The Relationship Between Resilience and Mental Health, Mobile Phone Addiction and Its Differences Across Levels of Parent-Child Conflict Among Left-Behind Adolescents: A Cross-Sectional Network Analysis

The Relationship Between Resilience and Mental Health, Mobile Phone Addiction and Its Differences Across Levels of Parent-Child Conflict Among Left-Behind Adolescents: A Cross-Sectional Network Analysis | 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 The Relationship Between Resilience and Mental Health, Mobile Phone Addiction and Its Differences Across Levels of Parent-Child Conflict Among Left-Behind Adolescents: A Cross-Sectional Network Analysis xiaoya yuan, Yaxin Mao, Xiaomin Xu, Ruolan Peng, Min Tang, Gang Dai, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5063332/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2025 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background mobile phone addiction and mental health problems have become increasingly prominent among left-behind adolescents in China. In recent years, some studies have focused on the important role of parent-child relationship and psychological resilience. Therefore, this study aims to explore the multidimensional relationships among resilience, mental health, and mobile phone addiction among left-behind adolescents, and to assess the impact of parent-child conflict level on these relationships. Methods The Brief Symptom Inventory (BSI-18), the Chinese version of the Mobile Phone Addiction Index (MPAI), the Resilience Scale for Children and Adolescents (RSCA), and the Parent-Child Conflict Scale were used to investigate 2,100 left-behind adolescents in Sichuan Province, and R was run to make network analysis and network comparison. Results (1) A structurally stable network relationship exists between left-behind adolescents' resilience, mental health, and mobile phone addiction; (2) BSI3 (Anxiety) is the most important node of the network model, followed by MPAI1 (the inability to control cravings subscale); (3) MPAI1 (the inability to control cravings subscale) and RSCA4 (family support) are key to connect resilience, mental health, and smartphone addiction in the study sample; (4) There was a significant difference in the network structure between the high- and low-level groups of parent-child conflict, no significant difference in the global strength of the network, and a significant difference in the centrality of strength and the centrality of bridge strength. Conclusions Chinese left-behind adolescents' resilience and mental health, mobile phone addiction are both independent and interact with each other to some extent. Specifically, high centrality dimensions such as anxiety, the inability to control cravings, and family support can be prioritised for intervention in related treatments, or reducing parent-child conflict and enhancing resilience to mitigate mobile phone addiction among left-behind adolescents, thus improving their mental health. Left-behind adolescents Resilience Mental health Mobile phone addiction Parent-child conflict Network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction With China's rapid political and economic development, a large number of people in underdeveloped regions of the central-west have begun to move to the developed regions searching for jobs, which gives rise to the problem of left-behind children due to family, policy, and other factors. What's more, left-behind children are minors under the age of 16 whose parents are both working away from their hometown, or one of whom is working away while the other is unable to take care of the children [ 1 ]. As of 2020, China saw 66.93 million left-behind children, and 138 million who were affected by population mobility, accounting for 46.4% of China's total child population [ 2 ]. The children, who are left behind are not able to stay with their parents for a long time, lack parental care, are prone to loneliness, anxiety, depression, and other negative emotions, which not only influence their studies and lives, but also may have long-term adverse effects on their mental health [ 3 ]. Research has found that, from the past to the present, migrant parents are more likely to hurt their mental health, including emotions and behaviour [ 4 ]. And the situation might become worse over time [ 5 ]. Meanwhile, these children tend to develop mobile phone addiction [ 6 ]. As a means of communication tools provided by parents for their children, smartphones expose children to addiction thanks to being alone for long periods and being spoiled by their elders [ 7 ]. This may lead to their indulgence in the virtual world, which may have a negative impact on their social skills, learning, behaviour and mental health [ 8 – 10 ]. Left-behind adolescents experiencing puberty have rapid physical and mental development and face more severe academic pressure and social environment [ 11 ]. Compared with non-left-behind adolescents, left-behind ones show higher risk propensity and prevalence of mental health problems and mobile phone addiction [ 12 ]. We should pay more attention to left-behind youth and provide them with more comprehensive and effective support and guidance. With the popularity of smartphones, the addictive problem has become a new form of Internet addiction in the mobile era, which is a behavioural addiction that causes psychological and behavioural phenomenon of users due to the misuse of mobile phones [ 13 ]. The 44th report of China Internet Network Information Centre [ 14 ] shows that the number of mobile phone holders in China has reached 847 million, of which 17% are teenagers aged 10 to 19 [ 15 ]. Empirical studies in different countries have found that adolescent mobile phone addiction is negatively associated with mental health [ 16 – 18 ]. Adolescents addicted to cell phones are more susceptible to anxiety, depression, and impulsivity at a high level [ 19 , 20 ]. Also, cross-lagged analyses show that individuals with higher depression and anxiety are subject to developing mobile phone addiction [ 21 ]. Smartphone addiction has a strong association with mental health, and there is even a risk of co-morbidity [ 22 ]. The cognitive-behavioural model of pathological Internet use proposed by Davis [ 23 ] argues that psychopathologies such as depression, anxiety, and substance dependence are distally necessary causes of pathological Internet use symptoms. According to the compensatory Internet use model proposed by Kardefelt-Winther, negative life situations can increase online behaviour to alleviate negative emotions [ 24 ]. Individuals with poor mental health are more vulnerable to negative emotions and behavioural change suffering negative life issues [ 25 ], which leads to mobile phone addiction[ 26 ]. Resilience is often described as the ability to revive or overcome certain adversity in order to extract a positive outcome from a negative event or situation [ 28 ]. Current research has found that resilience has an important role in the mental health and prevention of mobile phone addiction among left-behind adolescents [ 29 , 30 ]. A meta-analysis of 25 studies showed that despite differences in research objectives and instruments, higher resilience was associated with fewer mental health problems [ 31 ]. At the same time, resilience is also an important predictor of mobile phone addiction, and empirical studies have found that self-resilience related to "relationships", "curiosity" and "emotional control" have been found to moderate mobile phone use in both men and women [ 32 ]. Existing research suggests that resilience can both directly and negatively predict mobile phone addiction among Chinese adolescents [ 33 ] and studies from different countries have found that resilience can also serve as a mediator [ 34 , 35 , 7 ] or an adjustment [ 36 ] to influence mobile phone addiction tendencies. Results from a one-year longitudinal study also indicated that problematic mobile phone use and resilience predicted psychological disorders in college students, and that resilience mediated the association [ 37 ]. Meanwhile, mobile phone addiction depletes individuals' self-control ability [ 38 ], thus reducing their level of resilience. The resilience process model suggests that resilience is a protective mechanism under stress and adversity, reflecting an individual's ability to adapt and prepare for challenges positively [ 39 , 40 ]. Researchers have pointed out that resilience is regarded as one of the key protective elements of Internet addiction, and that Internet addiction often stems from the individual's lack of resilience in self-control and coping with stress and frustration [ 41 ]. Additionally, mobile phone and Internet addiction are both behavioural addictions with similarities and may have similar addiction mechanisms [ 42 ]. According to three key factors, foreign research suggests a link between adolescent resilience, psychological health, and mobile phone addiction [ 43 ], which has argued that the relationship between college students' resilience and mental health is mediated by Internet addiction, and that increasing resilience helps prevent Internet addiction and reduce the risk of depression [ 44 ]. Domestic studies have also shown that mobile phone addiction has a direct effect on college students' physical and mental health, and can also indirectly affect their health through resilience [ 45 ]. Internet addiction predicts depression and anxiety in Chinese rural left-behind children, and resilience plays an independent mediating role in the relationship between their Internet addiction and depression and anxiety symptoms [ 29 ]. Adverse mental health conditions such as depression, anxiety, stress, and coping styles significantly influence the risk of mobile phone addiction among adolescents and mediate the relationship between resilience and mobile phone addiction among Chinese adolescents [ 46 ]. However, current research still lacks insight into the network relationship between resilience, mental health, and mobile phone addiction. The family is a direct and dominant subsystem influencing adolescent development [ 47 ], so a harmonious family atmosphere is essential for the healthy physical and mental development of adolescents. During puberty, there is an increase in conflict and a decrease in interaction in parent-child relationships [ 48 ]. A Comparative Study of Parent-Child Relationships in the Internet Age in China, the United States, Japan, and Korea showed that 82.1% of Chinese primary and secondary school students had conflicts with their parents, and 25.2% of these conflicts were focused on Internet access. Studies have shown that the parent-child relationship is an important mediating mechanism in the family system that influences individual development and adaptation [ 49 ]. Conflict is an important part of the parent-child relationship, and adolescents with higher parent-child conflict are more likely to develop mobile phone addiction [ 50 , 51 ]. Substantial empirical studies have also demonstrated that parent-child conflict can negatively predict adolescent mental health [ 52 – 54 ]. In addition, there is an association mechanism between parent-child relationships (including parental support and parent-child conflict) and adolescent resilience [ 55 , 56 ]. According to the individual-situation interaction theory, situational factors may interact with an individual's characteristics [ 57 ]. The situational factors of parent-child conflict may interact with the resilience of individuals' psychological traits to influence individuals' psychological behavioural states. Therefore, this