A network analysis of academic burnout and its psychosocial correlates: Self-compassion, teacher support, and family cohesion among Chinese middle and high school students

A network analysis of academic burnout and its psychosocial correlates: Self-compassion, teacher support, and family cohesion among Chinese middle and high school students | 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 A network analysis of academic burnout and its psychosocial correlates: Self-compassion, teacher support, and family cohesion among Chinese middle and high school students Peng Li, Yiwei Li, Yidan Yuan, Yang Shao, Wenbin Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7325995/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Among adolescents, academic burnout has emerged as a pervasive psychological syndrome characterized by persistent emotional exhaustion, detached cynicism toward learning, and compromised academic efficacy. This phenomenon poses substantial risks to students' socioemotional development and cognitive functioning, particularly during the critical developmental transition of middle and high school. Although research has identified key protective factors, most studies still treat these interconnected constructs in isolation. This fragmented approach overlooks the dynamic and interconnected nature of these variables. To address this gap, we applied psychological network analysis to questionnaires from 1,229 Tianjin students (747 junior, 482 senior high) to examine how these factors jointly influence academic burnout. Network modeling revealed stage-specific patterns: teacher encouragement (Expected Influence = 1.55) was central for juniors, and teacher emotional care (EI = 1.49) was central for seniors. Furthermore, the network structure showed a complete disconnection of family cohesion in the high school subgroup, highlighting the shifting role of family support across developmental stages. These findings demonstrate the value of network analysis in identifying key intervention targets and capturing the evolving structure of psychosocial support systems. These findings provide theoretical insights into the synergistic mechanisms of protective resources and practical guidance for designing stage specific, ecosystem-based interventions to alleviate academic burnout among adolescents. Academic burnout Self-compassion Teacher support Family cohesion Network Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Academic burnout has emerged as a global psychological challenge among adolescents, demonstrating consistent cross-cultural prevalence (OECD, 2017 ; Zhou et al., 2024 ). Building on the foundational work of Schaufeli et al. ( 2002 ), contemporary research characterizes this syndrome through three core dimensions: chronic academic exhaustion, cognitive detachment from learning activities, and diminished personal achievement (Salmela-Aro et al., 2008 ). Academic burnout causes cognitive impairments in students in the form of reduced attention, problem-solving skills (May et al., 2015 ), and emotional regulation (Seibert et al., 2017 ). Longitudinal follow-up studies have shown that high levels of academic burnout exacerbate symptoms of depression and anxiety (Song et al., 2025 ) and decrease academic performance (Liou et al., 2022 ). Authority figures exert profound impacts on children throughout their development (Kohlberg, 1971 ), manifested in critical resources such as family cohesion and teacher support (Roorda et al., 2011b ). Conservation of resources theory (Hobfoll, 1989 ) highlights interactions between individual resources (e.g., self-compassion) and social resources (e.g., family cohesion, teacher support). Integrating two theories clarifies how protective resources coordinate across ecological levels, offering a comprehensive framework for understanding adolescent academic burnout. Although recent studies acknowledge multilevel determinants (Liu et al., 2023 ), prevailing research paradigms remain constrained by reductionist approaches that often examine isolated predictors of academic burnout, overlooking the mutual interplay among protective factors (Skinner & Kindermann, 2009). Network analysis addresses this gap by modeling variables as interconnected nodes, thus revealing not only direct associations but also bridge variables that transmit protective effects across systems (Borsboom & Cramer, 2013 ), providing a holistic understanding of burnout resistance. Therefore, this paper uses network analysis to examine the relationships among adolescents’ self-compassion, family cohesion, teacher support, and academic burnout from three perspectives: students’ own, family, and school perspectives. 1.1 Associations between Self-compassion, Family Cohesion, Teacher Support, and Academic Burnout Self-compassion (Neff, 2003 ) is a proactive self-regulatory capacity that enables individuals to effectively mitigate stress responses while maintaining personal accountability. On the basis of Conservation of Resources theory (Hobfoll, 1989 ), self-compassion mitigates academic burnout through a dual pathway. First, self-compassion reduces self-critical rumination by fostering a kind and accepting attitude toward personal shortcomings, which helps conserve cognitive-emotional resources and alleviate emotional exhaustion (Neff, 2003 ). Second, self-compassion enhances individuals’ emotional regulation abilities, enabling them to maintain psychological resilience under academic pressure and gradually accumulate positive psychological resources (Inwood & Ferrari, 2018 ; Neff & Germer, 2013 ;Qiang et al., 2024 ). Self-compassion significantly reduces burnout levels through a resource preservation effect, with individuals who participate in self-compassion training experiencing a 32% reduction in emotional exhaustion scores (intervention group vs. control group) (Neff & Germer, 2013 ). Family cohesion (Bowen, 1993 ) refers to the ‘safe base’ formed by emotional ties among family members and serves three primary functions in academic adaptation: first, postfailure emotional restoration; second, the promotion of challenge-seeking behaviors; and third, the provision of sustained emotional support. Research has shown that family cohesion is positively related to individuals' emotion regulation (Cheng et al., 2024 ), which may exert effort through second paths of self-compassion. Families can mitigate academic burnout in children by increasing traits such as optimism, hope, resilience, and self-efficacy (Yu et al., 2021 ), so students with greater family cohesion are less likely to experience academic burnout. Teacher support is conceptualized as a systematic interactive process involving emotional connection, cognitive guidance, and behavioral modeling to foster student resilience and growth (Pianta, 1999 ; Wentzel, 1998 ). According to self-determination theory (Ryan & Deci, 2000 ), teachers can alleviate the burnout experience by meeting students' three basic psychological needs—autonomy, competence, and belonging—in their educational practices (Roorda et al., 2011b ). One study revealed that high school students’ perceived teacher emotional support positively predicts their engagement in online learning (Kong et al., 2025 ); at the same time, teacher support and peer support significantly and negatively predict foreign language academic burnout (Xie & Chen, 2025 ). 1.2 School Age Differences in Self-compassion, Family Cohesion, Teacher Support, and Academic Burnout Under China's exam-oriented education system, academic burnout is more prevalent and varies across school stages (Jinqin & Zhiyan, 2016 ). From the perspective of developmental psychology, middle school (12–15 years old) and high school (15–18 years old) students are in a very different stage of psychosocial transition: middle school students are in early adolescence, when their cognitive control and emotion regulation systems have not yet matured fully (Steinberg, 2005 ), whereas high school students are in the late stage of adolescence, when their prefrontal cortex functioning tends to be perfected, and the need for independent decision-making is significantly increased (Crone & Dahl, 2012 ). This stage difference may lead to the divergent characteristics of the two groups in terms of their performance in self-compassion, family cohesion and teacher support. The transition from middle school to high school (approximately 15 years old) serves as a key turning point for increased academic burnout (Jiang et al., 2021 ). Specifically, high school cohorts face more intense academic competition and social comparisons, and their levels of self-compassion (especially the self-kindness dimension) are significantly lower than those of middle school students (Neff & and McGehee, 2010). Notably, the protective effects of family support systems show a phase reversal: among high school students, high family cohesion buffers the negative effects of low self-compassion on psychological distress (Hu et al., 2024 ), whereas middle school students’ psychological well-being relies more strongly on the direct effects of teachers’ emotional support (Roeser et al., 2000 ). This phenomenon may be rooted in the trajectory of adolescents’ need for autonomy, as they become more independent (Smetana et al., 2006 ), whereas middle school students still need teachers as external authorities to provide clear emotional anchors. 