Shedding Light on Self-Acceptance: Insights from Network Analysis for Youth Intervention

Shedding Light on Self-Acceptance: Insights from Network Analysis for Youth Intervention | 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 Shedding Light on Self-Acceptance: Insights from Network Analysis for Youth Intervention xinying li, Li Guo, yuting Li, yangtong niu, ying wu, yan ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6888824/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective: Self-acceptance is a multifaceted psychological phenomenon that has substantial clinical research implications. The structure of self-acceptance is not well understood, despite its significance for mental health. Methods: In the current study, self-acceptance was examined in a large sample ( n = 2460) drawn from a highly representative sample of the Chinese general population. In network analysis, a regularized partial correlation networks was estimated. The model depicted the topic items as nodes, with edges reflecting the regularized partial correlation between them. Nodes' connectedness to other points in the network is referred to as their centrality. To confirm the findings' trustworthiness, advanced stability, and accuracy analyses were done. Results: The study found that item 6 ("I am satisfied with myself") had the greatest strong centrality score. The centrality order of network edges and nodes was appropriately predicted. Conclusions: The network analysis uncovered intriguing correlations across self-acceptance indicators, necessitating further investigation into the implications of these findings for self-acceptance modeling. self-acceptance youth network analysis mental health psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Self-acceptance is an individual's positive attitude towards themselves, an acknowledgment and acceptance of their multifaceted positive and negative traits, and also a positive feeling about their past life. It entails acknowledging and embracing the complex positive and negative attributes of oneself while cultivating affirmative sentiments on one's prior experiences [1] . This facilitates personal development, well-being, and life success while also significantly enhancing positive self-image, mental health, and social adaptability [2] . Self-acceptance, with its beneficial effects on personal development and well-being, also affects the interactions and mental health of youth [3] . It can assist persons in achieving a commendable standard of mental health [4-5] . The youth period is critical for physical and mental development, characterized by physiological and psychological growth, the expansion of social experiences, and shifts in cognitive thinking styles. Under the pressures of competitive incentives, individuals may inevitably encounter various psychological disturbances or challenges related to learning, daily life, self-awareness, emotional regulation, interpersonal relationships, and future educational or employment opportunities [6] . Self-acceptance serves as the foundation for cultivating a healthy personality, and it is only through the acquisition of self-acceptance that an individual can preserve the integrity and harmony of their self within a dynamic and stressful environment. Elucidating the conceptual framework of juvenile self-acceptance is a significant priority for researchers and educators. Self-acceptance is an individual’s attitudinal characteristic directed towards the self, encompassing complex components including cognitive, affective, and behavioral dispositional elements [6] . In Rational-Emotive Behavior Therapy (REBT), self-acceptance refers to an individual's complete and unconditional acceptance of oneself, independent of the wisdom, correctness, or appropriateness of one's behavioral performance, as well as irrespective of external approval, respect, or love from others [7] . Shelley H. Carson and Ellen J. Langer (2006) identified two significant dimensions of self-acceptance from a cognitive standpoint. One significant aspect of self-acceptance is the ability and willingness to reveal one's true self to others; another crucial aspect is the capacity for appropriate self-evaluation [8] . Japanese scholars Hideaki Takagi and Yuki Tokunaga classified self-acceptance into four categories: self-acceptance of affirmative self-perceptions, self-acceptance of negative self-perceptions, self-rejection of affirmative self-perceptions, and self-rejection of negative self-perceptions, based on the aspect of self-perception [9] . In contrast, Chamberlain and Haaga's support for unconditional self-acceptance primarily embodies the philosophical understanding of this concept and its implementation as outlined in rational emotive behavioral therapy theory [10] . Certain scales mentioned above assess self-acceptance with differing emphases by evaluating perceptions of self-acceptance, conducting assessments, and exploring the correlation between self-acceptance and other variables. The extent to which the results derived in this manner accurately assess self-acceptance is yet to be examined. The nature of self-acceptance as either a singular dimension or a multidimensional construct requires further investigation. From a subject-centered viewpoint, Chinese researchers Cong Zhong and Gao Wenfeng identified the categories of self-acceptance among Chinese college students via questionnaire surveys and empirical validity assessments. Research revealed that Chinese college students' notions of self-acceptance had two dimensions: self-acceptance and self-evaluation [11] .Self-acceptance and self-evaluation offer a thorough assessment of an individual's attitudes toward both positive and negative self-aspects, with considerable implications for mental health. The researcher thus designed the Self-acceptance Questionnaire (SAQ) to assess the level of self-acceptance among college students in the aforementioned two dimensions, so enhancing the measurement's validity and practical applicability. Despite academics progressively advancing their investigations into the conceptual framework and assessment of self-acceptance, a comprehensive study examining the internal structure and direct interrelations among the constituent elements of self-acceptance in adolescents remains absent. This study aims to elucidate the fundamental dimensions of self-acceptance among college students and the interconnections among these dimensions by measuring a larger sample and employing network analysis, which examines the interactions among the various components of self-acceptance. Network analysis offers a novel perspective, shifting from the perception of mental constructs as outcomes of underlying diseases, as shown in the latent variable model, to an emphasis on the interactions among their components. While network modeling has predominantly been utilized in the examination of mental disorders, it has also been employed in other domains of psychological sciences, such as personality [12] , health-related quality of life [13] , intelligence [14] , and attitudes [15] . In the domain of network analysis modeling, psychological researchers have utilized this technique to investigate the inherent structure of multivariate data, particularly in psychological diagnostic and evaluation investigations. Recent study literature demonstrates the application of network modeling to ascertain the number of clusters of items in scales pertaining to post-traumatic stress disorder (PTSD), hence offering novel insights and methodologies for comprehending the psychological framework of PTSD [16]. This research expands the conceptual framework to encompass the psychological notion of self-acceptance. Network analysis enables the identification of interactions among psychological variables, such as items on self-report questionnaires, attributing the emergence and covariation of various constitutive features of the psychological construct to the direct interactions among the elements themselves [17] , facilitates intricate connections among several aspects through the creation of network diagrams, enabling the evaluation of the significance of the variables within the network [18] . Network analysis enhances comprehension of the methods by which mental conceptions are formed, sustained, and evolved by scrutinizing intricate interactions among variables of mental constructs. Network analysis offers quantitative metrics to assess the significance of nodes (topic items) inside a network, including the identification of core nodes via centrality metrics, which activate other nodes and influence the entire network [17] . In empirical research,comprehending the internal architecture and fundamental characteristics of psychological constructs can facilitate the creation of specialized psychological assessment instruments or the formulation of precise psychological interventions and therapies [19] . Boccaletti et al. (2006) indicated that network analysis serves as a valuable tool for examining the interrelationships among self-report scale items, providing insights into their correlations and significance within a network [20] . Centrality in a network denotes the extent, intensity, and proximity of a node's connections to other nodes, thus altering a node with high centrality influences a greater number of other nodes. Centrality serves as a crucial metric to delineate the attributes of an item; it quantifies the direct relationships between the item and other nodes [21-22] . Network analysis has been increasingly used in personality psychology [23] , social psychology [24] , and clinical psychology research [25] . For example, research on the Big Five personality traits indicates that agreeableness and extraversion exhibit significant overlap, implying that the correlations among measures of agreeableness do not exceed those between agreeableness and extraversion. This suggests that distinguishing between these two traits is challenging [23] . Previous studies have not specifically investigated networks of self-acceptance; however, McNally et al. (2017) utilized network analysis in PTSD research by examining the relationships among components of the 17-item PTSD Symptoms Scale. The study identified that physiological reactions to trauma reminders, dreams of a traumatic nature, and a diminished interest in previously enjoyed activities were the most prominent central symptoms within the network of indicators. Their associations with the nodes were robust. Examining the nodes at the network's center can elucidate the internal structure of mental constructs [19] . Individualized intervention programs can be developed around the central nodes to enhance mental health and foster positive development for each individual [22] . Currently, the majority of interventions focused on self-acceptance by domestic scholars utilize group counseling methods. The process of individual growth leads to self-acceptance; therefore, future research should concentrate on methods to cultivate high levels of self-acceptance in individuals. Group counseling has limitations; therefore, it is essential to develop a broader array of methods and approaches for intervening with individuals who exhibit low self-acceptance [5] . The network analysis method offers a robust framework for addressing this issue, as it effectively identifies core components and their interrelationships in youth self-acceptance, thereby highlighting the most impactful intervention targets [26] . Considering that changing a core node in a network may have a significant impact on the entire network [27] , upon activation of a node within a network, that node swiftly disseminates connections to other nodes, therefore triggering the activation of the entire network. Recognizing the fundamental elements of self-acceptance will furnish educators with clearer and more efficacious intervention objectives to enhance intervention efficacy [19] . In summary, this study evaluates the network model of self-acceptance by analyzing a substantial sample derived from a 16-item self-acceptance scale, which was developed based on the conceptual characterization of the two dimensions of self-acceptance, to ascertain the network structure and fundamental dimensions of self-acceptance in youth. 2. Research Methodology 2.1 Subjects Convenience sampling was employed to select 2460 undergraduate and graduate students as participants (Mage=24.28 years, SD=3.69) via online and offline questionnaires. There were 841 male students and 1,619 female pupils. Data were gathered from August to November 2024. All students provided informed consent prior to the survey, and they had the option to discontinue their responses at any moment. The study conducted a nonparametric normal transformation of the raw data to satisfy the requirements for normal distribution [28] . 2.2 Research tools This study utilized the Self-acceptance Questionnaire (SAQ), developed by domestic researchers Cong Zhong and Gao Wenfeng. The questionnaire comprises 16 items, encompassing two factors: self-evaluation (SE) and self-acceptance (SA). The reverse-scored items were all treated before the analysis.The scale has a Likert format with four features (1=very non-conforming, 4=very conforming); a higher score indicates a greater level of self-acceptance [11] . The Cronbach's alpha coefficients in this study were as follows: self-acceptance = 0.875 and self-evaluation = 0.871. 2.3 Data analysis methods This study used R 4.1.1 to conduct a network analysis of the data to explore the network structure relationships between the items of the Self-Acceptance Scale. 