study will also study the relationship between parent-child conflict and left-behind adolescents' resilience, mental health, and mobile phone addiction. At present, most domestic and international studies related to resilience, mental health, mobile phone addiction, and parent-child conflict adopt cross-sectional empirical research methods to explore the predictive mechanisms by constructing structural equation modelling [ 45 , 46 , 44 ]. While this approach can also deal with relationships between multivariate variables, it is mainly applied to validation factor analyses, focusing on verifying the pre-determined model structure [ 58 ], may have limitations for highly complex and dynamic systems, and may be insensitive to the discovery of new structures and patterns. Resilience [ 59 ] and good parent-child relationships [ 60 ] are protective factors in children's growth. "protective model" of adolescent development proposed by Fergus et al. [ 61 ], suggests that different protective factors may interact in predicting developmental outcomes, i.e., the predictive effect of one protective factor (e.g., resilience) on outcome variables (e.g., mobile phone addiction, mental health) may be influenced by another protective factor (e.g., parent-child relationship). In recent years, the network analysis model has rapidly emerged as a new method of describing individual psychological traits as a complement to latent variable models, providing new ideas for understanding human psychological phenomena [ 62 ]. In response to the neglect of symptom interactions in latent variable models of traditional psychological perspectives [ 63 ], Borsboom proposed a network theory of psychopathology, which suggests that symptoms are an integral part of mental disorders, and that the onset and persistence of mental disorders are driven by tightly intertwined causal relationships between symptoms and mutually reinforcing feedback mechanisms [ 64 ]. Based on this theory, the study by Cramer et al. used a Gaussian graph theory model to analyse the relational network of symptoms [ 65 ]. Subsequently, this model became the foundational method for employing network analysis to process transect data. The method refers to symptoms as nodes of a network graph, and links between symptoms as edges connected between nodes, with the weights of the edges representing the strength of the association between the nodes, which is usually visualised as the thickness of the edges in the network graph. The network analysis method can deal with complex interactions and dynamic relationships between variables, and can reveal the underlying structures and patterns in the system. Moreover, by displaying the associations between variables through graphical visualisation, it is possible to see which variables are closely related to each other and how these associations affect the whole system, which makes the results of the study more concise and easy to understand. Therefore, this study takes the group of left-behind adolescents in Sichuan Province, China, as the research object, and explores the multidimensional relationship between resilience, psychological health, and mobile phone addiction through network analysis, and assesses the characteristics of the network structure under different levels of parent-child conflict. This study is geographical and population-specific, combining psychology, sociology, and complex network analysis, which provides a novel theoretical framework and methodological tool for the study of the relationship between resilience, psychological health, and mobile phone addiction among left-behind adolescents, which can help to provide a scientific basis for the development of precise social intervention strategies. 2. Methodology 2.1 Participants This study used an online questionnaire platform to conduct a survey in 28 secondary schools in Sichuan Province for those who met the following criteria: (1) students in their first to third year of high school; (2) fulfilled the condition of being left behind, "neither parent can supervise or take care of me"; (3) gave informed consent and voluntarily took part in this research. A total of 2,824 questionnaires were distributed, excluding duplicates, missing questions, and consecutive cases with the same answers, and removing outliers according to the standard deviation of three times, resulting in 2,100 valid questionnaires, with an effective recovery rate of 74.4%. 2.2 Measurement tools 2.2.1 Mental health This study used the Brief Symptom Inventory 18 (BSI-18) prepared by Derogatis [ 66 ] to measures mental health. The scale consists of 18 questions with three dimensions, somatization, depression, and anxiety, and three subscales with six items each. All questions are scored on a 5-point scale (1 = never, 2 = mild, 3 = moderate, 4 = quite severe, 5 = severe), with higher scores indicating higher levels of psychological distress and lower levels of mental health. In this study, Cronbach's alpha coefficient for this scale was 0.926, with 0.845 for the somatization subscale, 0.850 for the depression subscale, and 0.846 for the anxiety subscale. 2.2.2 Mobile phone addiction The Chinese version of the Mobile Phone Dependence Index (MPAI) developed by Leung et al. [ 67 ] to measure mobile phone addiction. The scale consists of 17 questions, including four dimensions, namely, the inability to control cravings subscale, the feeling anxious and lost subscale, the withdrawal and escape subscale, and the productivity loss subscale, with the number of questions in each dimension ranging from 3–7. A 5-point scoring system was applied, with higher scores indicating higher levels of individual cell phone addiction. In this study, the Cronbach's alpha coefficient of the scale was 0.892, with 0.849 for the inability to control cravings subscale, 0.786 for the withdrawal and escape subscale, 0.764 for the feeling anxious and lost subscale, and 0.755 for the productivity loss subscale. 2.2.3 Resilience The Resilience Scale for Chinese Adolescents (RSCA) developed by Yue-Qin Hu and Yi-Qun Gan was used in this study [ 68 ] to measure resilience. The scale consists of 27 questions, including five dimensions: goal planning, emotional control, positive thinking, family support, and interpersonal assistance, with the number of questions in each dimension ranging from four to six. A 5-point scoring system was used, with higher scores indicating higher levels of resilience. In this study, the Cronbach's alpha coefficient of the scale was 0.874, with 0.763 for the goal-focused subscale, 0. 747 for the emotional control subscale, 0.762 for the positive thinking subscale, 0.793 for the family support subscale, and 0.745 for the interpersonal assistance subscale. 2.2.4 Parent-child conflict Based on Nelissen [ 69 ] 's study, the Parent-Child Conflict Scale consists of 6 questions on a 5-point scale, with higher scores indicating higher levels of parent-child conflict. The Cronbach's alpha coefficient for the scale in this study was 0.797. 2.3 Data analysis In this study, SPSS23.0 was applied for total score calculation, common method bias test, and descriptive statistical analysis, R (4.3.2) was used for network analysis, and R packages qgraph (1.9.8), mgm (1.2–14), networktools (1.5.2), and bootnet (1.5.6) were used for network estimation and visualisation, network centrality estimation and stability tests [ 70 ]. The top 27% of the total parent-child conflict score was taken as the high level of the parent-child conflict group, and the bottom 27% of the total score was taken as the low level of the parent-child conflict group [ 71 ], constructed the networks separately and compared them using the R package NetworkComparisonTest (2.2.2) [ 72 ]. 2.3.1 Data pre-processing Invalid questionnaires were filtered according to the following steps: firstly, questionnaires with less than 300s of response time were excluded, then questionnaires with missing items were deleted to facilitate subsequent data analysis, then duplicate cases were identified and deleted based on information such as IP, time of submission, age, school, etc., and those with more than 15 consecutive questions with the same response within the same scale were considered as invalid data were deleted, and finally, Z-scores for the respective scale and its dimensions were calculated, and extreme case data with Z-scores exceeding plus or minus 3 were removed to make the results more stable and reliable. Finally, the Z-scores of each scale and its dimension scores were calculated, and the data of extreme cases with Z-scores exceeding plus or minus 3 were deleted to make the results more stable and reliable. 2.3.2 Network estimation and visualisation In this study, each dimension of the Brief Symptom Scale 18, the Mobile Phone Dependence Index, and the Resilience Scale for Chinese Adolescents was used as a node, and the correlation between the dimensions were used to generate the edges of the network, and the partial correlation structured network was constructed and visualised using the R package qgraph (1.9.8) [ 73 ]. Applying the least absolute shrinkage and selection operator (LASSO) [ 74 ] and the extended Bayesian information criterion (EBIC) [ 75 ]. Regularisation was performed with a tuning parameter of 0.5 to prevent overfitting and obtain a concise and interpretable structure. The predictability of each node was calculated using the R package mgm(1.2–14) [ 76 ]. The more predictable a node is, the more it can be predicted or determined by other connected nodes in the network; conversely, if the predictability value is low, we need to intervene directly on the node or look for markers outside the network [ 62 , 77 ]. In addition, using the spinglass algorithm [ 78 ] for modular analysis of node clustering to reveal and optimise the structure of associations in the network. In the network, green edge lines represent positive correlations and red edge lines represent negative phases, and the thickness of the edges indicates the absolute magnitude of the correlation, with thicker edges indicating higher correlations. Using the Fruchterman-Reingold algorithm [ 79 ]. A visual network layout was performed so that nodes with strong and numerous connections were located in the centre of the network and nodes with weak and few connections were distributed at the periphery of the network. When performing network comparisons between the high and low level groups of parent-child conflict, the averageLayout function in the R package qgraph (1.9.8) was used to perform network layouts, presenting a consistent visual layout of nodes using the average position in the two networks. 2.3.3 Centrality estimates In network analysis, the centrality metric is an important metric used to describe how central a node is in the network. Using the centrality function in the R package qgraph (1.9.8) [ 73 ]. Calculate the strength, betweenness and closeness of the network nodes and use the bridge function in the R package networktools (1.5.2) [ 80 ] Calculate bridge centrality metrics for network nodes, including bridge strength, bridge betweenness, and bridge closeness. Previous studies [ 81 ] found that strength centrality is the most persuasive metric in psychometrics, and when the three metrics do not have the same numerical ordering, the result of the ordering of strength centrality generally prevails. Therefore, in this study, we chose the strength centrality and the bridge strength centrality of the node to be reported, and plotted the normalised (z-scored) values for each node. Where strength refers to the sum of the absolute value of the weights of all edges connecting the node, the larger its value indicates that the node is more closely connected to other nodes and has a greater effect on the whole network [ 82 ]; Bridge Strength refers to the sum of the absolute values of the weights of the edges of the nodes of other communities that are connected to this node, the higher its value the more influence this node has on the nodes of other communities [ 83 ]. 