1.3 Using Network Analysis to Explore the Complex Interactions among Self-compassion, Family Cohesion, Teacher Support, and Academic Burnout Current investigations of academic burnout (e.g., regression modeling and structural equation modeling) constrain a comprehensive understanding of complex psychological phenomena in two critical dimensions. First, such approaches employ reductionist analytical frameworks that presume static unidirectional relationships between operationalized variables. For example, regression models can calculate the independent contribution of self-compassion to academic burnout but fail to reveal whether family cohesion indirectly mitigates burnout by enhancing self-compassion or whether teacher support moderates this path. Second, traditional methods assume that relationships between variables are linear and superimposable, whereas in reality, psychological mechanisms often show nonlinear interactions. For example, the protective effect of family cohesion may suddenly fail when academic stress exceeds a certain threshold, but traditional linear models fail to capture such dynamic changes (Granic et al., 2003 ; Wang & Eccles, 2012 ). Network approaches (Borsboom, 2017 ) provide a methodological framework to model these nonlinear interactions. 1.4 Purpose of the Study This study examines how support from key authority figures (teachers and parents) impacts academic burnout to form a protective network against academic burnout across developmental stages. Therefore, the aim of this study is to address three major questions: 1. To explore the associative network structure of self-compassion, family cohesion, and teacher support in relation to academic burnout. 2. Identifying the variable dimension in the network that has the highest degree of bridging centrality may become a key target for intervening in academic burnout. 3. Which nodes exhibit significant differences in centrality between the psychological support networks of the two learning stages? 2. Method 2.1 Participants The questionnaire data were collected from 1,229 students (747 from junior high schools and 482 from senior high schools) in Tianjin, China. Except for 9 missing data points on gender variables, this study included 580 males and 640 females (the mean age for junior high school students was 13.01 years, and that for senior high school students was 16.06 years). The rate of missing data was 12.18% (91 participants) for junior high school students and 11.83% (57 participants) for senior high school students. 2.2 Ethics approval and consent to participate Data collection was conducted across six schools in Tianjin, China, in accordance with the Declaration of Helsinki and the ethical guidelines of the Tianjin Normal University’s Institutional Review Board.This study was approved by the Institutional Review Board of Tianjin Normal University(Reference Number: XL20240909a). 2.3 Procedure Teachers in six schools provided questionnaires for students in their classes and retrieved completed questionnaires. Each student was asked to sign the informed consent form before the survey. The participants completed the following measures. 2.3.1 Adolescent Student Burnout Inventory ASBI, which showed great reliability and validity in previous study (Wu et al., 2010; Wu et al., 2021), was used to measure students’ academic burnout. It includes 16 items that constitute 3 dimensions: exhaustion, learning cynicism, and reduced efficacy. Each item scored on a scale from 1 (strongly disagree) to 5 (strongly agree). The higher total scores indicated higher level of academic burnout. The Cronbach’s α was 0.89 for all items, 0.85 for reduced efficacy, 0.83 for learning cynicism, and 0.78 for exhaustion in this study. 2.3.2 Self-compassion Scale Self-compassion was measured by SCS which developed by Gong et al. (2014). Common humanity, self-kindness and mindfulness were three dimensions of SCS. 12 items required an answer on a 5-point scale, ranging from 1 (never) to 5 (very often). The Cronbach’s α for all items was 0.83 in the current research. The subscales’ Cronbach’s α were 0.77 for common humanity, 0.60 for self-kindness, and 0.84 for mindfulness.Self-kindness showed low reliability—likely from social-desirability bias—so interpret this dimension with caution. 2.2.3 Perceived Teachers’ Emotional Support Questionnaire PTESQ developed by Gao et al. (2017) was used to measure students’ perceived teachers’ emotional support. PTESQ consists of 18 items which are divided into 4 subscales: understanding students, care about students, respect students, and encourage students. Items are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating higher levels of perceived emotional support. Subscale scores are calculated by summing the responses to relevant items, and the total score is obtained by summing all 18 items. The Cronbach’s α of PTESQ was 0.96. Cronbach’s α for each subscale were 0.95 (understanding students), 0.91 (care about students), 0.90 (respect students), and 0.91 (encourage students). 2.3.4 Family Adaptability and Cohesion Evaluation Scales Family cohesion was measured by the Chinese version of FACES II, which had demonstrated have satisfactory reliability and validity (Fei et al., 1991; Shao et al., 2021). This study used family cohesion subscale which includes 16 items. The Cronbach’s α was 0.90 in this study. 2.3.5 Demographic questionnaire Participants required to provide their information about gender, age, and grade. 2.4 Data analysis 2.4.1 Descriptive analysis Descriptive analysis was conducted via SPSS 26.0. This step focused on the demographic information and main variables of junior and senior high school students. 2.4.2 Network analysis Owing to the missing values of the primary variables, multiple imputation was conducted through the MICE package in R to handle missing data before network analysis (van Buuren, 2018). Predictive mean matching was used to impute missing variables. Then, networks based on the imputed data for all samples were computed via the bootnet package (Epskamp, Borsboom, & Fried, 2018). Considering the unsatisfactory assumption of multivariate normality, Spearman correlation was selected as the correlation method. The graphical LASSO generates a sparse graphical model by setting edges with small partial correlation coefficients to zero (Friedman et al., 2008). The network was visualized via the Fruchterman-Reingold algorithm. 2.4.3 Network centrality The expected influence, strength, closeness, and betweenness were calculated as centrality indices for each node. Strength indicates a given node’s importance in the whole network, which is estimated by summing all the edges extending from this node. The expected influence considers the negative correlations based on the strength index. Closeness indicates the sum of distances from the given node to all the other nodes. Betweenness indicates the extent of the given node on the shortest path between two other nodes. 2.4.4 Accuracy and stability The bootstrap method was used to assess the accuracy of each edge. A narrower 95% CI indicates a more accurate evaluation. The stability of the centrality index for each node was assessed via the case-dropping method, which calculates a correlation stability (CS) coefficient between the original centrality index and the same index after the sample is dropped. Generally, CS coefficients should be greater than 0.5 (Epskamp et al., 2018). 2.4.5 Network comparison To compare the networks of junior and senior high school students, the difference in global strength, the maximum difference in edge weights, and the difference in each pair of edges and nodes’ centrality were tested through NetworkComparisonTest (van Borkulo et al., 2023). The p values were corrected via the Bonferroni method. 3. Results 3.1 Descriptive statistics Table 1 presents the results of the descriptive analysis. The gender distribution was well balanced across the two educational stages. The mean values of the other variables were also generally similar across subset samples. Table 1. Main variables and demographic information for students in two educational stages. Junior high school (n = 747) Senior high school (n = 482) All sample (n = 1229) Variables N(%) or M( SD ) N(%) or M( SD ) N(%) or M( SD ) Gender Male 370 (49.9) 210 (43.9) 580 (47.5) Female 372 (50.1) 268 (56.1) 640 (52.5) Age 13.10 (0.92) 16.06 (1.11) 14.26 (1.76) Academic burnout Exhaustion 3.11 (1.08) 3.24 (0.94) 3.16 (1.03) Reduced efficacy 2.06 (0.96) 2.18 (0.83) 2.11 (0.91) Learning cynicism 2.91 (0.85) 3.00 (0.77) 2.94 (0.82) Self-compassion Mindfulness 17.91 (5.23) 18.45 (4.15) 18.13 (4.84) Common humanity 12.13 (4.25) 11.96 (3.82) 12.07 (4.09) Self-kindness 10.27 (2.85) 10.26 (2.76) 10.27 (2.82) Family cohesion 56.94 (13.44) 57.51 (12.14) 57.16 (12.95) Teachers’ emotional support Understanding 13.35 (4.33) 13.43 (3.87) 13.38 (4.16) Care about 18.90 (4.53) 18.32 (4.26) 18.68 (4.43) Respect 16.31 (3.81) 15.70 (3.36) 16.07 (3.65) Encourage 19.14 (4.73) 18.49 (4.37) 18.89 (4.60) 3.2 Network structure for all samples The network structure is shown in Figure 1. Edges in red indicate negative relationships between two nodes; edges in blue indicate positive relationships. This network includes 11 nodes and 25 nonzero edges. The strongest association occurred between understanding students and caring about students (two dimensions of the PTESQ; standardized weight = 0.44). The weight of this edge was significantly greater than that of the other edge (see Figure S1 C). Figure S2 presents the 95% bootstrap CI of each edge weight, which indicates that these edges have acceptable stability. The weakest correlation was presented between care about students and reduced efficacy (ASBI dimension; standardized weight = -0.05). Generally, the nodes belonging to a given questionnaire had strong correlations with each other. Moreover, significant connections between two different questionnaires, such as the edges between common humanity and three dimensions of academic burnout, also exist in the network. The centrality index of each node is shown in Figure 2. The CS coefficients of closeness (0.28) and betweenness (0.05) were lower than 0.5, which suggested that these indices had insufficient stability in the current study (Figure S3). Therefore, the study presented only the strength and expected influence results. Compared with other nodes, students (standardized strength = 1.79, standardized expected influence = 1.47) and encouraged students (standardized strength = 1.20, standardized expected influence = 1.48) had the strongest influence on the whole network (see Figure S1 B). However, family cohesion had the fewest direct connections with other nodes (standardized strength = -1.27). 