Network analysis of key factors influencing subjective well-being among elderly with chronic disorders: A study based on the 2020 CFPS

Network analysis of key factors influencing subjective well-being among elderly with chronic disorders: A study based on the 2020 CFPS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Network analysis of key factors influencing subjective well-being among elderly with chronic disorders: A study based on the 2020 CFPS Chenyu Liang, Zilan Ye, Haifeng Yan, Jianfeng Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4778935/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives This study aims to delve into the mechanisms influencing the subjective well-being of elderly individuals with chronic disorders within the context of aging population. It comprehensively analyzes these factors, including cognitive schemas, social relationships, and demographics to provide a scientific basis for enhancing the subjective well-being of elderly individuals. Methods This study utilized data from the 2020 China Family Panel Studies (CFPS) and selected 863 individuals aged 65 and above with chronic disorders as subjects for analysis. Network analysis was conducted using JASP. Results This study has identified all the relationships between factors in the research and subjective well-being among the elderly with chronic disorders, and we have visualized them in graphical form. The findings indicate that social relationships (S1) are most closely associated with subjective well-being (W1), followed by level of confidence in the future (C1) and social status (B7). Further analysis found that C1 and S1 could be a mediator affecting the impact of B7 on W1. Conclusion Identify and delineate the key factors influencing the subjective well-being of elderly individuals with chronic disorders, as well as their interrelationships. This helps to further investigate the factors influencing the subjective well-being of the elderly. subjective well-being network analysis elderly chronic disorders Chinese Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Global statistics from the United Nations indicate that in 2020, individuals aged 65 and above accounted for 9.3% of the global population, with China ranking at 63rd place, still surpassing the global average of 9.3% (China Social Security Society, 2022). Since China embraced an aging society in 1999, the elderly population has been steadily growing, accelerating the pace of aging. The elderly now constitute a significant portion of the overall population, which will inevitably impact their daily lives and work dynamics (Fang et al., 2020 ). Existing research suggests that elderly individuals often experience psychological issues such as anxiety and depression, and these mental health challenges may lead to higher cognitive impairments, suicidal tendencies, dementia, and other health concerns (Ding & Kennedy, 2021 ), thereby increasing their vulnerability and adversely affecting their psychological well-being, consequently lowering their subjective well-being (Deng, 2019 ). Subjective well-being stands as a pivotal component of successful aging, and low levels of subjective well-being can hinder the process of successful aging (Gatz & Zarit, 1999 ). Successful aging among the elderly contributes to their overall physical and mental well-being (Friedman, 2020 ). Subjective well-being is an evaluation of a person's life from a subjective perspective, including emotional responses, domain satisfaction, and overall judgments of life satisfaction. It is an important component of people's quality of life (Diener, 1999). However, for the elderly with chronic disorders, their well-being appears to be more cared for. According to the "biopsychosocial model" theory, physical health status is one of the most important determinants of subjective well-being (Engel, 1977 ). Poor physical probrably reduces subjective well-being (Wang & Kim, 2020 ; Fei et al., 2023 ). Therefore, it is important to understand what kinds of factors affect well-being among elderly with chronic disorders. According to previous studies, there are quite lots of factors, such as some demographic factors (i.e., gender, residence, marital status, economic status, and educational level), cognitive schemas, physical health status, and social relationships, affecting the subjective well-being of the elderly. Firstly, in terms of demographics, previous research on gender differences in subjective well-being indicates that there may be differences between men and women, but it is unclear which gender tends to have higher levels of happiness. For example, some studies suggest that subjective well-being of elderly men is higher than that of women (Zhao et al., 2022 ). This difference is attributed to different social roles, with traditional norms often requiring men to spend more time and effort on their careers, while women are expected to prioritize caregiving roles, leading to more dissatisfied feelings among women in family care. At the same time, women tend to anticipate negative outcomes more, leading to higher levels of negative experiences and lower subjective well-being. However, some studies found that elderly women have higher levels of well-being than men, as they are more likely to derive satisfaction from interpersonal relationships, leading to higher levels of subjective well-being (Wang & Zhou, 2010 ). Some reported that there is no significant difference in subjective well-being between elderly men and women (Luo et al., 2023 ). Therefore, the question of whether there is a gender difference in subjective well-being remains uncertain. Regarding the influence mechanism of resident on subjective well-being, some researchers point out that elderly people with urban residence have higher subjective well-being than those in rural urban areas due to higher quality of life (Zhang et al., 2022 ; Cheng & Yan, 2021 ). Additionally, marital status also has varying degrees of impact on the subjective well-being of the elderly. For example, Zhou ( 2022 ) and Qin ( 2020 ) indicates that married individuals generally have higher levels of subjective well-being due to the emotional support they receive, compared to those in unhappy marital status such as divorce or widowhood. Some previous studies also found a significant positive correlation between social economic status and subjective well-being (Fard et al., 2022 ; Sawada & Toyosato, 2021 ; Kong et al., 2019 ). Elderly people with lower economic status may experience lower subjective well-being, possibly because lower economic status cannot provide a rich environment of mental stimulation, leading to facing the same life every day without the excitement of new things (Sawada & Toyosato, 2021 ). To some extent, economic status might be affected by educational degree, thus, elderly people with lower education levels are more likely to have lower subjective well-being (Lai et al., 2020 ; Sheikh et al., 2017 ). Secondly, cognitive schemas are also considered as one of the major factors influencing subjective well-being (Telef & Furlong, 2017 ). Cognitive schemas refer to the cognitive way in which the human brain processes information objects, comprising past reactions and experiences to form a relatively cohesive and enduring system of knowledge that shapes an individual's perception of objective realities (Segal, 1988 ). Previous studies have confirmed that having a positive and optimistic cognitive schema can significantly predict the subjective well-being of older adults, whereas a negative cognitive schema may lead to depression and decrease subjective well-being (Tang et al.,2022). Positive schemas include belief in one's ability to accomplish tasks and an optimistic attitude towards future events. According to previous studies, when individuals have more positive evaluations of past events and foster more positive expectations for the future, they are more likely to have a higher level of well-being (Chen et al., 2022 ). Positive cognitive schemas can increase optimism and promote healthy lifestyle choices and significantly impact physical and mental health adaptability to the environment, thereby enhancing subjective well-being (Seo & Lim, 2019 ). Finally, some previous studies also reported that the perception of social support among the elderly also influences subjective well-being (Fuller et al., 2020 ). This refers to individuals' subjective evaluations of whether they receive sufficient social support (Farriol-Baroni et al., 2021 ). Specifically, this encompasses material and emotional support from various sources such as family, relatives, friends, and social organizations (Xie et al., 2023 ). Social relationships influence subjective well-being in that good social relationships are good in alleviating stress through social-emotional support (Umberson & Karas, 2010). When elderly individuals with poor social relationships receive less social-emotional support, it is difficult for them to alleviate negative emotions ( Abramowska-Kmon & Timoszuk, 2020 ). From existing research, there are numerous factors influencing the subjective well-being of the elderly. However, few studies have integrated these factors for comprehensive analysis and focus on the elderly with chronic disorders. Therefore, to further know what the main factors in the elderly subjective well-being is, the present study will use symptom network analysis to explain. Symptom network analysis is the comprehensive systematic analysis of multiple variables, that can reveal associations at the symptom level, providing support for explaining the co-occurrence of two variables (Afzali et al., 2017 ). Network analysis has unique advantages, such as revealing relationships and interactions, visualization, social connectivity, and simulation and prediction. Leveraging these strengths, by consolidating the variables that influence the subjective well-being of the elderly as various nodes within a network, the main purposes of this study are to explore the relationships among these nodes (i.e., factors) and how they influence the dependent variable (i.e., well-being), and to measure the extent to which a node is determined by other nodes (Haslbeck & Fried, 2017 ). In conclusion, researching the subjective well-being of the elderly with chornic disorders is a prevailing trend, and employing social network analysis to uncover the connections between variables can provide guidance and predictive insights on how to enhance the subjective well-being of the elderly. 2. Method 2.1 Participants and Materials The data is derived from the China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University in 2020. CFPS aims to explore the development of society, encomic, education, health, and so on among Chinese population, including all aged populations (i.e., children, adolenscents and adults). The present study is to explore the factors influencing the subjective well-being of the elderly with chronic disorders. Therefore, the seletion criteria are: 1) aged over 65, 2) with chronic disoders in the past 6 months before the survey. After screening, a total of 1105 samples met the requirements. Next, samples with missing data were excluded, resulting in a final sample of 863 participants (F = 435, M = 428, mean aged = 71, SD = 5.07). 2.2 Research variables This study combines existing research on the subjective well-being of elderly individuals with chronic disorders to select relevant variables. For the convenience of the study, these variables are categorized into four major dimensions: demographic factors, cognitive schemas, and social relationships. Chronic disorders (QP401) were assessed by asking respondents, "In the past six months, have you been diagnosed by a doctor with any chronic diseases?" A response of 1 indicates the presence of a diagnosed chronic disease, while a response of 0 indicates no such diagnosis. This study focuses on elderly individuals who have been diagnosed with chronic diseases. Demographic factors includes age, gender, residence, marital status, economic status, education level and social status. Age (QA001B) represents the specific age of the survey respondents, all of whom are elderly individuals aged 65 and above in this study. Gender (QA002) is denoted by 1 for male and 0 for female. Residence (QA301) is classified as rural = 1, and urban = 2. Marital status (MARRIAGE_LAST) includes categories such as unmarried = 1, married = 2, cohabitation = 3, divorced = 4, and widowed = 5. Economic status (QI202) is measured using the monthly post-tax retirement pension of the elderly individuals, with the specific income amount self-reported by the respondents. Education level (EDU_LAST) represents the highest educational attainment of the elderly individuals, which was merged into three categories: illiterate/semi-literate or never attended school = 1, high school or below = 2, and college/bachelor's degree or above = 3. Social status (QN8012) is self-rated by the elderly individuals on a scale from 1 (very low) to 5 (very high). Cognitive schemas consist of three dimensions, including future schema (QN12016), negative schema (QN407), and cognition of others (QM6). Future schema is defined as the level of confidence in the future, represented by the respondent’s rating on a scale from 1 (no confidence) to 5 (very confident). Negative schema is defined as the respondent's level of trouble doing things on a scale from 1 = almost never (less than one day) to 4 = most of the time (5–7 days). The perception of others as helpful or selfish reflects the cognition of others, with helpful coded as 1 and selfish coded as 2. Social relationships (QM2011) are primarily subjectively judged by the respondents based on their interpersonal relationships, where respondents are from 0 (lowest) to 10 (highest). The subjective well-being (QM2016) score of the respondents is from 0 to 10, where 0 represents the lowest and 10 represents the highest level of subjective well-being. 