stable (i.e., case-dropping coefficient = 0.75) even dropping large proportions of the sample ( Fig. 2 ). The numerical interactions were showed in Table S3 using weighted adjacency matrix that represented the strength of associations between variables. The results of the reliability and stability analyses were presented in Figures S2- S4. Bridges and bridge centrality measures Figure 3 showed the bridges and bridge centrality indices. The top 20% scoring variables on bridge strength were W1(satisfaction with job itself; bridge strength: 0.25), W2 (job environment satisfaction; bridge strength: 0.19) and W3 (job reward satisfaction; bridge strength: 0.31) in job satisfaction community; H3 (interested in daily activities; bridge strength: 0.29) and H8 (feeling respectable; bridge strength: 0.18) in flourishing community (Fig. 3A). When identifying bridges with the top 25% bridge strengths, we observed that E10 (feeling frustrated at work; bridge strength: 0.14) in the occupational burnout community was additionally added as bridge linking other communities (Fig. 3B). Furthermore, H1 (leading a purposeful and meaningful life of flourishing community; bridge strength: 0.11) was added as a bridge when using the top 30% bridge strengths (Fig. 3C). Bridge strength was reported in Fig. 3D . Network comparisons between female and male The network structures and network centrality indices of male (n = 607) and female participants (n = 280) were showed in Fig. 4 , and the network structures were both stable ( Figures S5-S8 ). The number of edges in female network were 86, and in male network were 79. The network density of female and male were 0.37 and 0.34, respectively. The results of the NCT indicated significant sex differences in the distribution of partial correlation coefficients (M = 0.27, P = 0.017), and no significant gender differences were observed in network global strength (female: 10.63 vs. males: 9.91; global strength difference = 0.72, P = 0.071) ( Figure S9 ). We also calculated bridge centrality in females and males’ networks, separately (Fig. 5 ). No bridge was found with the top 20% and 25% bridge strengths, but seven bridges were found in both females and males’ networks with the top 30% bridge strength. However, there was a significant sex difference. For example, E10 (feeling frustrated at work of occupational burnout community; bridge strength: 0.14) was a bridge in the network of females, but no bridge was found in occupational burnout community in the network of males. Besides, H2 (having supportive and rewarding social relationships; bridge strength: 0.12) was a bridge in flourishing community in females’ network, however, H1 (leading a purposeful and meaningful life; bridge strength: 0.09) and H4 (contributing to the happiness and well-being of others; bridge strength: 0.08) were bridges in males’ network. Discussion This study estimated the network structure of occupational burnout, job satisfaction and flourishing, and firstly attempted to assess their interactions at the item level in healthcare workers. Our results indicated that variables feeling exhausted at work and feeling frustrated at work in occupational burnout community, and interested in daily activities in flourishing community were most central variables. Job reward satisfaction, satisfaction with job itself and job environment satisfaction in job satisfaction community, as well as interested in daily activities and feeling respectable in flourishing community were bridges when identifying bridges with the top 20% bridge strengths. Feeling frustrated at work in the occupational burnout community and leading a purposeful and meaningful life in the flourishing community became bridges connecting other communities with top 25% and 30% bridge strengths, respectively. Gender was associated with different distributions of partial correlation coefficients between their networks and network densities in females were higher than that in males. Central variables may give insights into the connectedness or importance of items within a network and may play a major role in causing the onset of and/or maintaining a syndrome [ 60 ]. Bridges mediate the transition among different syndromes and may increase risk of contagion to other disorders [ 61 ]. Variables with high centrality are especially important to the development, persistence, and remission of symptoms in networks, and bridges play the important role of bridging two or more communities. In this study, feeling exhausted at work was the most central variable with high centrality in the network, followed by interested in daily activities and feeling frustrated at work. Meanwhile, we found that interested in daily activities and feeling frustrated at work were also bridges connecting other communities. Thus, there may have some overlaps between central variables and bridges, and these overlaps deserve especial attention. It is easy for occupational population to develop negative emotions about work, not to mention healthcare workers who are responsible for the prevention and control of the diseases and treatment of the patients, etc. If occupational population are interested in their daily work, they may have high job satisfaction and less prone to have occupational burnout. Therefore, these central variables and bridges not only played an important role in understanding the structure of the network model, but also can serve as explicit intervention points to relieve all symptoms. It should be noted that bridges changed in different percentile cut-off of bridge strength (80%, 75%, and 70%), which were different from Jones's study that only focused on one cut-off [ 62 ]. The number of bridges would increase when the percentile cut-off point decreased, and therefore we identified at least one bridge from each community, which may guide us to carry out targeted interventions in the bridges corresponding to each community. In this study, the items belonging to the same communities tended to cluster together, and the relationships between occupational burnout, job satisfaction and flourishing were more subtle compared to previous studies' findings that only considering the total score or subscale total scores of the rating scales [ 63 – 65 ]. Thus, strategies and measures based on macro level, such as targeted policies formulated by society and related institutions for health workers, may be an effective method to improve health. Meanwhile, our study revealed that there were intrinsic interactions among variables of different communities, which suggested that interventions targeting crucial variables in each community may be beneficial to the whole network. Therefore, network analysis offers an additional tool to capture the dimensional nature of symptoms, focusing not only on the identification of symptom clusters but above all on their connecting patterns. Furthermore, our analysis documented both similarities and differences in the network structure of occupational burnout - job satisfaction - flourishing in females and males. The central variables were almost similar in both females and males. However, the bridges had a large difference in occupational burnout and flourishing community, which may be caused by the career expectation and attitudes towards problems between females and males [ 66 ]. We also examined sex differences in the structure of networks and found network densities between females and males were different. Network density describes the portion of the potential connections in a network that are actual connections and refers to the mean strength of the connections between variables in networks [ 59 ]. In the study, we found higher network densities in females than that in males, which showed that symptoms may transmit more quickly in females. The results were similar to previous studies which have found that females and males responded to conditions in different ways and females tend to develop internal symptoms[ 67 , 68 ]. The differences may be derived from particular