3.3 Differences between the networks of the two educational stages Figure 3 displays two networks of different educational stages. The junior high students’ network included 23 edges. The strongest edge existed between care about students and understanding students (standardized weight = 0.46), which was in accordance with the network for all samples. Its weight was significantly greater than that of the other 20 edges (except Care-Enc and Mdf-SK; see Figure S4 C). The edge between understanding students and reduced efficacy had the lowest weight (-0.07). For the senior high student network, only 15 edges were included, which resulted in a sparser network. The edge between caring about students and encouraging students still had the largest weight (0.42), which was significantly greater than the 9 edges (Figure S4 F). The weakest edge presented between care about students and reduced efficacy (standardized weight = -0.08). In summary, the consistency of two networks was reflected by the stable connections within each questionnaire. The reduced number of edges between questionnaires led to discordance between the networks in the two educational stages. For example, family cohesion does not have an edge in senior high school students’ networks. Figure 4 displays the strength and expected influence indices for each node. For junior high school students, the most important node in the whole network was encourage students (standardized strength = 1.38, standardized expected influence = 1.55). Its expected influence was significantly greater than that of the other 9 nodes, with the exception of students (see Figure S4 B). In the senior high school students’ network, care about students had a significantly greater influence than other nodes did, with the exception of encourage students (standardized strength = 1.77, standardized expected influence = 1.49; Figure S4 D). This is partially caused by decreased edges that encourage students and other nodes in the senior high school network. Family cohesion had the lowest level of strength in both junior and senior high school networks (junior: standardized strength = -1.34; senior: standardized strength = -2.34). The accuracy of the edges and stability of the nodes in each network can be found in Figures S5 and S6. The CSs for edges and the expected influence were greater than 0.5 in the two networks. In the junior high students’ network, the strength index did not show sufficient stability (Junior: CS = 0.36; Senior: CS = 0.60). The results of the network comparison indicated that the global strength of the junior high students’ network was significantly greater than that of the senior high students’ network (junior: global strength = 4.622677; senior: global strength = 3.742207; S = 0.88, p = 0.03). A network invariance test revealed that the value of the maximum difference in edge weights was not significantly different between the two networks (M = 0.13, p = 0.99). The weight of the edge between respect students and mindfulness differed across the two educational stages (p = 0.007; see Table S1). Specifically, this positive association occurred only in senior high school students’ networks. Additionally, the strength of family cohesion was greater in the network of junior high school students (p = 0.007). Owing to the absence of a negative association between respecting students and learning cynicism in senior high school students’ networks, the expected influence of respecting students was lower in senior high school students’ networks (p = 0.04; Table S2). However, the difference between each pair of nodes, as well as each pair of edges, of two networks was not significant after Bonferroni correction. 4. Discussion Through network analysis, this study examined developmental stage-dependent variations in the interplay among self-compassion, teacher support, family cohesion, and academic burnout in middle and high school students. The findings demonstrate that middle school students exhibit more pronounced interconnections among psychological variables and rely more heavily on external support systems. As students progress to high school, the structure and central protective factors shift accordingly. Moreover, students’ needs for teacher support vary across developmental stages. These findings provide novel insights into the dynamic nature of adolescent psychological adaptation mechanisms and highlight the imperative for developmentally sensitive intervention frameworks. 4.1 Main findings and theoretical interpretations 4.1.1 The overall effect of educational stage on network structure The network analysis revealed significantly greater global strength in middle school students than in their high school counterparts (p=0.03), indicating more robust interconnections among the psychological variables during early adolescence. This developmental difference may reflect middle school students' greater reliance on the holistic effects of external support systems (e.g., the synergistic effects of teacher support and family cohesion) for psychological adaptation, whereas high school students tend to diverge in the associations among psychological variables as they become more cognitively mature and autonomous (Van Lissa et al., 2019). For example, the loss of direct connections to other nodes in high school students' networks for “Family Cohesion” may suggest a diminished role for family support in psychological adjustment in late adolescence, a phenomenon that is consistent with the Family-Peer Support Transition Theory (Laursen & Collins, 2009) - a theory of family-peer support transitions (Laursen & Collins, 2009) that suggests that family support has an important role in psychological adjustment in late adolescence. This phenomenon is consistent with family-peer support transition theory (Laursen & Collins, 2009), which suggests that late adolescents are more likely to seek support from peers or teachers. 4.1.2 Educational Stage Specificity of Core Nodes Encouraging students was central to the middle school network (EI = 1.55), whereas “caring for students” was more prominent in the high school network (EI = 1.49). This developmental discrepancy may reflect stage-specific manifestations of teacher support needs. Middle school students undergoing the critical childhood-to-adolescence transition (ages 11--14) might benefit more from explicit motivational strategies (e.g., performance-contingent praise) that directly reinforce academic self-efficacy (Roeser et al., 2000), whereas high school students are under greater academic pressure, and teachers’ affective concerns (e.g., empathy and understanding) may be more helpful in alleviating burnout (Wang & Eccles, 2012). In addition, the edge between ‘respect for students’ and ‘positive thinking’ was present only in the high school students’ network (p=0.007), suggesting that high school students’ perceptions of teachers' respectful behaviors may contribute to the development of their self-regulatory skills, a result that supports the applicability of ‘respect needs theory’ (Deci & Ryan, 2000) in late adolescence. 4.1.3 The marginalizing role of family cohesion Family cohesion displayed the weakest node strength in both networks (middle school: –1.34; high school: –2.34) and became completely disconnected from other nodes in the senior-high network. This challenges the long-standing view that “family support is universally protective for adolescent mental health” (Suldo & Huebner, 2004) and likely reflects culture-specific family interactions in China. Chinese parents often emphasize academic supervision over emotional expression (Cheung & Pomerantz, 2011), a pattern that can provoke reactance in adolescents during high-stakes senior-high years. In addition, students’ deliberate distancing from parental support—driven by a quest for independence—may further erode the perceived warmth and efficacy of family ties. Future research should therefore distinguish between emotional support and academic control when operationalizing family cohesion to capture its genuinely protective elements (Steinberg & Silk, 2002). 