Educational Stage Differences in Symptom Networks and Bayesian Pathways of Non-Suicidal Self-Injury Among Ethnic Minority Adolescents in Ngawa Prefecture, China

Educational Stage Differences in Symptom Networks and Bayesian Pathways of Non-Suicidal Self-Injury Among Ethnic Minority Adolescents in Ngawa Prefecture, China | 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 Educational Stage Differences in Symptom Networks and Bayesian Pathways of Non-Suicidal Self-Injury Among Ethnic Minority Adolescents in Ngawa Prefecture, China Fan Wang, Wei Jin, Lan Hong, Zhaoxuan Liu, Jianuo Shi, Siyu Tong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9325556/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Objective To investigate the symptom network structure of non-suicidal self-injury (NSSI) among Chinese ethnic minority adolescents, with a particular focus on educational-stage differences and potential directional pathways linking psychiatric symptoms to NSSI. Methods A total of 2,353 adolescents from six junior and senior high schools in Ngawa Tibetan and Qiang Autonomous Prefecture, Sichuan Province, China participated, including 750 junior high students and 1,603 senior high students. Networks were estimated across 14 symptom dimensions, such as NSSI, depressive symptoms (PHQ), generalized anxiety symptoms (GAD), obsessive-compulsive symptoms (OCD), and various emotional and behavioral symptom dimensions. A Gaussian graphical model (GGM) and an Ising model were built separately. Centrality, bridge centrality, and predictability were calculated, and bootstrap techniques evaluated network accuracy and stability. Differences in network structure between educational stages were also analyzed. Additionally, a Bayesian directed acyclic graph (DAG) was used to explore potential directional relationships among symptoms. Results In the undirected network models, the strongest links were between SoP-PHQ and PHQ-OCD. PHQ was identified as the most central node in the entire sample. In subgroup analyses, PHQ remained the main node among junior high students, whereas OCD became the most central node among senior high students. PTSD became more prominent in the senior high school group within the Ising network. Bayesian DAG analysis revealed that educational stage and gender were relatively upstream variables, whereas PHQ, GAD, OCD, and PTSD occupied intermediate positions, with NSSI mainly downstream. Key pathways connecting symptoms to NSSI included PHQ→NSSI, PHQ→OCD→NSSI, and PHQ→GAD→Pa→NSSI. Stage-specific analyses indicated a pathway from gender to GAD to PHQ and then to NSSI in junior high students, whereas in senior high students, PHQ→NSSI was linked to Ma and TicD. Conclusion NSSI among ethnic minority adolescents seems to be a downstream behavioral sign within a psychopathological network dominated by clustered internalizing symptoms. Depressive, obsessive-compulsive, and trauma-related symptoms may serve as key targets for early identification and prevention of NSSI. ethnic minority adolescents non-suicidal self-injury symptom network analysis depressive symptoms obsessive-compulsive symptoms Bayesian network Figures Figure 1 Figure 2 Figure 3 1 Introduction 1.1 Research Background of NSSI Among Ethnic Minority Adolescents in Ngawa Non-suicidal self-injury (NSSI) is a prevalent and clinically important risk behavior during adolescence. Meta-analyses indicate that about 17.7% of adolescents worldwide engage in NSSI [ 1 , 2 ]. Meanwhile, a scoping review reports a lifetime prevalence of roughly 25% among Chinese adolescents [ 3 – 5 ], highlighting its significant public health impact in China. Besides its high occurrence, NSSI is strongly associated with depression, anxiety, functional impairment, and an increased risk of future suicide [ 1 , 3 – 6 ]. For example, in Shanghai's junior middle school students, the 12-month NSSI prevalence was 21.7%, with anxiety disorder being a common comorbidity [ 6 , 7 ]. Ethnic minority adolescents living in high-altitude, rural, and boarding-school contexts may face a distinct socioecological and cultural environment compared with adolescents in other regions. Factors such as plateau hypoxia, rural and pastoral lifestyles, boarding school experiences, caregiving differences, and cultural adaptation pressures can influence emotional regulation, symptom expression, and help-seeking behaviors [ 8 – 10 ]. Research has shown a considerable burden of depressive symptoms among children and adolescents in high-altitude western China [ 8 ]. In addition, studies from minority rural and pastoral communities in China have reported a high prevalence of NSSI and a significant association with help-seeking behaviors [ 11 ]. These findings suggest that NSSI in ethnic minority adolescents may not simply represent a regional variation of a common adolescent issue, but may also be shaped by specific contextual factors, resulting in distinctive symptom patterns [ 8 – 11 ]. Most existing studies in these populations have focused on prevalence, help-seeking, or isolated psychosocial factors. There is limited understanding of NSSI's role within a broader psychopathological system, including its immediate links to other symptoms and its organizational structure. In this context, NSSI may be better viewed as part of a larger symptom network involving depression, anxiety, trauma signs, and other negative psychological experiences [ 4 , 12 – 15 ]. Hence, a more comprehensive exploration of NSSI within this environment is essential. 1.2 The Complex Associations Between NSSI and Related Psychiatric Symptoms NSSI rarely occurs in isolation and is typically associated with depressive symptoms, anxiety, traumatic experiences, negative emotional states, and difficulties with emotion regulation [ 4 , 12 – 15 ]. Research consistently highlights strong links between NSSI and depressive symptoms, particularly depressed mood, negative self-view, and feelings of worthlessness [ 12 – 15 ]. Childhood trauma, especially emotional abuse, significantly influences adolescent NSSI. Research indicates NSSI, depressive symptoms, and childhood trauma are closely connected, with emotional abuse showing a strong link to NSSI. Additionally, in networks assessing NSSI and depressive symptoms, certain depressive symptoms and self-injury-related nodes can act as crucial bridging points [ 13 ]. In addition to depressive symptoms and trauma, NSSI frequently co-occurs with anxiety, sleep problems, peer issues, and emotional regulation difficulties [ 4 , 6 , 7 , 14 – 16 ]. For Chinese adolescents, anxiety disorder stands out as a common comorbidity associated with NSSI [ 6 ]. Longitudinal research suggests that the relationships between NSSI, depression, and anxiety are bidirectional at the symptom level, rather than unidirectional [ 14 , 15 ]. These findings overall imply that NSSI should not be viewed solely as a secondary consequence of a specific disorder or risk factor. Instead, it may function within a complex network of interconnected symptoms, which interact with highly linked symptoms to sustain psychological distress and risky behaviors [ 12 – 16 ]. This concern could be particularly significant for ethnic minority adolescents living in high-altitude western China. Their ecological and sociocultural environment might expose them to a more intricate set of mental health challenges across domains including sleep, trauma responses, and behavioral adaptation [ 8 – 10 , 17 , 18 ]. For instance, previous research has revealed notable differences in sleep quality among ethnic minority adolescents living at different altitudes, with poorer sleep observed at higher elevations [ 17 ]. Among ethnic minority youths affected by post-earthquake high-altitude conditions, the rate of probable post-traumatic stress disorder reached 17.8%, highlighting the prominence of trauma-related symptoms [ 18 ]. Moreover, some studies suggest that ethnic minority adolescents in western China may show elevated levels of problematic mobile phone use compared with Han adolescents, with negative effects on multiple aspects of quality of life [ 19 ]. Vulnerable groups, such as ethnic minority orphaned adolescents, may also face heightened mental health risks, with self-control potentially acting as a protective factor via self-esteem [ 20 ]. Overall, these data suggest that ethnic minority adolescents in such settings may experience a complex combination of trauma, sleep disturbances, emotional issues, and behavioral challenges, which are interlinked [ 8 – 11 , 17 – 20 ]. Traditional correlation or regression methods often cannot clearly define NSSI’s role in the symptom network or the strength of its connections. Therefore, a method that directly examines relationships between symptoms is needed. 1.3 Symptom network analysis in NSSI Recently, symptom network analysis has gained popularity in mental health research. It treats symptoms as an interconnected system in which they can influence and support each other directly. By studying these relationships, network analysis helps identify key symptoms, bridging symptoms, and local clusters [ 12 – 16 , 21 ]. Unlike traditional variable-centered approaches, this method is particularly valuable for exploring the internal structure of comorbid symptom networks and understanding the role of transdiagnostic risk behaviors, such as NSSI, within multidimensional psychopathological frameworks [ 12 – 16 , 21 ]. Past research has employed network analysis to examine how NSSI correlates with depressive symptoms, trauma, anxiety, and sleep issues in adolescents. These studies indicate that certain emotional, traumatic, and cognitive symptoms may be central or directly connected to NSSI [ 12 , 13 , 16 , 21 ]. Furthermore, combining techniques such as Bayesian networks or cross-lagged models with network analysis can provide preliminary insights into the directionality of symptom relationships, aiding in identifying upstream factors, proximal correlations, and potential transmission pathways related to NSSI [ 14 , 15 ]. This strategy not only underscores factors linked to NSSI but also clarifies which symptoms are most strongly associated, where NSSI fits within the symptom network, and which symptoms could be effective intervention targets [ 12 – 16 , 21 ]. However, evidence remains limited for ethnic minority adolescents living in high-altitude areas of western China, especially in school-based samples from Ngawa, Sichuan. Specifically, there is still a lack of studies that systematically examine the structural position of NSSI and its potential pathway relationships from the perspective of multidimensional symptom networks. Accordingly, the present study investigated NSSI among ethnic minority adolescents in Ngawa from a symptom network perspective. First, we constructed networks across 14 symptom dimensions to identify the structural position of NSSI and its key connections within the overall network. Second, we compared network structures across educational stages, with gender-stratified analyses serving as supplementary tests. Third, we used Bayesian directed network analysis to explore upstream factors, downstream links, and potential pathways related to NSSI. Based on a school sample from Ngawa, Sichuan, this study aimed to provide structural evidence to inform early identification, risk assessment, and intervention planning for ethnic minority adolescents in this region. 