psychological characteristics, unique perspective and experiences on work-related aspects in females which were different from males. Thus, preventions and interventions should mainly focus on females to prevent transmission and adverse effects of occupational burnout. But from a dialectical point of view, better outcomes may be achieved after interventions because of the fast transmit. This study has several strengths. First, the network model was designed to consider individual symptoms as variables in the study. In addition, we compared bridges under different thresholds, which can help to find the more important bridges in different communities and help design subtle interventions. Finally, NCT analysis explicitly tested gender as a sociodemographic influence on the network structure of occupational burnout - job satisfaction - flourishing of health workers, and indicated the heterogeneity in central and bridges for targeted intervention in two networks. Several limitations should also be mentioned. First, the cross-sectional data is limited to infer causal relations. To fully conceptualize how the variables interact with one another, future experimental and longitudinal studies are needed. However, other study has shown that network structures among cross-sectional and longitudinal studies did not differ [ 69 ]. Second, as a study relied on the self-report measure, the results may not rule out other possible factors, social desirability or recall biases, etc. Third, we only examined the difference in network structure between gender. However, other sociodemographic factors are also needed to examine in future studies. Finally, generalizations should be taken with caution since the sample comprised only a small proportion of healthcare workers in China. Therefore, researches on geographical and cultural diversity are warranted in the future. Conclusions Through network analysis, we estimated complex association between occupational burnout, job satisfaction, and flourishing among Chinese healthcare workers. Feeling frustrated at work of occupational burnout community and interested in daily activities of flourishing community were both as the central variables and bridges. Specific strategies and measures targeting to these variables should be given such as improving levels of flourishing and job satisfaction and reducing occupational burnout. Additionally, government and healthcare agencies should address occupational burnout and improve flourishing, especially taking initiatives to improve job satisfaction with effective interventions in healthcare workers. Declarations Funding This research was funded by the National Natural Science Foundation of China (81703279), Liangshan Key R & D projects of science and technology (2021DYF0045), and Sichuan Provincial foundation for AIDS prevention and control (2022ZC02, 2021ZC01, 2020zc05, 2020zc04, 2019sc01, 2018-WJW-03). Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Code availability R code available on request. Authors’ contributions SJ and SJY had taken a principal role in the study conception and design and methodologies, and drafting the manuscript. BY, CF, PJ and PX contributed to the writing of the study protocol and made revisions to the manuscript. SJ, SJY and BY contributed to the material preparation and data analysis. All authors read and approved the final manuscript. Competing interests The authors have not disclosed any conflict of interests. Ethics approval and consent to participate In this study, all methods were performed in accordance with the relevant guidelines and regulations. This study was approved by the Ethics Committee West China School of Public Health and West China Fourth Hospital, Sichuan University (Gwll2021059). 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Huang MB YLLB. Characterizing the HIV/AIDS epidemic in the United States and China. International Journal of Environmental Research and Public Health. 2016,13(1):30. Yuan FS, Liu L, Liu LH, Zeng YL, Zhang LL, He F, et al. Epidemiological and spatiotemporal analyses of HIV/AIDS prevalence among older adults in Sichuan, China between 2008 and 2019: a population-based study. Int J Infect Dis. 2021 2021 Apr,105:769-75. Maslach C, Jackson SE. MBI: maslach burnout inventory: manual research edition. 1986. Chaoping L, Kan S. The influence of distributive justice and procedural justice on job burnout. Xin Li Xue Bao. 2003(05):677-84. Diener E, Wirtz D, Tov W, Kim-Prieto C, Choi D, Oishi S, et al. New well-being measures: short scales to assess flourishing and positive and negative feelings. Soc Indic Res. 2010,97:143-56. Wenjie D, Dan X. Measuring adolescent flourishing: psychometric properties of flourishing scale in a sample of Chinese adolescents. J Psychoeduc Assess. 2016,37(1):131-35. 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Commentary: a network theory of mental disorders. Front Psychol. 2017 2017,8:1305. van Borkulo C, L B, D B, BW P, LJ W, RA S. Association of symptom network structure with the course of depression. 2015:1219-26. Pe ML, Kircanski K, Thompson RJ, Bringmann LF, Tuerlinckx F, Mestdagh M, et al. Emotion-network density in major depressive disorder. Clin Psychol Sci. 2015,3(2):292-300. BOCCALETTI S, LATORA V, MORENO Y, CHAVEZ M, HWANG D. Complex networks: structure and dynamics. Physics Reports. 2006,424(4-5):175-308. Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci. 2010 2010 Jun,33(2-3):137-50, 150-93. Jones PJ, Ma R, McNally RJ. Bridge centrality: a network approach to understanding comorbidity. Multivariate Behav Res. 2021 2021 Mar-Apr,56(2):353-67. Redelinghuys K, Rothmann S, Botha E. Flourishing-at-work: the role of positive organizational practices. Psychol Rep. 2019 2019 Apr,122(2):609-31. Kelly-Hedrick M, Rodriguez MM, Ruble AE, Wright SM, Chisolm MS. Measuring flourishing among internal medicine and psychiatry residents. J Grad Med Educ. 2020 2020 Jun,12(3):312-19. MH V, MK V, J F. Resilience, hope and flourishing are inversely associated with burnout among members of the society for gynecologic oncology. 2018:52-55. Sikora J, Pokropek A. Gender segregation of adolescent science career plans in 50 countries. Sci Educ. 2012(96):234-64. Hill TD, Needham BL. Rethinking gender and mental health: a critical analysis of three propositions. Soc Sci Med. 2013 2013 Sep,92:83-91. Slopen N, Williams DR, Fitzmaurice GM, Gilman SE. Sex, stressful life events, and adult onset depression and alcohol dependence: are men and women equally vulnerable? Soc Sci Med. 2011 2011 Aug,73(4):615-22. Miers AC, Weeda WD, Blote AW, Cramer A, Borsboom D, Westenberg PM. A cross-sectional and longitudinal network analysis approach to understanding connections among social anxiety components in youth. J Abnorm Psychol. 2020 2020 Jan,129(1):82-91. Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 03 Aug, 2023 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Major revision 11 May, 2023 Reviews received at journal 10 May, 2023 Reviews received at journal 06 May, 2023 Reviewers agreed at journal 29 Apr, 2023 Reviewers agreed at journal 28 Apr, 2023 Reviewers invited by journal 26 Apr, 2023 Editor assigned by journal 26 Apr, 2023 Editor invited by journal 24 Apr, 2023 Submission checks completed at journal 20 Apr, 2023 First submitted to journal 19 Apr, 2023 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. 