2.3. 4 Accuracy and stability test The accuracy and stability of the constructed network were calculated and verified using the R package bootnet (1.5.6) using the Bootstrapping method [ 70 ]. The estimation results were validated and analysed. Firstly, the 95% confidence interval (CI) of each edge weight in the network are calculated based on the non-parametric bootstrapping method. If the 95% confidence intervals of the sampling set and the original dataset overlap more, it means that the network edge weights are estimated more accurately. Secondly, based on removing the case-dropping subset bootstrap to assess the stability of the central indicator, delete a certain proportion of samples, and re-estimate the network, if the network structure of the central indicator order remains unchanged, the stability is good. And the correlation stability coefficient (CS-coefficient) of the network is calculated using the corStability function for assessment, which indicates that when the maximum proportion is removed, the correlation between the original centrality indicator and the network centrality indicator of the subset at 95% probability is higher than 0.7, the value of the CS-coefficient should not be lower than 0.25, and higher than 0.5 indicates good centrality stability [ 70 ]. Finally, the centrality difference test was conducted to assess the differences in centrality indicators between nodes and edges using the non-parametric bootstrap method. p < 0.05 was considered statistically significant. 2.3.5 Network comparison Using the R package NetworkComparisonTest (2.2.2) [ 72 ] Network Comparison Test (NCT) was performed on the high and low-level groups of parent-child conflict. Network invariance test and global strength invariance test were performed in 5000 permutations to assess whether the two networks differed in weight of edges and global strength. 3. Results 3.1 Common method bias test As all data in this study were collected using participant self-report, there may be common methodological bias. The Harman one-way method of testing was used in this study, which showed that there were a total of 12 factors with eigenvalues greater than 1, the first of which had a percentage of the variance of 23.4%, which did not exceed the critical value of 50% [ 85 ]. Therefore, common method bias had little effect on the results of this study. 3.2 Descriptive statistics Of the total of 2,100 adolescent secondary school students included in this study (Table 1 ), 905 (43.1%) were boys, and 1,195 (56.9%) were girls, with an average age of 15.60 years (age 12 to 20, SD = 1.78); the urban population was 386 (18.4%), the township population was 528 (25.1%), and the rural population was 1,186 (56.5%). An analysis of the respondents' left-behind status shows that 78 (3.7%) live with their fathers but cannot be under their guardianship or care, 75 (3.6%) have fathers who are away for less than three months a year, 415 (19.8%) have fathers who are away from three to six months a year, 722 (34.4%) have fathers who are away for more than six months a year, and 754 (35.9%) have fathers who are absent for almost all of the year (35.9%), 56 (2.7%) whose fathers had passed away; 135 (6.4%) who lived with their mothers but could not be under their guardianship or care, 103 (4.9%) whose mothers were away for less than three months a year, 391 (18.6%) whose mothers were away for three to six months a year, 691 (32.9%) whose mothers were away for more than six months a year and 737 (35.9%) whose mothers were away almost all the year. 737 (35.1%), and 43 (2.0%) whose mothers had passed away. Table 1 Basic information of participants Variable Number (%)/M(SD) Synthesis High-level Parent-Child Conflict Group Low-level Parent-Child Conflict Group Gender Boys 905 (43.1%) 219 (38.6%) 290 (51.1%) Girls 1,195 (56.9%) 348 (61.4%) 277 (48.9%) Age 15.60 (1.78) 15.94 (1.81) 15.30 (1.83) Residency Urban 386 (18.4%) 111 (19.6%) 91 (16.0%) Town 528 (25.1%) 151 (26.6%) 134 (23.6%) Rural 1,186 (56.5%) 305 (53.8%) 342 (60.3%) Living with my father or not Living with my father lack of his custody and care 78 (3.7%) 27 (4.8%) 16 (2.8%) Within 3 months of my father's absence annually 75 (3.6%) 36 (6.3%) 16 (2.8%) Within 3–6 months of my father's absence annually 415 (19.8%) 153 (27.0%) 89 (15.7%) More than 6 months of my father's absence annually 722 (34.4%) 168 (29.6%) 213 (37.6%) Almost a whole year of my father's absence 754 (35.9%) 172 (30.3%) 217 (38.3%) My father has passed away. 56 (2.7%) 11 (1.9%) 16 (2.8%) Living with my mother or not Living with my mother lack of his custody and care 135 (6.4%) 44 (7.8%) 23 (4.1%) Within 3 months of my mother's absence annually 103 (4.9%) 47 (8.3%) 19 (3.4%) Within 3–6 months of my mother's absence annually 391 (18.6%) 156 (27.5%) 94 (16.6%) More than 6 months of my father's absence annually 691 (32.9%) 143 (25.2%) 200 (35.3%) Almost a whole year of my mother's absence 737 (35.1%) 169 (29.8%) 214 (37.7%) My mother has passed away. 43 (2.0%) 8 (1.4%) 17 (3.0%) The high level of parent-child conflict group consisted of 567 individuals, 219 males (38.6%) and 348 females (61.4%), with a mean age of 15.94 years (age 12 to 20, SD = 1.81); the low level of parent-child conflict group consisted of 567 individuals, 290 males (51.1%) and 277 females (48.9%), with a mean age of 15.30 years (age 12 to 20, SD = 1.83). Comparing the differences between the high level of parent-child conflict group and the low level of parent-child conflict group on some demographic variables, the descriptive statistics showed that the two groups were essentially similar on the variables of gender, age, place of birth, and retention (see Table 1 ). Through t-tests, there were no statistically significant differences (p > 0.05) in demographic variables other than age, township, and rural birthplace, suggesting that in most respects the two groups were essentially equivalent. The overall sample mean total score for the BSI was 33.29 (SD = 10.70), the MPAI mean total score was 48.67 (SD = 12.18), the RSCA mean total score was 87.43 (SD = 14.61), and the Parent-Child Conflict Scale mean total score was 9.66 (SD = 3.42). The mean and standard deviation of each dimension of the scale, i.e., network nodes, are shown in Table 2 . The mean of the total score of the summary symptom scale for the high level of parent-child conflict group was 41.72 (SD = 10.53), the mean of the total score of the index of mobile phone dependence scale was 52.60 (SD = 11.19), the mean of the total score of the resilience scale for adolescents was 77.99 (SD = 12.29), and the mean of the total score of the parent-child conflict scale was 14.38 (SD = 2.54). The low level of parent-child conflict group had a total score mean of 26.58 (SD = 7.28) for the Brief Symptoms Scale, 42.37 (SD = 11.53) for the Mobile Phone Dependence Index Scale, 97.57 (SD = 13.97) for the Adolescent resilience Scale, and 6.38 (SD = 0.49) for the Parent-Child Conflict Scale. Table 2 Mean, standard deviation, predictability, and centrality indicators for each node Nodes Content Mean (M) Standard deviation (SD) Predictability Dissociation Bridge Strength BSI1 Somatisation 9.96 3.80 0.49 0.88 0.37 BSI2 Depression 11.65 4.14 0.64 1.10 0.48 BSI3 Anxiety 11.68 4.16 0.68 1.23 0.45 MPAI1 Inability to control cravings 20.05 6.04 0.51 1.21 0.64 MPAI2 Feeling anxious and lost 10.39 3.67 0.49 1.05 0.34 MPAI3 Withdrawal and escape 8.95 2.95 0.28 0.68 0.12 MPAI4 Productivity loss 9.28 2.85 0.39 1.09 0.32 RSCA1 Goal planning 16.26 3.81 0.35 0.79 0.16 RSCA2 Affect control 18.03 4.27 0.42 0.90 0.49 RSCA3 Positive thinking 14.06 3.17 0.31 0.88 0.33 RSCA4 Family support 19.95 4.87 0.45 0.95 0.60 RSCA5 Help-seeking 19.13 4.83 0.23 0.57 0.24 3.3 Network structure The network structure of the left-behind adolescents' mental health, mobile phone addiction, and resilience is demonstrated in Fig. 1 a. There are 12 nodes in the network, and a total of 45 non-zero edges actually exist, including 20 negative edges and 25 positive edges, accounting for 68.18% of the number of possible connected edges. The proportion of the circle around a node that is filled represents the predictability of that node, with a larger proportion of the filled portion indicating a higher predictability of that node, with an average predictability of 0.44 (range 0.23 to 0.68, Table 2 ). The network module analysis displayed that the nodes of mental health, mobile phone addiction, and resilience of the left-behind children clustered with each other to form three node communities (Fig. 1 b), which was consistent with the three research variables and their dimensions. The communities for mental health and mobile phone addiction were more strongly connected internally; whereas the communities for resilience were weaker except for RSCA1 (goal planning) and RSCA3 (positive thinking) which were deeply associated. The links between the three communities were also stronger, with the dimensional nodes interacting with each other. The strongest connections were between BSI2 (depression) and RSCA4 (family support), BSI3 (anxiety) with RSCA2 (affect control) and MPAI2 (the feeling anxious and lost), and MPAI1 (inability to control cravings) with RSCA4 (family support) and RSCA1 (goal planning) directly. 3.4 Indicators of centrality The results of the centrality index of psychological status, mobile phone addiction and resilience network of left-behind adolescents are shown in Fig. 2 , and the specific values are shown in Table 2 . Node BSI3 (anxiety, Strength = 1.23) has the highest strength centrality, and node MPAI1 (inability to control cravings, Strength = 1.21) is the second highest. In terms of bridge strength centrality, nodes MPAI1 (inability to control carving, Bridge Strength = 0.64) and RSCA4 (family support, Bridge Strength = 0.60) were significantly stronger than the other nodes. The results of the variability test for the centrality index also indicate that the high centrality nodes are stable and reliable. 3.5 Accuracy and stability of the network The results of the edge weight bootstrap procedure are shown in Fig. 3 , where the network estimation is moderately accurate and there is a partial overlap between the 95% CI of the edge weights. The results of the excluded cases bootstrap method are shown in Fig. 4 , with CS coefficients of 0.75 for strength, bridge strength, closeness, and edges, and 0.594 for betweenness, which are greater than 0.5, saying that the network estimation has good stability. The result of bootstrapped difference tests is shown in Fig. 5 , nodes and edges with strong centrality in the network are statistically more strongly different than other nodes in the network, further indicating that the results of centrality analysis are stable and generalisable. 