2 Methods 2.1 Participants and procedure This cross-sectional, school-based screening was conducted in six junior and senior high schools in Ngawa Tibetan and Qiang Autonomous Prefecture, Sichuan Province (hereinafter referred to as Ngawa Prefecture) from October 2022 to April 2023. The schools coordinated the survey, and students independently completed the questionnaires in a group setting. Informed written consent was obtained from all participants and their guardians prior to participation. The study received approval from the ethics committee of Wenzhou Kangning Hospital (2020-k021-02) and complied with the principles of the Declaration of Helsinki. The inclusion criteria included: 1) ages 12–18; 2) ability to independently complete the questionnaire; 3) normal vision, hearing, and cognitive function; 4) sufficient Chinese/Mandarin comprehension to understand the questions and response instructions; and 5) informed consent provided. The exclusion criteria encompassed: 1) severe physical illness; 2) cognitive impairments that hinder questionnaire understanding, whether known or self-reported; 3) ongoing psychiatric medication or psychological treatment; and 4) indications of careless or invalid responses. To ensure data quality, questionnaires underwent a two-step screening and cleaning process. First, 207 participants who did not meet the eligibility criteria were excluded. Second, 26 questionnaires considered low-quality or invalid were removed, including those with straight-line responses, logically inconsistent answers, very short completion times, or obviously implausible responses. The final sample comprised 2,353 adolescents, including 750 junior high and 1,603 senior high school students. Tibetan participants comprised 1,850 (78.6%), Hui participants 12.1%, Qiang participants 7.5%, and other ethnic minority groups 1.8%. Of all participants, 1,272 were boys (54.1%), and 1,081 were girls (45.9%) (Table 1 ). Table 1 Baseline characteristics of the screening sample by educational stage Characteristic Category Total Junior high (n = 750) Senior high (n = 1603) Statistic Effect P value Age (years), mean ± sd - 15.21 ± 1.52 13.61 ± 1.12 15.96 ± 1.02 t = -48.79 d = -2.23 < 0.001 Gender, N (%) Boy 1272 (54.1%) 393 (52.4%) 879 (54.8%) χ² = 1.12 φ = 0.02 0.289 Gril 1081 (45.9%) 357 (47.6%) 724 (45.2%) Ma, N (%) Positive 333 (14.2%) 174 (23.2%) 159 (9.9%) χ² = 73.09 φ = 0.18 < 0.001 Negative 2020 (85.8%) 576 (76.8%) 1444 (90.1%) Pa, N (%) Positive 298 (12.7%) 131 (17.5%) 167 (10.4%) χ² = 22.32 φ = 0.10 Negative 2055 (87.3%) 619 (82.5%) 1436 (89.6%) SeP, N (%) Positive 488 (20.7%) 222 (29.6%) 266 (16.6%) χ² = 51.79 φ = 0.15 Negative 1865 (79.3%) 528 (70.4%) 1337 (83.4%) SoP, N (%) Positive 607 (25.8%) 219 (29.2%) 388 (24.2%) χ² = 6.40 φ = 0.05 0.011 Negative 1746 (74.2%) 531 (70.8%) 1215 (75.8%) ScP, N (%) Positive 285 (12.1%) 82 (10.9%) 203 (12.7%) χ² = 1.28 φ = 0.02 0.258 Negative 2068 (87.9%) 668 (89.1%) 1400 (87.3%) PTSD, N (%) Positive 137 (5.8%) 91 (12.1%) 46 (2.9%) χ² = 78.28 φ = 0.18 < 0.001 Negative 2216 (94.2%) 659 (87.9%) 1557 (97.1%) TicD, N (%) Positive 617 (26.2%) 261 (34.8%) 356 (22.2%) χ² = 41.23 φ = 0.13 Negative 1736 (73.8%) 489 (65.2%) 1247 (77.8%) GAD, N (%) Positive 219 (9.3%) 93 (12.4%) 126 (7.9%) χ² = 11.94 φ = 0.07 Negative 2134 (90.7%) 657 (87.6%) 1477 (92.1%) CD, N (%) Positive 72 (3.1%) 41 (5.5%) 31 (1.9%) χ² = 20.32 φ = 0.09 Negative 2281 (96.9%) 709 (94.5%) 1572 (98.1%) Exc1, N (%) Positive 92 (3.9%) 40 (5.3%) 52 (3.2%) χ² = 5.39 φ = 0.05 0.02 Negative 2261 (96.1%) 710 (94.7%) 1551 (96.8%) Exc2, N (%) Positive 57 (2.4%) 33 (4.4%) 24 (1.5%) χ² = 17.01 φ = 0.09 < 0.001 Negative 2296 (97.6%) 717 (95.6%) 1579 (98.5%) PHQ, N (%) Positive 342 (14.5%) 166 (22.1%) 176 (11.0%) χ² = 50.28 φ = 0.15 Negative 2011 (85.5%) 584 (77.9%) 1427 (89.0%) OCD, N (%) Positive 701 (29.8%) 269 (35.9%) 432 (26.9%) χ² = 19.00 φ = 0.09 Negative 1652 (70.2%) 481 (64.1%) 1171 (73.1%) NSSI, N (%) Positive 76 (3.2%) 49 (6.5%) 27 (1.7%) χ² = 36.90 φ = 0.13 Negative 2277 (96.8%) 701 (93.5%) 1576 (98.3%) 1) Continuous variables are presented as mean ± SD and compared using independent-samples t tests. 2) Categorical variables are presented as N (%) and compared using chi-square tests. 3) Effect sizes are reported as Cohen’s for continuous variables and phi coefficient (φ) for categorical variables. 2.2 Measures A school-based mental health screening tool was employed to evaluate NSSI and associated symptoms. The network analyses included variables such as manic symptoms (Ma), panic symptoms (Pa), separation anxiety symptoms (SeP), social anxiety symptoms (SoP), school anxiety symptoms (ScP), post-traumatic stress symptoms (PTSD), tic symptoms (TicD), generalized anxiety symptoms (GAD), conduct problems (CD), substance/drug-related factors (Exc1), physical illness-related factors (Exc2), depressive symptoms (PHQ), obsessive-compulsive symptoms (OCD), and NSSI. For clarity and interpretation, these 14 symptom categories were generally grouped into five overarching spectra: anxiety (Pa, GAD, SoP, ScP, SeP), trauma-emotion-self-injury (PTSD, PHQ, NSSI), obsessive-compulsive/tics (OCD, TicD), externalizing/activation (CD, Ma), and substance-somatic factors (Exc1, Exc2). These symptom domains were developed based on widely used screening tools for children and adolescents. Depressive and obsessive-compulsive symptoms were evaluated using the Patient Health Questionnaire for Adolescents (PHQ-A) [ 22 ] and the Short OCD Screener (SOCS) [ 23 ], respectively. Non-suicidal self-injury (NSSI) was assessed with the Non-Suicidal Self-Injury Assessment Tool (NSSI-AT) [ 24 ]. The mania module relied on the Child Mania Rating Scale-Parent Version (CMRS-P) [ 25 ]. Anxiety-related symptoms—including panic, separation anxiety, social anxiety, school anxiety, and generalized anxiety—were measured with the Screen for Child Anxiety Related Emotional Disorders (SCARED) and its Chinese validation studies [ 26 , 27 ]. Other symptom areas, such as post-traumatic stress, tics, conduct problems, substance or drug use, and physical health issues, were informed by common screening frameworks and relevant instruments used in child and adolescent mental health research [ 28 – 32 ]. Each symptom domain was treated as an independent node to enhance clinical interpretability. Raw domain scores were used in the continuous network analyses, whereas binary screening status was used in the binary network analyses. Domains were classified as positive or negative according to predefined screening thresholds, as detailed in Supplementary Table S1 . 2.3 Statistical analysis All analyses were conducted in R version 4.5.2 using the readxl, dplyr, qgraph, bootnet, mgm, igraph, NetworkComparisonTest, and bnlearn packages. For the Gaussian graphical model (GGM), the tuning parameter γ was set to 0.25. A nonparametric bootstrap with 1,000 iterations was performed for both the GGM and Ising model. For the directed acyclic graph (DAG) analysis, 2,000 bootstrap resamples were used, and the average network retention threshold was set at 0.85. 2.3.1 Network estimation For raw symptom-domain scores, network structure was estimated using a Gaussian graphical model (GGM) implemented in bootnet::estimateNetwork with the default EBICglasso method based on Spearman correlations. Nodes represented symptom domains, and edges represented partial correlations after controlling for all other nodes. To examine associations among positive screening states, symptom domains were further recoded as binary variables based on predefined thresholds, and an Ising model was estimated using bootnet::estimateNetwork with the default IsingFit method. 2.3.2 Centrality, predictability, stability, and subgroup analysis Node centrality indices, including strength, betweenness, closeness, and expected influence, were calculated to assess the relative importance of symptom domains in the network. Node predictability was further estimated using the mgm package and visualized as an outer ring around each node. Regularization parameters were selected through 10-fold cross-validation with the AND rule. Predictability was expressed as R² for continuous variables and as 1 − nCC for categorical variables. Bootstrap procedures were used to evaluate network accuracy and stability. For both the GGM and Ising network, a nonparametric bootstrap was applied to estimate the accuracy of edge weights, and a case-dropping bootstrap was used to assess the stability of centrality indices under subsampling. Stability was primarily quantified using the correlation stability coefficient (CS-coefficient). To examine differences by educational stage and gender, GGM and Ising networks were estimated separately for junior high school versus senior high school students and for boys versus girls. Group differences in network structure, global strength, and specific edge weights were tested using the Network Comparison Test (NCT) with 1,000 permutations. 2.3.3 Bayesian network analysis To examine potential directional relationships among symptom domains, Bayesian network analysis was performed using directed acyclic graphs (DAGs). The analysis included all symptom domains, gender, and educational stage. Symptom domains were represented as numeric variables, while gender and educational stage were treated as categorical factors. The analysis used complete case data. The network structure was identified with a hill-climbing algorithm employing the bic-cg score from the bnlearn package. Edge stability was assessed using boot.strength with 2,000 bootstrap resamples, and the final network was formed with a strength threshold of 0.85. Regression-based edge effects were combined with the average DAG, with regression coefficients and statistical test results extracted for each retained edge to show the direction and strength of parent-child relationships. The direction indicated by the DAG reflects the estimated conditional dependencies based on the data and algorithm, suggesting possible orderings or pathways. 3 Results 3.1 Sample characteristics A total of 2,353 adolescents participated in the screening, comprising 750 junior high and 1,603 senior high students. The overall mean age was 15.21 ± 1.52 years, with junior high students averaging 13.61 ± 1.12 years, and senior high students averaging 15.96 ± 1.02 years; this age difference was statistically significant (t = -48.79, P < 0.001). There was no significant difference in gender distribution between the groups (χ² = 1.12, P = 0.29). The junior high group exhibited higher positive screening rates across most psychopathological dimensions compared to the senior high group. Notably, the junior high group had higher positive rates for manic, panic, separation anxiety, social anxiety, post-traumatic stress, tic, generalized anxiety, conduct problems, substance/drug factors, physical illness factors, depressive, obsessive-compulsive symptoms, and NSSI. However, no significant difference was noted in school anxiety symptoms (10.9% vs. 12.7%, P = 0.26). The baseline characteristics are summarized in Table 1 . 3.2 Network results 3.2.1 Overall network structure The GGM network showed dense conditional associations among the 14 nodes, with particularly strong connections observed within the internalizing symptom spectrum. The strongest connections in the GGM network were SeP-SoP (0.22), SoP-PHQ (0.21), and PHQ-OCD (0.17). The Ising network's overall structure was similar, with more prominent positive co-occurrence associations. The strongest edges here were Exc1-Exc2 (1.52) and CD-PHQ (1.3) (Fig. 1 and Supplementary Table S2-3). 3.2.2 Centrality results In the GGM network, PHQ showed the highest strength centrality (Strength = 1.15), followed by OCD (0.96), Pa (0.88), and GAD (0.84). The one-step expected influence (EI 1-step) results aligned with the strength estimates, with PHQ remaining the most central node. Similarly, the two-step expected influence (EI 2-step) results showed that PHQ (1.86), OCD (1.66), and Pa (1.48) were relatively central within the network (Fig. 2 and Supplementary Table S4). Stratified analyses by educational stage showed that the overall network structures were similar for junior and senior high school students, with differences in local core nodes and strong edge patterns. In the junior high school group, PHQ was the most central node (1.37), followed by OCD (0.87), GAD (0.86), and NSSI (0.84). In the senior high school group, OCD was the most central node (1.05), with PHQ next (1.03), then Pa (0.88), and GAD (0.81). Regarding local edge structures, the strongest edges in the junior high school group were SeP-SoP (0.21), SoP-PHQ (0.20), and PHQ-NSSI (0.18). In the senior high school group, the strongest edges were SeP-SoP (0.22), SoP-PHQ (0.21), and PHQ-OCD (0.19). In the Ising network based on positive screening status, PHQ was overall the most central node, followed by PTSD and OCD. After stratification by education level, PHQ remained most central among junior high school students (8.27), while PTSD was most central among senior high school students (8.19). (Fig. 2 and Supplementary Tables S4-S5) 3.2.3 Bridge centrality and stability results Bridge centrality analysis showed that in the GGM network, PHQ had the highest bridge expected influence (Bridge EI 1-step = 1.02), with OCD (0.86) and PTSD (0.68) following. For the bridge EI 2-step, PHQ (1.61) and OCD (1.50) remained the most significant bridge nodes. In the Ising network, the bridge effects were even stronger, with PHQ again leading in bridge expected influence, followed by PTSD and OCD. (Supplementary Figure S1 -2 and Tables S4) The case-dropping bootstrap analysis indicated good stability of strength centrality, with a CS-coefficient of 0.75, suggesting that the centrality results were relatively robust (Supplementary Figure S1 and Table S6). 