2.3 Data analyses The network is composed of nodes representing symptoms and edges representing conditional pairwise relationships between the two nodes that control all other nodes in the control network, connecting characteristics and information of a certain system, forming a network (Lazarov et al., 2020 ). Based on the network theory proposed by Lazarov et al. ( 2020 ), this study adopted the social network analysis method to quantitatively investigate the relationships between subjective well-being and its influencing factors in the social network. The descriptive statistics of this study were conducted using SPSS 26 to analyze demographic factors. The network analysis part was implemented in JASP software. The preliminary analysis of the data network was estimated using the EBICglasso parameter in the JASP (Epskamp et al., 2012 ). According to the study of Lazarov et al. ( 2020 ), it quantitatively explores the relationship between subjective well-being and its influencing factors in the social network. Nodes in the network represent the research subjects, while edges signify the connections between the research subjects (Zhou et al.,2023). In this study, nodes represent the subjective well-being of the elderly and the factors influencing their subjective well-being, while edges represent the connections between each pair of nodes. In line with the theory of Thomas, Fruchterman and Reingold (1991) on network centrality, the qgraph package positions nodes with strong correlations at the center of the network and those with weaker correlations towards the periphery. The correlations between nodes are depicted by lines, where blue lines indicate positive correlations between factors, while red lines denote negative correlations (Jones et al, 2018 ). Thicker lines represent stronger associations between nodes, and the closer the visual distance between nodes, the stronger their association (Epskamp et al, 2012 ). Moreover, these lines symbolize relative importance, revealing how one factor as a predictor influences another. They not only reflect the direct impact of Factor X on Factor Y but also show the indirect effect of Factor X on Factor Y after adjusting for all other factors in the network (Johnson & Lebreton, 2004 ). Thus, our study needs to consider both aspects of results. Based on the connectivity of nodes, we use the qgraph package to compute nodal centrality to explore which factors have the greatest impact on subjective well-being, thus quantifying and assessing their relative importance in the network (Robinaugh et al, 2016 ). Next is the evaluation of the accuracy and stability of the entire sample network. Firstly, estimating the accuracy of network edge weights, fitting within a 95% confidence interval (CI), where smaller overlaps between 95% CI indicate higher accuracy of edge estimates (Epskamp et al., 2018 ). Additionally, it's necessary to test the differences in edge weights to explore which edges significantly differ from others. Subsequently, using case-drop bootstrapping to assess the stability of centrality measures, the stability of centrality is quantified by the centrality stability coefficient (CS). The obtained CS coefficient should be at least 0.25, indicating moderate centrality stability, and surpassing 0.5 is considered more stable (Epskamp et al., 2018 ). Based on the results of network analysis, Structural Equation Modeling (SEM) would be used for further understanding of the inter-relationships between W1 and its key factors. R 4.3.3 would be use. According the model fit indices (Macfie et al., 2005 ), the Comparative Fit Index (CFI) should be greater than 0.9, the Tucker-Lewis Index (TLI) should exceed 0.90, the Standardized Root Mean Square Residual (SRMR) should be below 0.05, and the Root Mean Square Error of Approximation (RMSEA) should be less than 0.08 (Browne & Cudeck, 1993 ; Hu & Bentler, 1999 ). The bias-corrected bootstrap method was iterated 1000 times to test the indirect effects according to the 95% confidence interval. 3. Result 3.1 Descriptive statistics This study collected data from a total of 863 elderly individuals aged 65 and above with chornic disorders (females = 435, males = 428), with an average age of 71 years (SD = 5.07), ranging from 65 to 92 years old. Present data indicates that 59.4% of the elderly participants had rural residence, 81.2% of them were married; 17.6% were widowed and unmarried. The average post-tax pension amount was 1555 RMB. 42.2% of the elderly population was illiterate or had never attended school, while 54.2% had received primary or junior high school education, and only 3.6% had education beyond junior high school. 3.2 Network estimates The case-drop bootstrap results of this study indicate that the correlations of strengths in the symptom network are mostly above 0.5, demonstrating the stability of the network (see Fig. 4 ).The marginal weight results indicate smaller intervals, suggesting that the network has a certain level of accuracy (see Fig. 3 ). The mean, standard deviation and EI (expect influence) are presented in Table 1 . The expected influence value of B5 (economic status) is the highest, followed by B3 (residence) and W1 (subjective well-being) . Table 1 means, standard deviations, EI Variables Symptoms M SD EI Demographic factors Age (B1) 71.38 5.07 0.53 Gender(B2) 0.50 0.50 -0.69 Residence(B3) 1.41 0.49 1.31 Marital status (B4) 2.52 1.14 -1.53 Economic status (B5) 1555.01 1974.13 1.56 Education level (B6) 1.61 0.56 -0.06 Social status (B7) 3.51 1.07 0.27 Cognitive schema Level of confidence in the future (C1) 4.09 0.98 0.39 Trouble doing things (C2) 2.10 1.09 -1.24 Perception of others as helpful or selfish (C3) 2.01 1.74 -1.17 Social relationship Relationships (S1) 7.38 1.96 -0.15 Well-being Subjective well-being (W1) 7.74 2.11 0.77 Next, in this network, all nodes are interconnected directly or indirectly with other nodes. The positive and negative values of bridging edges correspond to blue and red edges, where positive values indicate positive influences, and negative values signify negative influences. The network results of demographic factors, cognitive schemas, social relationships, and subjective well-being are illustrated in Fig. 1 . The factors directly related to W1 (subjective well-being) include S1 (relationships), C1 (level of confidence in the future), B7 (social status), C3 (perception of others as helpful or selfish), B1 (age), B3 (residence), B4 (marital status), and C2 (trouble doing things) (see Table 2 ). Among these, S1, C1, and B7 show a stronger correlation with W1. Therefore, we have chosen to focus our discussion on these factors. To further understanding of their interrelationships, this study further analyse a Structural Equation Model (SEM). Table 2 Weights matrix of the regularized partial correlation network Variable Network B1 B2 B3 B4 B5 B6 B7 C1 C2 C3 S1 W1 B1 1 B2 0.06 1 B3 0.04 -0.13 1 B4 0.25 -0.25 0.00 1 B5 0.06 0.06 0.77 -0.02 1 B6 0.00 0.29 0.17 -0.13 0.17 1 B7 0.06 0.00 -0.05 -0.02 0.00 -0.04 1 C1 0.00 0.00 0.00 -0.02 0.00 -0.08 0.35 1 C2 0.00 0.00 -83.27 -0.04 -0.09 -0.07 0.00 -0.06 1 C3 0.00 0.00 0.01 0.00 0.00 0.00 -0.09 -0.01 0.12 1 S1 0.01 -0.02 0.00 0.00 0.00 0.00 0.09 0.05 0.00 -0.15 1 W1 0.06 0.00 0.04 -0.05 0.00 0.00 0.11 0.25 -0.03 -0.02 0.29 1 Note. B1 = age, B2 = gender, B3 = residence, B4 = marital status, B5 = economic status, B6 = education level, B7 = social status, C1 = level of confidence in the future, C2 = trouble doing things, C3 = perception of others as helpful or selfish, S1 = relationships, W1 = subjective well-being. 3.3 Mediation analysis The results showed that the structural equation model (see Fig. 2 ) with standardized path coefficients provided a good fit (CFI = 1.00, TLI = 1.00, SRMR = 0.00, RMSEA = 0.00). According to the results, B7 (social stauts) could predict S1 (relationships) (β = 0.427, SE = 0.060, p < 0.001) and C1 (level of confidence in the future) (β = 0.357, SE = 0.028, p < 0.001). S1 (relationships) (effect size = 0.142, SE = 0.024, p < 0.001) and C1 (level of confidence in the future) (effect size = 0.196, SE = 0.030, p < 0.001) both mediated the relationship between B7 (social status) and W1 (subjective well-being). 4. Discussion This study conducts a network analysis of the key factors influencing the subjective well-being of the elderly with chronic disorders, aiming to identify the pivotal factors among many potential influences, and then building a relation model between the key factors and subjective well-being. The results reveal the factors directly and strongly influencing the subjective well-being of the elderly are in turn social relationship (S1), level of confidence in the future (C1), social status (B7). The first finding of the network model is that the factor most closely related to subjective well-being (W1) is social relationships (S1). Elderly individuals with better social relationships tend to report higher subjective well-being, consistent with previous research (Su et al., 2022 ). Having good social relationships means being able to receive more social support from others, which can be explained through the theory of social support. Social support is broadly defined as the perception of being cared for and receiving help during difficult times (Cohen & Wills, 1985 ). On one hand, feeling a high level of social support can enhance self-worth and dignity, thereby increasing subjective well-being. On the other hand, having social support helps alleviate the psychological stress of illness and provides protective psychological resources (e.g., self-control and a sense of mastery), as well as promotes healthy behaviors (Thoits, 2011 ). Additionally, social support can motivate elderly individuals to develop a sense of meaning and belonging in life despite their illnesses, further enhancing their subjective well-being. In particular, for the elderly with chronic disorders, they need more care and emotional support. Research consistently shows that loneliness is a factor affecting the subjective well-being of the elderly, and having social support and connections can counteract the psychological decline caused by social isolation (Chen et al., 2022 ). Meanwhile, level of confidence in the future (C1) and social status (B7) are both positively correlated with subjective well-being. Level of confidence in the future can be understood as a form of positive self-schema in the elderly. Positive self-schema is defined as positive core beliefs about oneself, and preliminary research has found that positive self-schema implies higher subjective well-being (Chen et al., 2022 ). This is because individuals with a positive self-schema tend to have a more positive view of themselves, experience less anxiety regarding illness, and report higher subjective well-being (Xiang et al., 2020 a; Xiang et al., 2020 b). Moreover, elderly individuals with a higher social status (B7) who suffer from chronic illnesses can obtain more social resources to alleviate the pain caused by their illnesses, reduce the negative impacts of adverse emotions, and thus maintain their subjective well-being (Chen & Zhu, 2021 ). The second finding regarding the interrelationships between social relationships (S1), level of confidence in the future (C1), social status (B7), and subjective well-being (W1) is that an individual's social status can predict social relationships and confidence in the future, which in turn can predict subjective well-being. According to social identity theory (Tajfel et al., 1979 ), people derive identity and value from the status of their social groups. Additionally, social relationships function through reciprocity and resource exchange; individuals with higher subjective status attract more resources (e.g., wealth, power) (Ahmad et al., 2023 ), which can bring more recognition and respect, help them build more positive interpersonal relationships, and form a social support system. These supports can enhance the subjective well-being of the elderly (Thoits, 2011 ). Therefore, individuals with higher subjective status can establish a social support system through good interpersonal relationships, converting the obtained resources into emotional support, social recognition, and a sense of belonging, thereby improving their subjective well-being. Regarding the mediating role of confidence in the future (C1), individuals with higher social status often feel they have more rights and can control various aspects of life, including managing their illnesses and future development, thus usually having stronger confidence in the future (Li et al., 2023 ). At the same time, individuals with higher social status can obtain more resources and opportunities, increasing their chances of treating and coping with diseases, leading to higher expectations for the future and significantly enhancing their confidence in it (Yeung et al., 2022 ). Meanwhile, confidence in the future can bring self-affirmation and self-identity, establishing a positive self-schema, which enables elderly individuals with chronic diseases to maintain a positive attitude towards life experiences, thereby generating a sense of life meaning. The stronger the sense of life meaning, the higher the subjective well-being (Chen et al., 2022 ). Furthermore, individuals with lower social status have fewer social resources, making it harder for them to pursue and achieve health goals, leading to situational cognitive tendencies (Kraus et al., 2012 ). This means that individuals become more dependent on external resources. In such cases, whether they succeed or fail is subconsciously attributed to external factors. Over time, this may lead to a gradual loss of self-control, believing they cannot determine their health, ultimately losing confidence. When facing future challenges, they may exhibit avoidance behaviors, gradually reducing self-worth and decreasing subjective well-being (Compas et al., 2017 ). This indirectly confirms the relationship between positive cognitive schemas and subjective well-being, indicating that enhancing self-efficacy can indirectly improve subjective well-being (Wang et al., 2022 ). 