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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-2835947","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":193683133,"identity":"c17794ff-917a-4fd3-9ea5-0dcf86a58c96","order_by":0,"name":"Siyan Jia","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siyan","middleName":"","lastName":"Jia","suffix":""},{"id":193683134,"identity":"62d3c368-107a-4c1d-b682-6e5394a22387","order_by":1,"name":"Bin Yu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yu","suffix":""},{"id":193683135,"identity":"dd641ce6-edb3-43e8-8298-87d7d90f3f51","order_by":2,"name":"Chuanteng Feng","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuanteng","middleName":"","lastName":"Feng","suffix":""},{"id":193683136,"identity":"7576f997-1cd2-4784-9097-d7c3a0b47de5","order_by":3,"name":"Peng Jia","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Jia","suffix":""},{"id":193683137,"identity":"f97d6293-6b18-4d02-abdd-837d0a370759","order_by":4,"name":"Peng Xu","email":"","orcid":"","institution":"National Center for STD/AIDS Control and Prevention, China Center for Disease Control and Prevention","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Xu","suffix":""},{"id":193683138,"identity":"24b62df5-5303-412d-9ef7-02aa2c717781","order_by":5,"name":"Shujuan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYFACNoYDDAw2PPzsDaRpSZOR7DlAghYgOGxjcMOBSA3m7W2JhwvbzvMw3GBg/PAxhwgtMmeOHTg8s+02D+PsBmbJmduI0CIhkd5wmBeohVnmABszL1Fa5J+DtJzjYZNIIFaLBNsBoJYDPDzEa+FJSzjMcy6ZR4LnYDORfmE/ZvyZp8zO3v5488EPH4nRAgaM4LhhbCBWPQj8IUXxKBgFo2AUjDgAAHShM3FufyDzAAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shujuan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2023-04-19 09:44:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2835947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2835947/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-023-04959-7","type":"published","date":"2023-08-03T21:53:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":36223668,"identity":"4889c528-a920-4367-9bb2-db289563919b","added_by":"auto","created_at":"2023-04-24 14:22:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":823676,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks and strength centrality displaying the relationship between occupational burnout, job satisfaction and flourishing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The regularized network between occupational burnout, job satisfaction, and flourishing. \u003cstrong\u003e(B)\u003c/strong\u003e Standardized variable strength centrality for the network (z-scores). Variables are represented by nodes, numbers represent item numbers in the scale, partial correlations between the variables are represent by edges. The color of the edge indicates the direction of the correlation (red = negative, green =positive). The magnitude of the edge depicts the magnitude of the correlation, with thicker and saturated edges showing stronger correlations. Positive and negative correlations were shown in green and red, respectively. Variables identified as job satisfaction are colored in red circle, Variables identified as flourishing are colored in orange circle, Variables identified as occupational burnout are colored in green circle. The area in the rings around the variables indicate predictability (the upper bound of the variance of a given variable explained by the remaining variables in the network).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/d88e8136b0b65f1bae07ab9b.png"},{"id":36224542,"identity":"e14d7b86-8cbf-43b6-9580-bb7535adac8b","added_by":"auto","created_at":"2023-04-24 14:30:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33615,"visible":true,"origin":"","legend":"\u003cp\u003eStability of centrality indices by case dropping bootstrap for the network shown in Figure 1 (The bootstrap was repeated 1000 times). The x-axis represents the percentage of cases of original sample used at each step. The y-axis represents the average of correlations between the centrality indices from the original network and the centrality indices from the networks that were re-estimated after dropping increasing percentages of cases. Each line indicates the correlations of strength, while areas indicate 95% CI.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/841c5fa104624c40ef3e2833.png"},{"id":36223672,"identity":"4da0b284-f6c1-48de-b34f-43ecdb92053e","added_by":"auto","created_at":"2023-04-24 14:22:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1765994,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks and bridge strength centrality displaying the relationship between occupational burnout, job satisfaction and flourishing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The top 20% scoring variables on a given statistic and selected as predicted bridges. \u003cstrong\u003e(B)\u003c/strong\u003e The top 25% scoring variables on a given statistic and selected as predicted bridges. \u003cstrong\u003e(C)\u003c/strong\u003e The top 30% scoring variables on a given statistic and selected as predicted bridges. \u003cstrong\u003e(D)\u003c/strong\u003eBridge centrality estimates for each variable in the network, ordered by highest value. Variables are represented by nodes, numbers represent item numbers in the scale, partial correlations between the variables are represent by edges. The color of the edge indicates the direction of the correlation (red = negative, green =positive). The magnitude of the edge depicts the magnitude of the correlation, with thicker and saturated edges showing stronger correlations. Positive and negative correlations were shown in green and red, respectively. Variables identified as job satisfaction are colored in red circle, Variables identified as flourishing are colored in orange circle, Variables identified as occupational burnout are colored in green circle, Variables identified as bridges are drew with rectangles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/7f32187aff21e2bb7e9b3d3b.png"},{"id":36224541,"identity":"03e44a2b-b97d-43d2-8fd4-7773a3e8e0fd","added_by":"auto","created_at":"2023-04-24 14:30:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1535759,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks and strength centrality displaying the relationship between occupational burnout, job satisfaction and flourishing in females and males\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Graphical LASSO model of variables with occupational burnout, job satisfaction, and flourishing in the female participants (n= 627). \u003cstrong\u003e(B)\u003c/strong\u003e Graphical LASSO model of variables with job satisfaction, flourishing and occupational burnout in the male participants (n= 280). \u003cstrong\u003e(C) \u003c/strong\u003eComparison of network centrality indices between females and males. Variables are represented by nodes, numbers represent item numbers in the scale, partial correlations between the variables are represent by edges. The color of the edge indicates the direction of the correlation (red = negative, green =positive). The magnitude of the edge depicts the magnitude of the correlation, with thicker and saturated edges showing stronger correlations. Positive and negative correlations were shown in green and red, respectively. Variables identified as job satisfaction are colored in red circle, Variables identified as flourishing are colored in orange circle, Variables identified as occupational burnout are colored in green circle.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/0bfc4d26b833ca12e87679d2.png"},{"id":36223671,"identity":"a53ebabd-3ddb-4c88-9df6-8f4d933d3538","added_by":"auto","created_at":"2023-04-24 14:22:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1118007,"visible":true,"origin":"","legend":"\u003cp\u003eNetworks and bridge strength centrality displaying the relationship between occupational burnout, job satisfaction and flourishing in females and males\u003cstrong\u003e(A) \u003c/strong\u003eGraphical LASSO model of variables with occupational burnout, job satisfaction, and flourishing in the female participants of the sample (n= 687). \u003cstrong\u003e(B)\u003c/strong\u003eGraphical LASSO model of variables with job satisfaction, flourishing and occupational burnout in the male participants of the sample (n= 280). \u003cstrong\u003e(C) \u003c/strong\u003eBridge centrality estimates for each variable in the network of female, ordered by highest value.