3.2.4 Subgroup comparison results Subgroup analyses showed that global network connectivity was mostly similar across different educational levels and genders. When comparing educational stages, the difference in global strength was only 0.14 (P = 0.70) in the GGM network and 8.634 (P = 0.34) in the Ising network. For gender comparisons, the difference in global strength was minimal, with 0.00 (P = 1.00) in the GGM network and 0.166 (P = 0.97) in the Ising network. Although there were no significant differences in total network strength, some local connections varied between educational groups. Notable differences in the GGM network included edges like ScP-NSSI, Exc1-PHQ, and PTSD-Exc1. In the Ising network, the differing edges included SeP-ScP, Ma-PTSD, and CD-NSSI. (Supplementary Figure S3-4 and Tables S7-S8) 3.3 DAG results and edge-effect regression estimates The average DAG indicates that educational stage and gender are relatively prominent within the network. PHQ, GAD, OCD, and PTSD are situated in the middle layer and have numerous outgoing connections. Specifically, PHQ connects to Ma, Pa, SoP, and NSSI; GAD links to Pa, ScP, and OCD; OCD is connected to SeP, PTSD, TicD, and NSSI; and PTSD connects to CD. NSSI is mainly positioned downstream, with PHQ, OCD, gender, and educational stage identified as its direct parent nodes. Edge-effect regression analyses further reveal that PHQ (β = 0.07, P < 0.001), OCD (β = 0.35, P < 0.001), and female gender (β = 0.21, P < 0.001) are positively associated with NSSI. Conversely, being in senior high school shows a negative association with NSSI (β = -0.14, P < 0.001) (Fig. 3 and Supplementary Table S9). The typical network revealed three main pathways linking emotional symptoms to NSSI: PHQ→NSSI, PHQ→OCD→NSSI, and PHQ→GAD→Pa→NSSI. Stratified analysis indicated that in the junior high school group, a pathway from gender to GAD, then to PHQ, and finally to NSSI was found, along with a chain from NSSI to OCD, TicD, and PTSD. In the senior high school group, pathways such as PHQ→NSSI→Ma and PHQ→NSSI→TicD were identified. (Fig. 3 ) 4 Discussion 4.1 Strongest edges in the symptom network and their implications In the overall symptom network of ethnic minority adolescents, the strongest links connect separation anxiety with social anxiety, PHQ with SoP, and PHQ with OCD. This indicates that the symptom structure in this group primarily centers on closely related internalizing problems [ 33 – 35 ]. Rather than existing as distinct symptom clusters, anxiety, depression, and obsessive-compulsive symptoms often form interconnected groups that may reinforce each other's persistence throughout the network. The strong link between separation anxiety and social anxiety implies that the distress from separation and social judgment fears may develop simultaneously during adolescence. Past studies show that anxiety and depression symptoms often cluster within adolescent internalizing networks, with their connections intensifying under continuous stress [ 33 , 35 ]. In ethnic minority adolescents, this pattern might reflect typical developmental overlaps and contextual factors such as boarding-school experiences, changes in parent-child proximity, shifts in peer support, and stress from adapting to school [ 36 – 38 ]. Research suggests that life boarding and diminished social support are closely tied to emotional and internalizing problems in teens [ 36 – 38 ]. Clinically significant separation anxiety might indicate a broader social-emotional vulnerability rather than just an isolated anxiety disorder, emphasizing the need for comprehensive school screening [ 35 , 39 ]. The strong connection between PHQ and SoP indicates that in ethnic minority adolescents, low mood and social withdrawal may form a central internalizing axis. Past research shows that depressive symptoms and anxiety are often linked through factors such as low self-esteem, excessive worry, and social avoidance [ 33 , 34 ]. Avoidance related to social anxiety can restrict access to peer support and positive social interactions, leading to increased loneliness and depression. Additionally, symptoms like anhedonia, low energy, and self-devaluation can impair social functioning and lead to further withdrawal [ 40 , 41 ]. This reciprocal cycle suggests that social withdrawal at school should be viewed not merely as a temperament but as a potential warning sign of broader emotional risks. The strong connection between PHQ and OCD further implies that emotional distress and obsessive-compulsive thought patterns may be closely linked in ethnic minority adolescents. Prior studies have shown significant overlap between depressive and obsessive-compulsive symptoms, with rumination and intolerance of uncertainty likely serving as shared underlying mechanisms [ 42 – 45 ]. OCD symptoms have also been linked to self-harm and suicide risks, suggesting they can be important warning signs rather than just secondary effects of distress [ 46 – 48 ]. Practically, when depressive symptoms are detected during school screenings or clinical assessments, it’s important to also evaluate obsessive-compulsive symptoms and possible self-injury risk at the same time, instead of sequentially [ 42 , 48 ]. 4.2 Central nodes and differences by educational stage In the full network, PHQ was the most central node, suggesting that depressive symptoms might serve as a key hub within the symptom system of ethnic minority adolescents. This supports earlier findings indicating that depressive symptoms often have high centrality and bridge different symptom clusters in networks of adolescent depression and anxiety and are strongly linked to symptom severity and clinical outcomes [ 49 , 50 ]. In this setting, PHQ appears to reflect multiple symptom dimensions; it may represent a core process through which anxiety, obsessive-compulsive symptoms, trauma-related symptoms, and functional impairment become more interconnected. This centrality could be especially important during adolescence, a period when self-concept, social comparison, and emotion regulation are still developing [ 34 , 51 , 52 ]. High-altitude chronic hypoxia and related environmental stressors might also strengthen the connection among internalizing symptoms in this population [ 53 , 54 ]. In the junior high school group, PHQ remained the central node, suggesting that symptom organization in early adolescence might be more dominated by emotional distress. Early adolescence is characterized by rapid physical growth, cognitive changes, and heightened sensitivity to social evaluation, while regulatory skills are still developing. In this context, depressive symptoms are more prone to spread throughout the entire symptom network [ 51 , 52 ]. This finding supports previous research indicating that emotional symptoms tend to be more central during early developmental stages [ 35 , 55 ]. Clinically, school mental health programs for junior high students should prioritize early detection of low mood, feelings of helplessness, and initial functional impairments before symptoms become more widespread. In the senior high school group, OCD became the most central node, replacing PHQ, which suggests a reorganization of the network during development. As adolescents mature cognitively and face increased academic stress and performance demands, they may become more prone to repetitive thinking, intrusive thoughts, and control-focused coping strategies. This shift could strengthen obsessive-compulsive features within the overall symptom network [ 42 – 45 , 56 ]. When OCD symptoms occupy the center, they may elevate internal tension, feelings of loss of control, and functional burdens, further linking them to depression and anxiety symptoms [ 44 , 56 ]. Therefore, assessments in senior high school students should extend beyond general emotional distress to include behaviors like checking, reassurance seeking, excessive need for control, and cognitive rigidity. Furthermore, PTSD played a significant role in the senior high school group's Ising network, indicating trauma-related burden may influence co-occurring positive symptoms in late adolescence. This does not mean the network is mainly driven by trauma; rather, trauma-related symptoms might gain relevance once the burden exceeds a threshold identified by the Ising model. PTSD is linked to depression and anxiety and may increase connectivity through mechanisms like negative self-concept, hyperarousal, and emotional dysregulation [ 57 , 58 ]. Studies also show trauma exposure, bullying, adverse childhood experiences, and adolescent NSSI are related [ 58 – 60 ]. In ethnic minority adolescents living in high-altitude regions, environmental stressors such as earthquakes may heighten the importance of trauma within the symptom network of high-risk students [ 59 ]. These findings suggest trauma-related burdens underpin increasing symptom complexity in high-risk older adolescents, so screening should include symptoms like intrusive memories, hypervigilance, avoidance, and emotional numbness, beyond depression and anxiety, to better identify vulnerable individuals [ 57 – 60 ]. 4.3 Hierarchical structure and key pathways in the Bayesian network Educational stage and gender serve as foundational factors, functioning as background variables unaffected by symptoms. Prior studies show that educational level impacts symptom severity and the positioning of crucial nodes and NSSI interventions within adolescent networks [ 55 , 61 ]. The influence of gender indicates it affects risk through specific pathways rather than overall network differences [ 62 ]. These factors set the stage for symptom risk in this population. Furthermore, conditions like PHQ, GAD, OCD, and PTSD mainly appear in the middle layer. Symptoms here are often clinically important because they connect upstream vulnerabilities to downstream behaviors [ 63 , 64 ]. As a result, they may be key targets for interventions that block multiple pathways simultaneously. Furthermore, NSSI mainly appeared as a downstream outcome, with PHQ, OCD, gender, and educational stage serving as direct parent nodes. Self-injury is probably a distal behavioral result influenced by accumulated emotional, compulsive, and demographic risks, rather than the main driver within the symptom network. It often co-occurs with depressive symptoms, anxiety, emotional dysregulation, and other psychiatric disorders [ 15 , 65 , 66 ]. Key pathways indicate that the direct link from PHQ to NSSI shows depressive symptoms can directly lead to self-injury. Factors like low mood, helplessness, self-devaluation, and ongoing emotional pain are common triggers for adolescent self-injury, with NSSI often used as a maladaptive way to regulate emotions or regain temporary control [ 65 – 68 ]. Early detection and intervention for depressive symptoms are crucial in reducing NSSI risk among ethnic minority adolescents. Moreover, the pathway from PHQ through OCD to NSSI suggests that obsessive-compulsive symptoms act as a mediator, strengthening the link between emotional distress and self-injury. Negative emotions can increase discomfort with uncertainty and internal pain, leading to repetitive thoughts, checking behaviors, or rituals aimed at regaining control [ 43 – 45 ]. When depression and obsessive-compulsive symptoms occur together, it may indicate that the risk is particularly high or that the individual is at greater risk of self-harm [ 45 – 48 ]. Therefore, assessing NSSI risk should include not only depression but also rumination, the need for control, and behavioral rigidity. Educational-stage stratification reveals developmental differences in how pathways are organized. For junior high students, the sequence gender→GAD→PHQ→NSSI indicates that anxiety symptoms tend to appear earlier, and as depression symptoms increase, the risk of NSSI also rises [ 51 , 62 ]. Additionally, gender may indirectly influence NSSI through internalizing symptoms; females generally report higher levels of anxiety, depression, and NSSI, whereas males are more prone to externalizing problems [ 65 , 69 ]. Junior high school girls may face a higher risk of NSSI linked to the anxiety-depression pathway. For senior high school students, pathways such as PHQ→NSSI→Ma and PHQ→NSSI→TicD suggest that NSSI may become part of a more complex network of symptoms in later adolescence. At this stage, NSSI not only indicates immediate behavioral risks but also reflects greater psychological complexity [ 61 , 69 ]. Its connections with mania-like symptoms and tic-related symptoms imply that NSSI could serve as a warning sign of potential clinical issues [ 48 , 70 ]. Therefore, early detection and intervention for NSSI in senior high school students could help reduce both current self-injury risks and future clinical problems associated with more complex symptom patterns. 4.4 Clinical implications These findings suggest that mental health services for ethnic minority adolescents should adopt stage-specific screening and step intervention strategies across different educational levels. For junior high students, the symptom network mainly revolves around internalizing issues, particularly depression and anxiety, indicating this stage as a crucial early intervention opportunity in school-based mental health programs. Therefore, efforts should focus on early detection and low-level management of internalizing symptoms. In schools, emphasis might be placed on cognitive-behavioral therapy (CBT)-based psychoeducation, emotion regulation training, group activities, and collaboration between families and schools, with particular attention to warning signs such as low mood, helplessness, persistent worry, social withdrawal, and functional decline, ideally before symptoms escalate or NSSI appears [ 71 – 73 ]. Additionally, once NSSI is identified, it should be considered a critical warning sign, even if other symptoms are not yet evident, warranting prompt risk assessment and intervention support systems [ 74 – 76 ]. In senior high school students, intervention efforts may need to focus on integrated assessment frameworks and targeted treatment for obsessive-compulsive symptoms, trauma-related problems, and NSSI risk. For adolescents who mainly present with repetitive checking, reassurance seeking, and an excessive need for control, exposure and response prevention (ERP) may be considered [ 77 , 78 ]. Those with trauma-related symptoms such as intrusive memories, hyperarousal, avoidance, and emotional numbing may benefit from trauma-focused cognitive behavioral therapy (TF-CBT) [ 79 , 80 ]. For students with persistent NSSI risk, emotion dysregulation, or poor impulse control, DBT-based techniques may also be considered [ 81 – 83 ]. Overall, these findings highlight the importance of jointly evaluating obsessive-compulsive symptoms, trauma burden, and self-injury risk during screening and referral in senior high school students, in order to improve identification accuracy and better tailor interventions. 