4.1 Limitations and Implications The limitations of this study exist. First, the study uses cross-sectional data, so it cannot explore the causal relationships of subjective well-being among various factors in the elderly. Second, the index used to measure subjective well-being is in the form of subjective reports, which may introduce certain biases due to variations in individual definitions. Third, because the data collected are based on subjective reports from respondents, there is a possibility that social desirability may lead to concealment or the reporting of answers that align with societal expectations, introducing some bias and potentially not fully reflecting the actual situation. Fourth, this study's survey focuses on Chinese elderly people, and the limitations of cultural differences may restrict the generalizability of results to a global scale. Despite these limitations, the data results of this study provide insights into the network relationships among key factors influencing the subjective well-being of elderly individuals with chronic illnesses. For example, the study's findings demonstrate the factors most closely related to subjective well-being and their interrelationships. This can help social workers and policymakers in aging-related fields better understand the factors affecting the subjective well-being of elderly individuals with chronic illnesses. Such understanding can contribute to providing a more suitable environment and policy support for their care. 4.2 Conclusion In summary, this study has improved the understanding of the factors influencing the subjective well-being of elderly Chinese individuals with chronic illnesses and can help guide further research in this area. The results found that the key factors influencing subjective well-being include social relationships, confidence in the future, and social status. These factors formed a closer relationship cluster. Further analysis of the inter-relationships among these factors also revealed that social relationships and confidence in the future significantly affect the impact of social status on subjective well-being. These findings enhance our understanding of the factors influencing the subjective well-being of elderly individuals with chronic disorders. Declarations Ethics approval and consent to participate: The datasets generated during and/or analyses during the current study are available in the China Family Panel Studies (CFPS). Consent for publication: Not applicable. Availability of data and material: The datasets generated during and/or analyses during the current study are available in the China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. Competing interests: The authors declare that they have no conflict of interest. Funding: Hundred Youth Research Project Funding Plan of Guangdong Medical University in 2023 “Symptom Network Analysis of New Era Elderly Subjective Well-being” (广东医科大学2023 年学校百项青年研究项目资助计划“新时代老年人主观幸福感影响因素的症状网络研究”), Grand Code: GDMUD2023021 Graduated Research Project of Schoold of Humanities and Management in Guangdong Medical University in 2023 “Key Factors of Elderly Subejective Well-being” (广东医科大学人文与管理学院2023年本科生科研项目“影响老年人主观幸福感的关键因素研究”), Grand Code: RW2023002 Authors' contributions: CL: Conceptualization, Methodology, Writing - Original Draft, Review and Editing. ZY: Conceptualization, Formal Analysis, Writing, Supervision, Review and Editing. HY: Formal Analysis. JT: Supervision, Review Acknowledgements: No. References Abramowska-Kmon A, Timoszuk S. Family and social networks, subjective well-being and loneliness among older adults in Poland. Studia Demograficzne. 2020;1(177):9–36. https://doi.org/10.33119/SD.2020.1.1 . Afzali MH, Sunderland M, Teesson M, Carragher N, Mills K, Slade T. A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: The role of overlapping symptoms. J Affect Disord. 2017;208:490–6. https://doi.org/10.1016/j.jad.2016.10.037 . Ahmad R, Nawaz MR, Ishaq MI, Khan MM, Ashraf HA. Social exchange theory: Systematic review and future directions. Front Psychol. 2023;13:1015921. https://doi.org/10.3389/fpsyg.2022.1015921 . Browne MW, Cudeck R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long(Eds.). Testing Structural Equations Models , (pp. 136–162). Park, CA: Sage. Chen H, Zhu Z. Social trust and emotional health in rural older adults in China: the mediating and moderating role of subjective well-being and subjective social status. BMC Public Health. 2021;21(1):556. https://doi.org/10.1186/s12889-021-10617-y) . Chen W, Yang T, Luo J. Core self-evaluation and subjective wellbeing: A moderated mediation model. Front public health. 2022;10:1036071. https://doi.org/10.3389/fpubh.2022.1036071 . Cheng G, Yan Y. Sociodemographic, health-related, and social predictors of subjective well-being among Chinese oldest-old: a national community-based cohort study. BMC Geriatr. 2021;21(1):124. https://doi.org/10.1186/s12877-021-02071-7 . China Social Security Society(Ed.). (2022). China Aging Research Report 2022 . https://www.caoss.org.cn/UploadFile/pic/20229281791192316.pdf Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310–57. https://doi.org/10.1037/0033-2909.98.2.310 . Compas BE, Jaser SS, Bettis AH, Watson KH, Gruhn MA, Dunbar JP, Williams E, Thigpen JC. Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychol Bull. 2017;143(9):939–91. https://doi.org/10.1037/bul0000110 . Deng M. Social relations, mental health level and subjective well-being improvement of the elderly - an empirical analysis based on CGSS2015 data. Popul Dev. 2019;25(03):85–93. Diener E, Suh EM, Lucas RE, Smith HL. Subjective well-being: Three decades of progress. Psychol Bull. 1999;125(2):276–302. https://doi.org/10.1037/0033-2909.125.2.276 . Ding OJ, Kennedy GJ. Understanding Vulnerability to Late-Life Suicide. Curr psychiatry Rep. 2021;23(9):58. https://doi.org/10.1007/s11920-021-01268-2 . Engel GL. The need for a new medical model: a challenge for biomedicine. Sci (New York N Y). 1977;196(4286):129–36. https://doi.org/10.1126/science.847460 . Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. https://doi.org/10.3758/s13428-017-0862-1 . Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48(4):1–18. https://doi.org/10.18637/jss.v048.i04 . Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, Aman Y, Frank J, Liao J, Zou H, Wang NY, Wu J, Liu X, Li T, Fang Y, Niu Z, Yang G, Hong J, Wang Q, Chen G, Woo J. (2020). A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. Ageing research reviews, 64 , 101174. https://doi.org/10.1016/j.arr.2020.101174 Fard SS, Kavosi A, Sanagoo A, Jouybari L. Evaluation of the Relationship of Employment and Economic Status with the Feeling of Mental Well-Being in the Elderly Living in Northern Iran. Curr health Sci J. 2022;48(4):418–25. https://doi.org/10.12865/CHSJ.48.04.09 . Farriol-Baroni V, González-García L, Luque-García A, Postigo-Zegarra S, Pérez-Ruiz S. Influence of Social Support and Subjective Well-Being on the Perceived Overall Health of the Elderly. Int J Environ Res Public Health. 2021;18(10):5438. https://doi.org/10.3390/ijerph18105438 . Fei C, Zhu Y, Jiang L, Zhou H, Yu H. Social integration, physical and mental health and subjective well-being in the floating population-a moderated mediation analysis. Front public health. 2023;11:1167537. https://doi.org/10.3389/fpubh.2023.1167537 . Friedman SM. Lifestyle (Medicine) and Healthy Aging. Clin Geriatr Med. 2020;36(4):645–53. https://doi.org/10.1016/j.cger.2020.06.007 . Fuller HR, Ajrouch KJ, Antonucci TC. (2020), The Convoy Model and Later-Life Family Relationships. Journal of Family Theory & Review 12 (June 2020): 126–146. http://doi.org/10.1111/jftr.12376 Garfein AJ, Herzog AR. Robust aging among the young-old, old-old, and oldest-old. J Gerontol B. 1995;50(2):S77–87. https://doi.org/10.1093/geronb/50b.2.s77 . Gatz M, Zarit SH. A good old age: Paradox or possibility. In: Bengtson VL, Schaie KW, editors. Handbook of theories of aging. Springer Publishing Company; 1999. pp. 396–416. Haslbeck JMB, Fried EI. How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychol Med. 2017;47(16):2767–76. https://doi.org/10.1017/S0033291717001258 . Hu JS, Xiao J, Bai SY. Research on subjective well-being of rural elderly. Chin J Gerontol. 2006;26(03):314–7. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999;6(1):1–55. https://doi.org/10.1080/10705519909540118 . Johnson JW, Lebreton JM. History and Use of Relative Importance Indices in Organizational Research. Organizational Res Methods. 2004;7(3):238–57. https://doi.org/10.1177/1094428104266510 . Jones PJ, Mair P, McNally RJ. Visualizing Psychological Networks: A Tutorial in R. Front Psychol. 2018;9(1742). http://doi.org/10.3389/fpsyg.2018.01742 . Kong F, Xu L, Kong M, Li S, Zhou C, Li J, Sun L, et al. The relationship between socioeconomic status, mental health and long-term service support needs of the elderly in Shandong Province - a cross-sectional study. Int J Environ Res Public Health. 2019;16(4):526. http://doi.org/10.3390/ijerph16040526 . Kraus MW, Piff PK, Mendoza-Denton R, Rheinschmidt ML, Keltner D. Social class, solipsism, and contextualism: How the rich are different from the poor. Psychol Rev. 2012;119(3):546–72. https://doi.org/10.1037/a0028756 . Lai ET, Yu R, Woo J. The Associations of Income, Education and Income Inequality and Subjective Well-Being among Elderly in Hong Kong-A Multilevel Analysis. Int J Environ Res Public Health. 2020;17(4):1271. https://doi.org/10.3390/ijerph17041271 . Lazarov A, Suarez-Jimenez B, Levi O, Coppersmith DDL, Lubin G, Pine DS, Neria Y. Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients. Psychol Med. 2020;50(13):2154–70. 10.1017/S0033291719002034 . Li B, Wang SJ, Cui XY, Tang Z. Roles of indulgence versus restraint culture and ability to savor the moment in the link between income and subjective well-being. Int J Environ Res Public Health. 2022;19(12):6995. https://doi.org/10.3390/ijerph19126995 . Li B, Yu W, Hu X, et al. The impact of subjective social class on subjective well-being: examining the mediating roles of perceived scarcity and sense of control. Curr Psychol. 2023. https://doi.org/10.1007/s12144-023-05338-x . Li DM, Chen TY, Wu ZY. Quality of life and subjective well-being of the elderly in rural China. Chin J Gerontol. 2007;27(12):1193–6. Luo YH, Wang Q, Shi ZR, Zhou ZY. (2023). The relationship between self-concept, optimism, and the subjective well-being of the elderly. Journal of Hunan University of Science and Technology, 44 (03), 123–125. http://doi.org10.16336/j.cnki.cn43-1459/z.2023.03.018. Macfie J, McElwain NL, Houts RM, Cox MJ. Intergenerational transmission of role reversal between parent and child: Dyadic and family systems internal working models [Childrearing & Child Care 2956]. Attach Hum Dev. 2005;7(1):51–65. https://doi.org/10.1080/14616730500039663 . Qin YQ. The impact of marriage and health status on the subjective well-being of the elderly. Labor Secur World. 2020;09:33–4. Robinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol. 2016;125(6):747–57. https://doi.org/10.1037/abn0000181 . Sawada S, Toyosato T. The moderating effect of place attachment on the relationship between economic status and well-being among the older adults in Japan. Geriatr Nurs. 2021;42(3):721–6. https://doi.org/10.1016/j.gerinurse.2021.03.003 . Segal ZV. Appraisal of the self-schema construct in cognitive models of depression. Psychol Bull. 1988;103(2):147–62. https://doi.org/10.1037/0033-2909.103.2.147 . Seo MA, Lim YJ. Optimism and life satisfaction in persons with schizophrenia living in the community. Int J Soc Psychiatry. 