2.3.1. network estimation The Spearman correlations of the 16 ordered items were estimated, and a Gaussian graphical model (GGM) was used for data fitting and item network construction [29] . The GGM is an undirected network in which the nodes represent observed variables (16 items of the self-acceptance questionnaire in this study), and the correlation line between the two nodes represents their partial correlations [30] . The GGM was computed using a nonparametric Spearman correlation matrix. The GGM was regularized using a graphical lasso algorithm (minimum absolute contraction and selection operator), which contracted all edges and set edges with small partial correlations to zero, avoiding the estimation of spurious edges and making the network more stable and easier to interpret [31] . Nodes represent question items from self-admission. Edges are connections between two nodes: they are regularized partial correlations between two items in the questionnaire. Thus, an edge between two items implies the existence of a correlation after controlling for all other nodes in the network. Statistically, edges between items in a self-accepting network can be interpreted as follows: when two nodes A and B, are firmly connected, the observation group scores high on A. The observation group is also more likely to score high on B when controlling for all other nodes in the network [32] .To clearly display the relationships between nodes, this study applies the Fruchterman-Reingold algorithm to layout the display. When placing nodes in the network, the algorithm determines the position of a node based on the sum of its connections to other nodes. Each edge has a symbol: network structure. In the visualization network, green edges represent positive regularized partial correlation, while red edges represent negative regularized partial correlation. The corresponding thickness and saturation of an edge indicate its weight (i.e., strength of association) [33] . Thicker edges indicate a stronger correlation between nodes. 2.3.2 Network extrapolation The centrality graph illustrates the centrality of a node's connections with other nodes. Boccaletti et al. [20] described three types of centrality: strength(As shown in Figure 2), betweenness, and closeness.Strength centrality can be understood as the sum of direct connections that a given node has in the network; betweenness can be understood as the shortest path through the investigated node; and closeness measures the sum of the shortest paths from the investigated node to all other nodes in the network [34] . Since centrality represents the relative importance of the nodes in the network, three possible interpretations of the centrality item are conceptualized [35] : control, independence, or activity. Statistically, a central item shares the most significant variance with all other items. Psychological network analysis focuses on the relationships between psychological constructs, such as symptoms, cognitions, and behaviors. Degree centrality and betweenness centrality may not accurately capture the essence of these relationships because they primarily focus on the topological properties of the network structure rather than the dynamic interactions between psychological constructs. Therefore, this study has abandoned these two indicators and retained strength as the primary indicator of centrality in this paper.Conceptually, in the context of self-acceptance, which is a self-administered scale, we suggest that subjects' responses to the central item may predict the way subjects respond to the central item in a network with other items with which they share a connection. Centrality estimates are standardized with a mean of 0 and a standard deviation of 1. Strength centrality is the primary metric used in this paper because it is the most robust estimate of centrality described in the literature [36] . However, the centrality metric is relative because the centrality of each node is estimated by comparing it to other nodes (there is always a highly central node, no matter how weak the edges in the network are). 2.3.3 Estimation of centrality indicators The expected influence (EI) used in this study assesses the relative importance of each node in the network. The expected influence of a node is obtained by calculating the sum of all edge weights (i.e., regularized partial correlation coefficient in this study) of all edges connected to that node [37] . A higher value of the expected influence of a node indicates a higher degree of centrality of that node and a more important position in the network structure [38] . The predictability of the node was calculated using the MGM package to assess its controllability [39] . The predictability of a node is defined as the variance of that node, i.e., the degree to which all its neighboring nodes explain it. A high average predictability indicates that the network structure can be better predicted by its internal items from each other and is less affected by factors external to the network. In addition, the network tools package was used to calculate the bridge expected impact for each node and thus determine the bridge symptom, which is defined as the sum of the values of all edges connecting a particular node to nodes in other symptom clusters. Higher values of bridge expected impact indicate a more significant increase in the risk of transmission to other symptom clusters, using the bootnet package for network accuracy and stability assessment [36,40] . The above network construction and visualization were implemented using the qgraph package. 2.3.4 Network accuracy and stability estimates First, the accuracy of the edge weights was assessed by calculating 95% confidence intervals (CI) using a nonparametric self-help method (1000 bootstrap), and second, the stability of the expected impact of the nodes was assessed by calculating correlation stability (CS) coefficients using a sample descent self-help method (1000 bootstrap). The correlation stability coefficient should preferably be higher than 0.50 and should not be lower than 0.25. If the correlation stability coefficient (i.e., the CS coefficient) is higher than 0.25, but preferably higher than 0.5, the intensity is considered stable. The difference between the two intensity indicators was considered significant if the 2000-bootstrap 95% nonparametric confidence interval (CI) did not contain zero. Similarly, the 95% bootstrap CI was used to estimate edge precision, with larger CIs indicating a lower precision in edge estimation and narrower CIs implying a more trustworthy network. Significant differences between edges were estimated as CIs, and two edges were statistically different if they did not include zero [41] . Finally, tests of variance for edge weights, expectations, and bridge expectations were performed to assess whether there was a significant difference between edge weights, between expected values, or between bridge expectations (significance level α = 0.05) [42] . Since the study variables were self-reported by the respondents, Harman's common method bias test was used, and principal component analysis was used to test the results of the unrotated factor analysis. The explanatory rate of the largest common factor was 39.08%, which is less than the critical value of 40%, so it can be assumed that there is no common method bias [43] . 3. Results 3.1 Total sample network for self-acceptance The dimensional and question-item networks of self-acceptance were estimated individually, and Table 1 defines each node in the network. Figure 1 displays the estimated network of 16 items for self-admission. Overall, most items were positively connected inside the network. This network possesses various unusual characteristics, as outlined below. Out of 120 potential edges, 70 (58.33%) are non-zero, with 69 exhibiting positive correlation and one demonstrating negative correlation. Secondly, the most significantly correlated edges were seen in z5 “I am satisfied with my body shape and looks,” and z6 “I express satisfaction with myself” (edge weight value = 0.41), as well as z2 “My whole body is a good thing” and z3 “I feel confident that the opposite sex will find me appealing.” (marginal weight = 0.37), z14 “I consistently concern myself with the potential unhappiness of others” and z16 “I perpetually fear that others will regard me with disdain” (marginal weight = 0.37). “Disdain me” (marginal weight = 0.28), z9 “I consistently excel at acquiring new knowledge,” and z12 “I am capable of accomplishing all tasks independently” (marginal weight = 0.28). Item 16 (Always Worry About People Looking Down on Me), formerly categorized under the self-acceptance dimension, is now demonstrated to transition to the self-evaluation dimension within the network structure. An analysis of the local network structure identified two elements with the highest centrality index, which were statistically more central than the other nodes: z6 (I express happiness with myself) and z4 (I am perpetually apprehensive about undertaking tasks due to the fear of failure). The strength index in this sample is notably robust and reliable (cs = 0.75). The bootstrap 95% confidence intervals suggest that the stability of the side weights is dependable and precise. Figure 3 illustrates the results of evaluating the accuracy of the side weights using the bootstrap method. The gray line denotes the sample side weights, the black line indicates the average side weights determined by the bootstrap method, and the gray bars represent the confidence intervals. The narrow 95% confidence intervals suggest that the side weights are sufficiently precise. Figure 4 presents the results of the difference test for the edge weights. Gray squares denote the absence of a significant difference among the respective edge weights. Conversely, black squares denote a significant difference in the corresponding edge weights. Squares on the diagonal that are outlined in black indicate negatively correlated side weights, while those that are not outlined indicate positively correlated side weights. Table 2 presents the scores, anticipated impact (Z-scores), and predictability for each item of the self-acceptance questionnaire. Figures 5A and 5B illustrate the anticipated impact values for each node within the network and the expected impact values for the bridge, respectively (refer to Table 2 for detailed values). Figure 5A illustrates that the horizontal axis denotes the magnitude of the expected impact value, with positions further to the right indicating a higher expected impact. The nodes exhibiting the highest expected impact values are z6, “I am satisfied with myself,” z4, “I am always afraid to do things for fear of screwing up,” and z16, “I am always afraid that people will look down on me,” with corresponding impact values of 1.478, 1.264, and 1.007, respectively. In the self-acceptance community, the nodes with the highest impact values for bridging expectations included z15, which states, “I express liking for my personality traits.” "I am consistently preoccupied with the fear of making mistakes, which hinders my ability to take action." z8, "Consistently apprehensive about potential blame from others," and z13 "I perceive that I am not liked by anyone else." The Bridge Expectancy Impact Values were 0.212, 0.163, 0.154, and 0.143, respectively. Figures 6A and 6B present the results of stability tests for node expected impact values and bridge expected impact values, respectively. The lines represent the average correlation of node or bridge expected impacts across the entire sample and subsamples, while the orange areas denote the 2.5th to 97.5th percentiles. Figure 5 illustrates that the CS coefficients are uniformly 0.75, surpassing the threshold of 0.5. This indicates that both the expected impact and bridge expected impact values demonstrate adequate stability. The difference test is conducted for the expected impact and bridge expected impact values independently, with the results presented in Fig. 7A and Fig. 7B, respectively. The gray squares signify the absence of a significant difference between the anticipated impact and the bridge's expected impact related to the two nodes. The black squares signify a significant difference between the expected impact and the bridge expected impact associated with two nodes. The numerical values represented by diagonal lines within the white squares denote the anticipated impact value of the node or the bridge. 4. Discussion Self-acceptance is a multifaceted concept that is significantly associated with critical clinical outcomes, such as depression and anxiety [44] . Despite its significant role in mental health, the structure of its constituent elements and their interactions remain poorly understood. Building on prior research that utilized latent factor methodologies, network analysis was employed to enhance our comprehension of this phenomenon. This study employs network analysis to examine the relationships among items related to self-acceptance in Chinese youth. Aiming to identify central nodes and primary bridge nodes in self-acceptance, in order to gain an in-depth understanding of its network structure.The findings hold significant implications for elucidating the mechanisms underlying self-acceptance and for identifying potentially effective intervention strategies in this area. Examining the components of self-acceptance may yield new insights into their interactions. Certain items exhibit greater interconnectivity than others, with variations in centrality, and interactions occur among items across different components of self-acceptance. 