3.6 Comparison of networks Network Comparison Test (NCT) was performed on the Parent-Child Conflict High-Level Group and Low-Level Group. The results show that both networks contain 12 nodes, and the result of the parent-child conflict high-level group contains 47 edges, while the parent-child conflict low-level group contains 42 edges, and the visualisation of the network is shown in Fig. 6 . By centrality analysis, in the parent-child conflict high-level group, the core nodes and core bridge nodes are BSI3 (anxiety) and MPAI1 (inability to control cravings). In the low-level group of parent-child conflict, the core nodes were BSI3 (anxiety), MPAI2 (the feeling anxious and lost), and the core bridge nodes were MPAI1 (inability to control cravings), and RSCA4 (family support). The results of the network invariance test showed a significant difference in structure between the high and low-level groups of parent-child conflict (M = 0.265, p < 0.001), and the results of the global strength invariance test did not find a significant difference in the global strength of the network (high-level group: 5.526 vs. low-level group: 4.952; S = 0.566, p = 0.162). Tests of centrality invariance revealed significant differences in both intensity centrality (p < 0.001, cohen's d = 0.456) and bridge intensity centrality (p < 0.001, cohen's d = 0.828). A total of 6 node centrality of BSI1 (somatization), RSCA5 (interpersonal assistance), and BSI2 (depression) were significantly different (p < 0.05), accounting for 50% of the total. The results of the borderline weight invariance test showed that a total of 15 borderlines such as RSCA4 (family support) differed significantly (p < 0.05) from BSI2 (depression), MPAI1 (inability to control cravings), and RSCA2 (affect control), and BSI1 (somatization) differed significantly (p < 0.05) from BSI2 (depression), and MPAI4 (productivity loss), which accounted for about 28% of the total number of borderlines. 4. Discussion In this study, we used network analysis to explore in depth the associations between the dimensions of resilience and mental health and mobile phone addiction among Chinese left-behind adolescents, and to further compare the core dimensions and network structure differences in the networks of resilience and mental health and mobile phone addiction among left-behind adolescents with different levels of parent-child conflict. The results of the study found that: (1) there exists a structurally stable network relationship between resilience, psychological health, and mobile phone addiction among left-behind adolescents; (2) BSI3 (anxiety) is the most central node in the network model, followed by MPAI1 (inability to control cravings); (3) MPAI1 (inability to control cravings) and RSCA4 (family support) are the most central bridge nodes connecting resilience, psychological health and mobile phone addiction in the study sample; (4) there were significant differences in the network structure between the high and low-level groups of parent-child conflict, specifically no significant differences in the global strength of the network, and significant differences in both strength centrality and bridge strength centrality. 4.1 Network structure and its core dimensions of resilience, mental health, and mobile phone addiction of left-behind adolescents , 4.1.1 Network structure The study showed that there were three relatively independent clusters in the networks of resilience and mental health and mobile phone addiction among left-behind adolescents. The mental health and mobile phone addiction communities are more closely connected internally, in line with psychopathology network theory [ 86 ], i.e. some symptoms are more closely connected to each other than others, and the clusters form manifestations of mental disorders. Connections within the resilience community are looser overall, with only a strong positive correlation shown between goal planning and positive thinking, with the five dimensions representing different dimensions of the individual, the environment, and so on, which work together to contribute to the overall resilience of the individual. Looking at the network as a whole, mental health and mobile phone addiction in general have strong negative associations with resilience, while a positive correlation was shown between mental health and mobile phone addiction. Enhancing resilience may help to reduce the risk of mobile phone addiction and promote mental health among left-behind adolescents. 4.1.2 Network core dimensions This study found that inability to control carving occupies a crucial position in the network of left-behind adolescents' resilience and mental health, and mobile phone addiction. is both a core node and a core bridge node, which has a profound impact on the overall network structure. Inability to control carving is manifested in the individual's difficulty in self-regulation, investing too much time in mobile phone use without being able to manage it effectively [ 67 ]. The results of this study are similar to those of existing network analysis studies [ 87 , 88 ], and are also consistent with the Interaction of Person-Affect-Cognition-Executionmodel (I-PACE) proposed by Brand [ 89 ], diminished control over decision-making can be transferred to behavioural addictions and specific Internet-use disorders. Network visualisation results showed a stronger direct relationship between inability to control carving and goal planning and family support in resilience, possibly because the inability to control carving creates an attention bias toward the automation of addictions [ 90 ] that affects addicts' attention allocation and cognitive resource use. According to the social displacement hypothesis, addiction to smartphones can neglect face-to-face interactions with friends and family members [ 91 ] and lack of real-world social support [ 92 ] and left-behind adolescents are already more deprived of parental companionship, leading to lower levels of family support and interpersonal assistance. For mental health, uncontrollability was only directly related to depression. This may be due to the fact that both mobile phone addiction and depression are related to the dopamine system in the brain[ 93 , 94 ], and both show similar symptoms such as loss of interest, social withdrawal, and mood swings [ 95 ]. In contrast, adolescent somatization and anxiety are influenced by a wide range of factors, including genetics, environment, past experiences, psychological states, and psychological changes [ 96 – 98 ], and so were only indirectly affected by runaway sex in this network. Notably, the results of the present study showed that inability to control carving in mobile phone addiction was positively associated with positive perceptions of resilience, in contrast to existing research where hopeful attitudes may reduce adolescents' dependence on smartphones [ 99 ] The results are not consistent with the results of Left-behind adolescents may need to take on family responsibilities earlier because of the unique nature of their home environment, an experience that may allow them to hone their independence and coping strategies [ 100 ] and thus be able to adapt to adversity more quickly in certain situations, and to make self-determination, self-planning and problem-solving with a more optimistic and positive attitude. This also suggests that left-behind adolescents may realise the seriousness of the problem after experiencing uncontrolled mobile phone addiction, and may engage in self-reflection and seek adjustment, in which their positive thinking may be enhanced. In addition, similar to previous network analysis studies [ 101 , 102 ], anxiety is also one of the most central nodes of the network model in this study. Anxiety is the brain's response to danger, stimuli, and is a state that an organism will actively try to avoid [ 103 ]. In this network, anxiety mainly affects withdrawal or escape in mobile phone addiction. the over-comfort pathway in the pathway theory of problematic mobile phone use proposed by Billieux [ 104 ] states that individuals with increased anxiety contribute to their mobile phone dependence addiction out of needs such as comforting reassurance. Meanwhile, according to the Attention Gate Model (AGM), anxiety overestimates the time interval of negative stimuli [ 105 , 106 ] by paying attention to them [ 107 ]. When addicts are in withdrawal, their attention is more likely to be focused on the time they are waiting to use their mobile phones, leading to distorted perceptions of time and making withdrawal more difficult for addicts by making it feel more difficult. Emotional control and family support in the resilience of left-behind adolescents are directly and negatively affected by anxiety. Relevant studies have shown that chronic anxiety leads to a more sensitive response to external stimuli [ 108 ] which leads to difficulties in regulating negative emotions [ 109 ]. Due to parental absence and limited resources, left-behind adolescents have difficulties in obtaining immediate emotional support and proper guidance, and lack effective strategies for emotional expression and control. Anxiety may affect individuals' perception and utilisation of family support [ 110 ], or due to communication barriers [ 111 ], the inability to effectively access needed support from family members. Family support is another one of the core bridge nodes in the network structure of this study, connecting depression, anxiety, somatization in left-behind adolescents' mental health and uncontrollability and ineffectiveness in mobile phone addiction. It refers to the tolerant, respectful and supportive attitudes of family members [ 68 ], an important external factor in resilience [ 112 ]. Increased family support can enhance family members' psychological well-being [ 113 , 114 ]. In the present study, the family support of the left-behind adolescents also had direct effects on their depression, anxiety and somatization to varying degrees. Among them, the effect on depression was the most significant, and lack of parental care leading to depression was one of the most prominent problems among left-behind children [ 115 ]. The lack of direct parent-child interactions and emotional communication, and the sense of stress caused by the impairment of parental care resources or unmet needs of left-behind adolescents have both immediate and delayed negative predictive effects on depression [ 116 ]. The effect of family support on anxiety was also more significant, consistent with previous research findings [ 117 ]. The relatively weak effect on somatization, on the other hand, may be related to the indirect nature of somatization symptoms and the multiple influencing factors [ 96 ] of interference. Existing research suggests that family support significantly and negatively predicts adolescent mobile phone addiction [ 118 ], but in the network structure of this study, family support was negatively associated with inability to control carving and showed positive results with inefficacy. Inefficacy refers to excessive mobile phone use resulting in lower academic or work productivity [ 67 ] Ineffectiveness Left-behind adolescents usually grow up with their grandparents, who are more lenient than their parents based on their love for their grandchildren, and do not know whether to stop, encourage, or ignore adolescents' problematic behaviours due to their lack of knowledge and backwardness [ 119 ]. Parents who work outside the home tend to feel indebted to their children and indulge them completely, and lack proper guidance and supervision of their children's learning in education management. Such intergenerational education is often prone to spoiling, and although a certain degree of family support is provided for children, improper discipline in life also leads to problematic behaviours of excessive mobile phone use and affects learning efficiency. 