5. Limitations and Future Directions First, all the participants were drawn solely from Tianjin—a first-tier city characterized by exceptionally high academic pressure—which may introduce regional bias. Future studies should include urban–rural comparisons and cross-cultural replications to bolster their ecological validity. Second, the cross-sectional data could not infer causal relationships among variables, and longitudinal network analysis could further reveal the dynamic interaction mechanisms. Third, the cross-sectional snapshot cannot determine causal directions—e.g., whether teacher support reduces burnout or vice versa. Longitudinal network approaches, such as Dormant-State or dynamic structural equation models, are needed to trace bidirectional influences over time. 6. Conclusion This study provides the first empirical delineation of distinct developmental stage differences in adolescent psychosocial adjustment systems through network analysis. Middle school students rely on the overall synergy of external support, whereas high school students' networks are characterized by a “focus on core support” and a “weakening of family roles”. These findings provide educators with a theoretical basis for stage-specific interventions and call attention to the dynamic evolution of adolescents' psychological support systems. Future research needs to incorporate longitudinal designs and culturally sensitive indicators to deepen the understanding of adolescent psychological networks. Declarations Ethics approval and consent to participate All participants (or their legal guardians, for under-age participants) provided written informed consent before the experiment, which was conducted at Tianjin Normal University, China, in accordance with the Declaration of Helsinki and the guidelines of the Tianjin Normal University Ethics Committee. Consent for publication The manuscript does not contain any individual person’s data (identifiable details). Consent for publication is therefore Not applicable . Availability of data and materials Due to confidentiality agreements with participants and the study’s ethics approval conditions (XL20240909a), the datasets generated and/or analysed during the current study are not publicly available. They are, however, available from the corresponding author on reasonable request and with approval from the Tianjin Normal University Ethics Committee. Competing interests The authors declare that they have no competing business and family interests. Funding This work was supported by the following grants: 1. Key Laboratory of Child Cognitive Development and Mental Health of Provincial Universities (Yancheng Teachers University), Project No. 206110074. 2. Tianjin Municipal Higher Education Humanities and Social Sciences Research Project, Project No. 2024GX27. 3. Postgraduate Scientific Research Innovation Project of Tianjin Normal University, Project No. 2024KYCX137F. Authors' contributions P.L conceived the study and obtained ethical approval and collected the data and managed participant recruitment.Y.D.Y performed the statistical analyses.Y.W.L and Y.S drafted the manuscript. All authors critically revised the manuscript, read and approved the final version, and agree to be accountable for all aspects of the work. Acknowledgements The authors thank all participants for their time and commitment, and the staff of Tianjin Normal University and Yancheng Teachers University for logistical support. Authors' information (optional) Not applicable' for this section. References Borsboom D. A network theory of mental disorders. World psychiatry. 2017;16(1):5–13. Borsboom D, Cramer AOJ. Network analysis: An integrative approach to the structure of psychopathology. Ann Rev Clin Psychol. 2013;9:91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608 . Bowen M. Family therapy in clinical practice. Jason Aronson; 1993. Cheng WY, Cheung RYM, Chung KKH. 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Social support matters: Longitudinal effects of social support on three dimensions of school engagement from middle to high school. Child Dev. 2012;83(3):877–95. https://doi.org/10.1111/j.1467-8624.2012.01745.x . Wentzel KR. Social relationships and motivation in middle school: The role of parents, teachers, and peers. J Educ Psychol. 1998;90(2):202. Wu K, Wang F, Wang W, Li Y. Parents' Education Anxiety and Children's Academic Burnout: The Role of Parental Burnout and Family Function. Front Psychol. 2021;12:764824. https://doi.org/10.3389/fpsyg.2021.764824 . Wu Y, Dai X, Wen Z, Cui H. The Development of Adolescent Student Burnout Inventory. Chin J Clin Psychol. 2010;18(02):152–4. https://doi.org/10.16128/j.cnki.1005-3611.2010.02.018 . Xie TT, Chen MH. Unveiling the Predictive Effect of Perceived Social Support on Foreign Language Learning Burnout: The Mediating Role of L2 Selves. Asia-Pacific Educ Researcher. 2025. https://doi.org/10.1007/s40299-025-00994-y . Yu JC, Wang YF, Tang XQ, Wu YQ, Tang XM, Huang J. Impact of Family Cohesion and Adaptability on Academic Burnout of Chinese College Students: Serial Mediation of Peer Support and Positive Psychological Capital. Front Psychol. 2021;12:767616. https://doi.org/10.3389/fpsyg.2021.767616 . Zhou M, Ye B, Mynbayeva A, Yong L, Assilbek N. A cross-cultural comparison of academic burnout among Chinese and Kazakhstani secondary students. Curr Psychol. 2024;43(21):19140–52. https://doi.org/10.1007/s12144-024-05733-y . Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":2588434,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structure of academic burnout, self-compassion, family cohesion and teachers’ emotional support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Exh: exhaustion; RE: reduced efficacy; Cyn: learning cynicism; Mdf: mindfulness; CH: common humanity; SK: self-kindness; Und: understanding students; Care: care about students; Rsp: respect students; Enc: encourage students.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7325995/v1/48c22d98fdfefa613f309e9d.png"},{"id":91971535,"identity":"3721e555-be7e-4daa-ab3d-bcc8e7d8bc9e","added_by":"auto","created_at":"2025-09-23 09:18:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2875192,"visible":true,"origin":"","legend":"\u003cp\u003eStrength and expected influence of each nodes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The values of each index were standardized. The dimensions represented by each items were displayed in the note of figure 1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7325995/v1/98a48349774e09faac04eca4.png"},{"id":91971530,"identity":"c8aca7f0-e868-42ce-b5be-114984851705","added_by":"auto","created_at":"2025-09-23 09:18:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2198403,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structures of two educational stages.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e A: Network structure for junior high school students; B: Network structure for senior high school students.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7325995/v1/969f0c57421c30d3f4f6e255.png"},{"id":91973045,"identity":"c137b04f-2cf3-4d52-90a3-76256991c4db","added_by":"auto","created_at":"2025-09-23 09:26:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3689820,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of node centrality across educational stages.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7325995/v1/64e317496fd814b49fea6cac.png"},{"id":91975006,"identity":"4a8453e0-3373-4259-a0e6-0885f58261e0","added_by":"auto","created_at":"2025-09-23 09:50:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1645863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7325995/v1/9afe10da-6dea-4422-b430-33e3e3ac97c3.pdf"},{"id":91973400,"identity":"bf8331b9-c16a-4d9a-a62e-9e5ebf37bef5","added_by":"auto","created_at":"2025-09-23 09:34:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1934719,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial250911.docx","url":"https://assets-eu.researchsquare.com/files/rs-7325995/v1/b14531fdb2a631d2d2c7fab9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A network analysis of academic burnout and its psychosocial correlates: Self-compassion, teacher support, and family cohesion among Chinese middle and high school students","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcademic burnout has emerged as a global psychological challenge among adolescents, demonstrating consistent cross-cultural prevalence (OECD, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Building on the foundational work of Schaufeli et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), contemporary research characterizes this syndrome through three core dimensions: chronic academic exhaustion, cognitive detachment from learning activities, and diminished personal achievement (Salmela-Aro et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Academic burnout causes cognitive impairments in students in the form of reduced attention, problem-solving skills (May et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and emotional regulation (Seibert et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Longitudinal follow-up studies have shown that high levels of academic burnout exacerbate symptoms of depression and anxiety (Song et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and decrease academic performance (Liou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Authority figures exert profound impacts on children throughout their development (Kohlberg, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1971\u003c/span\u003e), manifested in critical resources such as family cohesion and teacher support (Roorda et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). Conservation of resources theory (Hobfoll, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) highlights interactions between individual resources (e.g., self-compassion) and social resources (e.g., family cohesion, teacher support). Integrating two theories clarifies how protective resources coordinate across ecological levels, offering a comprehensive framework for understanding adolescent academic burnout. Although recent studies acknowledge multilevel determinants (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), prevailing research paradigms remain constrained by reductionist approaches that often examine isolated predictors of academic burnout, overlooking the mutual interplay among protective factors (Skinner \u0026amp; Kindermann, 2009). Network analysis addresses this gap by modeling variables as interconnected nodes, thus revealing not only direct associations but also bridge variables that transmit protective effects across systems (Borsboom \u0026amp; Cramer, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), providing a holistic understanding of burnout resistance. Therefore, this paper uses network analysis to examine the relationships among adolescents\u0026rsquo; self-compassion, family cohesion, teacher support, and academic burnout from three perspectives: students\u0026rsquo; own, family, and school perspectives.