4.1 Core Question Items for Self-Acceptance This study employed Gaussian Graphical Models (GGM) for the first time to delineate the multidimensional network structure of young people's self-acceptance (SA). Firstly, the regularized partial correlation network algorithm identified two communities. The first community comprised items z2, z3, z5, z6, z9, z10, z12, and z15. The second community consisted of items z1, z7, z8, z4, z11, z14, z16, and z13. Network analysis confirmed the robustness of the SA and SE (Self-Esteem) two-factor structure [11][42] . However, a key dynamic connection was discovered: (1) Item z16 ("Worrying about being despised by others") migrated from the initially presupposed SA dimension to the SE dimension cluster. This corroborates the transmission pathway of "social evaluation cognition → self-worth judgment" in cognitive behavioral theory [43] . Items from specific communities were found to be connected with items from different communities, indicating from a network perspective that the SA communities interact within the network through these specific items. For example, a connection exists between items z4 and z15, which belong to the subscales of self-acceptance and self-esteem, respectively. (2) SA and SE formed strong coupling via cross-dimensional edges such as z4 ("Fear of failure")-z15 ("Personality acceptance"), suggesting bidirectional regulation between cognitive and affective components. This finding challenges the unidirectional causal assumption of traditional factor models and supports the theoretical framework of self-acceptance as a "cognitive-affective integrated system" [43][44] . Secondly, network structure analysis indicated that z6 ("I am satisfied with myself") and z4 ("I always worry about messing things up and am afraid to act") are among the two most central items in the network. Although targeting the most central nodes does not necessarily lead to effective changes in the network [29] , these findings suggest that a clinical strategy aimed at improving self-acceptance might focus on individuals' core self-perceptions. Items belonging to the self-acceptance community (z4 and z6) exhibited high centrality values; this finding supports Self-Concept Theory, which posits that an individual's views and evaluations of themselves are crucial factors influencing their behavior and psychological state [45] . "I am satisfied with myself" (z6) indicates a positive self-concept, while "I always worry about messing things up and am afraid to act" (z4) suggests self-doubt and negative self-evaluation. The central status of these two items supports the pivotal role of self-concept in an individual's psychology and behavior [45] . Item z6 showed the highest centrality value: to explain this finding, one must link the statistical significance of centrality and network connections (edges). As the node with the highest centrality in the network (EI = 1.478), z6 reflects the core status of the individual's self-concept. Its high connectivity suggests that reinforcing overall self-worth may generate a "ripple effect," positively activating associated cognitive nodes [29] . The identification of central and key nodes provides important clues for understanding the core dimensions of self-acceptance. For instance, "I am satisfied with myself" and "I always worry about messing things up and am afraid to act" might be key targets for self-acceptance interventions. By altering the state of these nodes, one can potentially effectively enhance an individual's level of self-acceptance. Item z4 ("Fear of failure leading to action inhibition"), as a regulatory hub (EI=1.264) within the SA dimension, reveals the dominant role of "avoidant cognition" in behavioral patterns. Its formation of a negative triangular structure with z8 ("Fear of blame") and z16 ("Anxiety of being looked down upon") may constitute a maintenance mechanism for low self-acceptance [46]. Bridge expectancy influence analysis further identified cross-boundary transmission nodes: z15 ("Personality acceptance") serves as a key bridge node (BEI=0.212), acting as an information relay between the SA and SE dimensions. Intervention targeting such nodes could potentially block the cross-dimensional spread of negative cognition, providing new targets for "cognitive dissonance-self reconstruction" therapy [23][47] . Finally, another of the network's most central items is "I always worry about others looking down on me" (z16), which relates to "I feel others don't like me" (z13) and "I always worry about making others unhappy" (z14). These worries reflect potential low self-esteem and social anxiety in the individual, indicating negative cognition in self-evaluation and interpersonal interactions, thereby affecting their self-acceptance [5] . Previous literature has already shown that self-acceptance is closely related to cognitive patterns involving negative expectations of others [46] . Therefore, it can be inferred that targeting biased cognitive processing may help improve the level of self-acceptance. In summary, this study not only revealed for the first time the multidimensional network structure of young people's self-acceptance and validated the robustness of its two-factor model, but more importantly, it identified key nodes (such as z4, z6, z15) and connection patterns within the network. Specifically, it illuminated the pivotal roles of core elements like fear of failure and self-satisfaction within the self-acceptance system, and how they influence the overall level through complex cognitive-emotional interactions. These findings not only challenge traditional unidirectional causal models, offering a new perspective of a "cognitive-affective integrated system" for self-acceptance, but also point the way for future development of more precise and effective psychological intervention strategies. For instance, targeting key nodes or bridge items could help break negative cycles and promote the formation of positive self-concepts. These network analysis results hold significant practical implications, as they help identify targets for developing precise self-acceptance interventions, potentially shifting traditional group counseling towards precision interventions based on core nodes. For example, the focus of intervention could be placed on highly central items like "I am satisfied with myself" (z6), or on regulatory nodes like "I always worry about messing things up and am afraid to act" (z4). By altering the state of these critical points, it becomes possible, much like tuning a network hub, to effectively enhance the stability of the entire self-acceptance system, ultimately helping individuals improve their level of self-acceptance. 4.2 Research limitations and future research This study has certain drawbacks. The networks in this study were only formed based on the youth's self-reported self-acceptance rating, so rendering them susceptible to the influence of social probability. Future research may employ diverse methodologies, including observational techniques, clinical interviews, and behavioral experiments, to acquire more comprehensive data and facilitate comparisons, or to mitigate the issues of social desirability bias inherent in self-reporting as evaluated by peers or educators. The data in this study are cross-sectional. In the future, more representative longitudinal data may be utilized to elucidate the network of self-acceptance items, their developmental trajectories, and the interconnections among various dimensions/items. Declarations Acknowledgement I’m deeply grateful to my friends for their inspiration during my research. Conversations, whether light or deep, often sparked ideas, opened new perspectives, and guided me in valuable directions, significantly enriching my work and saving me effort. My family’s unwavering support was essential, and I also appreciate friends who provided timely encouragement and help. To all friends who’ve supported and inspired me: thank you from the bottom of my heart. Thank you all! Data Availability Statement The data availability statement is completely consistent between the submission system and the main manuscript.The data that support the findings of this study are available from the corresponding author upon reasonable request.Data availability statement: The data are available upon reasonable request. Researchers requesting data access should contact [Guo Li] via [ [email protected] ]. Conflict of Interest Statement We hereby solemnly declare that, in the process of writing and publishing this article, I have no potential or actual conflicts of interest that could affect the objectivity, fairness, or interpretation of the results of this article. The conception of this article, the research (if applicable), data collection and analysis (if applicable), the expression of views, and the conclusions are all based on independent professional judgment and factual grounds. They have not been unduly influenced or pressured by any personal, group, institutional, or corporate economic, commercial, or other means. All cited materials and data have been strived to be accurate and reliable. Ethics Approval This study was approved by the Institutional Review Board (IRB) of Shanxi Medical University, with approval number 2023SJL71 (Date of Approval: 2020.03.13). The research was conducted in strict accordance with the approved protocol. Consent to Participate All participants were fully informed about the study's purpose, methods, potential risks, and benefits, and they all signed written informed consent forms before participating in the study. Privacy and Confidentiality This study strictly protects the privacy and anonymity of participants. All collected data are stored and processed in an encrypted or anonymized format. Any information that could identify participants has been removed or securely stored. Funding The content of this article does not represent the views of any organization or institution, nor has it received any form of financial support. Availability of data and materials Data availability statement: The data are available upon reasonable request. Researchers requesting data access should contact [Guo Li] via [ [email protected] ]. Consent for publication Consent to Publish Statement: All participants signed an electronic informed consent form, agreeing to the publication of data or related results from this study in open-access journals. Authors' Contributions Li Xinying was responsible for conceptualization, methodology, project administration, data curation, and resources.Li Yuting contributed to visualization, project administration, resources, conceptualization, and methodology.Guo Li was responsible for conceptualization, data curation, the writing of the original draft, and software development.Niu Yangtong contributed to software development, data curation, formal analysis, conceptualization, and visualization.Wu Ying contributed to the writing of the original draft, resources, conceptualization, and project administration.Ren Yan contributed to reviewing and editing, project administration, resources, conceptualization, the writing of the original draft, and supervision. References Ryff, C.D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology , 57(6), pp.1069–1081. doi:https://doi.org/10.1037//0022-3514.57.6.1069. SUN, D. and LU, B. (2017). Self-Acceptance:Concepts, Measures and Influences. Psychological Research , 10(6), pp.60–67.doi:https://doi.org/10.3969/j.issn.2095-1159.2017.06.009. ZHANG,E.(2017).Research progress on self-acceptance. The Guide of Scence & Education,(26),pp.37-41. doi:https://doi.org/10.16400/j.cnki.kjdkz.201709.018. LI, J. and ZHANG, J. (2011). Review and Prospect of the Research about Self-acceptance . Journal of Xiangyang Polytechnic , 10(2), pp.121–126. ZHEN, H. (2017). A Review of Self-Acceptance Research. Modern Communication , (4), pp.25–26. MENG, F. (2018). The Development of Secondary School Students’ Self- accentance Scale.[master's thesis]. [China]: SOUTHWEST University Pomeroy, L. (2013). Psychotherapy and the Value of a Human Being. pp.58–81. doi:https://doi.org/10.4324/9781315884080-11. Carson, S.H. and Langer, E. j (2006). Mindfulness and self-acceptance. J Rat-Emo Cognitive-Behav Ther , 24, pp.29–43. doi:https://doi.org/10.1007/s10942-006-0022-5. ZHANG, R. (1993). ‘Psychology of Youth : A Comparative Study of Youth Psychology in China and Japan.’ ZHANG Risheng ed. Beijing: Beijing Normal University Press. Chamberlain, J.M. and Haaga, D.A.F. (2001). Unconditional Self-Acceptance and Psychological Health. Journal of Rational-Emotive & Cognitive-Behavior Therapy , 19, pp.163–176. doi:https://doi.org/10.1023/A:1011189416600. CONG, Z. and GAO, W.(1999). The development and reliability and validity test of the Self-Acceptance Questionnaire. Chinese Journal of Behavioral Medicine and Brain Science , 8(1), pp.20-20. doi:https://doi.org/10.3760/cma.j.issn.1674-6554.1999.01.007 Costantini, G., Richetin, J., Borsboom, D., Fried, E.I., Rhemtulla, M. and Perugini, M. (2015). Development of Indirect Measures of Conscientiousness: Combining a Facets Approach and Network Analysis. European Journal of Personality , 29(5), pp.548–567. doi:https://doi.org/10.1002/per.2014. Kossakowski, J.J., Epskamp, S., Kieffer, J.M., van Borkulo, C.D., Rhemtulla, M. and Borsboom, D. (2016). The application of a network approach to Health-Related Quality of Life (HRQoL): introducing a new method for assessing HRQoL in healthy adults and cancer patients. Qual Life Res , 25(4), pp.781–792. doi:https://doi.org/10.1007/s11136-015-1127-z. Van Der Maas, H.L.J., Dolan, C.V., Grasman, R.P.P.P., Wicherts, J.M., Huizenga, H.M. and Raijmakers, M.E.J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review , 113(4), pp.842–861. doi:https://doi.org/10.1037/0033-295x.113.4.842. Dalege, J., Borsboom, D., van Harreveld, F. and van der Maas, H.L.J. (2017). Network Analysis on Attitudes. Social Psychological and Personality Science , 8(5), pp.528–537. doi:https://doi.org/10.1177/1948550617709827. Fried, E.I., Eidhof, M.B., Palic, S., Costantini, G., Huisman-van Dijk, H.M., Bockting, C.L.H., Engelhard, I., Armour, C., Nielsen, A.B.S. and Karstoft, K.-I. (2018). Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples. Clinical Psychological Science , 6(3), pp.335–351. doi:https://doi.org/10.1177/2167702617745092. Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry , 16(1), pp.5–13. doi:https://doi.org/10.1002/wps.20375. BORGATTL, S.P., MEHRA, A., BRASS, D.J. and LABIANCA, G. (2019). Network Analysis in the Social Sciences. Science , 323(5916), pp.892–895. doi:https://doi.org/10.1126/science.1165821. LIN Liang,XU Boya,YANG Ying,et al. Network analysis and core dimensions of adolescent pro-social behavior[J]. Psychological Journal : 1-30. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D.-U. (2006). Complex networks: Structure and dynamics. Physics Reports , 424(4-5), pp.175–308. doi:https://doi.org/10.1016/j.physrep.2005.10.009. Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L.J. and Cramer, A.O.J. (2015a). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality , 54, pp.13–29. doi:https://doi.org/10.1016/j.jrp.2014.07.003. CAI, Y., DONG, S., YUAN, S. and HU, C. (2020). Network analysis and its applications in psychology . Advances in Psychological Science, 28(1), pp.178–195. doi:https://doi.org/10.3724/SP.J.1042.2020.00178. Cramer, A.O.J., van der Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S.H., Kendler, K.S. and Borsboom, D. (2012). Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can’t Like Parties if You Don’t Like People. European Journal of Personality , 26(4), pp.414–431. doi:https://doi.org/10.1002/per.1866. Dalege, J., Borsboom, D., van Harreveld, F. and van der Maas, H.L.J. (2018). The Attitudinal Entropy (AE) Framework as a General Theory of Individual Attitudes. Psychological Inquiry , 29(4), pp.175–193. doi:https://doi.org/10.1080/1047840x.2018.1537246. Borsboom, D. and Cramer, A.O.J. (2013). Network Analysis: An Integrative Approach to the Structure of Psychopathology. Annual Review of Clinical Psychology , 9(1), pp.91–121. doi:https://doi.org/10.1146/annurev-clinpsy-050212-185608. McNally, R.J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy , 86, pp.95–104. doi:https://doi.org/10.1016/j.brat.2016.06.006. Blanchard, M.A. and Heeren, A. (2020). Why we should move from reductionism and embrace a network approach to parental burnout. New Directions for Child and Adolescent Development , 2020(174), pp.159–168. doi:https://doi.org/10.1002/cad.20377. Epskamp, S., Borsboom, D. and Fried, E.I. (2017). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods , [online] 50(1), pp.195–212. doi:https://doi.org/10.3758/s13428-017-0862-1. Epskamp, S., Waldorp, L.J., Mõttus, R. and Borsboom, D. (2018). The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivariate Behavioral Research , 53(4), pp.453–480. doi:https://doi.org/10.1080/00273171.2018.1454823. Epskamp, S. and Fried, E.I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods , [online] 23(4), pp.617–634. doi:https://doi.org/10.1037/met0000167. Foygel, R. and Mathias, D. (2010). Extended Bayesian Information Criteria for Gaussian Graphical Models. Advancesn neural information processing systems , [online] 23. Available at: http://gdmltest.u-ga.fr/item/1011.6640/. Brigantial, G., Kempenaers, C., Braun, S., Fried, E.I. and Linkowski, P. (2018). A Stéphanie-Braun, et al. Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults. Psychiatry Research , 265, pp.87–92. doi:https://doi.org/10.1016/j.psychres.2018.03.082. Fruchterman, T.M.J. and Reingold, E.M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience , 21(11), pp.1129–1164. doi:https://doi.org/10.1002/spe.4380211102. Opsahl, T., Agneessens, F. and Skvoretz, J. (2010). Node centrality in weighted networks: generalizing degree and shortest paths. Social Networks , 32(3), pp.245–251. doi:https://doi.org/10.1016/j.socnet.2010.03.006. Freeman, L.C. (1978). Centrality in social networks conceptual clarification. Social Networks , 1(3), pp.215–239. doi:https://doi.org/10.1016/0378-8733(78)90021-7. Epskamp, S., Borsboom, D. and Fried, E.I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behav Res 50, pp.195–212. https://doi.org/10.3758/s13428-017-0862-1. Haslbeck, J.M.B. and Fried, E.I. (2017). How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychological Medicine , 47(16), pp.2767–2776. doi:https://doi.org/10.1017/s0033291717001258. Hofmann, S.G., Curtiss, J. and McNally, R.J. (2016). A Complex Network Perspective on Clinical Science. Perspectives on Psychological Science , 11(5), pp.597–605. doi:https://doi.org/10.1177/1745691616639283. Jones, P.J., Ma, R. and McNally, R.J. (2019). Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivariate Behavioral Research , 56(2), pp.1–15. doi:https://doi.org/10.1080/00273171.2019.1614898. Borsboom, D., Robinaugh, D.J., Rhemtulla, M. and Cramer, A.O.J. (2018). Robustness and replicability of psychopathology networks. World Psychiatry , 17(2), pp.143–144. doi:https://doi.org/10.1002/wps.20515. ZHANG, F., WU, L., SONG, L., WANG, P., CAO, Y., LIU, J., ZHANG, Y., GAO, Z., WANG, J., LI, J. and WU, S. (2023). Network Analysis of Meaning in Life and Coping Styles among College Students. Chinese Medical Ethics , 36(6), pp.670–678. doi:https://doi.org/10.12026/j.issn.1001-8565.2023.06.14. ZHOU, H. and LONG, L. (2004). Statistical Remedies for Common Method Biases . Advances in Psychological Science , (6), pp.942–950. doi:https://doi.org/10.3969/j.issn.1671-3710.2004.06.018.. GAO, W. and CONG, Z. (2000). Relationship of social anxiety to self-esteem and self-acceptance in college students. China Journal of Health Psychology , 8(3), p.276. doi:https://doi.org/10.3969/j.issn.1005-1252.2000.03.017. Braun, S., Rosseel, Y., Kempenaers, C., Loas, G. and Linkowski, P. (2015). Self-Report of Empathy: A Shortened French Adaptation of the Interpersonal Reactivity Index (IRI) Using Two Large Belgian Samples. Psychological Reports , 117(3), pp.735–753. doi:https://doi.org/10.2466/08.02.pr0.117c23z6. Kruis, J. and Maris , G. (2016). Three representations of the Ising model. Scientific Reports , 6(1). doi:https://doi.org/10.1038/srep34175. Dolan, R.J. (2002). Emotion, Cognition, and Behavior. Science , 298(5596), pp.1191–1194. doi:https://doi.org/10.1126/science.1076358. Wylie, R. C. (1974). The Self-Concept: Theory and Research on Selected Topics. volume Two. revised Edition. U of Nebraska Press. Haslbeck, J.M.B. and Waldorp, L.J. (2020). mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data . Journal of Statistical Software , 93(8), pp.1–46. doi:https://doi.org/10.18637/jss.v093.i08. LIU, W. (2008). Effect of psychnlogical intervention on negative automatic thoughts,self acceptance and social support for patients with type 2 diabetic cataract. CHIINESE JOURNAL OF PRACTICAL NURSING , 24(24), pp.1–4. doi:https://doi.org/10.3760/cma.j.issn.1672-7088.2008.24.001. Tables Table 1 Basic information of each node in the topic network Coding subject Affiliated dimensions z1 I'm afraid to say what my heart desires self-acceptance z2 My whole body is good. self-evaluation z3 I think the opposite sex will definitely like me self-evaluation z4 I'm always afraid to do things for fear of screwing up. self-acceptance z5 I'm happy with my body and my looks self-evaluation z6 I'm satisfied with myself. self-evaluation z7 I can't do anything without the approval of others. self-acceptance z8 Always afraid of being blamed by others self-acceptance z9 I'm always faster when it comes to learning new things self-evaluation z10 I'm very pleased with my eloquence. self-evaluation z11 Always think of failure before you do something self-acceptance z12 I can do everything on my own. self-evaluation z13 I don't think anyone likes me. self-acceptance z14 Always worrying that you'll make others unhappy self-acceptance z15 Expressing a liking for your character traits self-evaluation z16 I'm always worried that people will look down on me. self-acceptance Table 2 Self-acceptance questionnaire items, dimension scores, expected impact and predictability encodings subject Expected impact (Z-score) predictability Anticipated impact of the bridge z1 I'm afraid to say what my heart desires -0.818 33.700 0.067 z2 My whole body is good. -0.759 42.597 0.016 z3 I think the opposite sex will definitely like me -0.435 44.230 0.022 z4 I'm always afraid to do things for fear of screwing up. 1.264 52.583 0.163 z5 I'm happy with my body and my looks 0.649 51.615 0.056 z6 I'm satisfied with myself. 1.478 55.113 0.136 z7 I can't do anything without the approval of others. -1.149 38.774 -0.001 z8 Always afraid of being blamed by others 0.994 53.115 0.154 z9 I'm always faster when it comes to learning new things -1.460 31.779 0.035 z10 I'm very pleased with my eloquence. 0.548 44.492 0.138 z11 Always think of failure before you do something -1.392 34.560 0.012 z12 I can do everything on my own. -0.875 34.928 0.056 z13 I don't think anyone likes me. -0.267 35.135 0.153 z14 Always worrying that you'll make others unhappy 0.385 50.416 0.044 z15 Expressing a liking for your character traits 0.829 43.146 0.212 z16 I'm always worried that people will look down on me. 1.007 51.080 0.079 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6888824","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485888854,"identity":"e5855c2b-af81-4518-912e-cfc973c131f2","order_by":0,"name":"xinying li","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"xinying","middleName":"","lastName":"li","suffix":""},{"id":485888857,"identity":"8402118a-6235-42a2-ad52-f4a6c3b6341a","order_by":1,"name":"Li 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13:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6888824/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6888824/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86968574,"identity":"ea080eb1-96a2-4f87-8d37-3603561f4dac","added_by":"auto","created_at":"2025-07-17 18:14:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuestion-item network of self-acceptance for the total sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: SA stands for self-acceptance dimension and SE stands for self-evaluation dimension\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/a48e8c4e3ec63a398aaa3948.png"},{"id":86968149,"identity":"f7bb7a2a-b919-459a-9b2a-912ece95b7be","added_by":"auto","created_at":"2025-07-17 18:06:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStrength of the topic items\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/cf816e1807fbc79541aada1b.png"},{"id":86968153,"identity":"d115088a-4231-46cf-9927-a81a3d355685","added_by":"auto","created_at":"2025-07-17 18:06:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67899,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAccuracy test of side weights\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/86e0551c38d6651c7dea6317.png"},{"id":86968575,"identity":"01ab642d-471f-4e3f-8fae-4d19b3696aea","added_by":"auto","created_at":"2025-07-17 18:14:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifference-in-difference test for side weights\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/6cced32e406706f311eb6ef0.png"},{"id":86968158,"identity":"e57e2080-9997-4d3e-b7d6-19f9bb57f1e3","added_by":"auto","created_at":"2025-07-17 18:06:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpected impact of nodes in the network and expected impact of bridges (raw values)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: A is the expected impact of the node; B is the expected impact of the node bridge; see Table 2 for the meaning of each node\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/f1b31a17dece21ec84dee858.png"},{"id":86968162,"identity":"d4c6146d-3f09-4666-8b11-80ad55c9d097","added_by":"auto","created_at":"2025-07-17 18:06:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability analysis of expected impacts of nodes and expected impacts of bridges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: A is a stability test for the expected impact of nodes; B is a stability test for the expected impact of node bridges\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/95500874384d898d590b97e2.png"},{"id":86968579,"identity":"dd4a9b85-eb43-41e4-a6e7-547c213bdaf1","added_by":"auto","created_at":"2025-07-17 18:14:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":82649,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifference-in-difference test between expected impacts of nodes and expected impacts of bridges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: A is a test of difference in expected impacts of nodes; B is a test of difference in expected impacts of node bridges\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/90bf68cd7556481a40268b01.png"},{"id":86968887,"identity":"31308e8a-18d8-49e1-b770-9211a9ff7dcc","added_by":"auto","created_at":"2025-07-17 18:22:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1647664,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6888824/v1/17254970-3e1f-444e-add6-9651c57eec04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shedding Light on Self-Acceptance: Insights from Network Analysis for Youth Intervention","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSelf-acceptance is an individual's positive attitude towards themselves, an acknowledgment and acceptance of their multifaceted positive and negative traits, and also a positive feeling about their past life. It entails acknowledging and embracing the complex positive and negative attributes of oneself while cultivating affirmative sentiments on one's prior experiences\u003csup\u003e[1]\u003c/sup\u003e. This facilitates personal development, well-being, and life success while also significantly enhancing positive self-image, mental health, and social adaptability\u003csup\u003e[2]\u003c/sup\u003e. Self-acceptance, with its beneficial effects on personal development and well-being, also affects the interactions and mental health of youth\u003csup\u003e[3]\u003c/sup\u003e.\u0026nbsp;It can assist persons in achieving a commendable standard of mental health\u003csup\u003e[4-5]\u003c/sup\u003e.\u0026nbsp;The youth period is critical for physical and mental development, characterized by physiological and psychological growth, the expansion of social experiences, and shifts in cognitive thinking styles. Under the pressures of competitive incentives, individuals may inevitably encounter various psychological disturbances or challenges related to learning, daily life, self-awareness, emotional regulation, interpersonal relationships, and future educational or employment opportunities\u003csup\u003e[6]\u003c/sup\u003e. Self-acceptance serves as the foundation for cultivating a healthy personality, and it is only through the acquisition of self-acceptance that an individual can preserve the integrity and harmony of their self within a dynamic and stressful environment. Elucidating the conceptual framework of juvenile self-acceptance is a significant priority for researchers and educators.\u003c/p\u003e\n\u003cp\u003eSelf-acceptance is an individual’s attitudinal characteristic directed towards the self, encompassing complex components including cognitive, affective, and behavioral dispositional elements\u003csup\u003e[6]\u003c/sup\u003e.\u0026nbsp;In Rational-Emotive Behavior Therapy (REBT), self-acceptance refers to an individual's complete and unconditional acceptance of oneself, independent of the wisdom, correctness, or appropriateness of one's behavioral performance, as well as irrespective of external approval, respect, or love from others\u003csup\u003e[7]\u003c/sup\u003e. Shelley H. Carson and Ellen J. Langer (2006) identified two significant dimensions of self-acceptance from a cognitive standpoint. One significant aspect of self-acceptance is the ability and willingness to reveal one's true self to others; another crucial aspect is the capacity for appropriate self-evaluation\u003csup\u003e[8]\u003c/sup\u003e. Japanese scholars Hideaki Takagi and Yuki Tokunaga classified self-acceptance into four categories: self-acceptance of affirmative self-perceptions, self-acceptance of negative self-perceptions, self-rejection of affirmative self-perceptions, and self-rejection of negative self-perceptions, based on the aspect of self-perception\u003csup\u003e[9]\u003c/sup\u003e. In contrast, Chamberlain and Haaga's support for unconditional self-acceptance primarily embodies the philosophical understanding of this concept and its implementation as outlined in rational emotive behavioral therapy theory\u003csup\u003e[10]\u003c/sup\u003e. Certain scales mentioned above assess self-acceptance with differing emphases by evaluating perceptions of self-acceptance, conducting assessments, and exploring the correlation between self-acceptance and other variables. The extent to which the results derived in this manner accurately assess self-acceptance is yet to be examined. The nature of self-acceptance as either a singular dimension or a multidimensional construct requires further investigation. \u0026nbsp;From a subject-centered viewpoint, Chinese researchers Cong Zhong and Gao Wenfeng identified the categories of self-acceptance among Chinese college students via questionnaire surveys and empirical validity assessments. Research revealed that Chinese college students' notions of self-acceptance had two dimensions: self-acceptance and self-evaluation\u003csup\u003e[11]\u003c/sup\u003e.Self-acceptance and self-evaluation offer a thorough assessment of an individual's attitudes toward both positive and negative self-aspects, with considerable implications for mental health. The researcher thus designed the Self-acceptance Questionnaire (SAQ) to assess the level of self-acceptance among college students in the aforementioned two dimensions, so enhancing the measurement's validity and practical applicability.