4.2 Differences in parent-child conflict levels between resilience, mental health, and mobile phone addiction networks in adolescents who are left behind In this study, it was found that the differences in network invariance test at different levels of parent-child conflict were significant, and the differences in the global strength of the networks were not significant. It indicated that the two networks had similar levels of connectivity overall and maintained some stability at different levels of parent-child conflict. Families with high levels of parent-child conflict were more connected within the mental health and mobile phone addiction communities, and distant within the resilience community, and even showed a significant negative internal correlation. This result reveals that parent-child conflict may undermine left-behind adolescents' resilience [ 56 ] to further disintegrate mental health and lead to mobile phone addiction problems. In addition, the network structure of the high-level parent-child conflict group had stronger direct associations among the three associations, the correlations among the nodes were more disordered, and changes in individual dimensions were more likely to spread across different associations. This suggests that intense parent-child conflict may lead to a rapid spread of risks or negative effects and cause individuals to show a high degree of flexibility and diversity in their psychological adaptations [ 120 ] that can cope with different situations through multiple psychological mechanisms. In contrast, in the group with low levels of parent-child conflict, inter-community connections were relatively looser, intro-community associations within resilience were stronger, and the direct link between mental health and mobile phone addiction communities was significantly reduced, suggesting that resilience effectively buffers the interplay between mental health and mobile phone addiction problems [ 29 , 45 ], playing a better protective role which played a better protective role. Somatization was the node with the most significant central difference in intensity, and was significantly more associated with depression, positive thinking, and inefficacy in left-behind adolescents with high levels of parent-child conflict than in the group with low levels of parent-child conflict. Somatization is a unique response to psychosocial stresses [ 66 ] and parent-adolescent conflict can exacerbate adolescent somatization symptoms [ 121 ].. Parent-child conflict is one of the stressors for adolescents in puberty [ 122 ], prolonged exposure to high-pressure and stressful environments increases the risk of mental health problems in adolescents [ 123 ] and is more likely to lead to multiple co-morbidities of psychological problems [ 124 ]. In the present study, somatization showed a stronger negative correlation with inefficacy in the high levels of parent-child conflict group, which may be due to the physical discomfort of somatization [ 125 ] and intense parent-child conflict [ 126 ] are the more dominant causes of academic inefficiency among left-behind adolescents. It is also possible that the separation anxiety of parents of left-behind adolescents (Scharf & Goldner, 2018) and severe family conflict [ 127 ] lead to stricter parental psychological and behavioural control, which prevents excessive mobile phone use from affecting the adolescents' efficiency. Positive thinking was the node with the most significant difference in bridge strength centrality. In the network structure of high parent-child conflict, positive thinking increased the direct positive correlation with lack of self-control, avoidance, and inefficiency in the mobile phone addiction community. This implies that more severe smartphone addiction problems such as lack of self-control, escapism, and inefficiency are intertwined with more optimistic attitudes, which is inconsistent with existing research [ 129 ]. In addition to the previously mentioned ability of left-behind adolescents to adapt more quickly to adversity, make positive self-decisions and plans, and potentially reflect deeply and seek change, it is also possible that negative cognition allows them to feel the realities of the situation such as family conflict more acutely, rather than being fully immersed in the world of mobile phones, which reduces performance in areas such as loss of control [ 100 ]. However, at the same time, according to the "loss of compensation" hypothesis [ 130 ], their addiction to mobile phones may be more of an emotional attachment, and their inner dependence on mobile phones will be strongly manifested during withdrawal. In addition, The connecting line between inability to control carving and family support is borderline with the most significant difference in weights and is strongly negatively correlated in the low-level group. Family members in families with low parent-child conflict tend to use positive coping strategies [ 131 ], and family support may be more accessible and effective. In contrast, if the parent-child conflict is high, although family support may be present, its effectiveness may be diminished by the conflict, and adolescents may seek other forms of social support to cope with the conflict, thus attenuating the direct effect of family support on mobile phone addiction loss of control, and instead having interpersonal assistance directly associated with loss of control. 4.3 Research limitations and future research perspectives This study used a network analysis model to examine the relationship between psychological variables, providing a multidimensional understanding of left-behind adolescents' resilience in relation to mental health, mobile phone addiction, and parent-child conflict from a cross-sectional perspective for psychological research, as well as expanding and deepening the theory of resilience. This study reveals that teachers and clinical interveners can maximise psychosocial interventions by focusing on the high centrality dimensions such as anxiety, inability to control carving, and family support when confronting left-behind adolescents' mental health and mobile phone addiction issues [ 88 ] The However, this study still has some limitations. First, this study only used cross-sectional data to construct a partial correlation network, and was unable to infer causal relationships. Although the important role of core nodes can be affirmed based on the network model centrality feature [ 88 ], it should be verified by longitudinal or experimental design in the future. Second, the average predictability of the nodes of the network analysis model in this study was not high, indicating that the networks could not predict each other well internally and were influenced by factors outside the network (e.g., environmental, biological factors, other psychological variables) [ 62 ]. Future research can collect more data with more representativeness and accuracy, identify and control for external variables that may affect the predictability of the model, and also incorporate multimodal indicators to construct the network based on relevant theories. Third, mobile phone addiction measured through self-report may be affected by social expectation bias. Some experiments have found that self-reported mobile phone use does not match the actual situation [ 132 ], future research should be cautious in interpreting estimated smartphone use with more objective metrics or a combination of personal interviews and guardian observations for evaluation. 5 Conclusion (1) The network structure of left-behind adolescents' resilience, mental health, and mobile phone addiction is stable, in which anxiety and inability to control cravings are core nodes, and controlling inability and family support are core bridge nodes. Practitioners should focus on the high centrality dimension for effective intervention for left-behind adolescents. (2) There are significant differences in the network structure of resilience, psychological health, and mobile phone addiction among left-behind adolescents at different levels of parent-child conflict. In families with higher levels of parent-child conflict, the network structure is more complex, and the resilience of left-behind adolescents is undermined, with risks and negative effects spreading faster; while in the lower counterpart, resilience has a protective effect. Declarations Ethics approval and consent to participate This study was conducted in strict accordance with the Declaration of Helsinki and received ethical approval from the Institutional Committee of Law School, Southwest University of Science and Technology in Mianyang, China (No. LL23001). Informed consent was signed by each adult participant, or their parent(s) or legal guardian(s) on behalf of adolescent participants. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are available from the corresponding author, upon reasonable request, immediately following publication and no end date. We can share individual participant data that underlie the results reported in this article, after deidentification (text, tables, figures and appendices). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the General Project of Sichuan Philosophy and Social Science Planning Fund of Sichuan Province [Project No. SCIJ23ND226], Steering Committee for Teaching Psychology in Higher Education, Ministry of Education [Project No. 20232010], and Institute of Psychology, Chinese Academy of Sciences [Project No.GJ202003]. Authors' contributions All authors contributed to the study conception and design. XY curated and analyzed the questionnaire data, visualized the results, interpreted the results of the network analysis model, and was the main contributor to writing the first draft of the manuscript. YM curated the data, conducted formal analyses, reviewed and edited the manuscript. XX investigated, curated the data and conducted a formal analysis. RH translated, reviewed and edited the manuscript. YW investigated and formally analyzed the data. MT investigated and formally analyzed the data. GD investigated and formally analyzed the data. XT investigated and formally analyzed the data. HF proposed the conceptualization and methodology and performed the result validation. XZ proposed the conceptualization and methodology, performed supervision, reviewed and edited the manuscript. GZ performed supervision, provided related resources, reviewed and edited the manuscript. 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Correlation analysis of mental toughness, family social support, and anxiety of nursing staff. Am J Transl Res. 2024;16(6):2563–70. Sun R, Gao Q, Xiang Y, Chen T, Liu T, Chen Q. Parent–child relationships and mobile phone addiction tendency among Chinese adolescents: The mediating role of psychological needs satisfaction and the moderating role of peer relationships. Children and Youth Services Review. 2020 Sep 1;116:105113. Wang CD, Hayslip B, Sun Q, Zhu W. Grandparents as the Primary Care Providers for Their Grandchildren: A Cross-Cultural Comparison of Chinese and U.S. Samples. Int J Aging Hum Dev. 2019 Dec 1;89(4):331–55. Moyer DN, Sandoz EK. The Role of Psychological Flexibility in the Relationship Between Parent and Adolescent Distress. J Child Fam Stud. 2015 May 1;24(5):1406–18. Bursch B, Lester P, Jiang L, Rotheram-Borus MJ, Weiss R. Psychosocial predictors of somatic symptoms in adolescents of parents with HIV: a six-year longitudinal study. AIDS Care. 2008 Jul;20(6):667–76. Anniko MK, Boersma K, Tillfors M. Sources of stress and worry in the development of stress-related mental health problems: A longitudinal investigation from early- to mid-adolescence. Anxiety, Stress, & Coping. 2019 Mar 4;32(2):155–67. Slavich G. Psychoneuroimmunology of Stress and Mental Health. 