\u003cstrong\u003e (D) \u003c/strong\u003eBridge centrality estimates for each variable in the network of male, ordered by highest value. Variables are represented by nodes, numbers represent item numbers in the scale, partial correlations between the variables are represent by edges. The color of the edge indicates the direction of the correlation (red = negative, green =positive). The magnitude of the edge depicts the magnitude of the correlation, with thicker and saturated edges showing stronger correlations. Positive and negative correlations were shown in green and red, respectively. Variables identified as job satisfaction are colored in red circle, Variables identified as flourishing are colored in orange circle, Variables identified as occupational burnout are colored in green circle, Variables identified as bridges are drew with rectangles.\u003cstrong\u003e \u003c/strong\u003eThe top 30% scoring variables on a given statistic and selected as predicted bridges in the female and male.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/4a24bc613ec18813e7a47de7.png"},{"id":44735619,"identity":"20b12fd0-04ac-430f-a661-7eb3ae6de351","added_by":"auto","created_at":"2023-10-16 22:26:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3139212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/72113893-026f-45d5-8181-e9e55611fe5b.pdf"},{"id":36223673,"identity":"3441d8d5-1bcb-47b4-81dc-aadd60ad111d","added_by":"auto","created_at":"2023-04-24 14:22:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5800114,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2835947/v1/dbc5f707330f5e710fd227ba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occupational burnout, job satisfaction and flourishing among healthcare workers in western China: a network analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eFrontline healthcare workers are well-known to experience excessive workload. They provide not only medical services, such as disease therapy, health monitoring and follow-up, but also health education and guidance, etc. Healthcare workers are a significant part of the health system. Despite this, healthcare workers may have anxiety over safety practices because of the occupational exposure risk [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore, overloaded work, high work-related demand and unsupportive environments may induce healthcare workers\u0026rsquo; occupational burnout [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A previous study on burnout syndrome in Brazilian healthcare workers showed that 26.4% were experiencing emotional exhaustion, 17.2% had an elevated level of depersonalization, and 10.5% had decreased personal accomplishment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Chinese healthcare workers, several studies showed a high prevalence of occupational burnout ranging from 75.5\u0026ndash;80.0% [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Occupational burnout is one of the critical reasons for poor quality of medical services, medical accidents, low efficacy of job performance [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], high absenteeism and turnover and low well-being [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Thus, it is urgent to understand occupational burnout and its comprehensive influencing factors among healthcare workers.\u003c/p\u003e \u003cp\u003eOccupational burnout of healthcare workers may be caused by negative psychological factors, such as anxiety, depression, negative coping strategies about events, and the poor aptitude for interpersonal relationships [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, positive psychological factors are beneficial in reducing occupational burnout [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Flourishing, which belongs to positive psychology research, goes beyond the confines of simple well-being and may better capture the complexity of positive symptoms. Recent studies suggested that flourishing, a state in which all aspects of one\u0026rsquo;s experience are well, can reflect the well-being and happiness of individuals and groups in a more comprehensive way and has more advantages in explaining complex relations and connotation compared with other tools [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Abundant studies found that employees with flourishing would exhibit positive individual and organizational outcomes, such as higher job satisfaction and lower risk of occupational burnout [\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To date, evidence on flourishing and the association between flourishing and occupation burnout in healthcare workers is scarce.\u003c/p\u003e \u003cp\u003eAnother factor that reflects the emotions of healthcare workers is job satisfaction, defined as people's feelings about their jobs, such as whether they like or dislike their jobs [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is reported that job satisfaction would greatly increase enthusiasm and reduce the absenteeism of workers, even in those with high occupational burnout [\u003cspan additionalcitationids=\"CR33 CR34 CR35 CR36\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Besides, job satisfaction was related to positive psychological factors (e.g., empathy and psychological capital) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and as described in the spillover model, higher levels of job satisfaction lead to higher levels of well-being [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For example, the dimensions of job satisfaction, such as satisfaction with job and job reward satisfaction, were both associated with well-being. If the job were not a good fit for the worker and do not fulfill the basic material needs, their well-being would be hampered [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, current studies only estimated the relationships between occupational burnout, flourishing, and job satisfaction based on the sum-scores of scales rather than individual items in a scale. Thus, the complex association among the dimensions of job satisfaction and flourishing on occupational burnout among healthcare workers remains to be elucidated more precisely.\u003c/p\u003e \u003cp\u003eExisting studies using ordinary least squares (OLS) regression cannot estimate the relationships among multi-dimensional variables at a time. In recent years, network analysis is increasingly being used in psychological research to quantify the relationships among individual psychological factors\u0026rsquo; dimensions and identify the most interconnected dimensions. Meanwhile, in a network, it is plausible that a group of nodes (variables) belong to a community (i.e., the same rating scale) where they are closely related, and within the set of variables there may be central variables that are closely related to other variables [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, network models describe an interacting web of symptoms and could help to understand the transdiagnostic symptom relationships that underlie the well-established association of occupational burnout, job satisfaction and flourishing. Network analysis can be a useful method to examine the bridges that are represented by one or more key variables bridging two or more communities [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These bridges may be used to develop targeted interventions, and deactivating bridges may constitute an effective strategy to block connections between items efficiently. For example, previous study identified bridge symptoms (i.e., \u0026ldquo;irritability\u0026rdquo;, \u0026ldquo;feeling afraid\u0026rdquo; and \u0026ldquo;sad mood\u0026rdquo;) that could be targeted in specific treatment and preventive measures for comorbid depressive and anxiety symptoms [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, few study, to our knowledge, used this method in occupational population who have a high probability of occupational burnout and other negative emotions.