4.5 Strengths and limitations This study focused on ethnic minority adolescents with a distinctive eco-cultural background and applied educational-stage-stratified symptom network analysis based on screening data from six schools in the study region. The findings revealed both shared and distinctive patterns of psychological symptoms across developmental stages and risk levels, with particular emphasis on identifying key symptoms and risk pathways. These results may provide a useful basis for school screening, clinical referral, and tiered intervention strategies. Several limitations should be acknowledged. First, because of the cross-sectional design, neither the network associations nor the Bayesian pathways can be interpreted as evidence of strict causality. Although the Bayesian framework provides useful clues about potential directional relationships, these findings still require further validation in larger, multicenter, and longitudinal samples [ 52 , 64 ]. Second, although ecological and contextual factors such as high-altitude environment, cultural stress, boarding-school experience, and social support were considered in interpretation, they were not directly included in the network models. Future studies should incorporate family functioning, trauma exposure, social support, adaptation to high-altitude living, and relevant physiological indicators to better explain the observed developmental differences and NSSI-related pathways [ 44 , 66 ]. Finally, the current study did not include intervention or predictive validation. Therefore, the findings mainly reveal structural relationships and cannot yet be used directly to develop mature screening tools. Future research could build on the core nodes, bridging pathways, and developmental differences identified here to create and validate stratified screening strategies and targeted interventions for real-world school settings [ 62 , 63 ]. Conclusion In this sample of ethnic minority adolescents in Ngawa, PHQ emerged as the most central element in the symptom network, with obsessive-compulsive and trauma-related symptoms also occupying important positions. NSSI was primarily located downstream, suggesting that early screening and prevention should prioritize depressive, obsessive-compulsive, and trauma-related symptoms. Differences in core nodes and potential pathways across educational stages further suggest that school-based screening and intervention strategies may benefit from being tailored separately for junior and senior high school students. Declarations Ethics approval and consent to participate The Ethics Committee approved this study under approval number 2020-k021-02. Information consent was obtained from all participants and their legal guardians. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant NO. LLSQN25H090001; Zhejiang Provincial Medical health Project under Grant NO. 2025KY1997; Wenzhou Science and Technology Bureau under Grant No. ZS2024001; Zhejiang Provincial Medical and Health Science Project Grant No. 2024KY590 and Lishui City Science and Technology Project Grant No. 2024GYX44. Author Contribution Ke Zhao was responsible for study design, manuscript revision, and overall supervision. Fan Wang and Wei Jin contributed equally to this work. Fan Wang conducted the data analysis and drafted the manuscript. Lan Hong, Zhaoxuan Liu, Jianuo Shi, and Si Yu Tong contributed to data collection. Gang Chen and Wei Tang contributed to manuscript revision. Acknowledgement The authors would like to thank all adolescents and their legal guardians for their participation in this study, as well as the staff who contributed to data collection. Data Availability All data generated or analyzed in this study are included in this published article and its supplementary information files. The datasets and analysis codes are available from the corresponding author upon reasonable request. References Lucena NL, Rossi TA, Azevedo LMG, Pereira M. Self-injury prevalence in adolescents: a global systematic review and meta-analysis. Child Youth Serv Rev. 2022;142:106634. https://doi.org/10.1016/j.childyouth.2022.106634 . Denton EG, Álvarez K. The global prevalence of nonsuicidal self-injury among adolescents. JAMA Netw Open. 2024;7(6):e2415406. https://doi.org/10.1001/jamanetworkopen.2024.15406 . 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Child Abuse Negl. 2022;134:105899. https://doi.org/10.1016/j.chiabu.2022.105899 . Webb C, Hayes A, Grasso D, Laurenceau JP, Deblinger E. Trauma-focused cognitive behavioral therapy for youth: effectiveness in a community setting. Psychol Trauma. 2014;6(5):555–62. https://doi.org/10.1037/a0037364 . Kothgassner OD, Goreis A, Robinson K, Huscsava MM, Schmahl C, Plener PL, et al. Efficacy of dialectical behavior therapy for adolescent self-harm and suicidal ideation: a systematic review and meta-analysis. Psychol Med. 2021;51(7):1057–67. https://doi.org/10.1017/S0033291721001355 . McCauley E, Berk MS, Asarnow JR, Adrian M, Cohen J, Korslund K, et al. Efficacy of dialectical behavior therapy for adolescents at high risk for suicide: a randomized clinical trial. JAMA Psychiatry. 2018;75(8):777–85. https://doi.org/10.1001/jamapsychiatry.2018.1109 . Bettis AH, Liu RT, Walsh BW, Klonsky ED. Treatments for self-injurious thoughts and behaviors in youth: progress and challenges. Evid Based Pract Child Adolesc Ment Health. 2020;5(3):354–64. https://doi.org/10.1080/23794925.2020.180675 . 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-9325556","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627603270,"identity":"6a7379fe-7962-4cec-9cb6-aba0921b3626","order_by":0,"name":"Fan Wang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wang","suffix":""},{"id":627603271,"identity":"c1041d22-18ba-4e2a-8bd5-434ba56cc1ac","order_by":1,"name":"Wei Jin","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Jin","suffix":""},{"id":627603273,"identity":"14353c43-e31f-4623-8441-a3d73d55c2d6","order_by":2,"name":"Lan Hong","email":"","orcid":"","institution":"Lishui Second People's Hospital Affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Hong","suffix":""},{"id":627603278,"identity":"a2b13718-6309-42f6-b4f4-9d378df90ef6","order_by":3,"name":"Zhaoxuan Liu","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoxuan","middleName":"","lastName":"Liu","suffix":""},{"id":627603282,"identity":"8a3d1875-fb70-47bd-b4ec-2b28ec0d472a","order_by":4,"name":"Jianuo Shi","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianuo","middleName":"","lastName":"Shi","suffix":""},{"id":627603287,"identity":"312f72da-e6cc-4b86-bafe-788bcea81100","order_by":5,"name":"Siyu Tong","email":"","orcid":"","institution":"Lishui Second People's Hospital Affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Tong","suffix":""},{"id":627603294,"identity":"823a1acc-e36e-456d-82a4-dc2eba387459","order_by":6,"name":"Gang Chen","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Chen","suffix":""},{"id":627603296,"identity":"697b5d84-a701-4de2-b8a9-1868dda591d7","order_by":7,"name":"Wei Tang","email":"","orcid":"","institution":"Qingdao Zhengyang Psychological Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Tang","suffix":""},{"id":627603305,"identity":"6003a6ea-8f01-4a5c-a5ae-300c93593adf","order_by":8,"name":"Ke Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDACCQYGxgYGGwiHhwQtaaRrOUyCFvnZzQ8fzqg4n7h2RgLjg7dtDPLmhLQwzjlmbLjhzO3EbTcSmA3ntjEY7mwgoIVZIsFM8mHb7VygFjZp3jaGBIMDBLSwSaR/k3z47xxIC/tvorTwSOSYSW5sOAC2hZkoLRISOcWGM44l128787BZcs45CcMNhLTIz0jf+LCnxs7Y7HjywQ9vymzkCdqCBIDRA46mUTAKRsEoGAWUAwCDfUI2dp8KaQAAAABJRU5ErkJggg==","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ke","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-04-05 10:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9325556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9325556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107656281,"identity":"8a6693fe-9f3b-4604-84da-2dc0cdc94e1c","added_by":"auto","created_at":"2026-04-23 15:56:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":706395,"visible":true,"origin":"","legend":"\u003cp\u003eGaussian graphical models of symptom networks across educational stages in adolescents.\u003c/p\u003e\n\u003cp\u003e(A) overall sample; (B) junior high school group; (C) senior high school group.\u003c/p\u003e","description":"","filename":"Figure1.GaussiangraphicalmodelsofsymptomnetworksacrosseducationalstagesinadolescentsinTibet.png","url":"https://assets-eu.researchsquare.com/files/rs-9325556/v1/aba78abe90767e0ae1dd489a.png"},{"id":107707694,"identity":"ec6d4df1-734f-41cb-91f6-d8bb6b814dfc","added_by":"auto","created_at":"2026-04-24 09:20:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1515208,"visible":true,"origin":"","legend":"\u003cp\u003eCentrality indices of the symptom network by educational stage among adolescents.\u003c/p\u003e","description":"","filename":"Figure2.CentralityindicesofthesymptomnetworkintheoverallsampleandbyeducationalstageamongadolescentsinTibet..png","url":"https://assets-eu.researchsquare.com/files/rs-9325556/v1/63285a99c4696292da5e1210.png"},{"id":107707725,"identity":"0744fbb9-faaa-45eb-b376-6b5656e205ae","added_by":"auto","created_at":"2026-04-24 09:21:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1601394,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian directed acyclic graph models of symptom networks by educational stage among adolescents.\u003c/p\u003e\n\u003cp\u003e(A) overall sample; (B) junior high school group; (C) senior high school group.\u003c/p\u003e","description":"","filename":"Figure3.BayesiandirectedacyclicgraphmodelsofsymptomnetworksintheoverallsampleandbyeducationalstageamongadolescentsinTibet..png","url":"https://assets-eu.researchsquare.com/files/rs-9325556/v1/b45e80c7f80bea05c4ff3e52.png"},{"id":108803681,"identity":"2345eddd-8a6b-4bab-88f8-83c95bd724fe","added_by":"auto","created_at":"2026-05-08 15:03:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1930537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9325556/v1/e252c0ef-2f80-4708-82b3-8be323125618.pdf"},{"id":107707427,"identity":"6baea329-5c95-46fc-933b-7213a6893ab3","added_by":"auto","created_at":"2026-04-24 09:20:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":132015533,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytablesandFigures20260409.docx","url":"https://assets-eu.researchsquare.com/files/rs-9325556/v1/287efb30365d371f08384b1a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Educational Stage Differences in Symptom Networks and Bayesian Pathways of Non-Suicidal Self-Injury Among Ethnic Minority Adolescents in Ngawa Prefecture, China","fulltext":[{"header":"1 Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Research Background of NSSI Among Ethnic Minority Adolescents in Ngawa\u003c/h2\u003e \u003cp\u003eNon-suicidal self-injury (NSSI) is a prevalent and clinically important risk behavior during adolescence. Meta-analyses indicate that about 17.7% of adolescents worldwide engage in NSSI [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Meanwhile, a scoping review reports a lifetime prevalence of roughly 25% among Chinese adolescents [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], highlighting its significant public health impact in China. Besides its high occurrence, NSSI is strongly associated with depression, anxiety, functional impairment, and an increased risk of future suicide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, in Shanghai's junior middle school students, the 12-month NSSI prevalence was 21.7%, with anxiety disorder being a common comorbidity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEthnic minority adolescents living in high-altitude, rural, and boarding-school contexts may face a distinct socioecological and cultural environment compared with adolescents in other regions. Factors such as plateau hypoxia, rural and pastoral lifestyles, boarding school experiences, caregiving differences, and cultural adaptation pressures can influence emotional regulation, symptom expression, and help-seeking behaviors [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research has shown a considerable burden of depressive symptoms among children and adolescents in high-altitude western China [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, studies from minority rural