2019;65(7–8):615–20. https://doi.org/10.1177/0020764019868256 . Sheikh MA, Abelsen B, Olsen JA. Education and health and well-being: direct and indirect effects with multiple mediators and interactions with multiple imputed data in Stata. J Epidemiol Commun Health. 2017;71(11):1037–45. https://doi.org/10.1136/jech-2016-208671 . Su H, Zhou Y, Wang H, Xing L. Social support, self-worth, and subjective well-being in older adults of rural China: a cross-sectional study. Psychol Health Med. 2022;27(7):1602–8. https://doi.org/10.1080/13548506.2021.1905861 . Tajfel H, Turner JC, Austin WG, Worchel S. An integrative theory of intergroup conflict. Brooks/Cole; 1979. Tang L, Mira Y, Hu Y, Zhang H. The relationship between optimistic personality, depression, and subjective well-being in the elderly. J Chin Gerontol. 2022;40(06):1328–31. Telef BB, Furlong MJ. Social and Emotional Psychological Factors Associated With Subjective Well-Being: A Comparison of Turkish and California Adolescents. Cross-Cultural Res. 2017;51(5):491–520. https://doi.org/10.1177/1069397117694815 . Thoits PA. Mechanisms linking social ties and support to physical and mental health. J Health Soc Behav. 2011;52(2):145–61. https://doi.org/10.1177/0022146510395592 . Thomas M, Fruchterman J, Edward M, Reingold. Graph drawing by force-directed placement. Softw - Pract Experience. 1991;21(11):1129–64. http://doi.org/10.1002/SPE.4380211102 . Umberson D, Karas Montez J. Social Relationships and Health: A Flashpoint for Health Policy. J Health Soc Behav. 2010;51(suppl):S54–66. https://doi.org/10.1177/0022146510383501 . Wang K, Li Y, Zhang T, Luo J. The Relationship among College Students' Physical Exercise, Self-Efficacy, Emotional Intelligence, and Subjective Well-Being. Int J Environ Res Public Health. 2022;19(18):11596. https://doi.org/10.3390/ijerph191811596 . Wang SY, Kim G. The Relationship between Physical-Mental Comorbidity and Subjective Well-Being among Older Adults. Clin gerontologist. 2020;43(4):455–64. https://doi.org/10.1080/07317115.2019.1580810 . Wang XH, Zhou HF. Current status and interrelationships of quality of life, loneliness and subjective well-being among older adults. Chin J Gerontol. 2010;30(05):676–7. Xiang GC, Teng ZJ, Li QQ, Chen H, Guo C. The influence of perceived social support on hope: A longitudinal study of older-aged adolescents in China. Child Youth Serv Rev. 2020;119. http://doi.org/10.1016/j.childyouth.2020.105616 . Xiang G, Li Q, Du X, et al. Links between family cohesion and subjective well-being in adolescents and early adults: The mediating role of self-concept clarity and hope. Curr Psychol. 2022;41(1):76–85. https://doi.org/10.1007/s12144-020-00795-0 . Xie C, Li L, Zhou L, Sun C, Zhang Y, Li Y. Mediating role of learned helplessness' components in the association between health literacy/social support and self-management among maintenance haemodialysis patients in Changsha, China: a cross-sectional study. BMJ Open. 2023;13(8):e068601. https://doi.org/10.1136/bmjopen-2022-068601 . Yeung SSS, King RB, Nalipay MJN, Cai Y. Exploring the interplay between socioeconomic status and reading achievement: An expectancy-value perspective. Br J Educ Psychol. 2022;92(3):1196–214. https://doi.org/10.1111/bjep.12495 . Zhang J, Xiao S, Shi L, Xue Y, Zheng X, Dong F, Xue B, Zhang C. Differences in Health-Related Quality of Life and Its Associated Factors Among Older Adults in Urban and Rural Areas. Risk Manage Healthc policy. 2022;15:1447–57. https://doi.org/10.2147/RMHP.S373077 . Zhao L, Zhang K, Gao Y, Jia Z, Han S. The relationship between gender, marital status and depression among Chinese middle-aged and older people: Mediation by subjective well-being and moderation by degree of digitization. Front Psychol. 2022;13:923597. https://doi.org/10.3389/fpsyg.2022.923597 . Zhou LJ. (2022). Research on the relationship between residents’ lifestyle, social attitude, health status and subjective well-being [Master dissertation, Nanjing University of Posts and Telecommunications]. CNKI. Zhou M, Gu X, Cheng K, et al. Exploration of symptom clusters during hemodialysis and symptom network analysis of older maintenance hemodialysis patients: a cross-sectional study. BMC Nephrol. 2023;24:115. https://doi.org/10.1186/s12882-023-03176-4 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4778935","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342547973,"identity":"3a765bda-dfbd-4393-8878-d3de3a8023c9","order_by":0,"name":"Chenyu Liang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Liang","suffix":""},{"id":342547974,"identity":"2eae2880-4bca-49be-a24b-3d906c416898","order_by":1,"name":"Zilan Ye","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zilan","middleName":"","lastName":"Ye","suffix":""},{"id":342547975,"identity":"9aecc7c1-fff1-4de7-9d49-9836473d13c4","order_by":2,"name":"Haifeng Yan","email":"","orcid":"","institution":"The First Dongguan Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Yan","suffix":""},{"id":342547976,"identity":"6f6a7b18-aa87-41ba-84f0-212f098045e7","order_by":3,"name":"Jianfeng Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIie3RMQrCMBTG8S8UdKl2fV08Q90ES71KpFAXB6fOmZzEOeIpvEFKVqmbCHHoEQouLoLVHiDtJpj/Fng/XuABLtdPVlSgOAbU5zHoAHzoqaAs60W8qUCme5AF08Nqxi/J/nqOUOcawVFYP8YEcZMezDpistSgu7KRSyHCp0mjhnijrUZEvNOWsiWvHkQlX8I6EaWZJJ7y0GSbYleufLpZyFBqPIgni7FJT9Uzn08CaSEgBY+ApUB7Gt8y3xQIsLq5j33S5XK5/rY3fPpHbEKv5IcAAAAASUVORK5CYII=","orcid":"","institution":"The First Dongguan Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-07-22 03:45:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4778935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4778935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62983901,"identity":"7d512fa7-5846-4ce0-9232-919ed3ee28c2","added_by":"auto","created_at":"2024-08-21 18:34:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176938,"visible":true,"origin":"","legend":"\u003cp\u003enetwork graph\u003c/p\u003e\n\u003cp\u003eNote. Factors influencing the subjective well-being of the elderly, with blue edges indicating positive correlation and red edges indicating negative correlation. The thickness and brightness of the edges represent the strength of the correlation. B1=age, B2=gender, B3=residence, B4=marital status, B5=economic status, B6=education level, B7=social status, C1=level of confidence in the future, C2=trouble doing things, C3=perception of others as helpful or selfish, S1=relationships, W1=subjective well-being.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4778935/v1/346bc75c0efffb12b3ab8cd6.png"},{"id":62983717,"identity":"b5bdfa8d-253c-4e6c-acd3-531ca49efe80","added_by":"auto","created_at":"2024-08-21 18:26:57","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92852,"visible":true,"origin":"","legend":"\u003cp\u003estructural equation model\u003c/p\u003e\n\u003cp\u003eNote. *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; B7=social status, C1=level of confidence in the future, S1=relationships, W1=subjective well-being.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4778935/v1/b6cdbdee82d860cdbeeacf1d.jpeg"},{"id":62983719,"identity":"93f4ad7e-cfc8-4f1a-8324-b5123770c959","added_by":"auto","created_at":"2024-08-21 18:26:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148595,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrap confidence intervals for the edge weights of the network\u003c/p\u003e\n\u003cp\u003eNotes. The red line represents the sample value, and the gray area represents the bootstrapped CI. each horizontal line represents an edge in the network, in order from the edge with the highest edge weight to the edge with the lowest edge weight.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4778935/v1/fc5572cc832e908884727d17.png"},{"id":62983718,"identity":"66e1af79-57e1-4a64-b48a-cb46a4e977b9","added_by":"auto","created_at":"2024-08-21 18:26:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81325,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation stability coefficient of network\u003c/p\u003e\n\u003cp\u003eNotes. Correlation stability coefficient of symptom network. The CS of strength in the network were all surpassing 0.5.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4778935/v1/ca6c7517c2fde5f8a6b8bbf0.png"},{"id":73072063,"identity":"ba2690dc-094f-4a49-bc15-f663cfa143d2","added_by":"auto","created_at":"2025-01-06 12:47:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1044010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4778935/v1/9e3d37f9-9754-4931-ba59-fd91e8e64081.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network analysis of key factors influencing subjective well-being among elderly with chronic disorders: A study based on the 2020 CFPS","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal statistics from the United Nations indicate that in 2020, individuals aged 65 and above accounted for 9.3% of the global population, with China ranking at 63rd place, still surpassing the global average of 9.3% (China Social Security Society, 2022). Since China embraced an aging society in 1999, the elderly population has been steadily growing, accelerating the pace of aging. The elderly now constitute a significant portion of the overall population, which will inevitably impact their daily lives and work dynamics (Fang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Existing research suggests that elderly individuals often experience psychological issues such as anxiety and depression, and these mental health challenges may lead to higher cognitive impairments, suicidal tendencies, dementia, and other health concerns (Ding \u0026amp; Kennedy, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thereby increasing their vulnerability and adversely affecting their psychological well-being, consequently lowering their subjective well-being (Deng, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Subjective well-being stands as a pivotal component of successful aging, and low levels of subjective well-being can hinder the process of successful aging (Gatz \u0026amp; Zarit, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Successful aging among the elderly contributes to their overall physical and mental well-being (Friedman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Subjective well-being is an evaluation of a person's life from a subjective perspective, including emotional responses, domain satisfaction, and overall judgments of life satisfaction. It is an important component of people's quality of life (Diener, 1999). However, for the elderly with chronic disorders, their well-being appears to be more cared for. According to the \"biopsychosocial model\" theory, physical health status is one of the most important determinants of subjective well-being (Engel, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Poor physical probrably reduces subjective well-being (Wang \u0026amp; Kim, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fei et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, it is important to understand what kinds of factors affect well-being among elderly with chronic disorders.\u003c/p\u003e \u003cp\u003eAccording to previous studies, there are quite lots of factors, such as some demographic factors (i.e., gender, residence, marital status, economic status, and educational level), cognitive schemas, physical health status, and social relationships, affecting the subjective well-being of the elderly. Firstly, in terms of demographics, previous research on gender differences in subjective well-being indicates that there may be differences between men and women, but it is unclear which gender tends to have higher levels of happiness. For example, some studies suggest that subjective well-being of elderly men is higher than that of women (Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This difference is attributed to different social roles, with traditional norms often requiring men to spend more time and effort on their careers, while women are expected to prioritize caregiving roles, leading to more dissatisfied feelings among women in family care. At the same time, women tend to anticipate negative outcomes more, leading to higher levels of negative experiences and lower subjective well-being. However, some studies found that elderly women have higher levels of well-being than men, as they are more likely to derive satisfaction from interpersonal relationships, leading to higher levels of subjective well-being (Wang \u0026amp; Zhou, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Some reported that there is no significant difference in subjective well-being between elderly men and women (Luo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the question of whether there is a gender difference in subjective well-being remains uncertain.