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Associations between Self-compassion, Family Cohesion, Teacher Support, and Academic Burnout\u003c/h2\u003e\u003cp\u003eSelf-compassion (Neff, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) is a proactive self-regulatory capacity that enables individuals to effectively mitigate stress responses while maintaining personal accountability. On the basis of Conservation of Resources theory (Hobfoll, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), self-compassion mitigates academic burnout through a dual pathway. First, self-compassion reduces self-critical rumination by fostering a kind and accepting attitude toward personal shortcomings, which helps conserve cognitive-emotional resources and alleviate emotional exhaustion (Neff, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Second, self-compassion enhances individuals\u0026rsquo; emotional regulation abilities, enabling them to maintain psychological resilience under academic pressure and gradually accumulate positive psychological resources (Inwood \u0026amp; Ferrari, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Neff \u0026amp; Germer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e;Qiang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Self-compassion significantly reduces burnout levels through a resource preservation effect, with individuals who participate in self-compassion training experiencing a 32% reduction in emotional exhaustion scores (intervention group vs. control group) (Neff \u0026amp; Germer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFamily cohesion (Bowen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) refers to the \u0026lsquo;safe base\u0026rsquo; formed by emotional ties among family members and serves three primary functions in academic adaptation: first, postfailure emotional restoration; second, the promotion of challenge-seeking behaviors; and third, the provision of sustained emotional support. Research has shown that family cohesion is positively related to individuals' emotion regulation (Cheng et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which may exert effort through second paths of self-compassion. Families can mitigate academic burnout in children by increasing traits such as optimism, hope, resilience, and self-efficacy (Yu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), so students with greater family cohesion are less likely to experience academic burnout.\u003c/p\u003e\u003cp\u003eTeacher support is conceptualized as a systematic interactive process involving emotional connection, cognitive guidance, and behavioral modeling to foster student resilience and growth (Pianta, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Wentzel, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). According to self-determination theory (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), teachers can alleviate the burnout experience by meeting students' three basic psychological needs\u0026mdash;autonomy, competence, and belonging\u0026mdash;in their educational practices (Roorda et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e). One study revealed that high school students\u0026rsquo; perceived teacher emotional support positively predicts their engagement in online learning (Kong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); at the same time, teacher support and peer support significantly and negatively predict foreign language academic burnout (Xie \u0026amp; Chen, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 School Age Differences in Self-compassion, Family Cohesion, Teacher Support, and Academic Burnout\u003c/h2\u003e\u003cp\u003eUnder China's exam-oriented education system, academic burnout is more prevalent and varies across school stages (Jinqin \u0026amp; Zhiyan, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). From the perspective of developmental psychology, middle school (12\u0026ndash;15 years old) and high school (15\u0026ndash;18 years old) students are in a very different stage of psychosocial transition: middle school students are in early adolescence, when their cognitive control and emotion regulation systems have not yet matured fully (Steinberg, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), whereas high school students are in the late stage of adolescence, when their prefrontal cortex functioning tends to be perfected, and the need for independent decision-making is significantly increased (Crone \u0026amp; Dahl, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This stage difference may lead to the divergent characteristics of the two groups in terms of their performance in self-compassion, family cohesion and teacher support.\u003c/p\u003e\u003cp\u003eThe transition from middle school to high school (approximately 15 years old) serves as a key turning point for increased academic burnout (Jiang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, high school cohorts face more intense academic competition and social comparisons, and their levels of self-compassion (especially the self-kindness dimension) are significantly lower than those of middle school students (Neff \u0026amp; and McGehee, 2010). Notably, the protective effects of family support systems show a phase reversal: among high school students, high family cohesion buffers the negative effects of low self-compassion on psychological distress (Hu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), whereas middle school students\u0026rsquo; psychological well-being relies more strongly on the direct effects of teachers\u0026rsquo; emotional support (Roeser et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This phenomenon may be rooted in the trajectory of adolescents\u0026rsquo; need for autonomy, as they become more independent (Smetana et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), whereas middle school students still need teachers as external authorities to provide clear emotional anchors.\u003c/p\u003e\u003cp\u003e\u003cb\u003e1.3 Using Network Analysis to Explore the Complex Interactions among Self-compassion, Family Cohesion, Teacher Support, and Academic Burnout\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCurrent investigations of academic burnout (e.g., regression modeling and structural equation modeling) constrain a comprehensive understanding of complex psychological phenomena in two critical dimensions. First, such approaches employ reductionist analytical frameworks that presume static unidirectional relationships between operationalized variables. For example, regression models can calculate the independent contribution of self-compassion to academic burnout but fail to reveal whether family cohesion indirectly mitigates burnout by enhancing self-compassion or whether teacher support moderates this path. Second, traditional methods assume that relationships between variables are linear and superimposable, whereas in reality, psychological mechanisms often show nonlinear interactions. For example, the protective effect of family cohesion may suddenly fail when academic stress exceeds a certain threshold, but traditional linear models fail to capture such dynamic changes (Granic et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wang \u0026amp; Eccles, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Network approaches (Borsboom, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) provide a methodological framework to model these nonlinear interactions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Purpose of the Study\u003c/h2\u003e\u003cp\u003eThis study examines how support from key authority figures (teachers and parents) impacts academic burnout to form a protective network against academic burnout across developmental stages. Therefore, the aim of this study is to address three major questions: 1. To explore the associative network structure of self-compassion, family cohesion, and teacher support in relation to academic burnout. 2. Identifying the variable dimension in the network that has the highest degree of bridging centrality may become a key target for intervening in academic burnout. 3. Which nodes exhibit significant differences in centrality between the psychological support networks of the two learning stages?\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Method","content":"\u003cp\u003e\u003cstrong\u003e2.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire data were collected from 1,229 students (747 from junior high schools and 482 from senior high schools) in Tianjin, China. Except for 9 missing data points on gender variables, this study included 580 males and 640 females (the mean age for junior high school students was 13.01 years, and that for senior high school students was 16.06 years). The rate of missing data was 12.18% (91 participants) for junior high school students and 11.83% (57 participants) for senior high school students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection was conducted across six schools in Tianjin, China, in accordance with the Declaration of Helsinki and the ethical guidelines of the Tianjin Normal University\u0026rsquo;s Institutional Review Board.This study was approved by the\u0026nbsp;Institutional Review Board of Tianjin Normal University(Reference Number: XL20240909a).