and pastoral communities in China have reported a high prevalence of NSSI and a significant association with help-seeking behaviors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings suggest that NSSI in ethnic minority adolescents may not simply represent a regional variation of a common adolescent issue, but may also be shaped by specific contextual factors, resulting in distinctive symptom patterns [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Most existing studies in these populations have focused on prevalence, help-seeking, or isolated psychosocial factors. There is limited understanding of NSSI's role within a broader psychopathological system, including its immediate links to other symptoms and its organizational structure. In this context, NSSI may be better viewed as part of a larger symptom network involving depression, anxiety, trauma signs, and other negative psychological experiences [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hence, a more comprehensive exploration of NSSI within this environment is essential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 The Complex Associations Between NSSI and Related Psychiatric Symptoms\u003c/h2\u003e \u003cp\u003eNSSI rarely occurs in isolation and is typically associated with depressive symptoms, anxiety, traumatic experiences, negative emotional states, and difficulties with emotion regulation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Research consistently highlights strong links between NSSI and depressive symptoms, particularly depressed mood, negative self-view, and feelings of worthlessness [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Childhood trauma, especially emotional abuse, significantly influences adolescent NSSI. Research indicates NSSI, depressive symptoms, and childhood trauma are closely connected, with emotional abuse showing a strong link to NSSI. Additionally, in networks assessing NSSI and depressive symptoms, certain depressive symptoms and self-injury-related nodes can act as crucial bridging points [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to depressive symptoms and trauma, NSSI frequently co-occurs with anxiety, sleep problems, peer issues, and emotional regulation difficulties [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For Chinese adolescents, anxiety disorder stands out as a common comorbidity associated with NSSI [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Longitudinal research suggests that the relationships between NSSI, depression, and anxiety are bidirectional at the symptom level, rather than unidirectional [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These findings overall imply that NSSI should not be viewed solely as a secondary consequence of a specific disorder or risk factor. Instead, it may function within a complex network of interconnected symptoms, which interact with highly linked symptoms to sustain psychological distress and risky behaviors [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis concern could be particularly significant for ethnic minority adolescents living in high-altitude western China. Their ecological and sociocultural environment might expose them to a more intricate set of mental health challenges across domains including sleep, trauma responses, and behavioral adaptation [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For instance, previous research has revealed notable differences in sleep quality among ethnic minority adolescents living at different altitudes, with poorer sleep observed at higher elevations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among ethnic minority youths affected by post-earthquake high-altitude conditions, the rate of probable post-traumatic stress disorder reached 17.8%, highlighting the prominence of trauma-related symptoms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, some studies suggest that ethnic minority adolescents in western China may show elevated levels of problematic mobile phone use compared with Han adolescents, with negative effects on multiple aspects of quality of life [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Vulnerable groups, such as ethnic minority orphaned adolescents, may also face heightened mental health risks, with self-control potentially acting as a protective factor via self-esteem [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Overall, these data suggest that ethnic minority adolescents in such settings may experience a complex combination of trauma, sleep disturbances, emotional issues, and behavioral challenges, which are interlinked [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Traditional correlation or regression methods often cannot clearly define NSSI\u0026rsquo;s role in the symptom network or the strength of its connections. Therefore, a method that directly examines relationships between symptoms is needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Symptom network analysis in NSSI\u003c/h2\u003e \u003cp\u003eRecently, symptom network analysis has gained popularity in mental health research. It treats symptoms as an interconnected system in which they can influence and support each other directly. By studying these relationships, network analysis helps identify key symptoms, bridging symptoms, and local clusters [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Unlike traditional variable-centered approaches, this method is particularly valuable for exploring the internal structure of comorbid symptom networks and understanding the role of transdiagnostic risk behaviors, such as NSSI, within multidimensional psychopathological frameworks [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Past research has employed network analysis to examine how NSSI correlates with depressive symptoms, trauma, anxiety, and sleep issues in adolescents. These studies indicate that certain emotional, traumatic, and cognitive symptoms may be central or directly connected to NSSI [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, combining techniques such as Bayesian networks or cross-lagged models with network analysis can provide preliminary insights into the directionality of symptom relationships, aiding in identifying upstream factors, proximal correlations, and potential transmission pathways related to NSSI [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This strategy not only underscores factors linked to NSSI but also clarifies which symptoms are most strongly associated, where NSSI fits within the symptom network, and which symptoms could be effective intervention targets [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, evidence remains limited for ethnic minority adolescents living in high-altitude areas of western China, especially in school-based samples from Ngawa, Sichuan. Specifically, there is still a lack of studies that systematically examine the structural position of NSSI and its potential pathway relationships from the perspective of multidimensional symptom networks. Accordingly, the present study investigated NSSI among ethnic minority adolescents in Ngawa from a symptom network perspective. First, we constructed networks across 14 symptom dimensions to identify the structural position of NSSI and its key connections within the overall network. Second, we compared network structures across educational stages, with gender-stratified analyses serving as supplementary tests. Third, we used Bayesian directed network analysis to explore upstream factors, downstream links, and potential pathways related to NSSI. Based on a school sample from Ngawa, Sichuan, this study aimed to provide structural evidence to inform early identification, risk assessment, and intervention planning for ethnic minority adolescents in this region.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and procedure\u003c/h2\u003e \u003cp\u003eThis cross-sectional, school-based screening was conducted in six junior and senior high schools in Ngawa Tibetan and Qiang Autonomous Prefecture, Sichuan Province (hereinafter referred to as Ngawa Prefecture) from October 2022 to April 2023. The schools coordinated the survey, and students independently completed the questionnaires in a group setting. Informed written consent was obtained from all participants and their guardians prior to participation. The study received approval from the ethics committee of Wenzhou Kangning Hospital (2020-k021-02) and complied with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eThe inclusion criteria included: 1) ages 12\u0026ndash;18; 2) ability to independently complete the questionnaire; 3) normal vision, hearing, and cognitive function; 4) sufficient Chinese/Mandarin comprehension to understand the questions and response instructions; and 5) informed consent provided. The exclusion criteria encompassed: 1) severe physical illness; 2) cognitive impairments that hinder questionnaire understanding, whether known or self-reported; 3) ongoing psychiatric medication or psychological treatment; and 4) indications of careless or invalid responses.\u003c/p\u003e \u003cp\u003eTo ensure data quality, questionnaires underwent a two-step screening and cleaning process. First, 207 participants who did not meet the eligibility criteria were excluded. Second, 26 questionnaires considered low-quality or invalid were removed, including those with straight-line responses, logically inconsistent answers, very short completion times, or obviously implausible responses. The final sample comprised 2,353 adolescents, including 750 junior high and 1,603 senior high school students. Tibetan participants comprised 1,850 (78.6%), Hui participants 12.1%, Qiang participants 7.5%, and other ethnic minority groups 1.8%. Of all participants, 1,272 were boys (54.1%), and 1,081 were girls (45.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the screening sample by educational stage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJunior high (n\u0026thinsp;=\u0026thinsp;750)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSenior high (n\u0026thinsp;=\u0026thinsp;1603)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003et = -48.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed = -2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1272 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e393 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e879 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1081 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e357 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e724 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMa, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174 (23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 73.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 (85.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e576 (76.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1444 (90.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePa, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (17.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 22.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2055 (87.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e619 (82.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1436 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeP, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 51.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1865 (79.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e528 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1337 (83.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoP, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e388 (24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1746 (74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e531 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1215 (75.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScP, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2068 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e668 (89.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1400 (87.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePTSD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 78.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2216 (94.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1557 (97.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTicD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e617 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e356 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 41.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1736 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e489 (65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1247 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 11.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2134 (90.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e657 (87.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1477 (92.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 20.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2281 (96.