2018 Jun 7; von Klitzing K, White LO, Otto Y, Fuchs S, Egger HL, Klein AM. Depressive comorbidity in preschool anxiety disorder. J Child Psychol Psychiatry. 2014 Oct;55(10):1107–16. Ayoub MA. Ergonomic deficiencies: I. Pain at work. J Occup Med. 1990 Jan;32(1):52–7. Dotterer AM, Hoffman L, Crouter AC, McHale SM. A Longitudinal Examination of the Bi-Directional Links between Academic Achievement and Parent-Adolescent Conflict. J Fam Issues. 2008 Jun 1;29(6):762–79. Steeger CM, Gondoli DM. Mother–adolescent conflict as a mediator between adolescent problem behaviors and maternal psychological control. Developmental Psychology. 2013;49(4):804–14. Scharf M, Goldner L. “If you really love me, you will do/be…”: Parental psychological control and its implications for children’s adjustment. Developmental Review. 2018 Sep 1;49:16–30. Guan J, Ma W, Liu C. Fear of missing out and problematic smartphone use among Chinese college students: The roles of positive and negative metacognitions about smartphone use and optimism. PLoS One. 2023 Nov 28;18(11):e0294505. Gao W, Chen Z. A Study on Psychopathology and Psychotherapy of Internet Addiction. Advances in Psychological Science. 2006 Jul 15;14(4):596. Camisasca E, Miragoli S, Di Blasio P, Grych J. Children’s Coping Strategies to Inter-Parental Conflict: The Moderating Role of Attachment. J Child Fam Stud. 2017 Apr 1;26(4):1099–111. Andrews S, Ellis DA, Shaw H, Piwek L. Beyond Self-Report: Tools to Compare Estimated and Real-World Smartphone Use. PLOS ONE. 2015 Oct 28;10(10):e0139004. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 17 Sep, 2024 Editor assigned by journal 17 Sep, 2024 Submission checks completed at journal 13 Sep, 2024 First submitted to journal 10 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5063332","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":355056056,"identity":"360cd011-0fc3-48ca-8c44-7d72434aac6c","order_by":0,"name":"xiaoya yuan","email":"","orcid":"","institution":"Southwest University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"xiaoya","middleName":"","lastName":"yuan","suffix":""},{"id":355056059,"identity":"9521c9ac-2d87-4dcb-81e6-0ffe5219c757","order_by":1,"name":"Yaxin Mao","email":"","orcid":"","institution":"Southwest University of Science and 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09:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5063332/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5063332/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-22105-8","type":"published","date":"2025-03-10T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70506049,"identity":"76da446c-a06d-462f-bb81-9856b4b9b887","added_by":"auto","created_at":"2024-12-03 23:32:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15612485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork structure diagram (a) and cluster diagram (b) of mobile phone addiction, mental health and resilience among left-behind adolescents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Nodes are psychological variable dimensions, and connecting lines are partial correlations between node dimensions. The thicker the line, the higher the correlation, and the colour of the line indicates the direction of the correlation (green for positive correlation, red for negative).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/18a3836b1a7bd077c6d58e1c.png"},{"id":70506714,"identity":"28a7ce34-87a4-4879-9fa2-e02684d6aa14","added_by":"auto","created_at":"2024-12-03 23:40:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5290633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentrality index of each node of the network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: BSI1, somatization; BSI2, depression; BSI3, anxiety; MPAI1, inability to control carving; MPAI2, feeling anxious and lost; MPAI3, withdrawal or escape; MPAI4, productivity losse; RSCA1, goal planning; RSCA2, affect control; RSCA3, positive thinking; RSCA4, family support; RSCA5, help-seeking.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/a1bb16d4c38b25ea312d59f1.png"},{"id":70506048,"identity":"b34f89cd-3972-40ee-83f7-e90fc4f5f0d5","added_by":"auto","created_at":"2024-12-03 23:32:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":874088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBootstrap confidence intervals for edge weights in network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Red dots indicate sample values, black dots indicate values for each edge weight, and grey areas indicate 95 % confidence intervals.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/51431ff087fb75f8e8c80263.png"},{"id":70506046,"identity":"f70a7164-14ac-41b6-87d4-d0d0c2c8c5b9","added_by":"auto","created_at":"2024-12-03 23:32:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1076615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStability test diagram for the case-dropping subset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Lines represent the average relationship between the original sample centrality and the subsamples. Regional color blocks indicate the range between the first quartile and the third quartile.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/263d1f74f078b600a3391a23.png"},{"id":70506715,"identity":"dfb9c42b-d98d-4ae3-8704-02a63c800f4d","added_by":"auto","created_at":"2024-12-03 23:40:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12530801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBootstrapped difference tests result of the network strength (a), bridge strength (b) and edges (c)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Black boxes indicate significant differences between two nodes (α= 0.05). BSI1, somatization; BSI2, depression; BSI3, anxiety; MPAI1, inability to control carving; MPAI2, feeling anxious and lost; MPAI3, withdrawal or escape; MPAI4, productivity losse; RSCA1, goal planning; RSCA2, affect control; RSCA3, positive thinking; RSCA4, family support; RSCA5, help-seeking.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/f406c175f919635019d98a90.png"},{"id":70506052,"identity":"00a11236-f873-4c77-bf39-fb4c91c66159","added_by":"auto","created_at":"2024-12-03 23:32:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15262293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of network structure between high- and low-level groups of parent-child conflict\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Nodes are dimensions of psychological variables, and lines are partial correlations between the dimensions of nodes. Thicker lines indicate higher correlation, and line colors indicate the direction of correlation (green for positive correlation, red for negative correlation).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/243d5bb0fbb16cf29b0a1066.png"},{"id":70506050,"identity":"2cba4195-7700-48dd-84ed-6aa32802ad58","added_by":"auto","created_at":"2024-12-03 23:32:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":7740016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of network centrality indicators between high- and low-level groups of parent-child conflict\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The centrality graph depicts the strength centrality (z-score) and the bridge strength centrality (z-score) of each node in the network, with higher scores representing greater influence of nodes in the network. BSI1, somatization; BSI2, depression; BSI3, anxiety; MPAI1, inability to control carving; MPAI2, feeling anxious and lost; MPAI3, withdrawal or escape; MPAI4, productivity losse; RSCA1, goal planning; RSCA2, affect control; RSCA3, positive thinking; RSCA4, family support; RSCA5, help-seeking.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/5d4edaf58d2efd5b501aa96a.png"},{"id":78688920,"identity":"c3cc7809-e1a9-4b73-b078-8c7a4b812f8d","added_by":"auto","created_at":"2025-03-17 16:07:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":48472225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5063332/v1/d814daed-39fb-4af1-a67a-bb618cd06494.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship Between Resilience and Mental Health, Mobile Phone Addiction and Its Differences Across Levels of Parent-Child Conflict Among Left-Behind Adolescents: A Cross-Sectional Network Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith China's rapid political and economic development, a large number of people in underdeveloped regions of the central-west have begun to move to the developed regions searching for jobs, which gives rise to the problem of left-behind children due to family, policy, and other factors. What's more, left-behind children are minors under the age of 16 whose parents are both working away from their hometown, or one of whom is working away while the other is unable to take care of the children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of 2020, China saw 66.93\u0026nbsp;million left-behind children, and 138\u0026nbsp;million who were affected by population mobility, accounting for 46.4% of China's total child population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The children, who are left behind are not able to stay with their parents for a long time, lack parental care, are prone to loneliness, anxiety, depression, and other negative emotions, which not only influence their studies and lives, but also may have long-term adverse effects on their mental health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Research has found that, from the past to the present, migrant parents are more likely to hurt their mental health, including emotions and behaviour [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. And the situation might become worse over time [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Meanwhile, these children tend to develop mobile phone addiction [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a means of communication tools provided by parents for their children, smartphones expose children to addiction thanks to being alone for long periods and being spoiled by their elders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This may lead to their indulgence in the virtual world, which may have a negative impact on their social skills, learning, behaviour and mental health [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Left-behind adolescents experiencing puberty have rapid physical and mental development and face more severe academic pressure and social environment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Compared with non-left-behind adolescents, left-behind ones show higher risk propensity and prevalence of mental health problems and mobile phone addiction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We should pay more attention to left-behind youth and provide them with more comprehensive and effective support and guidance.