\u003c/p\u003e\n\u003cp\u003eDespite academics progressively advancing their investigations into the conceptual framework and assessment of self-acceptance, a comprehensive study examining the internal structure and direct interrelations among the constituent elements of self-acceptance in adolescents remains absent. This study aims to elucidate the fundamental dimensions of self-acceptance among college students and the interconnections among these dimensions by measuring a larger sample and employing network analysis, which examines the interactions among the various components of self-acceptance. Network analysis offers a novel perspective, shifting from the perception of mental constructs as outcomes of underlying diseases, as shown in the latent variable model, to an emphasis on the interactions among their components. While network modeling has predominantly been utilized in the examination of mental disorders, it has also been employed in other domains of psychological sciences, such as personality \u003csup\u003e[12]\u003c/sup\u003e, health-related quality of life \u003csup\u003e[13]\u003c/sup\u003e, intelligence \u003csup\u003e[14]\u003c/sup\u003e, and attitudes \u003csup\u003e[15]\u003c/sup\u003e. In the domain of network analysis modeling, psychological researchers have utilized this technique to investigate the inherent structure of multivariate data, particularly in psychological diagnostic and evaluation investigations. Recent study literature demonstrates the application of network modeling to ascertain the number of clusters of items in scales pertaining to post-traumatic stress disorder (PTSD), hence offering novel insights and methodologies for comprehending the psychological framework of PTSD\u003csup\u003e[16].\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research expands the conceptual framework to encompass the psychological notion of self-acceptance. Network analysis enables the identification of interactions among psychological variables, such as items on self-report questionnaires, attributing the emergence and covariation of various constitutive features of the psychological construct to the direct interactions among the elements themselves\u003csup\u003e[17]\u003c/sup\u003e, facilitates intricate connections among several aspects through the creation of network diagrams, enabling the evaluation of the significance of the variables within the network\u003csup\u003e[18]\u003c/sup\u003e. Network analysis enhances comprehension of the methods by which mental conceptions are formed, sustained, and evolved by scrutinizing intricate interactions among variables of mental constructs. Network analysis offers quantitative metrics to assess the significance of nodes (topic items) inside a network, including the identification of core nodes via centrality metrics, which activate other nodes and influence the entire network\u003csup\u003e[17]\u003c/sup\u003e. In empirical research,comprehending the internal architecture and fundamental characteristics of psychological constructs can facilitate the creation of specialized psychological assessment instruments or the formulation of precise psychological interventions and therapies\u003csup\u003e[19]\u003c/sup\u003e. Boccaletti et al. (2006) indicated that network analysis serves as a valuable tool for examining the interrelationships among self-report scale items, providing insights into their correlations and significance within a network \u003csup\u003e[20]\u003c/sup\u003e. Centrality in a network denotes the extent, intensity, and proximity of a node's connections to other nodes, thus altering a node with high centrality influences a greater number of other nodes. Centrality serves as a crucial metric to delineate the attributes of an item; it quantifies the direct relationships between the item and other nodes\u003csup\u003e[21-22]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eNetwork analysis has been increasingly used in personality psychology \u003csup\u003e[23]\u003c/sup\u003e, social psychology \u003csup\u003e[24]\u003c/sup\u003e, and clinical psychology research \u003csup\u003e[25]\u003c/sup\u003e. For example, research on the Big Five personality traits indicates that agreeableness and extraversion exhibit significant overlap, implying that the correlations among measures of agreeableness do not exceed those between agreeableness and extraversion. This suggests that distinguishing between these two traits is challenging\u003csup\u003e[23]\u003c/sup\u003e. Previous studies have not specifically investigated networks of self-acceptance; however, McNally et al. (2017) utilized network analysis in PTSD research by examining the relationships among components of the 17-item PTSD Symptoms Scale. The study identified that physiological reactions to trauma reminders, dreams of a traumatic nature, and a diminished interest in previously enjoyed activities were the most prominent central symptoms within the network of indicators. Their associations with the nodes were robust. Examining the nodes at the network's center can elucidate the internal structure of mental constructs \u003csup\u003e[19]\u003c/sup\u003e. Individualized intervention programs can be developed around the central nodes to enhance mental health and foster positive development for each individual \u003csup\u003e[22]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCurrently, the majority of interventions focused on self-acceptance by domestic scholars utilize group counseling methods. The process of individual growth leads to self-acceptance; therefore, future research should concentrate on methods to cultivate high levels of self-acceptance in individuals. Group counseling has limitations; therefore, it is essential to develop a broader array of methods and approaches for intervening with individuals who exhibit low self-acceptance \u003csup\u003e[5]\u003c/sup\u003e. The network analysis method offers a robust framework for addressing this issue, as it effectively identifies core components and their interrelationships in youth self-acceptance, thereby highlighting the most impactful intervention targets \u003csup\u003e[26]\u003c/sup\u003e. Considering that changing a core node in a network may have a significant impact on the entire network \u003csup\u003e[27]\u003c/sup\u003e, upon activation of a node within a network, that node swiftly disseminates connections to other nodes, therefore triggering the activation of the entire network. \u0026nbsp;Recognizing the fundamental elements of self-acceptance will furnish educators with clearer and more efficacious intervention objectives to enhance intervention efficacy \u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, this study evaluates the network model of self-acceptance by analyzing a substantial sample derived from a 16-item self-acceptance scale, which was developed based on the conceptual characterization of the two dimensions of self-acceptance, to ascertain the network structure and fundamental dimensions of self-acceptance in youth.\u003c/p\u003e"},{"header":"2. Research Methodology","content":"\u003cp\u003e\u003cstrong\u003e2.1 Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConvenience sampling was employed to select 2460 undergraduate and graduate students as participants (Mage=24.28 years, SD=3.69) via online and offline questionnaires. \u0026nbsp;There were 841 male students and 1,619 female pupils. Data were gathered from August to November 2024. All students provided informed consent prior to the survey, and they had the option to discontinue their responses at any moment. The study conducted a nonparametric normal transformation of the raw data to satisfy the requirements for normal distribution\u003csup\u003e[28]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Research tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the Self-acceptance Questionnaire (SAQ), developed by domestic researchers Cong Zhong and Gao Wenfeng. The questionnaire comprises 16 items, encompassing two factors: self-evaluation (SE) and self-acceptance (SA). The reverse-scored items were all treated before the analysis.The scale has a Likert format with four features (1=very non-conforming, 4=very conforming); a higher score indicates a greater level of self-acceptance\u003csup\u003e[11]\u003c/sup\u003e. The Cronbach's alpha coefficients in this study were as follows: self-acceptance = 0.875 and self-evaluation = 0.871.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Data analysis methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used R 4.1.1 to conduct a network analysis of the data to explore the network structure relationships between the items of the Self-Acceptance Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1. network estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Spearman correlations of the 16 ordered items were estimated, and a Gaussian graphical model (GGM) was used for data fitting and item network construction\u003csup\u003e[29]\u003c/sup\u003e. The GGM is an undirected network in which the nodes represent observed variables (16 items of the self-acceptance questionnaire in this study), and the correlation line between the two nodes represents their partial correlations\u003csup\u003e[30]\u003c/sup\u003e. The GGM was computed using a nonparametric Spearman correlation matrix. The GGM was regularized using a graphical lasso algorithm (minimum absolute contraction and selection operator), which contracted all edges and set edges with small partial correlations to zero, avoiding the estimation of spurious edges and making the network more stable and easier to interpret\u003csup\u003e[31]\u003c/sup\u003e. Nodes represent question items from self-admission. Edges are connections between two nodes: they are regularized partial correlations between two items in the questionnaire. Thus, an edge between two items implies the existence of a correlation after controlling for all other nodes in the network. Statistically, edges between items in a self-accepting network can be interpreted as follows: when two nodes A and B, are firmly connected, the observation group scores high on A. The observation group is also more likely to score high on B when controlling for all other nodes in the network\u003csup\u003e[32]\u003c/sup\u003e.To clearly display the relationships between nodes, this study applies the Fruchterman-Reingold algorithm to layout the display. When placing nodes in the network, the algorithm determines the position of a node based on the sum of its connections to other nodes. Each edge has a symbol: network structure. In the visualization network, green edges represent positive regularized partial correlation, while red edges represent negative regularized partial correlation. The corresponding thickness and saturation of an edge indicate its weight (i.e., strength of association)\u003csup\u003e[33]\u003c/sup\u003e. Thicker edges indicate a stronger correlation between nodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2 Network extrapolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe centrality graph illustrates the centrality of a node's connections with other nodes. Boccaletti et al.\u003csup\u003e[20]\u003c/sup\u003e described three types of centrality: strength(As shown in Figure 2), betweenness, and closeness.Strength centrality can be understood as the sum of direct connections that a given node has in the network; betweenness can be understood as the shortest path through the investigated node; and closeness measures the sum of the shortest paths from the investigated node to all other nodes in the network\u003csup\u003e[34]\u003c/sup\u003e. Since centrality represents the relative importance of the nodes in the network, three possible interpretations of the centrality item are conceptualized\u003csup\u003e[35]\u003c/sup\u003e: control, independence, or activity. Statistically, a central item shares the most significant variance with all other items. Psychological network analysis focuses on the relationships between psychological constructs, such as symptoms, cognitions, and behaviors. Degree centrality and betweenness centrality may not accurately capture the essence of these relationships because they primarily focus on the topological properties of the network structure rather than the dynamic interactions between psychological constructs. Therefore, this study has abandoned these two indicators and retained strength as the primary indicator of centrality in this paper.Conceptually, in the context of self-acceptance, which is a self-administered scale, we suggest that subjects' responses to the central item may predict the way subjects respond to the central item in a network with other items with which they share a connection. Centrality estimates are standardized with a mean of 0 and a standard deviation of 1. Strength centrality is the primary metric used in this paper because it is the most robust estimate of centrality described in the literature\u003csup\u003e[36]\u003c/sup\u003e. However, the centrality metric is relative because the centrality of each node is estimated by comparing it to other nodes (there is always a highly central node, no matter how weak the edges in the network are).