\u003c/p\u003e \u003cp\u003eRegarding the influence mechanism of resident on subjective well-being, some researchers point out that elderly people with urban residence have higher subjective well-being than those in rural urban areas due to higher quality of life (Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cheng \u0026amp; Yan, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, marital status also has varying degrees of impact on the subjective well-being of the elderly. For example, Zhou (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Qin (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicates that married individuals generally have higher levels of subjective well-being due to the emotional support they receive, compared to those in unhappy marital status such as divorce or widowhood. Some previous studies also found a significant positive correlation between social economic status and subjective well-being (Fard et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sawada \u0026amp; Toyosato, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Elderly people with lower economic status may experience lower subjective well-being, possibly because lower economic status cannot provide a rich environment of mental stimulation, leading to facing the same life every day without the excitement of new things (Sawada \u0026amp; Toyosato, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To some extent, economic status might be affected by educational degree, thus, elderly people with lower education levels are more likely to have lower subjective well-being (Lai et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sheikh et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, cognitive schemas are also considered as one of the major factors influencing subjective well-being (Telef \u0026amp; Furlong, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cognitive schemas refer to the cognitive way in which the human brain processes information objects, comprising past reactions and experiences to form a relatively cohesive and enduring system of knowledge that shapes an individual's perception of objective realities (Segal, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Previous studies have confirmed that having a positive and optimistic cognitive schema can significantly predict the subjective well-being of older adults, whereas a negative cognitive schema may lead to depression and decrease subjective well-being (Tang et al.,2022). Positive schemas include belief in one's ability to accomplish tasks and an optimistic attitude towards future events. According to previous studies, when individuals have more positive evaluations of past events and foster more positive expectations for the future, they are more likely to have a higher level of well-being (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Positive cognitive schemas can increase optimism and promote healthy lifestyle choices and significantly impact physical and mental health adaptability to the environment, thereby enhancing subjective well-being (Seo \u0026amp; Lim, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, some previous studies also reported that the perception of social support among the elderly also influences subjective well-being (Fuller et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This refers to individuals' subjective evaluations of whether they receive sufficient social support (Farriol-Baroni et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, this encompasses material and emotional support from various sources such as family, relatives, friends, and social organizations (Xie et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Social relationships influence subjective well-being in that good social relationships are good in alleviating stress through social-emotional support (Umberson \u0026amp; Karas, 2010). When elderly individuals with poor social relationships receive less social-emotional support, it is difficult for them to alleviate negative emotions ( Abramowska-Kmon \u0026amp; Timoszuk, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom existing research, there are numerous factors influencing the subjective well-being of the elderly. However, few studies have integrated these factors for comprehensive analysis and focus on the elderly with chronic disorders. Therefore, to further know what the main factors in the elderly subjective well-being is, the present study will use symptom network analysis to explain. Symptom network analysis is the comprehensive systematic analysis of multiple variables, that can reveal associations at the symptom level, providing support for explaining the co-occurrence of two variables (Afzali et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Network analysis has unique advantages, such as revealing relationships and interactions, visualization, social connectivity, and simulation and prediction. Leveraging these strengths, by consolidating the variables that influence the subjective well-being of the elderly as various nodes within a network, the main purposes of this study are to explore the relationships among these nodes (i.e., factors) and how they influence the dependent variable (i.e., well-being), and to measure the extent to which a node is determined by other nodes (Haslbeck \u0026amp; Fried, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, researching the subjective well-being of the elderly with chornic disorders is a prevailing trend, and employing social network analysis to uncover the connections between variables can provide guidance and predictive insights on how to enhance the subjective well-being of the elderly.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and Materials\u003c/h2\u003e \u003cp\u003eThe data is derived from the China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University in 2020. CFPS aims to explore the development of society, encomic, education, health, and so on among Chinese population, including all aged populations (i.e., children, adolenscents and adults). The present study is to explore the factors influencing the subjective well-being of the elderly with chronic disorders. Therefore, the seletion criteria are: 1) aged over 65, 2) with chronic disoders in the past 6 months before the survey. After screening, a total of 1105 samples met the requirements. Next, samples with missing data were excluded, resulting in a final sample of 863 participants (F\u0026thinsp;=\u0026thinsp;435, M\u0026thinsp;=\u0026thinsp;428, mean aged\u0026thinsp;=\u0026thinsp;71, SD\u0026thinsp;=\u0026thinsp;5.07).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research variables\u003c/h2\u003e \u003cp\u003eThis study combines existing research on the subjective well-being of elderly individuals with chronic disorders to select relevant variables. For the convenience of the study, these variables are categorized into four major dimensions: demographic factors, cognitive schemas, and social relationships.\u003c/p\u003e \u003cp\u003eChronic disorders (QP401) were assessed by asking respondents, \"In the past six months, have you been diagnosed by a doctor with any chronic diseases?\" A response of 1 indicates the presence of a diagnosed chronic disease, while a response of 0 indicates no such diagnosis. This study focuses on elderly individuals who have been diagnosed with chronic diseases.\u003c/p\u003e \u003cp\u003eDemographic factors includes age, gender, residence, marital status, economic status, education level and social status. Age (QA001B) represents the specific age of the survey respondents, all of whom are elderly individuals aged 65 and above in this study. Gender (QA002) is denoted by 1 for male and 0 for female. Residence (QA301) is classified as rural\u0026thinsp;=\u0026thinsp;1, and urban\u0026thinsp;=\u0026thinsp;2. Marital status (MARRIAGE_LAST) includes categories such as unmarried\u0026thinsp;=\u0026thinsp;1, married\u0026thinsp;=\u0026thinsp;2, cohabitation\u0026thinsp;=\u0026thinsp;3, divorced\u0026thinsp;=\u0026thinsp;4, and widowed\u0026thinsp;=\u0026thinsp;5. Economic status (QI202) is measured using the monthly post-tax retirement pension of the elderly individuals, with the specific income amount self-reported by the respondents. Education level (EDU_LAST) represents the highest educational attainment of the elderly individuals, which was merged into three categories: illiterate/semi-literate or never attended school\u0026thinsp;=\u0026thinsp;1, high school or below =\u0026thinsp;2, and college/bachelor's degree or above =\u0026thinsp;3. Social status (QN8012) is self-rated by the elderly individuals on a scale from 1 (very low) to 5 (very high).\u003c/p\u003e \u003cp\u003eCognitive schemas consist of three dimensions, including future schema (QN12016), negative schema (QN407), and cognition of others (QM6). Future schema is defined as the level of confidence in the future, represented by the respondent\u0026rsquo;s rating on a scale from 1 (no confidence) to 5 (very confident). Negative schema is defined as the respondent's level of trouble doing things on a scale from 1\u0026thinsp;=\u0026thinsp;almost never (less than one day) to 4\u0026thinsp;=\u0026thinsp;most of the time (5\u0026ndash;7 days). The perception of others as helpful or selfish reflects the cognition of others, with helpful coded as 1 and selfish coded as 2.\u003c/p\u003e \u003cp\u003eSocial relationships (QM2011) are primarily subjectively judged by the respondents based on their interpersonal relationships, where respondents are from 0 (lowest) to 10 (highest).\u003c/p\u003e \u003cp\u003eThe subjective well-being (QM2016) score of the respondents is from 0 to 10, where 0 represents the lowest and 10 represents the highest level of subjective well-being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analyses\u003c/h2\u003e \u003cp\u003eThe network is composed of nodes representing symptoms and edges representing conditional pairwise relationships between the two nodes that control all other nodes in the control network, connecting characteristics and information of a certain system, forming a network (Lazarov et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Based on the network theory proposed by Lazarov et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this study adopted the social network analysis method to quantitatively investigate the relationships between subjective well-being and its influencing factors in the social network.\u003c/p\u003e \u003cp\u003eThe descriptive statistics of this study were conducted using SPSS 26 to analyze demographic factors. The network analysis part was implemented in JASP software. The preliminary analysis of the data network was estimated using the EBICglasso parameter in the JASP (Epskamp et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). According to the study of Lazarov et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it quantitatively explores the relationship between subjective well-being and its influencing factors in the social network. Nodes in the network represent the research subjects, while edges signify the connections between the research subjects (Zhou et al.,2023). In this study, nodes represent the subjective well-being of the elderly and the factors influencing their subjective well-being, while edges represent the connections between each pair of nodes.\u003c/p\u003e \u003cp\u003eIn line with the theory of Thomas, Fruchterman and Reingold (1991) on network centrality, the qgraph package positions nodes with strong correlations at the center of the network and those with weaker correlations towards the periphery. The correlations between nodes are depicted by lines, where blue lines indicate positive correlations between factors, while red lines denote negative correlations (Jones et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thicker lines represent stronger associations between nodes, and the closer the visual distance between nodes, the stronger their association (Epskamp et al, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Moreover, these lines symbolize relative importance, revealing how one factor as a predictor influences another. They not only reflect the direct impact of Factor X on Factor Y but also show the indirect effect of Factor X on Factor Y after adjusting for all other factors in the network (Johnson \u0026amp; Lebreton, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Thus, our study needs to consider both aspects of results. Based on the connectivity of nodes, we use the qgraph package to compute nodal centrality to explore which factors have the greatest impact on subjective well-being, thus quantifying and assessing their relative importance in the network (Robinaugh et al, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNext is the evaluation of the accuracy and stability of the entire sample network. Firstly, estimating the accuracy of network edge weights, fitting within a 95% confidence interval (CI), where smaller overlaps between 95% CI indicate higher accuracy of edge estimates (Epskamp et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, it's necessary to test the differences in edge weights to explore which edges significantly differ from others. Subsequently, using case-drop bootstrapping to assess the stability of centrality measures, the stability of centrality is quantified by the centrality stability coefficient (CS). The obtained CS coefficient should be at least 0.25, indicating moderate centrality stability, and surpassing 0.5 is considered more stable (Epskamp et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the results of network analysis, Structural Equation Modeling (SEM) would be used for further understanding of the inter-relationships between W1 and its key factors. R 4.3.3 would be use. According the model fit indices (Macfie et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the Comparative Fit Index (CFI) should be greater than 0.9, the Tucker-Lewis Index (TLI) should exceed 0.90, the Standardized Root Mean Square Residual (SRMR) should be below 0.05, and the Root Mean Square Error of Approximation (RMSEA) should be less than 0.08 (Browne \u0026amp; Cudeck, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Hu \u0026amp; Bentler, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The bias-corrected bootstrap method was iterated 1000 times to test the indirect effects according to the 95% confidence interval.