\u003c/p\u003e \u003cp\u003eWith the popularity of smartphones, the addictive problem has become a new form of Internet addiction in the mobile era, which is a behavioural addiction that causes psychological and behavioural phenomenon of users due to the misuse of mobile phones [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The 44th report of China Internet Network Information Centre [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] shows that the number of mobile phone holders in China has reached 847\u0026nbsp;million, of which 17% are teenagers aged 10 to 19 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Empirical studies in different countries have found that adolescent mobile phone addiction is negatively associated with mental health [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Adolescents addicted to cell phones are more susceptible to anxiety, depression, and impulsivity at a high level [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Also, cross-lagged analyses show that individuals with higher depression and anxiety are subject to developing mobile phone addiction [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Smartphone addiction has a strong association with mental health, and there is even a risk of co-morbidity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The cognitive-behavioural model of pathological Internet use proposed by Davis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] argues that psychopathologies such as depression, anxiety, and substance dependence are distally necessary causes of pathological Internet use symptoms. According to the compensatory Internet use model proposed by Kardefelt-Winther, negative life situations can increase online behaviour to alleviate negative emotions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Individuals with poor mental health are more vulnerable to negative emotions and behavioural change suffering negative life issues [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which leads to mobile phone addiction[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResilience is often described as the ability to revive or overcome certain adversity in order to extract a positive outcome from a negative event or situation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Current research has found that resilience has an important role in the mental health and prevention of mobile phone addiction among left-behind adolescents [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A meta-analysis of 25 studies showed that despite differences in research objectives and instruments, higher resilience was associated with fewer mental health problems [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. At the same time, resilience is also an important predictor of mobile phone addiction, and empirical studies have found that self-resilience related to \"relationships\", \"curiosity\" and \"emotional control\" have been found to moderate mobile phone use in both men and women [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Existing research suggests that resilience can both directly and negatively predict mobile phone addiction among Chinese adolescents [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and studies from different countries have found that resilience can also serve as a mediator [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or an adjustment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] to influence mobile phone addiction tendencies. Results from a one-year longitudinal study also indicated that problematic mobile phone use and resilience predicted psychological disorders in college students, and that resilience mediated the association [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Meanwhile, mobile phone addiction depletes individuals' self-control ability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], thus reducing their level of resilience. The resilience process model suggests that resilience is a protective mechanism under stress and adversity, reflecting an individual's ability to adapt and prepare for challenges positively [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Researchers have pointed out that resilience is regarded as one of the key protective elements of Internet addiction, and that Internet addiction often stems from the individual's lack of resilience in self-control and coping with stress and frustration [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, mobile phone and Internet addiction are both behavioural addictions with similarities and may have similar addiction mechanisms [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to three key factors, foreign research suggests a link between adolescent resilience, psychological health, and mobile phone addiction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which has argued that the relationship between college students' resilience and mental health is mediated by Internet addiction, and that increasing resilience helps prevent Internet addiction and reduce the risk of depression [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Domestic studies have also shown that mobile phone addiction has a direct effect on college students' physical and mental health, and can also indirectly affect their health through resilience [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Internet addiction predicts depression and anxiety in Chinese rural left-behind children, and resilience plays an independent mediating role in the relationship between their Internet addiction and depression and anxiety symptoms [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Adverse mental health conditions such as depression, anxiety, stress, and coping styles significantly influence the risk of mobile phone addiction among adolescents and mediate the relationship between resilience and mobile phone addiction among Chinese adolescents [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, current research still lacks insight into the network relationship between resilience, mental health, and mobile phone addiction.\u003c/p\u003e \u003cp\u003eThe family is a direct and dominant subsystem influencing adolescent development [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], so a harmonious family atmosphere is essential for the healthy physical and mental development of adolescents. During puberty, there is an increase in conflict and a decrease in interaction in parent-child relationships [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A Comparative Study of Parent-Child Relationships in the Internet Age in China, the United States, Japan, and Korea showed that 82.1% of Chinese primary and secondary school students had conflicts with their parents, and 25.2% of these conflicts were focused on Internet access. Studies have shown that the parent-child relationship is an important mediating mechanism in the family system that influences individual development and adaptation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Conflict is an important part of the parent-child relationship, and adolescents with higher parent-child conflict are more likely to develop mobile phone addiction [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Substantial empirical studies have also demonstrated that parent-child conflict can negatively predict adolescent mental health [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In addition, there is an association mechanism between parent-child relationships (including parental support and parent-child conflict) and adolescent resilience [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. According to the individual-situation interaction theory, situational factors may interact with an individual's characteristics [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The situational factors of parent-child conflict may interact with the resilience of individuals' psychological traits to influence individuals' psychological behavioural states. Therefore, this study will also study the relationship between parent-child conflict and left-behind adolescents' resilience, mental health, and mobile phone addiction.\u003c/p\u003e \u003cp\u003eAt present, most domestic and international studies related to resilience, mental health, mobile phone addiction, and parent-child conflict adopt cross-sectional empirical research methods to explore the predictive mechanisms by constructing structural equation modelling [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. While this approach can also deal with relationships between multivariate variables, it is mainly applied to validation factor analyses, focusing on verifying the pre-determined model structure [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], may have limitations for highly complex and dynamic systems, and may be insensitive to the discovery of new structures and patterns. Resilience [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and good parent-child relationships [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] are protective factors in children's growth. \"protective model\" of adolescent development proposed by Fergus et al. [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], suggests that different protective factors may interact in predicting developmental outcomes, i.e., the predictive effect of one protective factor (e.g., resilience) on outcome variables (e.g., mobile phone addiction, mental health) may be influenced by another protective factor (e.g., parent-child relationship).\u003c/p\u003e \u003cp\u003eIn recent years, the network analysis model has rapidly emerged as a new method of describing individual psychological traits as a complement to latent variable models, providing new ideas for understanding human psychological phenomena [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In response to the neglect of symptom interactions in latent variable models of traditional psychological perspectives [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], Borsboom proposed a network theory of psychopathology, which suggests that symptoms are an integral part of mental disorders, and that the onset and persistence of mental disorders are driven by tightly intertwined causal relationships between symptoms and mutually reinforcing feedback mechanisms [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Based on this theory, the study by Cramer et al. used a Gaussian graph theory model to analyse the relational network of symptoms [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Subsequently, this model became the foundational method for employing network analysis to process transect data. The method refers to symptoms as nodes of a network graph, and links between symptoms as edges connected between nodes, with the weights of the edges representing the strength of the association between the nodes, which is usually visualised as the thickness of the edges in the network graph. The network analysis method can deal with complex interactions and dynamic relationships between variables, and can reveal the underlying structures and patterns in the system. Moreover, by displaying the associations between variables through graphical visualisation, it is possible to see which variables are closely related to each other and how these associations affect the whole system, which makes the results of the study more concise and easy to understand.\u003c/p\u003e \u003cp\u003eTherefore, this study takes the group of left-behind adolescents in Sichuan Province, China, as the research object, and explores the multidimensional relationship between resilience, psychological health, and mobile phone addiction through network analysis, and assesses the characteristics of the network structure under different levels of parent-child conflict. This study is geographical and population-specific, combining psychology, sociology, and complex network analysis, which provides a novel theoretical framework and methodological tool for the study of the relationship between resilience, psychological health, and mobile phone addiction among left-behind adolescents, which can help to provide a scientific basis for the development of precise social intervention strategies.