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3 Estimation of centrality indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expected influence (EI) used in this study assesses the relative importance of each node in the network. The expected influence of a node is obtained by calculating the sum of all edge weights (i.e., regularized partial correlation coefficient in this study) of all edges connected to that node\u003csup\u003e[37]\u003c/sup\u003e. A higher value of the expected influence of a node indicates a higher degree of centrality of that node and a more important position in the network structure\u003csup\u003e[38]\u003c/sup\u003e. The predictability of the node was calculated using the MGM package to assess its controllability\u003csup\u003e[39]\u003c/sup\u003e. The predictability of a node is defined as the variance of that node, i.e., the degree to which all its neighboring nodes explain it. A high average predictability indicates that the network structure can be better predicted by its internal items from each other and is less affected by factors external to the network. In addition, the network tools package was used to calculate the bridge expected impact for each node and thus determine the bridge symptom, which is defined as the sum of the values of all edges connecting a particular node to nodes in other symptom clusters. Higher values of bridge expected impact indicate a more significant increase in the risk of transmission to other symptom clusters, using the bootnet package for network accuracy and stability assessment\u003csup\u003e[36,40]\u003c/sup\u003e. The above network construction and visualization were implemented using the qgraph package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.4 Network accuracy and stability estimates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the accuracy of the edge weights was assessed by calculating 95% confidence intervals (CI) using a nonparametric self-help method (1000 bootstrap), and second, the stability of the expected impact of the nodes was assessed by calculating correlation stability (CS) coefficients using a sample descent self-help method (1000 bootstrap). The correlation stability coefficient should preferably be higher than 0.50 and should not be lower than 0.25. If the correlation stability coefficient (i.e., the CS coefficient) is higher than 0.25, but preferably higher than 0.5, the intensity is considered stable. The difference between the two intensity indicators was considered significant if the 2000-bootstrap 95% nonparametric confidence interval (CI) did not contain zero. Similarly, the 95% bootstrap CI was used to estimate edge precision, with larger CIs indicating a lower precision in edge estimation and narrower CIs implying a more trustworthy network. Significant differences between edges were estimated as CIs, and two edges were statistically different if they did not include zero\u003csup\u003e[41]\u003c/sup\u003e. Finally, tests of variance for edge weights, expectations, and bridge expectations were performed to assess whether there was a significant difference between edge weights, between expected values, or between bridge expectations (significance level α = 0.05) \u003csup\u003e[42]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSince the study variables were self-reported by the respondents, Harman's common method bias test was used, and principal component analysis was used to test the results of the unrotated factor analysis. The explanatory rate of the largest common factor was 39.08%, which is less than the critical value of 40%, so it can be assumed that there is no common method bias\u003csup\u003e[43]\u003c/sup\u003e .\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Total sample network for self-acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dimensional and question-item networks of self-acceptance were estimated individually, and Table 1 defines each node in the network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 displays the estimated network of 16 items for self-admission. Overall, most items were positively connected inside the network.\u003c/p\u003e\n\u003cp\u003eThis network possesses various unusual characteristics, as outlined below. Out of 120 potential edges, 70 (58.33%) are non-zero, with 69 exhibiting positive correlation and one demonstrating negative correlation. Secondly, the most significantly correlated edges were seen in z5 “I am satisfied with my body shape and looks,” and z6 “I express satisfaction with myself” (edge weight value = 0.41), as well as z2 “My whole body is a good thing” and z3 “I feel confident that the opposite sex will find me appealing.” (marginal weight = 0.37), z14 “I consistently concern myself with the potential unhappiness of others” and z16 “I perpetually fear that others will regard me with disdain” (marginal weight = 0.37). “Disdain me” (marginal weight = 0.28), z9 “I consistently excel at acquiring new knowledge,” and z12 “I am capable of accomplishing all tasks independently” (marginal weight = 0.28). Item 16 (Always Worry About People Looking Down on Me), formerly categorized under the self-acceptance dimension, is now demonstrated to transition to the self-evaluation dimension within the network structure.\u003c/p\u003e\n\u003cp\u003eAn analysis of the local network structure identified two elements with the highest centrality index, which were statistically more central than the other nodes: z6 (I express happiness with myself) and z4 (I am perpetually apprehensive about undertaking tasks due to the fear of failure). \u0026nbsp;The strength index in this sample is notably robust and reliable (cs = 0.75).\u003c/p\u003e\n\u003cp\u003eThe bootstrap 95% confidence intervals suggest that the stability of the side weights is dependable and precise. Figure 3 illustrates the results of evaluating the accuracy of the side weights using the bootstrap method. The gray line denotes the sample side weights, the black line indicates the average side weights determined by the bootstrap method, and the gray bars represent the confidence intervals. The narrow 95% confidence intervals suggest that the side weights are sufficiently precise.\u003c/p\u003e\n\u003cp\u003eFigure 4 presents the results of the difference test for the edge weights. \u0026nbsp;Gray squares denote the absence of a significant difference among the respective edge weights. \u0026nbsp;Conversely, black squares denote a significant difference in the corresponding edge weights. \u0026nbsp;Squares on the diagonal that are outlined in black indicate negatively correlated side weights, while those that are not outlined indicate positively correlated side weights.\u003c/p\u003e\n\u003cp\u003eTable 2 presents the scores, anticipated impact (Z-scores), and predictability for each item of the self-acceptance questionnaire.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figures 5A and 5B illustrate the anticipated impact values for each node within the network and the expected impact values for the bridge, respectively (refer to Table 2 for detailed values).\u003c/p\u003e\n\u003cp\u003eFigure 5A illustrates that the horizontal axis denotes the magnitude of the expected impact value, with positions further to the right indicating a higher expected impact. The nodes exhibiting the highest expected impact values are z6, “I am satisfied with myself,” z4, “I am always afraid to do things for fear of screwing up,” and z16, “I am always afraid that people will look down on me,” with corresponding impact values of 1.478, 1.264, and 1.007, respectively. \u0026nbsp;In the self-acceptance community, the nodes with the highest impact values for bridging expectations included z15, which states, “I express liking for my personality traits.” \"I am consistently preoccupied with the fear of making mistakes, which hinders my ability to take action.\" z8, \"Consistently apprehensive about potential blame from others,\" and z13 \"I perceive that I am not liked by anyone else.\" The Bridge Expectancy Impact Values were 0.212, 0.163, 0.154, and 0.143, respectively.\u003c/p\u003e\n\u003cp\u003eFigures 6A and 6B present the results of stability tests for node expected impact values and bridge expected impact values, respectively. The lines represent the average correlation of node or bridge expected impacts across the entire sample and subsamples, while the orange areas denote the 2.5th to 97.5th percentiles. \u0026nbsp;Figure 5 illustrates that the CS coefficients are uniformly 0.75, surpassing the threshold of 0.5. This indicates that both the expected impact and bridge expected impact values demonstrate adequate stability.\u003c/p\u003e\n\u003cp\u003eThe difference test is conducted for the expected impact and bridge expected impact values independently, with the results presented in Fig. 7A and Fig. 7B, respectively. The gray squares signify the absence of a significant difference between the anticipated impact and the bridge's expected impact related to the two nodes. The black squares signify a significant difference between the expected impact and the bridge expected impact associated with two nodes. The numerical values represented by diagonal lines within the white squares denote the anticipated impact value of the node or the bridge.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSelf-acceptance is a multifaceted concept that is significantly associated with critical clinical outcomes, such as depression and anxiety\u003csup\u003e[44]\u003c/sup\u003e. Despite its significant role in mental health, the structure of its constituent elements and their interactions remain poorly understood. Building on prior research that utilized latent factor methodologies, network analysis was employed to enhance our comprehension of this phenomenon. This study employs network analysis to examine the relationships among items related to self-acceptance in Chinese youth. Aiming to identify central nodes and primary bridge nodes in self-acceptance, in order to gain an in-depth understanding of its network structure.The findings hold significant implications for elucidating the mechanisms underlying self-acceptance and for identifying potentially effective intervention strategies in this area.\u003c/p\u003e\n\u003cp\u003eExamining the components of self-acceptance may yield new insights into their interactions. \u0026nbsp;Certain items exhibit greater interconnectivity than others, with variations in centrality, and interactions occur among items across different components of self-acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Core Question Items for Self-Acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed Gaussian Graphical Models (GGM) for the first time to delineate the multidimensional network structure of young people's self-acceptance (SA). Firstly, the regularized partial correlation network algorithm identified two communities. The first community comprised items z2, z3, z5, z6, z9, z10, z12, and z15. The second community consisted of items z1, z7, z8, z4, z11, z14, z16, and z13. Network analysis confirmed the robustness of the SA and SE (Self-Esteem) two-factor structure\u003csup\u003e[11][42]\u003c/sup\u003e. However, a key dynamic connection was discovered: (1) Item z16 (\"Worrying about being despised by others\") migrated from the initially presupposed SA dimension to the SE dimension cluster. This corroborates the transmission pathway of \"social evaluation cognition → self-worth judgment\" in cognitive behavioral theory\u003csup\u003e\u0026nbsp;[43]\u003c/sup\u003e. Items from specific communities were found to be connected with items from different communities, indicating from a network perspective that the SA communities interact within the network through these specific items. For example, a connection exists between items z4 and z15, which belong to the subscales of self-acceptance and self-esteem, respectively. (2) SA and SE formed strong coupling via cross-dimensional edges such as z4 (\"Fear of failure\")-z15 (\"Personality acceptance\"), suggesting bidirectional regulation between cognitive and affective components. This finding challenges the unidirectional causal assumption of traditional factor models and supports the theoretical framework of self-acceptance as a \"cognitive-affective integrated system\" \u003csup\u003e[43][44]\u003c/sup\u003e. Secondly, network structure analysis indicated that z6 (\"I am satisfied with myself\") and z4 (\"I always worry about messing things up and am afraid to act\") are among the two most central items in the network. Although targeting the most central nodes does not necessarily lead to effective changes in the network\u003csup\u003e\u0026nbsp;[29]\u003c/sup\u003e, these findings suggest that a clinical strategy aimed at improving self-acceptance might focus on individuals' core self-perceptions. Items belonging to the self-acceptance community (z4 and z6) exhibited high centrality values; this finding supports Self-Concept Theory, which posits that an individual's views and evaluations of themselves are crucial factors influencing their behavior and psychological state\u003csup\u003e\u0026nbsp;[45]\u003c/sup\u003e. \"I am satisfied with myself\" (z6) indicates a positive self-concept, while \"I always worry about messing things up and am afraid to act\" (z4) suggests self-doubt and negative self-evaluation. The central status of these two items supports the pivotal role of self-concept in an individual's psychology and behavior\u003csup\u003e\u0026nbsp;[45]\u003c/sup\u003e. Item z6 showed the highest centrality value: to explain this finding, one must link the statistical significance of centrality and network connections (edges). As the node with the highest centrality in the network (EI = 1.478), z6 reflects the core status of the individual's self-concept. Its high connectivity suggests that reinforcing overall self-worth may generate a \"ripple effect,\" positively activating associated cognitive nodes\u003csup\u003e\u0026nbsp;[29]\u003c/sup\u003e. The identification of central and key nodes provides important clues for understanding the core dimensions of self-acceptance. For instance, \"I am satisfied with myself\" and \"I always worry about messing things up and am afraid to act\" might be key targets for self-acceptance interventions. By altering the state of these nodes, one can potentially effectively enhance an individual's level of self-acceptance.\u003c/p\u003e\n\u003cp\u003eItem z4 (\"Fear of failure leading to action inhibition\"), as a regulatory hub (EI=1.264) within the SA dimension, reveals the dominant role of \"avoidant cognition\" in behavioral patterns. Its formation of a negative triangular structure with z8 (\"Fear of blame\") and z16 (\"Anxiety of being looked down upon\") may constitute a maintenance mechanism for low self-acceptance [46]. Bridge expectancy influence analysis further identified cross-boundary transmission nodes: z15 (\"Personality acceptance\") serves as a key bridge node (BEI=0.212), acting as an information relay between the SA and SE dimensions. Intervention targeting such nodes could potentially block the cross-dimensional spread of negative cognition, providing new targets for \"cognitive dissonance-self reconstruction\" therapy \u003csup\u003e[23][47]\u003c/sup\u003e. Finally, another of the network's most central items is \"I always worry about others looking down on me\" (z16), which relates to \"I feel others don't like me\" (z13) and \"I always worry about making others unhappy\" (z14). These worries reflect potential low self-esteem and social anxiety in the individual, indicating negative cognition in self-evaluation and interpersonal interactions, thereby affecting their self-acceptance\u003csup\u003e\u0026nbsp;[5]\u003c/sup\u003e. Previous literature has already shown that self-acceptance is closely related to cognitive patterns involving negative expectations of others\u003csup\u003e\u0026nbsp;[46]\u003c/sup\u003e. Therefore, it can be inferred that targeting biased cognitive processing may help improve the level of self-acceptance.