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThis study collected data from a total of 863 elderly individuals aged 65 and above with chornic disorders (females\u0026thinsp;=\u0026thinsp;435, males\u0026thinsp;=\u0026thinsp;428), with an average age of 71 years (SD\u0026thinsp;=\u0026thinsp;5.07), ranging from 65 to 92 years old. Present data indicates that 59.4% of the elderly participants had rural residence, 81.2% of them were married; 17.6% were widowed and unmarried. The average post-tax pension amount was 1555 RMB. 42.2% of the elderly population was illiterate or had never attended school, while 54.2% had received primary or junior high school education, and only 3.6% had education beyond junior high school.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Network estimates\u003c/h2\u003e \u003cp\u003eThe case-drop bootstrap results of this study indicate that the correlations of strengths in the symptom network are mostly above 0.5, demonstrating the stability of the network (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).The marginal weight results indicate smaller intervals, suggesting that the network has a certain level of accuracy (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean, standard deviation and EI (expect influence) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The expected influence value of B5 (economic status) is the highest, followed by B3 (residence) and W1 (subjective well-being) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\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\u003emeans, standard deviations, EI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \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\u003eSymptoms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eDemographic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge (B1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender(B2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidence(B3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status (B4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic status (B5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1555.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1974.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation level (B6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial status (B7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCognitive schema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of confidence in the future (C1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrouble doing things (C2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception of others as helpful or selfish (C3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelationships (S1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubjective well-being (W1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, in this network, all nodes are interconnected directly or indirectly with other nodes. The positive and negative values of bridging edges correspond to blue and red edges, where positive values indicate positive influences, and negative values signify negative influences. The network results of demographic factors, cognitive schemas, social relationships, and subjective well-being are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The factors directly related to W1 (subjective well-being) include S1 (relationships), C1 (level of confidence in the future), B7 (social status), C3 (perception of others as helpful or selfish), B1 (age), B3 (residence), B4 (marital status), and C2 (trouble doing things) (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these, S1, C1, and B7 show a stronger correlation with W1. Therefore, we have chosen to focus our discussion on these factors. To further understanding of their interrelationships, this study further analyse a Structural Equation Model (SEM).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeights matrix of the regularized partial correlation network\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNetwork\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-83.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote. B1\u0026thinsp;=\u0026thinsp;age, B2\u0026thinsp;=\u0026thinsp;gender, B3\u0026thinsp;=\u0026thinsp;residence, B4\u0026thinsp;=\u0026thinsp;marital status, B5\u0026thinsp;=\u0026thinsp;economic status, B6\u0026thinsp;=\u0026thinsp;education level, B7\u0026thinsp;=\u0026thinsp;social status, C1\u0026thinsp;=\u0026thinsp;level of confidence in the future, C2\u0026thinsp;=\u0026thinsp;trouble doing things, C3\u0026thinsp;=\u0026thinsp;perception of others as helpful or selfish, S1\u0026thinsp;=\u0026thinsp;relationships, W1\u0026thinsp;=\u0026thinsp;subjective well-being.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mediation analysis\u003c/h2\u003e \u003cp\u003eThe results showed that the structural equation model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with standardized path coefficients provided a good fit (CFI\u0026thinsp;=\u0026thinsp;1.00, TLI\u0026thinsp;=\u0026thinsp;1.00, SRMR\u0026thinsp;=\u0026thinsp;0.00, RMSEA\u0026thinsp;=\u0026thinsp;0.00). According to the results, B7 (social stauts) could predict S1 (relationships) (β\u0026thinsp;=\u0026thinsp;0.427, SE\u0026thinsp;=\u0026thinsp;0.060, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and C1 (level of confidence in the future) (β\u0026thinsp;=\u0026thinsp;0.357, SE\u0026thinsp;=\u0026thinsp;0.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). S1 (relationships) (effect size\u0026thinsp;=\u0026thinsp;0.142, SE\u0026thinsp;=\u0026thinsp;0.024, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and C1 (level of confidence in the future) (effect size\u0026thinsp;=\u0026thinsp;0.196, SE\u0026thinsp;=\u0026thinsp;0.030, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) both mediated the relationship between B7 (social status) and W1 (subjective well-being).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study conducts a network analysis of the key factors influencing the subjective well-being of the elderly with chronic disorders, aiming to identify the pivotal factors among many potential influences, and then building a relation model between the key factors and subjective well-being. The results reveal the factors directly and strongly influencing the subjective well-being of the elderly are in turn social relationship (S1), level of confidence in the future (C1), social status (B7).\u003c/p\u003e \u003cp\u003eThe first finding of the network model is that the factor most closely related to subjective well-being (W1) is social relationships (S1). Elderly individuals with better social relationships tend to report higher subjective well-being, consistent with previous research (Su et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Having good social relationships means being able to receive more social support from others, which can be explained through the theory of social support. Social support is broadly defined as the perception of being cared for and receiving help during difficult times (Cohen \u0026amp; Wills, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). On one hand, feeling a high level of social support can enhance self-worth and dignity, thereby increasing subjective well-being. On the other hand, having social support helps alleviate the psychological stress of illness and provides protective psychological resources (e.g., self-control and a sense of mastery), as well as promotes healthy behaviors (Thoits, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, social support can motivate elderly individuals to develop a sense of meaning and belonging in life despite their illnesses, further enhancing their subjective well-being. In particular, for the elderly with chronic disorders, they need more care and emotional support. Research consistently shows that loneliness is a factor affecting the subjective well-being of the elderly, and having social support and connections can counteract the psychological decline caused by social isolation (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeanwhile, level of confidence in the future (C1) and social status (B7) are both positively correlated with subjective well-being. Level of confidence in the future can be understood as a form of positive self-schema in the elderly. Positive self-schema is defined as positive core beliefs about oneself, and preliminary research has found that positive self-schema implies higher subjective well-being (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is because individuals with a positive self-schema tend to have a more positive view of themselves, experience less anxiety regarding illness, and report higher subjective well-being (Xiang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea; Xiang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb). Moreover, elderly individuals with a higher social status (B7) who suffer from chronic illnesses can obtain more social resources to alleviate the pain caused by their illnesses, reduce the negative impacts of adverse emotions, and thus maintain their subjective well-being (Chen \u0026amp; Zhu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second finding regarding the interrelationships between social relationships (S1), level of confidence in the future (C1), social status (B7), and subjective well-being (W1) is that an individual's social status can predict social relationships and confidence in the future, which in turn can predict subjective well-being. According to social identity theory (Tajfel et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), people derive identity and value from the status of their social groups. Additionally, social relationships function through reciprocity and resource exchange; individuals with higher subjective status attract more resources (e.g., wealth, power) (Ahmad et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can bring more recognition and respect, help them build more positive interpersonal relationships, and form a social support system. These supports can enhance the subjective well-being of the elderly (Thoits, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, individuals with higher subjective status can establish a social support system through good interpersonal relationships, converting the obtained resources into emotional support, social recognition, and a sense of belonging, thereby improving their subjective well-being.\u003c/p\u003e \u003cp\u003eRegarding the mediating role of confidence in the future (C1), individuals with higher social status often feel they have more rights and can control various aspects of life, including managing their illnesses and future development, thus usually having stronger confidence in the future (Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the same time, individuals with higher social status can obtain more resources and opportunities, increasing their chances of treating and coping with diseases, leading to higher expectations for the future and significantly enhancing their confidence in it (Yeung et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Meanwhile, confidence in the future can bring self-affirmation and self-identity, establishing a positive self-schema, which enables elderly individuals with chronic diseases to maintain a positive attitude towards life experiences, thereby generating a sense of life meaning. The stronger the sense of life meaning, the higher the subjective well-being (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, individuals with lower social status have fewer social resources, making it harder for them to pursue and achieve health goals, leading to situational cognitive tendencies (Kraus et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This means that individuals become more dependent on external resources. In such cases, whether they succeed or fail is subconsciously attributed to external factors. Over time, this may lead to a gradual loss of self-control, believing they cannot determine their health, ultimately losing confidence. When facing future challenges, they may exhibit avoidance behaviors, gradually reducing self-worth and decreasing subjective well-being (Compas et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This indirectly confirms the relationship between positive cognitive schemas and subjective well-being, indicating that enhancing self-efficacy can indirectly improve subjective well-being (Wang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations and Implications\u003c/h2\u003e \u003cp\u003eThe limitations of this study exist. First, the study uses cross-sectional data, so it cannot explore the causal relationships of subjective well-being among various factors in the elderly. Second, the index used to measure subjective well-being is in the form of subjective reports, which may introduce certain biases due to variations in individual definitions. Third, because the data collected are based on subjective reports from respondents, there is a possibility that social desirability may lead to concealment or the reporting of answers that align with societal expectations, introducing some bias and potentially not fully reflecting the actual situation. Fourth, this study's survey focuses on Chinese elderly people, and the limitations of cultural differences may restrict the generalizability of results to a global scale.