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTeachers in six schools provided questionnaires for students in their classes and retrieved completed questionnaires. Each student was asked to sign the informed consent form before the survey. The participants completed the following measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.1 Adolescent Student Burnout Inventory\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eASBI, which showed great reliability and validity in previous study (Wu et al., 2010; Wu et al., 2021), was used to measure students\u0026rsquo; academic burnout. It includes 16 items that constitute 3 dimensions: exhaustion, learning cynicism, and reduced efficacy. Each item scored on a scale from 1 (strongly disagree) to 5 (strongly agree). The higher total scores indicated higher level of academic burnout. The Cronbach\u0026rsquo;s \u0026alpha; was 0.89 for all items, 0.85 for reduced efficacy, 0.83 for learning cynicism, and 0.78 for exhaustion in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.2 Self-compassion Scale\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelf-compassion was measured by SCS which developed by Gong et al. (2014). Common humanity, self-kindness and mindfulness were three dimensions of SCS. 12 items required an answer on a 5-point scale, ranging from 1 (never) to 5 (very often). The Cronbach\u0026rsquo;s \u0026alpha; for all items was 0.83 in the current research. The subscales\u0026rsquo; Cronbach\u0026rsquo;s \u0026alpha; were 0.77 for common humanity, 0.60 for self-kindness, and 0.84 for mindfulness.Self-kindness showed low reliability\u0026mdash;likely from social-desirability bias\u0026mdash;so interpret this dimension with caution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2.3 Perceived Teachers\u0026rsquo; Emotional Support Questionnaire\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePTESQ developed by Gao et al. (2017) was used to measure students\u0026rsquo; perceived teachers\u0026rsquo; emotional support. PTESQ consists of 18 items which are divided into 4 subscales: understanding students, care about students, respect students, and encourage students. Items are rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating higher levels of perceived emotional support. Subscale scores are calculated by summing the responses to relevant items, and the total score is obtained by summing all 18 items. The Cronbach\u0026rsquo;s \u0026alpha; of PTESQ was 0.96. Cronbach\u0026rsquo;s \u0026alpha; for each subscale were 0.95 (understanding students), 0.91 (care about students), 0.90 (respect students), and 0.91 (encourage students).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.4 Family Adaptability and Cohesion Evaluation Scales\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFamily cohesion was measured by the Chinese version of FACES II, which had demonstrated have satisfactory reliability and validity (Fei et al., 1991; Shao et al., 2021). This study used family cohesion subscale which includes 16 items. The Cronbach\u0026rsquo;s \u0026alpha; was 0.90 in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3.5 Demographic questionnaire\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants required to provide their information about gender, age, and grade.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.1 Descriptive analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive analysis was conducted via SPSS 26.0. This step focused on the demographic information and main variables of junior and senior high school students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.2 Network analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOwing to the missing values of the primary variables, multiple imputation was conducted through the \u003cem\u003eMICE\u003c/em\u003e package in R to handle missing data before network analysis (van Buuren, 2018). Predictive mean matching was used to impute missing variables. Then, networks based on the imputed data for all samples were computed via the \u003cem\u003ebootnet\u003c/em\u003e package (Epskamp, Borsboom, \u0026amp; Fried, 2018). Considering the unsatisfactory assumption of multivariate normality, Spearman correlation was selected as the correlation method. The graphical LASSO generates a sparse graphical model by setting edges with small partial correlation coefficients to zero (Friedman et al., 2008). The network was visualized via the Fruchterman-Reingold algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.3 Network centrality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expected influence, strength, closeness, and betweenness were calculated as centrality indices for each node. Strength indicates a given node\u0026rsquo;s importance in the whole network, which is estimated by summing all the edges extending from this node. The expected influence considers the negative correlations based on the strength index. Closeness indicates the sum of distances from the given node to all the other nodes. Betweenness indicates the extent of the given node on the shortest path between two other nodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.4 Accuracy and stability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bootstrap method was used to assess the accuracy of each edge. A narrower 95% CI indicates a more accurate evaluation. The stability of the centrality index for each node was assessed via the case-dropping method, which calculates a correlation stability (CS) coefficient between the original centrality index and the same index after the sample is dropped. Generally, CS coefficients should be greater than 0.5 (Epskamp et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4.5 Network comparison\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo compare the networks of junior and senior high school students, the difference in global strength, the maximum difference in edge weights, and the difference in each pair of edges and nodes\u0026rsquo; centrality were tested through \u003cem\u003eNetworkComparisonTest\u003c/em\u003e (van Borkulo et al., 2023). The \u003cem\u003ep\u003c/em\u003e values were corrected via the Bonferroni method.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Descriptive statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 presents the results of the descriptive analysis. The gender distribution was well balanced across the two educational stages. The mean values of the other variables were also generally similar across subset samples.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1.\u003c/em\u003e Main variables and demographic information for students in two educational stages.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eJunior high school (n = 747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSenior high school (n = 482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAll sample (n = 1229)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eN(%) or M(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eN(%) or M(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eN(%) or M(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e370 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e210 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e580 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e372 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e268 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e640 (52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e13.10 (0.92)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e16.06 (1.11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e14.26 (1.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcademic burnout\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eExhaustion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e3.11 (1.08)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.24 (0.94)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.16 (1.03)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eReduced efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2.06 (0.96)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.18 (0.83)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.11 (0.91)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eLearning cynicism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e2.91 (0.85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e3.00 (0.77)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2.94 (0.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-compassion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eMindfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e17.91 (5.23)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e18.45 (4.15)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e18.13 (4.84)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eCommon humanity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e12.13 (4.25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e11.96 (3.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e12.07 (4.09)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eSelf-kindness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e10.27 (2.85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e10.26 (2.76)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e10.27 (2.