\u003c/p\u003e\n\u003cp\u003eIn summary, this study not only revealed for the first time the multidimensional network structure of young people's self-acceptance and validated the robustness of its two-factor model, but more importantly, it identified key nodes (such as z4, z6, z15) and connection patterns within the network. Specifically, it illuminated the pivotal roles of core elements like fear of failure and self-satisfaction within the self-acceptance system, and how they influence the overall level through complex cognitive-emotional interactions. These findings not only challenge traditional unidirectional causal models, offering a new perspective of a \"cognitive-affective integrated system\" for self-acceptance, but also point the way for future development of more precise and effective psychological intervention strategies. For instance, targeting key nodes or bridge items could help break negative cycles and promote the formation of positive self-concepts. These network analysis results hold significant practical implications, as they help identify targets for developing precise self-acceptance interventions, potentially shifting traditional group counseling towards precision interventions based on core nodes. For example, the focus of intervention could be placed on highly central items like \"I am satisfied with myself\" (z6), or on regulatory nodes like \"I always worry about messing things up and am afraid to act\" (z4). By altering the state of these critical points, it becomes possible, much like tuning a network hub, to effectively enhance the stability of the entire self-acceptance system, ultimately helping individuals improve their level of self-acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Research limitations and future research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has certain drawbacks. The networks in this study were only formed based on the youth's self-reported self-acceptance rating, so rendering them susceptible to the influence of social probability. Future research may employ diverse methodologies, including observational techniques, clinical interviews, and behavioral experiments, to acquire more comprehensive data and facilitate comparisons, or to mitigate the issues of social desirability bias inherent in self-reporting as evaluated by peers or educators. The data in this study are cross-sectional. In the future, more representative longitudinal data may be utilized to elucidate the network of self-acceptance items, their developmental trajectories, and the interconnections among various dimensions/items.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI\u0026rsquo;m deeply grateful to my friends for their inspiration during my research. Conversations, whether light or deep, often sparked ideas, opened new perspectives, and guided me in valuable directions, significantly enriching my work and saving me effort. My family\u0026rsquo;s unwavering support was essential, and I also appreciate friends who provided timely encouragement and help. To all friends who\u0026rsquo;ve supported and inspired me: thank you from the bottom of my heart. Thank you all!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;Availability\u0026nbsp;Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data availability statement is completely consistent between the submission system and the main manuscript.The data that support the findings of this study are available from the corresponding author upon reasonable request.Data availability statement: The data are available upon reasonable request. Researchers requesting data access should contact [Guo Li] via [[email protected]].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hereby solemnly declare that, in the process of writing and publishing this article, I have no potential or actual conflicts of interest that could affect the objectivity, fairness, or interpretation of the results of this article. The conception of this article, the research (if applicable), data collection and analysis (if applicable), the expression of views, and the conclusions are all based on independent professional judgment and factual grounds. They have not been unduly influenced or pressured by any personal, group, institutional, or corporate economic, commercial, or other means. All cited materials and data have been strived to be accurate and reliable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB) of Shanxi Medical University, with approval number 2023SJL71 (Date of Approval: 2020.03.13). The research was conducted in strict accordance with the approved protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were fully informed about the study\u0026apos;s purpose, methods, potential risks, and benefits, and they all signed written informed consent forms before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrivacy and Confidentiality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study strictly protects the privacy and anonymity of participants. All collected data are stored and processed in an encrypted or anonymized format. Any information that could identify participants has been removed or securely stored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe content of this article does not represent the views of any organization or institution, nor has it received any form of financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u0026nbsp;of\u0026nbsp;data\u0026nbsp;and\u0026nbsp;materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData availability statement: The data are available upon reasonable request. Researchers requesting data access should contact [Guo Li] via [[email protected]].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u0026nbsp;for\u0026nbsp;publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to Publish Statement: All participants signed an electronic informed consent form, agreeing to the publication of data or related results from this study in open-access journals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLi Xinying was responsible for conceptualization, methodology, project administration, data curation, and resources.Li Yuting contributed to visualization, project administration, resources, conceptualization, and methodology.Guo Li was responsible for conceptualization, data curation, the writing of the original draft, and software development.Niu Yangtong contributed to software development, data curation, formal analysis, conceptualization, and visualization.Wu Ying contributed to the writing of the original draft, resources, conceptualization, and project administration.Ren Yan contributed to reviewing and editing, project administration, resources, conceptualization, the writing of the original draft, and supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRyff, C.D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, 57(6), pp.1069\u0026ndash;1081. doi:https://doi.org/10.1037//0022-3514.57.6.1069.\u003c/li\u003e\n\u003cli\u003eSUN, D. and LU, B. (2017). Self-Acceptance:Concepts, Measures and Influences. \u003cem\u003ePsychological Research\u003c/em\u003e, 10(6), pp.60\u0026ndash;67.doi:https://doi.org/10.3969/j.issn.2095-1159.2017.06.009.\u003c/li\u003e\n\u003cli\u003eZHANG,E.(2017).Research progress on self-acceptance. The Guide of Scence \u0026amp; Education,(26),pp.37-41. doi:https://doi.org/10.16400/j.cnki.kjdkz.201709.018.\u003c/li\u003e\n\u003cli\u003eLI, J. and ZHANG, J. (2011). Review and Prospect of the Research about Self-acceptance . \u003cem\u003eJournal of Xiangyang Polytechnic\u003c/em\u003e, 10(2), pp.121\u0026ndash;126.\u003c/li\u003e\n\u003cli\u003eZHEN, H. (2017). A Review of Self-Acceptance Research. \u003cem\u003eModern Communication\u003c/em\u003e, (4), pp.25\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eMENG, F. (2018). The Development of Secondary School Students\u0026rsquo; Self- accentance Scale.[master\u0026apos;s thesis]. [China]: SOUTHWEST University\u003c/li\u003e\n\u003cli\u003ePomeroy, L. (2013). Psychotherapy and the Value of a Human Being. pp.58\u0026ndash;81. doi:https://doi.org/10.4324/9781315884080-11.\u003c/li\u003e\n\u003cli\u003eCarson, S.H. and Langer, E. j (2006). Mindfulness and self-acceptance. \u003cem\u003eJ Rat-Emo Cognitive-Behav Ther\u003c/em\u003e, 24, pp.29\u0026ndash;43. doi:https://doi.org/10.1007/s10942-006-0022-5.\u003c/li\u003e\n\u003cli\u003eZHANG, R. (1993). \u0026lsquo;Psychology of Youth : A Comparative Study of Youth Psychology in China and Japan.\u0026rsquo; ZHANG Risheng ed. Beijing: Beijing Normal University Press.\u003c/li\u003e\n\u003cli\u003eChamberlain, J.M. and Haaga, D.A.F. (2001). Unconditional Self-Acceptance and Psychological Health. \u003cem\u003eJournal of Rational-Emotive \u0026amp; Cognitive-Behavior Therapy\u003c/em\u003e, 19, pp.163\u0026ndash;176. doi:https://doi.org/10.1023/A:1011189416600.\u003c/li\u003e\n\u003cli\u003eCONG, Z. and GAO, W.(1999). The development and reliability and validity test of the Self-Acceptance Questionnaire. \u003cem\u003eChinese Journal of Behavioral Medicine and Brain Science\u003c/em\u003e, 8(1), pp.20-20. doi:https://doi.org/10.3760/cma.j.issn.1674-6554.1999.01.007\u003c/li\u003e\n\u003cli\u003eCostantini, G., Richetin, J., Borsboom, D., Fried, E.I., Rhemtulla, M. and Perugini, M. (2015). Development of Indirect Measures of Conscientiousness: Combining a Facets Approach and Network Analysis. \u003cem\u003eEuropean Journal of Personality\u003c/em\u003e, 29(5), pp.548\u0026ndash;567. doi:https://doi.org/10.1002/per.2014.\u003c/li\u003e\n\u003cli\u003eKossakowski, J.J., Epskamp, S., Kieffer, J.M., van Borkulo, C.D., Rhemtulla, M. and Borsboom, D. (2016). The application of a network approach to Health-Related Quality of Life (HRQoL): introducing a new method for assessing HRQoL in healthy adults and cancer patients.\u003cem\u003e Qual Life Res\u003c/em\u003e, 25(4), pp.781\u0026ndash;792. doi:https://doi.org/10.1007/s11136-015-1127-z.\u003c/li\u003e\n\u003cli\u003eVan Der Maas, H.L.J., Dolan, C.V., Grasman, R.P.P.P., Wicherts, J.M., Huizenga, H.M. and Raijmakers, M.E.J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. \u003cem\u003ePsychological Review\u003c/em\u003e, 113(4), pp.842\u0026ndash;861. doi:https://doi.org/10.1037/0033-295x.113.4.842.\u003c/li\u003e\n\u003cli\u003eDalege, J., Borsboom, D., van Harreveld, F. and van der Maas, H.L.J. (2017). Network Analysis on Attitudes. \u003cem\u003eSocial Psychological and Personality Science\u003c/em\u003e, 8(5), pp.528\u0026ndash;537. doi:https://doi.org/10.1177/1948550617709827.\u003c/li\u003e\n\u003cli\u003eFried, E.I., Eidhof, M.B., Palic, S., Costantini, G., Huisman-van Dijk, H.M., Bockting, C.L.H., Engelhard, I., Armour, C., Nielsen, A.B.S. and Karstoft, K.-I. (2018). Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples. \u003cem\u003eClinical Psychological Science\u003c/em\u003e, 6(3), pp.335\u0026ndash;351. doi:https://doi.org/10.1177/2167702617745092.\u003c/li\u003e\n\u003cli\u003eBorsboom, D. (2017). A network theory of mental disorders. \u003cem\u003eWorld Psychiatry\u003c/em\u003e, 16(1), pp.5\u0026ndash;13. doi:https://doi.org/10.1002/wps.20375.\u003c/li\u003e\n\u003cli\u003eBORGATTL, S.P., MEHRA, A., BRASS, D.J. and LABIANCA, G. (2019). Network Analysis in the Social Sciences. \u003cem\u003eScience\u003c/em\u003e, 323(5916), pp.892\u0026ndash;895. doi:https://doi.org/10.1126/science.1165821.\u003c/li\u003e\n\u003cli\u003eLIN Liang,XU Boya,YANG Ying,et al. Network analysis and core dimensions of adolescent pro-social behavior[J]. \u003cem\u003ePsychological Journal\u003c/em\u003e: 1-30.\u003c/li\u003e\n\u003cli\u003eBoccaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D.-U. (2006). Complex networks: Structure and dynamics. \u003cem\u003ePhysics Reports\u003c/em\u003e, 424(4-5), pp.175\u0026ndash;308. doi:https://doi.org/10.1016/j.physrep.2005.10.009.\u003c/li\u003e\n\u003cli\u003eCostantini, G., Epskamp, S., Borsboom, D., Perugini, M., M\u0026otilde;ttus, R., Waldorp, L.J. and Cramer, A.O.J. (2015a). State of the aRt personality research: A tutorial on network analysis of personality data in R. \u003cem\u003eJournal of Research in Personality\u003c/em\u003e, 54, pp.13\u0026ndash;29. doi:https://doi.org/10.1016/j.jrp.2014.07.003.\u003c/li\u003e\n\u003cli\u003eCAI, Y., DONG, S., YUAN, S. and HU, C. (2020). Network analysis and its applications in psychology . \u003cem\u003eAdvances in Psychological Science,\u003c/em\u003e 28(1), pp.178\u0026ndash;195. doi:https://doi.org/10.3724/SP.J.1042.2020.00178.\u003c/li\u003e\n\u003cli\u003eCramer, A.O.J., van der Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S.H., Kendler, K.S. and Borsboom, D. (2012). Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can\u0026rsquo;t Like Parties if You Don\u0026rsquo;t Like People. \u003cem\u003eEuropean Journal of Personality\u003c/em\u003e, 26(4), pp.414\u0026ndash;431. doi:https://doi.org/10.1002/per.1866.\u003c/li\u003e\n\u003cli\u003eDalege, J., Borsboom, D., van Harreveld, F. and van der Maas, H.L.J. (2018). The Attitudinal Entropy (AE) Framework as a General Theory of Individual Attitudes. \u003cem\u003ePsychological Inquiry\u003c/em\u003e, 29(4), pp.175\u0026ndash;193. doi:https://doi.org/10.1080/1047840x.2018.1537246.\u003c/li\u003e\n\u003cli\u003eBorsboom, D. and Cramer, A.O.J. (2013). Network Analysis: An Integrative Approach to the Structure of Psychopathology. \u003cem\u003eAnnual Review of Clinical Psychology\u003c/em\u003e, 9(1), pp.91\u0026ndash;121. doi:https://doi.org/10.1146/annurev-clinpsy-050212-185608.\u003c/li\u003e\n\u003cli\u003eMcNally, R.J. (2016). Can network analysis transform psychopathology? \u003cem\u003eBehaviour Research and Therapy\u003c/em\u003e, 86, pp.95\u0026ndash;104. doi:https://doi.org/10.1016/j.brat.2016.06.006.