\u003c/p\u003e \u003cp\u003eDespite these limitations, the data results of this study provide insights into the network relationships among key factors influencing the subjective well-being of elderly individuals with chronic illnesses. For example, the study's findings demonstrate the factors most closely related to subjective well-being and their interrelationships. This can help social workers and policymakers in aging-related fields better understand the factors affecting the subjective well-being of elderly individuals with chronic illnesses. Such understanding can contribute to providing a more suitable environment and policy support for their care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Conclusion\u003c/h2\u003e \u003cp\u003eIn summary, this study has improved the understanding of the factors influencing the subjective well-being of elderly Chinese individuals with chronic illnesses and can help guide further research in this area. The results found that the key factors influencing subjective well-being include social relationships, confidence in the future, and social status. These factors formed a closer relationship cluster. Further analysis of the inter-relationships among these factors also revealed that social relationships and confidence in the future significantly affect the impact of social status on subjective well-being. These findings enhance our understanding of the factors influencing the subjective well-being of elderly individuals with chronic disorders.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyses during the current study are available in the China Family Panel Studies (CFPS).\u0026nbsp;\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\u003eAvailability of data and material:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyses during the current study are available in the China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHundred Youth Research Project Funding Plan\u0026nbsp;of Guangdong Medical University\u0026nbsp;in 2023 \u0026ldquo;Symptom Network Analysis of New Era Elderly Subjective Well-being\u0026rdquo;\u0026nbsp;(广东医科大学2023\u0026nbsp;年学校百项青年研究项目资助计划\u0026ldquo;新时代老年人主观幸福感影响因素的症状网络研究\u0026rdquo;),\u0026nbsp;Grand Code: GDMUD2023021\u003c/li\u003e\n \u003cli\u003eGraduated Research Project of Schoold of Humanities and Management in Guangdong Medical University\u0026nbsp;in 2023 \u0026ldquo;Key Factors of Elderly Subejective Well-being\u0026rdquo;\u0026nbsp;(广东医科大学人文与管理学院2023年本科生科研项目\u0026ldquo;影响老年人主观幸福感的关键因素研究\u0026rdquo;), Grand Code: RW2023002\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCL: Conceptualization, Methodology, Writing - Original Draft, Review and Editing.\u003c/p\u003e\n\u003cp\u003eZY: Conceptualization, Formal Analysis, Writing,\u0026nbsp;Supervision, Review\u0026nbsp;and Editing.\u003c/p\u003e\n\u003cp\u003eHY:\u0026nbsp;Formal Analysis.\u003c/p\u003e\n\u003cp\u003eJT:\u0026nbsp;Supervision, Review\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbramowska-Kmon A, Timoszuk S. Family and social networks, subjective well-being and loneliness among older adults in Poland. Studia Demograficzne. 2020;1(177):9\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33119/SD.2020.1.1\u003c/span\u003e\u003cspan address=\"10.33119/SD.2020.1.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfzali MH, Sunderland M, Teesson M, Carragher N, Mills K, Slade T. A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: The role of overlapping symptoms. J Affect Disord. 2017;208:490\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2016.10.037\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2016.10.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad R, Nawaz MR, Ishaq MI, Khan MM, Ashraf HA. Social exchange theory: Systematic review and future directions. Front Psychol. 2023;13:1015921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2022.1015921\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.1015921\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrowne MW, Cudeck R. (1993). Alternative ways of assessing model fit. In K. A. Bollen \u0026amp; J. S. Long(Eds.). \u003cem\u003eTesting Structural Equations Models\u003c/em\u003e, (pp. 136\u0026ndash;162). Park, CA: Sage.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Zhu Z. Social trust and emotional health in rural older adults in China: the mediating and moderating role of subjective well-being and subjective social status. BMC Public Health. 2021;21(1):556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-021-10617-y)\u003c/span\u003e\u003cspan address=\"10.1186/s12889-021-10617-y)\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Yang T, Luo J. Core self-evaluation and subjective wellbeing: A moderated mediation model. Front public health. 2022;10:1036071. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2022.1036071\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.1036071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng G, Yan Y. Sociodemographic, health-related, and social predictors of subjective well-being among Chinese oldest-old: a national community-based cohort study. BMC Geriatr. 2021;21(1):124. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-021-02071-7\u003c/span\u003e\u003cspan address=\"10.1186/s12877-021-02071-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina Social Security Society(Ed.). (2022). \u003cem\u003eChina Aging Research Report 2022\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.caoss.org.cn/UploadFile/pic/20229281791192316.pdf\u003c/span\u003e\u003cspan address=\"https://www.caoss.org.cn/UploadFile/pic/20229281791192316.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull. 1985;98(2):310\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.98.2.310\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.98.2.310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCompas BE, Jaser SS, Bettis AH, Watson KH, Gruhn MA, Dunbar JP, Williams E, Thigpen JC. Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychol Bull. 2017;143(9):939\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/bul0000110\u003c/span\u003e\u003cspan address=\"10.1037/bul0000110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng M. Social relations, mental health level and subjective well-being improvement of the elderly - an empirical analysis based on CGSS2015 data. Popul Dev. 2019;25(03):85\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiener E, Suh EM, Lucas RE, Smith HL. Subjective well-being: Three decades of progress. Psychol Bull. 1999;125(2):276\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.125.2.276\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.125.2.276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing OJ, Kennedy GJ. Understanding Vulnerability to Late-Life Suicide. Curr psychiatry Rep. 2021;23(9):58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11920-021-01268-2\u003c/span\u003e\u003cspan address=\"10.1007/s11920-021-01268-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngel GL. The need for a new medical model: a challenge for biomedicine. Sci (New York N Y). 1977;196(4286):129\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.847460\u003c/span\u003e\u003cspan address=\"10.1126/science.847460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3758/s13428-017-0862-1\u003c/span\u003e\u003cspan address=\"10.3758/s13428-017-0862-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEpskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48(4):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v048.i04\u003c/span\u003e\u003cspan address=\"10.18637/jss.v048.i04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, Aman Y, Frank J, Liao J, Zou H, Wang NY, Wu J, Liu X, Li T, Fang Y, Niu Z, Yang G, Hong J, Wang Q, Chen G, Woo J. (2020). A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. \u003cem\u003eAgeing research reviews, 64\u003c/em\u003e, 101174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2020.101174\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2020.101174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFard SS, Kavosi A, Sanagoo A, Jouybari L. Evaluation of the Relationship of Employment and Economic Status with the Feeling of Mental Well-Being in the Elderly Living in Northern Iran. Curr health Sci J. 2022;48(4):418\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12865/CHSJ.48.04.09\u003c/span\u003e\u003cspan address=\"10.12865/CHSJ.48.04.09\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarriol-Baroni V, Gonz\u0026aacute;lez-Garc\u0026iacute;a L, Luque-Garc\u0026iacute;a A, Postigo-Zegarra S, P\u0026eacute;rez-Ruiz S. Influence of Social Support and Subjective Well-Being on the Perceived Overall Health of the Elderly. Int J Environ Res Public Health. 2021;18(10):5438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18105438\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18105438\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFei C, Zhu Y, Jiang L, Zhou H, Yu H. Social integration, physical and mental health and subjective well-being in the floating population-a moderated mediation analysis. Front public health. 2023;11:1167537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpubh.2023.1167537\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2023.1167537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman SM. Lifestyle (Medicine) and Healthy Aging. Clin Geriatr Med. 2020;36(4):645\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cger.2020.06.007\u003c/span\u003e\u003cspan address=\"10.1016/j.cger.2020.06.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuller HR, Ajrouch KJ, Antonucci TC. (2020), The Convoy Model and Later-Life Family Relationships. \u003cem\u003eJournal of Family Theory \u0026amp; Review 12\u003c/em\u003e (June 2020): 126\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1111/jftr.12376\u003c/span\u003e\u003cspan address=\"10.1111/jftr.12376\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarfein AJ, Herzog AR. Robust aging among the young-old, old-old, and oldest-old. J Gerontol B. 1995;50(2):S77\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/geronb/50b.2.s77\u003c/span\u003e\u003cspan address=\"10.1093/geronb/50b.2.s77\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGatz M, Zarit SH. A good old age: Paradox or possibility. In: Bengtson VL, Schaie KW, editors. Handbook of theories of aging. Springer Publishing Company; 1999. pp. 396\u0026ndash;416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaslbeck JMB, Fried EI. How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychol Med. 2017;47(16):2767\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0033291717001258\u003c/span\u003e\u003cspan address=\"10.1017/S0033291717001258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu JS, Xiao J, Bai SY. Research on subjective well-being of rural elderly. Chin J Gerontol. 2006;26(03):314\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999;6(1):1\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705519909540118\u003c/span\u003e\u003cspan address=\"10.1080/10705519909540118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson JW, Lebreton JM. History and Use of Relative Importance Indices in Organizational Research. Organizational Res Methods. 2004;7(3):238\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1094428104266510\u003c/span\u003e\u003cspan address=\"10.1177/1094428104266510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PJ, Mair P, McNally RJ. Visualizing Psychological Networks: A Tutorial in R. Front Psychol. 2018;9(1742). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3389/fpsyg.2018.01742\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2018.01742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong F, Xu L, Kong M, Li S, Zhou C, Li J, Sun L, et al. The relationship between socioeconomic status, mental health and long-term service support needs of the elderly in Shandong Province - a cross-sectional study. Int J Environ Res Public Health. 