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily cohesion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e56.94 (13.44)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e57.51 (12.14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e57.16 (12.95)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeachers\u0026rsquo; emotional support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eUnderstanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e13.35 (4.33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e13.43 (3.87)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e13.38 (4.16)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eCare about\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e18.90 (4.53)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e18.32 (4.26)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e18.68 (4.43)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eRespect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e16.31 (3.81)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e15.70 (3.36)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e16.07 (3.65)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eEncourage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e19.14 (4.73)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e18.49 (4.37)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e18.89 (4.60)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e3.2 Network structure for all samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network structure is shown in Figure 1. Edges in red indicate negative relationships between two nodes; edges in blue indicate positive relationships. This network includes 11 nodes and 25 nonzero edges. The strongest association occurred between understanding students and caring about students (two dimensions of the PTESQ; standardized weight = 0.44). The weight of this edge was significantly greater than that of the other edge (see Figure S1 C). Figure S2 presents the 95% bootstrap CI of each edge weight, which indicates that these edges have acceptable stability. The weakest correlation was presented between care about students and reduced efficacy (ASBI dimension; standardized weight = -0.05). Generally, the nodes belonging to a given questionnaire had strong correlations with each other. Moreover, significant connections between two different questionnaires, such as the edges between common humanity and three dimensions of academic burnout, also exist in the network.\u003c/p\u003e\n\u003cp\u003eThe centrality index of each node is shown in Figure 2. The CS coefficients of closeness (0.28) and betweenness (0.05) were lower than 0.5, which suggested that these indices had insufficient stability in the current study (Figure S3). Therefore, the study presented only the strength and expected influence results. Compared with other nodes, students (standardized strength = 1.79, standardized expected influence = 1.47) and encouraged students (standardized strength = 1.20, standardized expected influence = 1.48) had the strongest influence on the whole network (see Figure S1 B). However, family cohesion had the fewest direct connections with other nodes (standardized strength = -1.27).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Differences between the networks of the two educational stages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 displays two networks of different educational stages. The junior high students\u0026rsquo; network included 23 edges. The strongest edge existed between care about students and understanding students (standardized weight = 0.46), which was in accordance with the network for all samples. Its weight was significantly greater than that of the other 20 edges (except Care-Enc and Mdf-SK; see Figure S4 C). The edge between understanding students and reduced efficacy had the lowest weight (-0.07). For the senior high student network, only 15 edges were included, which resulted in a sparser network. The edge between caring about students and encouraging students still had the largest weight (0.42), which was significantly greater than the 9 edges (Figure S4 F). The weakest edge presented between care about students and reduced efficacy (standardized weight = -0.08). In summary, the consistency of two networks was reflected by the stable connections within each questionnaire. The reduced number of edges between questionnaires led to discordance between the networks in the two educational stages. For example, family cohesion does not have an edge in senior high school students\u0026rsquo; networks.\u003c/p\u003e\n\u003cp\u003eFigure 4 displays the strength and expected influence indices for each node. For junior high school students, the most important node in the whole network was encourage students (standardized strength = 1.38, standardized expected influence = 1.55). Its expected influence was significantly greater than that of the other 9 nodes, with the exception of students (see Figure S4 B). In the senior high school students\u0026rsquo; network, care about students had a significantly greater influence than other nodes did, with the exception of encourage students (standardized strength = 1.77, standardized expected influence = 1.49; Figure S4 D). This is partially caused by decreased edges that encourage students and other nodes in the senior high school network. Family cohesion had the lowest level of strength in both junior and senior high school networks (junior: standardized strength = -1.34; senior: standardized strength = -2.34).\u003c/p\u003e\n\u003cp\u003eThe accuracy of the edges and stability of the nodes in each network can be found in Figures S5 and S6. The CSs for edges and the expected influence were greater than 0.5 in the two networks. In the junior high students\u0026rsquo; network, the strength index did not show sufficient stability (Junior: CS = 0.36; Senior: CS = 0.60).\u003c/p\u003e\n\u003cp\u003eThe results of the network comparison indicated that the global strength of the junior high students\u0026rsquo; network was significantly greater than that of the senior high students\u0026rsquo; network (junior: global strength = 4.622677; senior: global strength = 3.742207; S = 0.88, p = 0.03). A network invariance test revealed that the value of the maximum difference in edge weights was not significantly different between the two networks (M = 0.13, p = 0.99). The weight of the edge between respect students and mindfulness differed across the two educational stages (p = 0.007; see Table S1). Specifically, this positive association occurred only in senior high school students\u0026rsquo; networks. Additionally, the strength of family cohesion was greater in the network of junior high school students (p = 0.007). Owing to the absence of a negative association between respecting students and learning cynicism in senior high school students\u0026rsquo; networks, the expected influence of respecting students was lower in senior high school students\u0026rsquo; networks (p = 0.04; Table S2). However, the difference between each pair of nodes, as well as each pair of edges, of two networks was not significant after Bonferroni correction.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThrough network analysis, this study examined\u0026nbsp;developmental\u0026nbsp;stage-dependent variations\u0026nbsp;in the interplay among self-compassion, teacher support, family cohesion, and academic burnout in middle and high school students. The findings demonstrate\u0026nbsp;that middle school students exhibit more pronounced interconnections among psychological variables and rely more heavily on external support systems. As students progress to high school, the structure and central protective factors shift accordingly. Moreover, students\u0026rsquo; needs for teacher support vary across developmental stages. These findings provide novel insights into the dynamic nature of adolescent psychological adaptation mechanisms and highlight the imperative for developmentally sensitive intervention frameworks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Main findings and theoretical interpretations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1.1 The overall effect of educational stage on network structure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network analysis revealed significantly greater global strength in middle school students than in their high school counterparts (p=0.03), indicating\u0026nbsp;more robust interconnections among the psychological variables during early adolescence. This developmental difference\u0026nbsp;may reflect middle school students\u0026apos; greater reliance on the holistic effects of external support systems (e.g., the synergistic effects of teacher support and family cohesion) for psychological adaptation, whereas high school students tend to diverge in the associations among psychological variables as they become more cognitively mature and autonomous (Van Lissa et al., 2019). For example, the loss of direct connections to other nodes in high school students\u0026apos; networks for \u0026ldquo;Family Cohesion\u0026rdquo; may suggest a diminished role for family support in psychological adjustment in late adolescence, a phenomenon that is consistent with the Family-Peer Support Transition Theory (Laursen \u0026amp; Collins, 2009) - a theory of family-peer support transitions (Laursen \u0026amp; Collins, 2009) that suggests that family support has an important role in psychological adjustment in late adolescence. This phenomenon is consistent with family-peer support transition theory (Laursen \u0026amp; Collins, 2009), which suggests that late adolescents are more likely to seek support from peers or teachers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1.2 Educational Stage Specificity of Core Nodes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEncouraging students was central to the middle school network (EI = 1.55), whereas \u0026ldquo;caring for students\u0026rdquo; was more prominent in the high school network (EI = 1.49). This developmental discrepancy may reflect stage-specific manifestations of teacher support needs. Middle school students\u0026nbsp;undergoing the critical childhood-to-adolescence transition (ages 11--14) might benefit more from explicit motivational strategies (e.g., performance-contingent praise) that directly reinforce academic self-efficacy (Roeser et al., 2000), whereas high school students are under greater academic pressure, and teachers\u0026rsquo; affective concerns (e.g., empathy and understanding) may be more helpful in alleviating burnout (Wang \u0026amp; Eccles, 2012). In addition, the edge between \u0026lsquo;respect for students\u0026rsquo; and \u0026lsquo;positive thinking\u0026rsquo; was present only in the high school students\u0026rsquo; network (p=0.007), suggesting that high school students\u0026rsquo; perceptions of teachers\u0026apos; respectful behaviors may contribute to the development of their self-regulatory skills, a result that supports the applicability of \u0026lsquo;respect needs theory\u0026rsquo; (Deci \u0026amp; Ryan, 2000) in late adolescence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1.3 The marginalizing role of family cohesion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFamily cohesion displayed the weakest node strength in both networks (middle school: \u0026ndash;1.34; high school: \u0026ndash;2.34) and became completely disconnected from other nodes in the senior-high network. This challenges the long-standing view that \u0026ldquo;family support is universally protective for adolescent mental health\u0026rdquo; (Suldo \u0026amp; Huebner, 2004) and likely reflects culture-specific family interactions in China. Chinese parents often emphasize academic supervision over emotional expression (Cheung \u0026amp; Pomerantz, 2011), a pattern that can provoke reactance in adolescents during high-stakes senior-high years. In addition, students\u0026rsquo; deliberate distancing from parental support\u0026mdash;driven by a quest for independence\u0026mdash;may further erode the perceived warmth and efficacy of family ties. Future research should therefore distinguish between emotional support and academic control when operationalizing family cohesion to capture its genuinely protective elements (Steinberg \u0026amp; Silk, 2002).\u003c/p\u003e"},{"header":"5. Limitations and Future Directions","content":"\u003cp\u003eFirst, all the participants were drawn solely from Tianjin\u0026mdash;a first-tier city characterized by exceptionally high academic pressure\u0026mdash;which may introduce regional bias. Future studies should include urban\u0026ndash;rural comparisons and cross-cultural replications to bolster their ecological validity. Second, the cross-sectional data could not infer causal relationships among variables, and longitudinal network analysis could further reveal the dynamic interaction mechanisms. Third, the cross-sectional snapshot cannot determine causal directions\u0026mdash;e.g., whether teacher support reduces burnout or vice versa. Longitudinal network approaches, such as Dormant-State or dynamic structural equation models, are needed to trace bidirectional influences over time.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study provides the first empirical delineation of distinct developmental stage differences in adolescent psychosocial adjustment systems through network analysis. Middle school students rely on the overall synergy of external support, whereas high school students\u0026apos; networks are characterized by a \u0026ldquo;focus on core support\u0026rdquo; and a \u0026ldquo;weakening of family roles\u0026rdquo;. These findings provide educators with a theoretical basis for stage-specific interventions and call attention to the dynamic evolution of adolescents\u0026apos; psychological support systems. Future research needs to incorporate longitudinal designs and culturally sensitive indicators to deepen the understanding of adolescent psychological networks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants (or their legal guardians, for under-age participants) provided written informed consent before the experiment, which was conducted at Tianjin Normal University, China, in accordance with the Declaration of Helsinki and the guidelines of the Tianjin Normal University Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript does not contain any individual person\u0026rsquo;s data (identifiable details). Consent for publication is therefore \u003cstrong\u003eNot applicable\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to confidentiality agreements with participants and the study\u0026rsquo;s ethics approval conditions (XL20240909a), the datasets generated and/or analysed during the current study are not publicly available. They are, however, available from the corresponding author on reasonable request and with approval from the Tianjin Normal University Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing business and family interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the following grants:\u003c/p\u003e\n\u003cp\u003e1. Key Laboratory of Child Cognitive Development and Mental Health of Provincial Universities (Yancheng Teachers University), Project No. 206110074.\u003c/p\u003e\n\u003cp\u003e2. Tianjin Municipal Higher Education Humanities and Social Sciences Research Project, Project No. 2024GX27.\u003c/p\u003e\n\u003cp\u003e3. Postgraduate Scientific Research Innovation Project of Tianjin Normal University, Project No. 2024KYCX137F.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.L conceived the study and obtained ethical approval and collected the data and managed participant recruitment.Y.D.Y performed the statistical analyses.Y.W.L and Y.S drafted the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors critically revised the manuscript, read and approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all participants for their time and commitment, and the staff of Tianjin Normal University and Yancheng Teachers University for logistical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026apos; for this section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBorsboom D. 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Curr Psychol. 2024;43(21):19140\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12144-024-05733-y\u003c/span\u003e\u003cspan address=\"10.1007/s12144-024-05733-y\" 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":true,"hideJournal":false,"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":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Academic burnout, Self-compassion, Teacher support, Family cohesion, Network","lastPublishedDoi":"10.21203/rs.3.rs-7325995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7325995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmong adolescents, academic burnout has emerged as a pervasive psychological syndrome characterized by persistent emotional exhaustion, detached cynicism toward learning, and compromised academic efficacy. This phenomenon poses substantial risks to students' socioemotional development and cognitive functioning, particularly during the critical developmental transition of middle and high school. Although research has identified key protective factors, most studies still treat these interconnected constructs in isolation. This fragmented approach overlooks the dynamic and interconnected nature of these variables.\u003c/p\u003e\u003cp\u003eTo address this gap, we applied psychological network analysis to questionnaires from 1,229 Tianjin students (747 junior, 482 senior high) to examine how these factors jointly influence academic burnout. Network modeling revealed stage-specific patterns: teacher encouragement (Expected Influence\u0026thinsp;=\u0026thinsp;1.55) was central for juniors, and teacher emotional care (EI\u0026thinsp;=\u0026thinsp;1.49) was central for seniors. Furthermore, the network structure showed a complete disconnection of family cohesion in the high school subgroup, highlighting the shifting role of family support across developmental stages.\u003c/p\u003e\u003cp\u003eThese findings demonstrate the value of network analysis in identifying key intervention targets and capturing the evolving structure of psychosocial support systems. These findings provide theoretical insights into the synergistic mechanisms of protective resources and practical guidance for designing stage specific, ecosystem-based interventions to alleviate academic burnout among adolescents.\u003c/p\u003e","manuscriptTitle":"A network analysis of academic burnout and its psychosocial correlates: Self-compassion, teacher support, and family cohesion among Chinese middle and high school students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 09:18:02","doi":"10.21203/rs.3.rs-7325995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-06T12:40:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T10:03:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-12T11:06:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T02:03:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-09-11T02:00:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d69429f4-b59b-4655-9b7c-c07ff24322ae","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T12:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 09:18:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7325995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7325995","identity":"rs-7325995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}