\u003c/p\u003e \u003cp\u003eIn the light of the above gaps, we used network analysis to (1) explore the associations of occupational burnout, job satisfaction, and flourishing in a constructed network; (2) identify central variables in network structure of occupational burnout - job satisfaction - flourishing; and (3) seek bridges linking occupational burnout, job satisfaction and flourishing. Considering the diversity of medical services of health workers and limitations of implementation and data collection in real-world data, this study used a survey dataset of HIV/AIDS healthcare workers. The findings of this study would help obtain a straightforward view of variables\u0026rsquo; interactions to clarify critical issues and guide more effective interventions to improve the health and professional happiness of healthcare workers in China. More broadly, our finding would help policy-making for improving the psychological health of other healthcare workers.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study is an observational and cross-sectional design in the Sichuan province of China, where is one of the most HIV-affected province in China [45\u003csup\u003e,\u003c/sup\u003e46]. In such a scenario, HIV/AIDS healthcare workers in Sichuan province may undertake overloaded work and high work-related demands. The current study used a stratified cluster sampling method to obtain a sample. First, we randomly selected three cities based on the prevalence of HIV as our study sites. Second, all related healthcare workers in selected cities were recruited for investigation (e.g., from the Centers for Disease Control and Prevention (CDC) and Designated Hospitals for HIV treatment) (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Finally, 922 HIV/AIDS healthcare workers were enrolled to complete the electronic survey. All the participants gave informed assent forms before data collection; it has been stated on the cover page that the participants cannot proceed with the online survey without consent. Ethics approval was obtained from the Ethics Committee West China School of Public Health and West China Fourth Hospital, Sichuan University (Gwll2021059).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData for the analysis was collected between July 2021and October 2021 through an online questionnaire designed by an expert panel. The panel consisted of two epidemiologists, one health psychologist, and two HIV/AIDS healthcare workers. A pretest was conducted in 10 HIV/AIDS healthcare workers to test the questionnaire's comprehension and the feasibility of investigation process. Their feedback was used to revise and finalize the questionnaire. Then, the online questionnaire link was released by the HIV/AIDS management department in the Health Commission of the three cities through WeChat and other social media platforms and forwarded to all the HIV/AIDS healthcare workers. The questionnaire took about 20 minutes to complete on average.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eOccupational burnout scale\u003c/h2\u003e \u003cp\u003eThe widely used scale to measure occupational burnout was the version of the Maslach Burnout Inventory-General Survey (MBI-GS) by Maslach and Jackson [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which had good reliability and validity in China [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Our study used a self-designed scale developed based on the MBI-GS to better adapt the local culture of HIV/AIDS healthcare workers, and to ensure the high-quality results, that is, within the endurance limits to be interviewed. The scale used 11 items to assess the occurrence of occupational burnout, including emotional exhaustion (3 items), work fatigue (1 item), emotional numbness (3 items), work frustration (2 items), and work pressure (2 items). All items had five degrees to choose, from strongly disagree (1-point) to strongly agree (5-point). A sum score ranging from 11 to 55 was calculated, and higher scores indicated more severe occupational burnout. Cronbach\u0026rsquo;s alpha for the occupational burnout scale was 0.895 in this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFlourishing scale\u003c/h2\u003e \u003cp\u003eThe flourishing scale (FS) was used to assess the flourishing of HIV/AIDS healthcare workers. The flourishing scale consists of 8 items describing essential aspects of human functioning and human needs, including the meaning of life, interpersonal relationship, life and work engagement, helpful to others, competence, self-esteem, optimism and respect from others [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Each item was answered on a 7-item scale ranging from strong disagreement (1-point) to strong agreement (7-point). The total score of all items ranged from 8 (strong disagreement) to 56 (strong agreement). A higher total score indicated a greater sense of thriving, which showed a person had a positive attitude toward life and owed many psychological resources and advantages. The Chinese version of flourishing scale had good validity and reliability [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Cronbach\u0026rsquo;s alpha for the flourishing scale was 0.932 in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eJob satisfaction scale\u003c/h2\u003e \u003cp\u003eJob satisfaction was measured by a self-developed scale and was revised and finalized after the pilot study. Three dimensions of job satisfaction, including satisfaction with job itself (10 items), job environment satisfaction (6 items) and job reward satisfaction (8 items), were recorded. All items had five degrees ranging from disagree (1-point) to agree (5-point). The total score of each dimension was calculated, and higher scores indicated higher job satisfaction in the corresponding dimension. The Cronbach\u0026rsquo;s alpha coefficients for the three dimensions of job satisfaction were 0.853, 0.834 and 0.808 in this study, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic and work-related characteristics\u003c/h2\u003e \u003cp\u003eThe collected socio-demographic characteristics included age, sex, race, marital status, educational level, personal monthly income and living situation. Besides, work-related factors included professional status, job tenure, years working in HIV/AIDS units, and types and levels of institutions. All relevant descriptions and explanations were detailed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eData were summarized by descriptive statistics, with frequency and percentage for categorical variables, and median and interquartile range (IQR) for continuous variables (\u003cb\u003eTable S2\u003c/b\u003e). We used R version 4.0.3 for data management and all statistical analyses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNetwork estimation\u003c/h2\u003e \u003cp\u003eWe assessed the associations among occupational burnout, job satisfaction and flourishing based on the network of Graphical Gaussian Model (GGM). Partial correlation analysis was conducted to indicate the association of each pairwise variable and form networks; nodes in the network represented variables, and edges represented partial correlation coefficients between two variables. Stronger correlations were shown in thicker and more saturated edges. Positive and negative correlations were shown in green and red, respectively. In this network estimation, an Extended Bayesian Information Criterion (EBIC) model with the least absolute shrinkage and selection operator (LASSO) were used to get a sparse and intelligible network [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. We also estimated the predictability, the upper bound of variance (measured in R\u003csup\u003e2\u003c/sup\u003e) of a given variable explained by all the other variables in the network [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCentrality and bridge symptoms\u003c/h2\u003e \u003cp\u003eFor the constructed networks, we calculated strength centrality (i.e., the sum of the absolute value of all partial correlation coefficients for a given variable) to identify the most central variables, using the \u003cem\u003ecentralityplot\u003c/em\u003e function in the \u003cem\u003eqgraph\u003c/em\u003e