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e709 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1572 (98.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExc1, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2261 (96.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e710 (94.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1551 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExc2, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 17.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2296 (97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e717 (95.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1579 (98.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePHQ, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e342 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 50.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011 (85.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e584 (77.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1427 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOCD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e701 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e432 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1652 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e481 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1171 (73.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNSSI, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eχ\u0026sup2; = 36.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eφ\u0026thinsp;=\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2277 (96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e701 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1576 (98.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e1) Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared using independent-samples t tests.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e2) Categorical variables are presented as N (%) and compared using chi-square tests.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e3) Effect sizes are reported as Cohen\u0026rsquo;s for continuous variables and phi coefficient (φ) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cp\u003eA school-based mental health screening tool was employed to evaluate NSSI and associated symptoms. The network analyses included variables such as manic symptoms (Ma), panic symptoms (Pa), separation anxiety symptoms (SeP), social anxiety symptoms (SoP), school anxiety symptoms (ScP), post-traumatic stress symptoms (PTSD), tic symptoms (TicD), generalized anxiety symptoms (GAD), conduct problems (CD), substance/drug-related factors (Exc1), physical illness-related factors (Exc2), depressive symptoms (PHQ), obsessive-compulsive symptoms (OCD), and NSSI. For clarity and interpretation, these 14 symptom categories were generally grouped into five overarching spectra: anxiety (Pa, GAD, SoP, ScP, SeP), trauma-emotion-self-injury (PTSD, PHQ, NSSI), obsessive-compulsive/tics (OCD, TicD), externalizing/activation (CD, Ma), and substance-somatic factors (Exc1, Exc2).\u003c/p\u003e \u003cp\u003eThese symptom domains were developed based on widely used screening tools for children and adolescents. Depressive and obsessive-compulsive symptoms were evaluated using the Patient Health Questionnaire for Adolescents (PHQ-A) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the Short OCD Screener (SOCS) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], respectively. Non-suicidal self-injury (NSSI) was assessed with the Non-Suicidal Self-Injury Assessment Tool (NSSI-AT) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The mania module relied on the Child Mania Rating Scale-Parent Version (CMRS-P) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Anxiety-related symptoms\u0026mdash;including panic, separation anxiety, social anxiety, school anxiety, and generalized anxiety\u0026mdash;were measured with the Screen for Child Anxiety Related Emotional Disorders (SCARED) and its Chinese validation studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Other symptom areas, such as post-traumatic stress, tics, conduct problems, substance or drug use, and physical health issues, were informed by common screening frameworks and relevant instruments used in child and adolescent mental health research [\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEach symptom domain was treated as an independent node to enhance clinical interpretability. Raw domain scores were used in the continuous network analyses, whereas binary screening status was used in the binary network analyses. Domains were classified as positive or negative according to predefined screening thresholds, as detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R version 4.5.2 using the readxl, dplyr, qgraph, bootnet, mgm, igraph, NetworkComparisonTest, and bnlearn packages. For the Gaussian graphical model (GGM), the tuning parameter γ was set to 0.25. A nonparametric bootstrap with 1,000 iterations was performed for both the GGM and Ising model. For the directed acyclic graph (DAG) analysis, 2,000 bootstrap resamples were used, and the average network retention threshold was set at 0.85.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Network estimation\u003c/h2\u003e \u003cp\u003eFor raw symptom-domain scores, network structure was estimated using a Gaussian graphical model (GGM) implemented in bootnet::estimateNetwork with the default EBICglasso method based on Spearman correlations. Nodes represented symptom domains, and edges represented partial correlations after controlling for all other nodes. To examine associations among positive screening states, symptom domains were further recoded as binary variables based on predefined thresholds, and an Ising model was estimated using bootnet::estimateNetwork with the default IsingFit method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Centrality, predictability, stability, and subgroup analysis\u003c/h2\u003e \u003cp\u003eNode centrality indices, including strength, betweenness, closeness, and expected influence, were calculated to assess the relative importance of symptom domains in the network. Node predictability was further estimated using the mgm package and visualized as an outer ring around each node. Regularization parameters were selected through 10-fold cross-validation with the AND rule. Predictability was expressed as R\u0026sup2; for continuous variables and as 1\u0026thinsp;\u0026minus;\u0026thinsp;nCC for categorical variables.\u003c/p\u003e \u003cp\u003eBootstrap procedures were used to evaluate network accuracy and stability. For both the GGM and Ising network, a nonparametric bootstrap was applied to estimate the accuracy of edge weights, and a case-dropping bootstrap was used to assess the stability of centrality indices under subsampling. Stability was primarily quantified using the correlation stability coefficient (CS-coefficient).\u003c/p\u003e \u003cp\u003eTo examine differences by educational stage and gender, GGM and Ising networks were estimated separately for junior high school versus senior high school students and for boys versus girls. Group differences in network structure, global strength, and specific edge weights were tested using the Network Comparison Test (NCT) with 1,000 permutations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Bayesian network analysis\u003c/h2\u003e \u003cp\u003eTo examine potential directional relationships among symptom domains, Bayesian network analysis was performed using directed acyclic graphs (DAGs). The analysis included all symptom domains, gender, and educational stage. Symptom domains were represented as numeric variables, while gender and educational stage were treated as categorical factors. The analysis used complete case data. The network structure was identified with a hill-climbing algorithm employing the bic-cg score from the bnlearn package. Edge stability was assessed using boot.strength with 2,000 bootstrap resamples, and the final network was formed with a strength threshold of 0.85. Regression-based edge effects were combined with the average DAG, with regression coefficients and statistical test results extracted for each retained edge to show the direction and strength of parent-child relationships. The direction indicated by the DAG reflects the estimated conditional dependencies based on the data and algorithm, suggesting possible orderings or pathways.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eA total of 2,353 adolescents participated in the screening, comprising 750 junior high and 1,603 senior high students. The overall mean age was 15.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52 years, with junior high students averaging 13.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 years, and senior high students averaging 15.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 years; this age difference was statistically significant (t = -48.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no significant difference in gender distribution between the groups (χ\u0026sup2; = 1.12, P\u0026thinsp;=\u0026thinsp;0.29). The junior high group exhibited higher positive screening rates across most psychopathological dimensions compared to the senior high group. Notably, the junior high group had higher positive rates for manic, panic, separation anxiety, social anxiety, post-traumatic stress, tic, generalized anxiety, conduct problems, substance/drug factors, physical illness factors, depressive, obsessive-compulsive symptoms, and NSSI. However, no significant difference was noted in school anxiety symptoms (10.9% vs. 12.7%, P\u0026thinsp;=\u0026thinsp;0.26). The baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Network results\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Overall network structure\u003c/h2\u003e \u003cp\u003eThe GGM network showed dense conditional associations among the 14 nodes, with particularly strong connections observed within the internalizing symptom spectrum. The strongest connections in the GGM network were SeP-SoP (0.22), SoP-PHQ (0.21), and PHQ-OCD (0.17). The Ising network's overall structure was similar, with more prominent positive co-occurrence associations. The strongest edges here were Exc1-Exc2 (1.52) and CD-PHQ (1.3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table S2-3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Centrality results\u003c/h2\u003e \u003cp\u003eIn the GGM network, PHQ showed the highest strength centrality (Strength\u0026thinsp;=\u0026thinsp;1.15), followed by OCD (0.96), Pa (0.88), and GAD (0.84). The one-step expected influence (EI 1-step) results aligned with the strength estimates, with PHQ remaining the most central node. Similarly, the two-step expected influence (EI 2-step) results showed that PHQ (1.86), OCD (1.66), and Pa (1.48) were relatively central within the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratified analyses by educational stage showed that the overall network structures were similar for junior and senior high school students, with differences in local core nodes and strong edge patterns. In the junior high school group, PHQ was the most central node (1.37), followed by OCD (0.87), GAD (0.86), and NSSI (0.84). In the senior high school group, OCD was the most central node (1.05), with PHQ next (1.03), then Pa (0.88), and GAD (0.81). Regarding local edge structures, the strongest edges in the junior high school group were SeP-SoP (0.21), SoP-PHQ (0.20), and PHQ-NSSI (0.18). In the senior high school group, the strongest edges were SeP-SoP (0.22), SoP-PHQ (0.21), and PHQ-OCD (0.19). In the Ising network based on positive screening status, PHQ was overall the most central node, followed by PTSD and OCD. After stratification by education level, PHQ remained most central among junior high school students (8.27), while PTSD was most central among senior high school students (8.19). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Tables S4-S5)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Bridge centrality and stability results\u003c/h2\u003e \u003cp\u003eBridge centrality analysis showed that in the GGM network, PHQ had the highest bridge expected influence (Bridge EI 1-step\u0026thinsp;=\u0026thinsp;1.02), with OCD (0.86) and PTSD (0.68) following. For the bridge EI 2-step, PHQ (1.61) and OCD (1.50) remained the most significant bridge nodes. In the Ising network, the bridge effects were even stronger, with PHQ again leading in bridge expected influence, followed by PTSD and OCD. (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-2 and Tables S4)\u003c/p\u003e \u003cp\u003eThe case-dropping bootstrap analysis indicated good stability of strength centrality, with a CS-coefficient of 0.75, suggesting that the centrality results were relatively robust (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Subgroup comparison results\u003c/h2\u003e \u003cp\u003eSubgroup analyses showed that global network connectivity was mostly similar across different educational levels and genders. When comparing educational stages, the difference in global strength was only 0.14 (P\u0026thinsp;=\u0026thinsp;0.70) in the GGM network and 8.634 (P\u0026thinsp;=\u0026thinsp;0.34) in the Ising network. For gender comparisons, the difference in global strength was minimal, with 0.00 (P\u0026thinsp;=\u0026thinsp;1.00) in the GGM network and 0.166 (P\u0026thinsp;=\u0026thinsp;0.97) in the Ising network. Although there were no significant differences in total network strength, some local connections varied between educational groups. Notable differences in the GGM network included edges like ScP-NSSI, Exc1-PHQ, and PTSD-Exc1. In the Ising network, the differing edges included SeP-ScP, Ma-PTSD, and CD-NSSI. (Supplementary Figure S3-4 and Tables S7-S8)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 DAG results and edge-effect regression estimates\u003c/h2\u003e \u003cp\u003eThe average DAG indicates that educational stage and gender are relatively prominent within the network. PHQ, GAD, OCD, and PTSD are situated in the middle layer and have numerous outgoing connections. Specifically, PHQ connects to Ma, Pa, SoP, and NSSI; GAD links to Pa, ScP, and OCD; OCD is connected to SeP, PTSD, TicD, and NSSI; and PTSD connects to CD. NSSI is mainly positioned downstream, with PHQ, OCD, gender, and educational stage identified as its direct parent nodes. Edge-effect regression analyses further reveal that PHQ (β\u0026thinsp;=\u0026thinsp;0.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), OCD (β\u0026thinsp;=\u0026thinsp;0.35, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and female gender (β\u0026thinsp;=\u0026thinsp;0.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are positively associated with NSSI. Conversely, being in senior high school shows a negative association with NSSI (β = -0.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table S9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe typical network revealed three main pathways linking emotional symptoms to NSSI: PHQ\u0026rarr;NSSI, PHQ\u0026rarr;OCD\u0026rarr;NSSI, and PHQ\u0026rarr;GAD\u0026rarr;Pa\u0026rarr;NSSI. Stratified analysis indicated that in the junior high school group, a pathway from gender to GAD, then to PHQ, and finally to NSSI was found, along with a chain from NSSI to OCD, TicD, and PTSD. In the senior high school group, pathways such as PHQ\u0026rarr;NSSI\u0026rarr;Ma and PHQ\u0026rarr;NSSI\u0026rarr;TicD were identified. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Strongest edges in the symptom network and their implications\u003c/h2\u003e \u003cp\u003eIn the overall symptom network of ethnic minority adolescents, the strongest links connect separation anxiety with social anxiety, PHQ with SoP, and PHQ with OCD. This indicates that the symptom structure in this group primarily centers on closely related internalizing problems [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Rather than existing as distinct symptom clusters, anxiety, depression, and obsessive-compulsive symptoms often form interconnected groups that may reinforce each other's persistence throughout the network.\u003c/p\u003e \u003cp\u003eThe strong link between separation anxiety and social anxiety implies that the distress from separation and social judgment fears may develop simultaneously during adolescence. Past studies show that anxiety and depression symptoms often cluster within adolescent internalizing networks, with their connections intensifying under continuous stress [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In ethnic minority adolescents, this pattern might reflect typical developmental overlaps and contextual factors such as boarding-school experiences, changes in parent-child proximity, shifts in peer support, and stress from adapting to school [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Research suggests that life boarding and diminished social support are closely tied to emotional and internalizing problems in teens [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Clinically significant separation anxiety might indicate a broader social-emotional vulnerability rather than just an isolated anxiety disorder, emphasizing the need for comprehensive school screening [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strong connection between PHQ and SoP indicates that in ethnic minority adolescents, low mood and social withdrawal may form a central internalizing axis. Past research shows that depressive symptoms and anxiety are often linked through factors such as low self-esteem, excessive worry, and social avoidance [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Avoidance related to social anxiety can restrict access to peer support and positive social interactions, leading to increased loneliness and depression. Additionally, symptoms like anhedonia, low energy, and self-devaluation can impair social functioning and lead to further withdrawal [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This reciprocal cycle suggests that social withdrawal at school should be viewed not merely as a temperament but as a potential warning sign of broader emotional risks.\u003c/p\u003e \u003cp\u003eThe strong connection between PHQ and OCD further implies that emotional distress and obsessive-compulsive thought patterns may be closely linked in ethnic minority adolescents. Prior studies have shown significant overlap between depressive and obsessive-compulsive symptoms, with rumination and intolerance of uncertainty likely serving as shared underlying mechanisms [\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. OCD symptoms have also been linked to self-harm and suicide risks, suggesting they can be important warning signs rather than just secondary effects of distress [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Practically, when depressive symptoms are detected during school screenings or clinical assessments, it\u0026rsquo;s important to also evaluate obsessive-compulsive symptoms and possible self-injury risk at the same time, instead of sequentially [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Central nodes and differences by educational stage\u003c/h2\u003e \u003cp\u003eIn the full network, PHQ was the most central node, suggesting that depressive symptoms might serve as a key hub within the symptom system of ethnic minority adolescents. This supports earlier findings indicating that depressive symptoms often have high centrality and bridge different symptom clusters in networks of adolescent depression and anxiety and are strongly linked to symptom severity and clinical outcomes [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In this setting, PHQ appears to reflect multiple symptom dimensions; it may represent a core process through which anxiety, obsessive-compulsive symptoms, trauma-related symptoms, and functional impairment become more interconnected. This centrality could be especially important during adolescence, a period when self-concept, social comparison, and emotion regulation are still developing [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. High-altitude chronic hypoxia and related environmental stressors might also strengthen the connection among internalizing symptoms in this population [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the junior high school group, PHQ remained the central node, suggesting that symptom organization in early adolescence might be more dominated by emotional distress. Early adolescence is characterized by rapid physical growth, cognitive changes, and heightened sensitivity to social evaluation, while regulatory skills are still developing. In this context, depressive symptoms are more prone to spread throughout the entire symptom network [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This finding supports previous research indicating that emotional symptoms tend to be more central during early developmental stages [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Clinically, school mental health programs for junior high students should prioritize early detection of low mood, feelings of helplessness, and initial functional impairments before symptoms become more widespread.\u003c/p\u003e \u003cp\u003eIn the senior high school group, OCD became the most central node, replacing PHQ, which suggests a reorganization of the network during development. As adolescents mature cognitively and face increased academic stress and performance demands, they may become more prone to repetitive thinking, intrusive thoughts, and control-focused coping strategies. This shift could strengthen obsessive-compulsive features within the overall symptom network [\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. When OCD symptoms occupy the center, they may elevate internal tension, feelings of loss of control, and functional burdens, further linking them to depression and anxiety symptoms [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Therefore, assessments in senior high school students should extend beyond general emotional distress to include behaviors like checking, reassurance seeking, excessive need for control, and cognitive rigidity.\u003c/p\u003e \u003cp\u003eFurthermore, PTSD played a significant role in the senior high school group's Ising network, indicating trauma-related burden may influence co-occurring positive symptoms in late adolescence. This does not mean the network is mainly driven by trauma; rather, trauma-related symptoms might gain relevance once the burden exceeds a threshold identified by the Ising model. PTSD is linked to depression and anxiety and may increase connectivity through mechanisms like negative self-concept, hyperarousal, and emotional dysregulation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Studies also show trauma exposure, bullying, adverse childhood experiences, and adolescent NSSI are related [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In ethnic minority adolescents living in high-altitude regions, environmental stressors such as earthquakes may heighten the importance of trauma within the symptom network of high-risk students [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These findings suggest trauma-related burdens underpin increasing symptom complexity in high-risk older adolescents, so screening should include symptoms like intrusive memories, hypervigilance, avoidance, and emotional numbness, beyond depression and anxiety, to better identify vulnerable individuals [\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Hierarchical structure and key pathways in the Bayesian network\u003c/h2\u003e \u003cp\u003eEducational stage and gender serve as foundational factors, functioning as background variables unaffected by symptoms. Prior studies show that educational level impacts symptom severity and the positioning of crucial nodes and NSSI interventions within adolescent networks [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The influence of gender indicates it affects risk through specific pathways rather than overall network differences [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These factors set the stage for symptom risk in this population. Furthermore, conditions like PHQ, GAD, OCD, and PTSD mainly appear in the middle layer. Symptoms here are often clinically important because they connect upstream vulnerabilities to downstream behaviors [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. As a result, they may be key targets for interventions that block multiple pathways simultaneously. Furthermore, NSSI mainly appeared as a downstream outcome, with PHQ, OCD, gender, and educational stage serving as direct parent nodes. Self-injury is probably a distal behavioral result influenced by accumulated emotional, compulsive, and demographic risks, rather than the main driver within the symptom network. It often co-occurs with depressive symptoms, anxiety, emotional dysregulation, and other psychiatric disorders [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKey pathways indicate that the direct link from PHQ to NSSI shows depressive symptoms can directly lead to self-injury. Factors like low mood, helplessness, self-devaluation, and ongoing emotional pain are common triggers for adolescent self-injury, with NSSI often used as a maladaptive way to regulate emotions or regain temporary control [\u003cspan additionalcitationids=\"CR66 CR67\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Early detection and intervention for depressive symptoms are crucial in reducing NSSI risk among ethnic minority adolescents. Moreover, the pathway from PHQ through OCD to NSSI suggests that obsessive-compulsive symptoms act as a mediator, strengthening the link between emotional distress and self-injury. Negative emotions can increase discomfort with uncertainty and internal pain, leading to repetitive thoughts, checking behaviors, or rituals aimed at regaining control [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. When depression and obsessive-compulsive symptoms occur together, it may indicate that the risk is particularly high or that the individual is at greater risk of self-harm [\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, assessing NSSI risk should include not only depression but also rumination, the need for control, and behavioral rigidity.