2019;16(4):526. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3390/ijerph16040526\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16040526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraus MW, Piff PK, Mendoza-Denton R, Rheinschmidt ML, Keltner D. Social class, solipsism, and contextualism: How the rich are different from the poor. Psychol Rev. 2012;119(3):546\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/a0028756\u003c/span\u003e\u003cspan address=\"10.1037/a0028756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai ET, Yu R, Woo J. The Associations of Income, Education and Income Inequality and Subjective Well-Being among Elderly in Hong Kong-A Multilevel Analysis. Int J Environ Res Public Health. 2020;17(4):1271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17041271\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17041271\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazarov A, Suarez-Jimenez B, Levi O, Coppersmith DDL, Lubin G, Pine DS, Neria Y. Symptom structure of PTSD and co-morbid depressive symptoms \u0026ndash; a network analysis of combat veteran patients. Psychol Med. 2020;50(13):2154\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/S0033291719002034\u003c/span\u003e\u003cspan address=\"10.1017/S0033291719002034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Wang SJ, Cui XY, Tang Z. Roles of indulgence versus restraint culture and ability to savor the moment in the link between income and subjective well-being. Int J Environ Res Public Health. 2022;19(12):6995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph19126995\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19126995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Yu W, Hu X, et al. The impact of subjective social class on subjective well-being: examining the mediating roles of perceived scarcity and sense of control. Curr Psychol. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12144-023-05338-x\u003c/span\u003e\u003cspan address=\"10.1007/s12144-023-05338-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi DM, Chen TY, Wu ZY. Quality of life and subjective well-being of the elderly in rural China. Chin J Gerontol. 2007;27(12):1193\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo YH, Wang Q, Shi ZR, Zhou ZY. (2023). The relationship between self-concept, optimism, and the subjective well-being of the elderly. \u003cem\u003eJournal of Hunan University of Science and Technology, 44\u003c/em\u003e(03), 123\u0026ndash;125. http://doi.org10.16336/j.cnki.cn43-1459/z.2023.03.018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacfie J, McElwain NL, Houts RM, Cox MJ. Intergenerational transmission of role reversal between parent and child: Dyadic and family systems internal working models [Childrearing \u0026amp; Child Care 2956]. Attach Hum Dev. 2005;7(1):51\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14616730500039663\u003c/span\u003e\u003cspan address=\"10.1080/14616730500039663\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin YQ. The impact of marriage and health status on the subjective well-being of the elderly. Labor Secur World. 2020;09:33\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. J Abnorm Psychol. 2016;125(6):747\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/abn0000181\u003c/span\u003e\u003cspan address=\"10.1037/abn0000181\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSawada S, Toyosato T. The moderating effect of place attachment on the relationship between economic status and well-being among the older adults in Japan. Geriatr Nurs. 2021;42(3):721\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gerinurse.2021.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.gerinurse.2021.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegal ZV. Appraisal of the self-schema construct in cognitive models of depression. Psychol Bull. 1988;103(2):147\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.103.2.147\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.103.2.147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo MA, Lim YJ. Optimism and life satisfaction in persons with schizophrenia living in the community. Int J Soc Psychiatry. 2019;65(7\u0026ndash;8):615\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0020764019868256\u003c/span\u003e\u003cspan address=\"10.1177/0020764019868256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheikh MA, Abelsen B, Olsen JA. Education and health and well-being: direct and indirect effects with multiple mediators and interactions with multiple imputed data in Stata. J Epidemiol Commun Health. 2017;71(11):1037\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech-2016-208671\u003c/span\u003e\u003cspan address=\"10.1136/jech-2016-208671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu H, Zhou Y, Wang H, Xing L. Social support, self-worth, and subjective well-being in older adults of rural China: a cross-sectional study. Psychol Health Med. 2022;27(7):1602\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13548506.2021.1905861\u003c/span\u003e\u003cspan address=\"10.1080/13548506.2021.1905861\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTajfel H, Turner JC, Austin WG, Worchel S. An integrative theory of intergroup conflict. Brooks/Cole; 1979.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang L, Mira Y, Hu Y, Zhang H. The relationship between optimistic personality, depression, and subjective well-being in the elderly. J Chin Gerontol. 2022;40(06):1328\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTelef BB, Furlong MJ. Social and Emotional Psychological Factors Associated With Subjective Well-Being: A Comparison of Turkish and California Adolescents. Cross-Cultural Res. 2017;51(5):491\u0026ndash;520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1069397117694815\u003c/span\u003e\u003cspan address=\"10.1177/1069397117694815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThoits PA. Mechanisms linking social ties and support to physical and mental health. J Health Soc Behav. 2011;52(2):145\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022146510395592\u003c/span\u003e\u003cspan address=\"10.1177/0022146510395592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas M, Fruchterman J, Edward M, Reingold. Graph drawing by force-directed placement. Softw - Pract Experience. 1991;21(11):1129\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1002/SPE.4380211102\u003c/span\u003e\u003cspan address=\"10.1002/SPE.4380211102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmberson D, Karas Montez J. Social Relationships and Health: A Flashpoint for Health Policy. J Health Soc Behav. 2010;51(suppl):S54\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0022146510383501\u003c/span\u003e\u003cspan address=\"10.1177/0022146510383501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Li Y, Zhang T, Luo J. The Relationship among College Students' Physical Exercise, Self-Efficacy, Emotional Intelligence, and Subjective Well-Being. Int J Environ Res Public Health. 2022;19(18):11596. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph191811596\u003c/span\u003e\u003cspan address=\"10.3390/ijerph191811596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang SY, Kim G. The Relationship between Physical-Mental Comorbidity and Subjective Well-Being among Older Adults. Clin gerontologist. 2020;43(4):455\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07317115.2019.1580810\u003c/span\u003e\u003cspan address=\"10.1080/07317115.2019.1580810\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang XH, Zhou HF. Current status and interrelationships of quality of life, loneliness and subjective well-being among older adults. Chin J Gerontol. 2010;30(05):676\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang GC, Teng ZJ, Li QQ, Chen H, Guo C. The influence of perceived social support on hope: A longitudinal study of older-aged adolescents in China. Child Youth Serv Rev. 2020;119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.childyouth.2020.105616\u003c/span\u003e\u003cspan address=\"10.1016/j.childyouth.2020.105616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang G, Li Q, Du X, et al. Links between family cohesion and subjective well-being in adolescents and early adults: The mediating role of self-concept clarity and hope. Curr Psychol. 2022;41(1):76\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12144-020-00795-0\u003c/span\u003e\u003cspan address=\"10.1007/s12144-020-00795-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie C, Li L, Zhou L, Sun C, Zhang Y, Li Y. Mediating role of learned helplessness' components in the association between health literacy/social support and self-management among maintenance haemodialysis patients in Changsha, China: a cross-sectional study. BMJ Open. 2023;13(8):e068601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2022-068601\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2022-068601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung SSS, King RB, Nalipay MJN, Cai Y. Exploring the interplay between socioeconomic status and reading achievement: An expectancy-value perspective. Br J Educ Psychol. 2022;92(3):1196\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/bjep.12495\u003c/span\u003e\u003cspan address=\"10.1111/bjep.12495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Xiao S, Shi L, Xue Y, Zheng X, Dong F, Xue B, Zhang C. Differences in Health-Related Quality of Life and Its Associated Factors Among Older Adults in Urban and Rural Areas. Risk Manage Healthc policy. 2022;15:1447\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/RMHP.S373077\u003c/span\u003e\u003cspan address=\"10.2147/RMHP.S373077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao L, Zhang K, Gao Y, Jia Z, Han S. The relationship between gender, marital status and depression among Chinese middle-aged and older people: Mediation by subjective well-being and moderation by degree of digitization. Front Psychol. 2022;13:923597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2022.923597\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2022.923597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou LJ. (2022). Research on the relationship between residents\u0026rsquo; lifestyle, social attitude, health status and subjective well-being [Master dissertation, Nanjing University of Posts and Telecommunications]. CNKI.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou M, Gu X, Cheng K, et al. Exploration of symptom clusters during hemodialysis and symptom network analysis of older maintenance hemodialysis patients: a cross-sectional study. BMC Nephrol. 2023;24:115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12882-023-03176-4\u003c/span\u003e\u003cspan address=\"10.1186/s12882-023-03176-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"subjective well-being, network analysis, elderly, chronic disorders, Chinese","lastPublishedDoi":"10.21203/rs.3.rs-4778935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4778935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aims to delve into the mechanisms influencing the subjective well-being of elderly individuals with chronic disorders within the context of aging population. It comprehensively analyzes these factors, including cognitive schemas, social relationships, and demographics to provide a scientific basis for enhancing the subjective well-being of elderly individuals.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2020 China Family Panel Studies (CFPS) and selected 863 individuals aged 65 and above with chronic disorders as subjects for analysis. Network analysis was conducted using JASP.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study has identified all the relationships between factors in the research and subjective well-being among the elderly with chronic disorders, and we have visualized them in graphical form. The findings indicate that social relationships (S1) are most closely associated with subjective well-being (W1), followed by level of confidence in the future (C1) and social status (B7). Further analysis found that C1 and S1 could be a mediator affecting the impact of B7 on W1.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIdentify and delineate the key factors influencing the subjective well-being of elderly individuals with chronic disorders, as well as their interrelationships. This helps to further investigate the factors influencing the subjective well-being of the elderly.\u003c/p\u003e","manuscriptTitle":"Network analysis of key factors influencing subjective well-being among elderly with chronic disorders: A study based on the 2020 CFPS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 18:26:53","doi":"10.21203/rs.3.rs-4778935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8019f66a-395b-4dc4-97e9-baf164e2e0b2","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T12:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 18:26:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4778935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4778935","identity":"rs-4778935","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}