package in R [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In addition, we conducted a stability and reliability analysis of our results using a commonly applied bootstrapping procedure in R with \u003cem\u003ebootnet\u003c/em\u003e package, which showed whether the networks remained stable when dropping 75% of the sample, and whether the results of bootstrap 95% CI for edges were narrow, indicating the trustworthy of edges [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe used the term community to designate a group of items or dimensions, which were supposed to be related according to scale classification, independent of the actual network structure [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We also estimated variables that acted as bridges connecting communities, which allowed us to identify which items were the most interconnected across occupational burnout, job satisfaction and flourishing communities. The bridges were estimated with different scoring variable thresholds (i.e., variables that are most strongly connected to all the variables of different communities) using the \u003cem\u003ebridge\u003c/em\u003e function in the \u003cem\u003enetworktools\u003c/em\u003e package in R [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNetwork comparison test\u003c/h2\u003e \u003cp\u003eNetwork Comparison Test (NCT) is a permutation-based hypothesis test for invariance of network structure (i.e., how the connections between variables within a network differ across samples) and global strength of connections (i.e., how the density of the network differs across samples \u0026ndash; the sum of all edge strengths). To examine sex differences in the structure of networks, we ran the NCT using the \u003cem\u003eNetwork Comparison Test\u003c/em\u003e package in R [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. We compared the distribution of partial correlation coefficients in each network to characterize the network structure. Then, we compared differences in strength for each edge of networks between females and males, after controlling for multiple tests using a Holm-Bonferroni correction. Additionally, the network densities (the actual number of variables in the network / the theoretical number of variables in the network[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]) between females and males were also compared.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic and work characteristics\u003c/h2\u003e \u003cp\u003eA total of 907 (98.37%) HIV/AIDS healthcare workers were included in the final analysis, with a mean age of 38.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 years. Among them, 69.1% (n\u0026thinsp;=\u0026thinsp;627) were women, 49.5% (n\u0026thinsp;=\u0026thinsp;449) graduated from junior colleges, 77.8% (n\u0026thinsp;=\u0026thinsp;706) were married or had a spouse, 87.3% lived with their parents (n\u0026thinsp;=\u0026thinsp;792), and 27.9% (n\u0026thinsp;=\u0026thinsp;253) had a personal income below 3,000 Renminbi (RMB) per month. There were 318 (35.1%) participants working for 2\u0026ndash;5 years, and 464 (51.2%) working in county-level medical institutions. Further details were provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eBaseline characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorce/Widowed/Living separately\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePersonal monthly income (RMB)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4000\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving situation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith friends or others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith parents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional status\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV/AIDS care workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic health physicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJob tenure (years)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u0026ndash;\u003c/b\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears working in HIV/AIDS units\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTypes of institution\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesignated Hospital for treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty Governments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity Health Service Centers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevels of institution\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMunicipal-level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistrict-level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty-level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: CDC, Centers for Disease Control and Prevention.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003e Others is a self-selected designation that indicates the institution is not listed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eNetwork structure and centrality measures\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe network structure showed a high degree of interrelations between variables from occupational burnout, job satisfaction and flourishing communities, and the variables in the same communities tended to cluster together. Most of the associations between variables of different communities were negative (Fig.\u0026nbsp;1). The predictabilities of variables were shown as ring shaped pie charts, and variables with the highest centrality were E2 (feeling exhausted at work in occupational burnout community; strength: 1.42), H3 (interested in daily activities in flourishing community; strength: 1.32) and E10 (feeling frustrated at work in occupational burnout community; strength: 1.27). We also found the stability of the network remained stable (i.e., case-dropping coefficient\u0026thinsp;=\u0026thinsp;0.75) even dropping large proportions of the sample \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The numerical interactions were showed in \u003cb\u003eTable S3\u003c/b\u003e using weighted adjacency matrix that represented the strength of associations between variables. The results of the reliability and stability analyses were presented in \u003cb\u003eFigures S2- S4.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBridges and bridge centrality measures\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure 3\u003c/b\u003e showed the bridges and bridge centrality indices. The top 20% scoring variables on bridge strength were W1(satisfaction with job itself; bridge strength: 0.25), W2 (job environment satisfaction; bridge strength: 0.19) and W3 (job reward satisfaction; bridge strength: 0.31) in job satisfaction community; H3 (interested in daily activities; bridge strength: 0.29) and H8 (feeling respectable; bridge strength: 0.18) in flourishing community (Fig.\u0026nbsp;3A). When identifying bridges with the top 25% bridge strengths, we observed that E10 (feeling frustrated at work; bridge strength: 0.14) in the occupational burnout community was additionally added as bridge linking other communities (Fig.\u0026nbsp;3B). Furthermore, H1 (leading a purposeful and meaningful life of flourishing community; bridge strength: 0.11) was added as a bridge when using the top 30% bridge strengths (Fig.\u0026nbsp;3C). Bridge strength was reported in \u003cb\u003eFig.