\u003c/p\u003e \u003cp\u003eEducational-stage stratification reveals developmental differences in how pathways are organized. For junior high students, the sequence gender\u0026rarr;GAD\u0026rarr;PHQ\u0026rarr;NSSI indicates that anxiety symptoms tend to appear earlier, and as depression symptoms increase, the risk of NSSI also rises [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Additionally, gender may indirectly influence NSSI through internalizing symptoms; females generally report higher levels of anxiety, depression, and NSSI, whereas males are more prone to externalizing problems [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Junior high school girls may face a higher risk of NSSI linked to the anxiety-depression pathway. For senior high school students, pathways such as PHQ\u0026rarr;NSSI\u0026rarr;Ma and PHQ\u0026rarr;NSSI\u0026rarr;TicD suggest that NSSI may become part of a more complex network of symptoms in later adolescence. At this stage, NSSI not only indicates immediate behavioral risks but also reflects greater psychological complexity [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Its connections with mania-like symptoms and tic-related symptoms imply that NSSI could serve as a warning sign of potential clinical issues [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Therefore, early detection and intervention for NSSI in senior high school students could help reduce both current self-injury risks and future clinical problems associated with more complex symptom patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Clinical implications\u003c/h2\u003e \u003cp\u003eThese findings suggest that mental health services for ethnic minority adolescents should adopt stage-specific screening and step intervention strategies across different educational levels. For junior high students, the symptom network mainly revolves around internalizing issues, particularly depression and anxiety, indicating this stage as a crucial early intervention opportunity in school-based mental health programs. Therefore, efforts should focus on early detection and low-level management of internalizing symptoms. In schools, emphasis might be placed on cognitive-behavioral therapy (CBT)-based psychoeducation, emotion regulation training, group activities, and collaboration between families and schools, with particular attention to warning signs such as low mood, helplessness, persistent worry, social withdrawal, and functional decline, ideally before symptoms escalate or NSSI appears [\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Additionally, once NSSI is identified, it should be considered a critical warning sign, even if other symptoms are not yet evident, warranting prompt risk assessment and intervention support systems [\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn senior high school students, intervention efforts may need to focus on integrated assessment frameworks and targeted treatment for obsessive-compulsive symptoms, trauma-related problems, and NSSI risk. For adolescents who mainly present with repetitive checking, reassurance seeking, and an excessive need for control, exposure and response prevention (ERP) may be considered [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Those with trauma-related symptoms such as intrusive memories, hyperarousal, avoidance, and emotional numbing may benefit from trauma-focused cognitive behavioral therapy (TF-CBT) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. For students with persistent NSSI risk, emotion dysregulation, or poor impulse control, DBT-based techniques may also be considered [\u003cspan additionalcitationids=\"CR82\" citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Overall, these findings highlight the importance of jointly evaluating obsessive-compulsive symptoms, trauma burden, and self-injury risk during screening and referral in senior high school students, in order to improve identification accuracy and better tailor interventions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study focused on ethnic minority adolescents with a distinctive eco-cultural background and applied educational-stage-stratified symptom network analysis based on screening data from six schools in the study region. The findings revealed both shared and distinctive patterns of psychological symptoms across developmental stages and risk levels, with particular emphasis on identifying key symptoms and risk pathways. These results may provide a useful basis for school screening, clinical referral, and tiered intervention strategies.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, because of the cross-sectional design, neither the network associations nor the Bayesian pathways can be interpreted as evidence of strict causality. Although the Bayesian framework provides useful clues about potential directional relationships, these findings still require further validation in larger, multicenter, and longitudinal samples [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Second, although ecological and contextual factors such as high-altitude environment, cultural stress, boarding-school experience, and social support were considered in interpretation, they were not directly included in the network models. Future studies should incorporate family functioning, trauma exposure, social support, adaptation to high-altitude living, and relevant physiological indicators to better explain the observed developmental differences and NSSI-related pathways [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Finally, the current study did not include intervention or predictive validation. Therefore, the findings mainly reveal structural relationships and cannot yet be used directly to develop mature screening tools. Future research could build on the core nodes, bridging pathways, and developmental differences identified here to create and validate stratified screening strategies and targeted interventions for real-world school settings [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this sample of ethnic minority adolescents in Ngawa, PHQ emerged as the most central element in the symptom network, with obsessive-compulsive and trauma-related symptoms also occupying important positions. NSSI was primarily located downstream, suggesting that early screening and prevention should prioritize depressive, obsessive-compulsive, and trauma-related symptoms. Differences in core nodes and potential pathways across educational stages further suggest that school-based screening and intervention strategies may benefit from being tailored separately for junior and senior high school students.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The Ethics Committee approved this study under approval number 2020-k021-02. Information consent was obtained from all participants and their legal guardians.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant NO. LLSQN25H090001; Zhejiang Provincial Medical health Project under Grant NO. 2025KY1997; Wenzhou Science and Technology Bureau under Grant No. ZS2024001; Zhejiang Provincial Medical and Health Science Project Grant No. 2024KY590 and Lishui City Science and Technology Project Grant No. 2024GYX44.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKe Zhao was responsible for study design, manuscript revision, and overall supervision. Fan Wang and Wei Jin contributed equally to this work. Fan Wang conducted the data analysis and drafted the manuscript. Lan Hong, Zhaoxuan Liu, Jianuo Shi, and Si Yu Tong contributed to data collection. Gang Chen and Wei Tang contributed to manuscript revision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank all adolescents and their legal guardians for their participation in this study, as well as the staff who contributed to data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed in this study are included in this published article and its supplementary information files. The datasets and analysis codes are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLucena NL, Rossi TA, Azevedo LMG, Pereira M. Self-injury prevalence in adolescents: a global systematic review and meta-analysis. 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Evid Based Pract Child Adolesc Ment Health. 2020;5(3):354\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23794925.2020.180675\u003c/span\u003e\u003cspan address=\"10.1080/23794925.2020.180675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ethnic minority adolescents, non-suicidal self-injury, symptom network analysis, depressive symptoms, obsessive-compulsive symptoms, Bayesian network","lastPublishedDoi":"10.21203/rs.3.rs-9325556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9325556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the symptom network structure of non-suicidal self-injury (NSSI) among Chinese ethnic minority adolescents, with a particular focus on educational-stage differences and potential directional pathways linking psychiatric symptoms to NSSI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A total of 2,353 adolescents from six junior and senior high schools in Ngawa Tibetan and Qiang Autonomous Prefecture, Sichuan Province, China participated, including 750 junior high students and 1,603 senior high students. Networks were estimated across 14 symptom dimensions, such as NSSI, depressive symptoms (PHQ), generalized anxiety symptoms (GAD), obsessive-compulsive symptoms (OCD), and various emotional and behavioral symptom dimensions. A Gaussian graphical model (GGM) and an Ising model were built separately. Centrality, bridge centrality, and predictability were calculated, and bootstrap techniques evaluated network accuracy and stability. Differences in network structure between educational stages were also analyzed. Additionally, a Bayesian directed acyclic graph (DAG) was used to explore potential directional relationships among symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the undirected network models, the strongest links were between SoP-PHQ and PHQ-OCD. PHQ was identified as the most central node in the entire sample. In subgroup analyses, PHQ remained the main node among junior high students, whereas OCD became the most central node among senior high students. PTSD became more prominent in the senior high school group within the Ising network. Bayesian DAG analysis revealed that educational stage and gender were relatively upstream variables, whereas PHQ, GAD, OCD, and PTSD occupied intermediate positions, with NSSI mainly downstream. Key pathways connecting symptoms to NSSI included PHQ\u0026rarr;NSSI, PHQ\u0026rarr;OCD\u0026rarr;NSSI, and PHQ\u0026rarr;GAD\u0026rarr;Pa\u0026rarr;NSSI. Stage-specific analyses indicated a pathway from gender to GAD to PHQ and then to NSSI in junior high students, whereas in senior high students, PHQ\u0026rarr;NSSI was linked to Ma and TicD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNSSI among ethnic minority adolescents seems to be a downstream behavioral sign within a psychopathological network dominated by clustered internalizing symptoms. Depressive, obsessive-compulsive, and trauma-related symptoms may serve as key targets for early identification and prevention of NSSI.\u003c/p\u003e","manuscriptTitle":"Educational Stage Differences in Symptom Networks and Bayesian Pathways of Non-Suicidal Self-Injury Among Ethnic Minority Adolescents in Ngawa Prefecture, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 15:56:29","doi":"10.21203/rs.3.rs-9325556/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T14:17:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T15:48:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T08:55:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126658125329497310318388528391721126717","date":"2026-04-22T07:16:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T09:19:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233856023710876042728922299262487606624","date":"2026-04-21T08:14:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227476436794995990353800834892380160305","date":"2026-04-16T08:16:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95427166496228648484093155570140999870","date":"2026-04-15T21:28:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T18:07:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T15:46:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T08:56:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-10T08:55:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-04-05T10:08:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1872d521-2a20-4733-8241-28c54f5c4e3f","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T14:17:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T15:48:46+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T14:26:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 15:56:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9325556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9325556","identity":"rs-9325556","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}