\u003c/li\u003e\n\u003cli\u003eBlanchard, M.A. and Heeren, A. (2020). Why we should move from reductionism and embrace a network approach to parental burnout. \u003cem\u003eNew Directions for Child and Adolescent Development\u003c/em\u003e, 2020(174), pp.159\u0026ndash;168. doi:https://doi.org/10.1002/cad.20377.\u003c/li\u003e\n\u003cli\u003eEpskamp, S., Borsboom, D. and Fried, E.I. (2017). Estimating psychological networks and their accuracy: A tutorial paper. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, [online] 50(1), pp.195\u0026ndash;212. doi:https://doi.org/10.3758/s13428-017-0862-1.\u003c/li\u003e\n\u003cli\u003eEpskamp, S., Waldorp, L.J., M\u0026otilde;ttus, R. and Borsboom, D. (2018). The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, 53(4), pp.453\u0026ndash;480. doi:https://doi.org/10.1080/00273171.2018.1454823.\u003c/li\u003e\n\u003cli\u003eEpskamp, S. and Fried, E.I. (2018). A tutorial on regularized partial correlation networks. \u003cem\u003ePsychological Methods\u003c/em\u003e, [online] 23(4), pp.617\u0026ndash;634. doi:https://doi.org/10.1037/met0000167.\u003c/li\u003e\n\u003cli\u003eFoygel, R. and Mathias, D. (2010). Extended Bayesian Information Criteria for Gaussian Graphical Models. \u003cem\u003eAdvancesn neural information processing systems\u003c/em\u003e, [online] 23. Available at: http://gdmltest.u-ga.fr/item/1011.6640/.\u003c/li\u003e\n\u003cli\u003eBrigantial, G., Kempenaers, C., Braun, S., Fried, E.I. and Linkowski, P. (2018). A St\u0026eacute;phanie-Braun, et al. Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults. \u003cem\u003ePsychiatry Research\u003c/em\u003e, 265, pp.87\u0026ndash;92. doi:https://doi.org/10.1016/j.psychres.2018.03.082.\u003c/li\u003e\n\u003cli\u003eFruchterman, T.M.J. and Reingold, E.M. (1991). Graph drawing by force-directed placement. \u003cem\u003eSoftware: Practice and Experience\u003c/em\u003e, 21(11), pp.1129\u0026ndash;1164. doi:https://doi.org/10.1002/spe.4380211102.\u003c/li\u003e\n\u003cli\u003eOpsahl, T., Agneessens, F. and Skvoretz, J. (2010). Node centrality in weighted networks: generalizing degree and shortest paths. \u003cem\u003eSocial Networks\u003c/em\u003e, 32(3), pp.245\u0026ndash;251. doi:https://doi.org/10.1016/j.socnet.2010.03.006.\u003c/li\u003e\n\u003cli\u003eFreeman, L.C. (1978). Centrality in social networks conceptual clarification. \u003cem\u003eSocial Networks\u003c/em\u003e, 1(3), pp.215\u0026ndash;239. doi:https://doi.org/10.1016/0378-8733(78)90021-7.\u003c/li\u003e\n\u003cli\u003eEpskamp, S., Borsboom, D. and Fried, E.I. (2018). Estimating psychological networks and their accuracy: A tutorial paper.\u003cem\u003e Behav Res\u003c/em\u003e 50, pp.195\u0026ndash;212. https://doi.org/10.3758/s13428-017-0862-1.\u003c/li\u003e\n\u003cli\u003eHaslbeck, J.M.B. and Fried, E.I. (2017). How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. \u003cem\u003ePsychological Medicine\u003c/em\u003e, 47(16), pp.2767\u0026ndash;2776. doi:https://doi.org/10.1017/s0033291717001258.\u003c/li\u003e\n\u003cli\u003eHofmann, S.G., Curtiss, J. and McNally, R.J. (2016). A Complex Network Perspective on Clinical Science. \u003cem\u003ePerspectives on Psychological Science\u003c/em\u003e, 11(5), pp.597\u0026ndash;605. doi:https://doi.org/10.1177/1745691616639283.\u003c/li\u003e\n\u003cli\u003eJones, P.J., Ma, R. and McNally, R.J. (2019). Bridge Centrality: A Network Approach to Understanding Comorbidity. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, 56(2), pp.1\u0026ndash;15. doi:https://doi.org/10.1080/00273171.2019.1614898.\u003c/li\u003e\n\u003cli\u003eBorsboom, D., Robinaugh, D.J., Rhemtulla, M. and Cramer, A.O.J. (2018). Robustness and replicability of psychopathology networks. \u003cem\u003eWorld Psychiatry\u003c/em\u003e, 17(2), pp.143\u0026ndash;144. doi:https://doi.org/10.1002/wps.20515.\u003c/li\u003e\n\u003cli\u003eZHANG, F., WU, L., SONG, L., WANG, P., CAO, Y., LIU, J., ZHANG, Y., GAO, Z., WANG, J., LI, J. and WU, S. (2023). Network Analysis of Meaning in Life and Coping Styles among College Students. \u003cem\u003eChinese Medical Ethics\u003c/em\u003e, 36(6), pp.670\u0026ndash;678. doi:https://doi.org/10.12026/j.issn.1001-8565.2023.06.14.\u003c/li\u003e\n\u003cli\u003eZHOU, H. and LONG, L. (2004). Statistical Remedies for Common Method Biases . \u003cem\u003eAdvances in Psychological Science\u003c/em\u003e, (6), pp.942\u0026ndash;950. doi:https://doi.org/10.3969/j.issn.1671-3710.2004.06.018..\u003c/li\u003e\n\u003cli\u003eGAO, W. and CONG, Z. (2000). Relationship of social anxiety to self-esteem and self-acceptance in college students. \u003cem\u003eChina Journal of Health Psychology\u003c/em\u003e, 8(3), p.276. doi:https://doi.org/10.3969/j.issn.1005-1252.2000.03.017.\u003c/li\u003e\n\u003cli\u003eBraun, S., Rosseel, Y., Kempenaers, C., Loas, G. and Linkowski, P. (2015). Self-Report of Empathy: A Shortened French Adaptation of the Interpersonal Reactivity Index (IRI) Using Two Large Belgian Samples. \u003cem\u003ePsychological Reports\u003c/em\u003e, 117(3), pp.735\u0026ndash;753. doi:https://doi.org/10.2466/08.02.pr0.117c23z6.\u003c/li\u003e\n\u003cli\u003eKruis, J. and Maris , G. (2016). Three representations of the Ising model. \u003cem\u003eScientific Reports\u003c/em\u003e, 6(1). doi:https://doi.org/10.1038/srep34175.\u003c/li\u003e\n\u003cli\u003eDolan, R.J. (2002). Emotion, Cognition, and Behavior. \u003cem\u003eScience\u003c/em\u003e, 298(5596), pp.1191\u0026ndash;1194. doi:https://doi.org/10.1126/science.1076358.\u003c/li\u003e\n\u003cli\u003eWylie, R. C. (1974). The Self-Concept: Theory and Research on Selected Topics. volume Two. revised Edition. U of Nebraska Press.\u003c/li\u003e\n\u003cli\u003eHaslbeck, J.M.B. and Waldorp, L.J. (2020). mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data . \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, 93(8), pp.1\u0026ndash;46. doi:https://doi.org/10.18637/jss.v093.i08.\u003c/li\u003e\n\u003cli\u003eLIU, W. (2008). Effect of psychnlogical intervention on negative automatic thoughts,self acceptance and social support for patients with type 2 diabetic cataract. \u003cem\u003eCHIINESE JOURNAL OF PRACTICAL NURSING\u003c/em\u003e, 24(24), pp.1\u0026ndash;4. doi:https://doi.org/10.3760/cma.j.issn.1672-7088.2008.24.001.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 Basic information of each node in the topic network\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCoding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003esubject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAffiliated dimensions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m afraid to say what my heart desires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eMy whole body is good.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI think the opposite sex will definitely like me\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m always afraid to do things for fear of screwing up.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m happy with my body and my looks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m satisfied with myself.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI can\u0026apos;t do anything without the approval of others.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eAlways afraid of being blamed by others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m always faster when it comes to learning new things\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m very pleased with my eloquence.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eAlways think of failure before you do something\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI can do everything on my own.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI don\u0026apos;t think anyone likes me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eAlways worrying that you\u0026apos;ll make others unhappy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eExpressing a liking for your character traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-evaluation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ez16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eI\u0026apos;m always worried that people will look down on me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eself-acceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2 Self-acceptance questionnaire items, dimension scores, expected impact and predictability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eencodings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003esubject\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eExpected impact\u0026nbsp;(Z-score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003epredictability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAnticipated impact of the bridge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m afraid to say what my heart desires\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e33.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eMy whole body is good.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e42.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI think the opposite sex will definitely like me\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e44.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m always afraid to do things for fear of screwing up.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e52.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m happy with my body and my looks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e51.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m satisfied with myself.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e55.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI can\u0026apos;t do anything without the approval of others.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e38.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eAlways afraid of being blamed by others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e53.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m always faster when it comes to learning new things\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-1.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e31.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m very pleased with my eloquence.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e44.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eAlways think of failure before you do something\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-1.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e34.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI can do everything on my own.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e34.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI don\u0026apos;t think anyone likes me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e35.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eAlways worrying that you\u0026apos;ll make others unhappy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e50.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eExpressing a liking for your character traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e43.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003ez16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003eI\u0026apos;m always worried that people will look down on me.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e51.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"self-acceptance, youth, network analysis, mental health, psychology","lastPublishedDoi":"10.21203/rs.3.rs-6888824/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6888824/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eSelf-acceptance is a multifaceted psychological phenomenon that has substantial clinical research implications. The structure of self-acceptance is not well understood, despite its significance for mental health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn the current study, self-acceptance was examined in a large sample (\u003cem\u003en \u003c/em\u003e= 2460) drawn from a highly representative sample of the Chinese general population. In network analysis, a regularized partial correlation networks was estimated. The model depicted the topic items as nodes, with edges reflecting the regularized partial correlation between them. Nodes' connectedness to other points in the network is referred to as their centrality. To confirm the findings' trustworthiness, advanced stability, and accuracy analyses were done.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study found that item 6 (\"I am satisfied with myself\") had the greatest strong centrality score. The centrality order of network edges and nodes was appropriately predicted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe network analysis uncovered intriguing correlations across self-acceptance indicators, necessitating further investigation into the implications of these findings for self-acceptance modeling.\u003c/p\u003e","manuscriptTitle":"Shedding Light on Self-Acceptance: Insights from Network Analysis for Youth Intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 18:06:54","doi":"10.21203/rs.3.rs-6888824/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-28T19:16:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T08:42:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T22:09:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17986540103254895276802051152035753988","date":"2025-08-21T05:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224567432932517369517888406702959626581","date":"2025-08-11T06:15:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136462152360494731709679615992366108780","date":"2025-07-17T15:48:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T15:41:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T15:33:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-25T04:54:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-18T10:28:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Mental Health","date":"2025-06-18T09:53:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-mental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dimh","sideBox":"Learn more about [Discover Mental Health](https://www.springer.com/44192)","snPcode":"","submissionUrl":"","title":"Discover Mental Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fe68bb4-51fc-4288-a585-65afd05a648b","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T19:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 18:06:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6888824","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6888824","identity":"rs-6888824","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}