\u0026nbsp;3D\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNetwork comparisons between female and male\u003c/h2\u003e \u003cp\u003eThe network structures and network centrality indices of male (n\u0026thinsp;=\u0026thinsp;607) and female participants (n\u0026thinsp;=\u0026thinsp;280) were showed in \u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e, and the network structures were both stable (\u003cb\u003eFigures S5-S8\u003c/b\u003e). The number of edges in female network were 86, and in male network were 79. The network density of female and male were 0.37 and 0.34, respectively. The results of the NCT indicated significant sex differences in the distribution of partial correlation coefficients (M\u0026thinsp;=\u0026thinsp;0.27, P\u0026thinsp;=\u0026thinsp;0.017), and no significant gender differences were observed in network global strength (female: 10.63 vs. males: 9.91; global strength difference\u0026thinsp;=\u0026thinsp;0.72, P\u0026thinsp;=\u0026thinsp;0.071) (\u003cb\u003eFigure S9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe also calculated bridge centrality in females and males\u0026rsquo; networks, separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e). No bridge was found with the top 20% and 25% bridge strengths, but seven bridges were found in both females and males\u0026rsquo; networks with the top 30% bridge strength. However, there was a significant sex difference. For example, E10 (feeling frustrated at work of occupational burnout community; bridge strength: 0.14) was a bridge in the network of females, but no bridge was found in occupational burnout community in the network of males. Besides, H2 (having supportive and rewarding social relationships; bridge strength: 0.12) was a bridge in flourishing community in females\u0026rsquo; network, however, H1 (leading a purposeful and meaningful life; bridge strength: 0.09) and H4 (contributing to the happiness and well-being of others; bridge strength: 0.08) were bridges in males\u0026rsquo; network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study estimated the network structure of occupational burnout, job satisfaction and flourishing, and firstly attempted to assess their interactions at the item level in healthcare workers. Our results indicated that variables feeling exhausted at work and feeling frustrated at work in occupational burnout community, and interested in daily activities in flourishing community were most central variables. Job reward satisfaction, satisfaction with job itself and job environment satisfaction in job satisfaction community, as well as interested in daily activities and feeling respectable in flourishing community were bridges when identifying bridges with the top 20% bridge strengths. Feeling frustrated at work in the occupational burnout community and leading a purposeful and meaningful life in the flourishing community became bridges connecting other communities with top 25% and 30% bridge strengths, respectively. Gender was associated with different distributions of partial correlation coefficients between their networks and network densities in females were higher than that in males.\u003c/p\u003e \u003cp\u003eCentral variables may give insights into the connectedness or importance of items within a network and may play a major role in causing the onset of and/or maintaining a syndrome [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Bridges mediate the transition among different syndromes and may increase risk of contagion to other disorders [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Variables with high centrality are especially important to the development, persistence, and remission of symptoms in networks, and bridges play the important role of bridging two or more communities. In this study, feeling exhausted at work was the most central variable with high centrality in the network, followed by interested in daily activities and feeling frustrated at work. Meanwhile, we found that interested in daily activities and feeling frustrated at work were also bridges connecting other communities. Thus, there may have some overlaps between central variables and bridges, and these overlaps deserve especial attention. It is easy for occupational population to develop negative emotions about work, not to mention healthcare workers who are responsible for the prevention and control of the diseases and treatment of the patients, etc. If occupational population are interested in their daily work, they may have high job satisfaction and less prone to have occupational burnout. Therefore, these central variables and bridges not only played an important role in understanding the structure of the network model, but also can serve as explicit intervention points to relieve all symptoms. It should be noted that bridges changed in different percentile cut-off of bridge strength (80%, 75%, and 70%), which were different from Jones's study that only focused on one cut-off [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The number of bridges would increase when the percentile cut-off point decreased, and therefore we identified at least one bridge from each community, which may guide us to carry out targeted interventions in the bridges corresponding to each community.\u003c/p\u003e \u003cp\u003eIn this study, the items belonging to the same communities tended to cluster together, and the relationships between occupational burnout, job satisfaction and flourishing were more subtle compared to previous studies' findings that only considering the total score or subscale total scores of the rating scales [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Thus, strategies and measures based on macro level, such as targeted policies formulated by society and related institutions for health workers, may be an effective method to improve health. Meanwhile, our study revealed that there were intrinsic interactions among variables of different communities, which suggested that interventions targeting crucial variables in each community may be beneficial to the whole network. Therefore, network analysis offers an additional tool to capture the dimensional nature of symptoms, focusing not only on the identification of symptom clusters but above all on their connecting patterns.\u003c/p\u003e \u003cp\u003eFurthermore, our analysis documented both similarities and differences in the network structure of occupational burnout - job satisfaction - flourishing in females and males. The central variables were almost similar in both females and males. However, the bridges had a large difference in occupational burnout and flourishing community, which may be caused by the career expectation and attitudes towards problems between females and males [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. We also examined sex differences in the structure of networks and found network densities between females and males were different. Network density describes the portion of the potential connections in a network that are actual connections and refers to the mean strength of the connections between variables in networks [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In the study, we found higher network densities in females than that in males, which showed that symptoms may transmit more quickly in females. The results were similar to previous studies which have found that females and males responded to conditions in different ways and females tend to develop internal symptoms[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The differences may be derived from particular psychological characteristics, unique perspective and experiences on work-related aspects in females which were different from males. Thus, preventions and interventions should mainly focus on females to prevent transmission and adverse effects of occupational burnout. But from a dialectical point of view, better outcomes may be achieved after interventions because of the fast transmit.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, the network model was designed to consider individual symptoms as variables in the study. In addition, we compared bridges under different thresholds, which can help to find the more important bridges in different communities and help design subtle interventions. Finally, NCT analysis explicitly tested gender as a sociodemographic influence on the network structure of occupational burnout - job satisfaction - flourishing of health workers, and indicated the heterogeneity in central and bridges for targeted intervention in two networks.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be mentioned. First, the cross-sectional data is limited to infer causal relations. To fully conceptualize how the variables interact with one another, future experimental and longitudinal studies are needed. However, other study has shown that network structures among cross-sectional and longitudinal studies did not differ [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Second, as a study relied on the self-report measure, the results may not rule out other possible factors, social desirability or recall biases, etc. Third, we only examined the difference in network structure between gender. However, other sociodemographic factors are also needed to examine in future studies. Finally, generalizations should be taken with caution since the sample comprised only a small proportion of healthcare workers in China. Therefore, researches on geographical and cultural diversity are warranted in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThrough network analysis, we estimated complex association between occupational burnout, job satisfaction, and flourishing among Chinese healthcare workers. Feeling frustrated at work of occupational burnout community and interested in daily activities of flourishing community were both as the central variables and bridges. Specific strategies and measures targeting to these variables should be given such as improving levels of flourishing and job satisfaction and reducing occupational burnout. Additionally, government and healthcare agencies should address occupational burnout and improve flourishing, especially taking initiatives to improve job satisfaction with effective interventions in healthcare workers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (81703279), Liangshan Key R \u0026amp; D projects of science and technology (2021DYF0045), and Sichuan Provincial foundation for AIDS prevention and control (2022ZC02, 2021ZC01, 2020zc05, 2020zc04, 2019sc01, 2018-WJW-03).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR code available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSJ and SJY had taken a principal role in the study conception and design and methodologies, and drafting the manuscript. BY, CF, PJ and PX contributed to the writing of the study protocol and made revisions to the manuscript. SJ, SJY and BY contributed to the material preparation and data analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not disclosed any conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, all methods were performed in accordance with the relevant guidelines and regulations. This study was approved by the Ethics Committee West China School of Public Health and West China Fourth Hospital, Sichuan University (Gwll2021059). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the staff in the HIV/AIDS management department of the Health Commissions who helped to distribute the questionnaire. \u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSanchez TH, Kelley CF, Rosenberg E, Luisi N, O\u0026apos;Hara B, Lambert R, et al. Lack of awareness of human immunodeficiency virus (HIV) infection: problems and solutions with self-reported HIV serostatus of men who have sex with men. Open Forum Infect Dis. 2014 2014 Sep,1(2):u84.\u003c/li\u003e\n\u003cli\u003eAnyangwe SC, Mtonga C. Inequities in the global health workforce: the greatest impediment to health in Sub-Saharan Africa. 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J Abnorm Psychol. 2020 2020 Jan,129(1):82-91.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Occupational burnout, job satisfaction, flourishing, network analysis, healthcare workers","lastPublishedDoi":"10.21203/rs.3.rs-2835947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2835947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHealthcare workers suffered with high prevalence of occupational burnout, which might be related with their job satisfaction and well-being. This study aimed to provide evidence of complex interrelations among occupational burnout, job satisfaction, and flourishing, and identify key variables from the perspective of network structure among healthcare workers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA cross-sectional study was conducted between July and October 2021, and 922 healthcare workers were recruited to report their sociodemographic characteristics, occupational burnout, job satisfaction, and flourishing. Network analysis was conducted to investigate the interrelations of dimensions in occupational burnout, job satisfaction, and flourishing communities, and identify central variables and bridges connecting different dimensions with different bridge strength thresholds in the network structure. The Network Comparison Test (NCT) was conducted to examine the gender differences in networks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the network, feeling exhausted at work (strength: 1.42) and feeling frustrated at work (1.27) in occupational burnout community, and interested in daily activities (1.32) in flourishing community were central variables. Bridges in the network were job reward satisfaction (bridge strength: 0.31), satisfaction with job itself (0.25), and job environment satisfaction (0.19) in job satisfaction community, as well as interested in daily activities (0.29) and feeling respectable (0.18) in flourishing community, with bridges selected with top 20% bridge strengths. Feeling frustrated at work (0.14) in occupational burnout community and leading a purposeful and meaningful life (0.11) in flourishing community became bridges when using thresholds of top 25% and 30% bridge strengths, respectively. We also observed higher network densities in females (network density: 0.37) than that in males (0.34) and gender differences in the distribution of partial correlation coefficients (M\u0026thinsp;=\u0026thinsp;0.27, P\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the network structure of occupational burnout-job satisfaction-flourishing, feeling frustrated at work in occupational burnout community and interested in daily activities in flourishing community were both central variables and bridges, which may be targeted variables to intervene to alleviate the overall level of symptoms in the network and therefore prevent poor health outcomes in healthcare workers.\u003c/p\u003e","manuscriptTitle":"Occupational burnout, job satisfaction and flourishing among healthcare workers in western China: a network analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-24 14:22:23","doi":"10.21203/rs.3.rs-2835947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-11T07:10:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-11T02:09:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-06T07:51:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22f141ae-f54f-419b-ad56-04a33cea9b10","date":"2023-04-30T01:53:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5fd05c40-4989-4f6e-b8a5-4e3f696eea5a_SNPRID","date":"2023-04-28T13:58:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-04-26T16:55:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-04-26T06:35:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-04-24T09:11:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-04-20T15:04:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2023-04-19T09:42:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2045274-54ac-421a-9a9f-3ffa54988655","owner":[],"postedDate":"April 24th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T22:12:03+00:00","versionOfRecord":{"articleIdentity":"rs-2835947","link":"https://doi.org/10.1186/s12888-023-04959-7","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2023-08-03 21:53:06","publishedOnDateReadable":"August 3rd, 2023"},"versionCreatedAt":"2023-04-24 14:22:23","video":"","vorDoi":"10.1186/s12888-023-04959-7","vorDoiUrl":"https://doi.org/10.1186/s12888-023-04959-7","workflowStages":[]},"version":"v1","identity":"rs-2835947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2835947","identity":"rs-2835947","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}