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThis study used an online questionnaire platform to conduct a survey in 28 secondary schools in Sichuan Province for those who met the following criteria: (1) students in their first to third year of high school; (2) fulfilled the condition of being left behind, \"neither parent can supervise or take care of me\"; (3) gave informed consent and voluntarily took part in this research. A total of 2,824 questionnaires were distributed, excluding duplicates, missing questions, and consecutive cases with the same answers, and removing outliers according to the standard deviation of three times, resulting in 2,100 valid questionnaires, with an effective recovery rate of 74.4%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement tools\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Mental health\u003c/h2\u003e \u003cp\u003eThis study used the Brief Symptom Inventory 18 (BSI-18) prepared by Derogatis [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] to measures mental health. The scale consists of 18 questions with three dimensions, somatization, depression, and anxiety, and three subscales with six items each. All questions are scored on a 5-point scale (1\u0026thinsp;=\u0026thinsp;never, 2\u0026thinsp;=\u0026thinsp;mild, 3\u0026thinsp;=\u0026thinsp;moderate, 4\u0026thinsp;=\u0026thinsp;quite severe, 5\u0026thinsp;=\u0026thinsp;severe), with higher scores indicating higher levels of psychological distress and lower levels of mental health. In this study, Cronbach's alpha coefficient for this scale was 0.926, with 0.845 for the somatization subscale, 0.850 for the depression subscale, and 0.846 for the anxiety subscale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Mobile phone addiction\u003c/h2\u003e \u003cp\u003eThe Chinese version of the Mobile Phone Dependence Index (MPAI) developed by Leung et al. [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] to measure mobile phone addiction. The scale consists of 17 questions, including four dimensions, namely, the inability to control cravings subscale, the feeling anxious and lost subscale, the withdrawal and escape subscale, and the productivity loss subscale, with the number of questions in each dimension ranging from 3\u0026ndash;7. A 5-point scoring system was applied, with higher scores indicating higher levels of individual cell phone addiction. In this study, the Cronbach's alpha coefficient of the scale was 0.892, with 0.849 for the inability to control cravings subscale, 0.786 for the withdrawal and escape subscale, 0.764 for the feeling anxious and lost subscale, and 0.755 for the productivity loss subscale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Resilience\u003c/h2\u003e \u003cp\u003eThe Resilience Scale for Chinese Adolescents (RSCA) developed by Yue-Qin Hu and Yi-Qun Gan was used in this study [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] to measure resilience. The scale consists of 27 questions, including five dimensions: goal planning, emotional control, positive thinking, family support, and interpersonal assistance, with the number of questions in each dimension ranging from four to six. A 5-point scoring system was used, with higher scores indicating higher levels of resilience. In this study, the Cronbach's alpha coefficient of the scale was 0.874, with 0.763 for the goal-focused subscale, 0. 747 for the emotional control subscale, 0.762 for the positive thinking subscale, 0.793 for the family support subscale, and 0.745 for the interpersonal assistance subscale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Parent-child conflict\u003c/h2\u003e \u003cp\u003eBased on Nelissen [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] 's study, the Parent-Child Conflict Scale consists of 6 questions on a 5-point scale, with higher scores indicating higher levels of parent-child conflict. The Cronbach's alpha coefficient for the scale in this study was 0.797.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e \u003cp\u003eIn this study, SPSS23.0 was applied for total score calculation, common method bias test, and descriptive statistical analysis, R (4.3.2) was used for network analysis, and R packages qgraph (1.9.8), mgm (1.2\u0026ndash;14), networktools (1.5.2), and bootnet (1.5.6) were used for network estimation and visualisation, network centrality estimation and stability tests [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The top 27% of the total parent-child conflict score was taken as the high level of the parent-child conflict group, and the bottom 27% of the total score was taken as the low level of the parent-child conflict group [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], constructed the networks separately and compared them using the R package NetworkComparisonTest (2.2.2) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Data pre-processing\u003c/h2\u003e \u003cp\u003eInvalid questionnaires were filtered according to the following steps: firstly, questionnaires with less than 300s of response time were excluded, then questionnaires with missing items were deleted to facilitate subsequent data analysis, then duplicate cases were identified and deleted based on information such as IP, time of submission, age, school, etc., and those with more than 15 consecutive questions with the same response within the same scale were considered as invalid data were deleted, and finally, Z-scores for the respective scale and its dimensions were calculated, and extreme case data with Z-scores exceeding plus or minus 3 were removed to make the results more stable and reliable. Finally, the Z-scores of each scale and its dimension scores were calculated, and the data of extreme cases with Z-scores exceeding plus or minus 3 were deleted to make the results more stable and reliable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Network estimation and visualisation\u003c/h2\u003e \u003cp\u003eIn this study, each dimension of the Brief Symptom Scale 18, the Mobile Phone Dependence Index, and the Resilience Scale for Chinese Adolescents was used as a node, and the correlation between the dimensions were used to generate the edges of the network, and the partial correlation structured network was constructed and visualised using the R package qgraph (1.9.8) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Applying the least absolute shrinkage and selection operator (LASSO) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] and the extended Bayesian information criterion (EBIC) [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Regularisation was performed with a tuning parameter of 0.5 to prevent overfitting and obtain a concise and interpretable structure. The predictability of each node was calculated using the R package mgm(1.2\u0026ndash;14) [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. The more predictable a node is, the more it can be predicted or determined by other connected nodes in the network; conversely, if the predictability value is low, we need to intervene directly on the node or look for markers outside the network [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. In addition, using the spinglass algorithm [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] for modular analysis of node clustering to reveal and optimise the structure of associations in the network.\u003c/p\u003e \u003cp\u003eIn the network, green edge lines represent positive correlations and red edge lines represent negative phases, and the thickness of the edges indicates the absolute magnitude of the correlation, with thicker edges indicating higher correlations. Using the Fruchterman-Reingold algorithm [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. A visual network layout was performed so that nodes with strong and numerous connections were located in the centre of the network and nodes with weak and few connections were distributed at the periphery of the network. When performing network comparisons between the high and low level groups of parent-child conflict, the averageLayout function in the R package qgraph (1.9.8) was used to perform network layouts, presenting a consistent visual layout of nodes using the average position in the two networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Centrality estimates\u003c/h2\u003e \u003cp\u003eIn network analysis, the centrality metric is an important metric used to describe how central a node is in the network. Using the centrality function in the R package qgraph (1.9.8) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Calculate the strength, betweenness and closeness of the network nodes and use the bridge function in the R package networktools (1.5.2) [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] Calculate bridge centrality metrics for network nodes, including bridge strength, bridge betweenness, and bridge closeness. Previous studies [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] found that strength centrality is the most persuasive metric in psychometrics, and when the three metrics do not have the same numerical ordering, the result of the ordering of strength centrality generally prevails. Therefore, in this study, we chose the strength centrality and the bridge strength centrality of the node to be reported, and plotted the normalised (z-scored) values for each node. Where strength refers to the sum of the absolute value of the weights of all edges connecting the node, the larger its value indicates that the node is more closely connected to other nodes and has a greater effect on the whole network [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]; Bridge Strength refers to the sum of the absolute values of the weights of the edges of the nodes of other communities that are connected to this node, the higher its value the more influence this node has on the nodes of other communities [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3. 4 Accuracy and stability test\u003c/h2\u003e \u003cp\u003eThe accuracy and stability of the constructed network were calculated and verified using the R package bootnet (1.5.6) using the Bootstrapping method [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The estimation results were validated and analysed. Firstly, the 95% confidence interval (CI) of each edge weight in the network are calculated based on the non-parametric bootstrapping method. If the 95% confidence intervals of the sampling set and the original dataset overlap more, it means that the network edge weights are estimated more accurately. Secondly, based on removing the case-dropping subset bootstrap to assess the stability of the central indicator, delete a certain proportion of samples, and re-estimate the network, if the network structure of the central indicator order remains unchanged, the stability is good. And the correlation stability coefficient (CS-coefficient) of the network is calculated using the corStability function for assessment, which indicates that when the maximum proportion is removed, the correlation between the original centrality indicator and the network centrality indicator of the subset at 95% probability is higher than 0.7, the value of the CS-coefficient should not be lower than 0.25, and higher than 0.5 indicates good centrality stability [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Finally, the centrality difference test was conducted to assess the differences in centrality indicators between nodes and edges using the non-parametric bootstrap method. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Network comparison\u003c/h2\u003e \u003cp\u003eUsing the R package NetworkComparisonTest (2.2.2) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] Network Comparison Test (NCT) was performed on the high and low-level groups of parent-child conflict. Network invariance test and global strength invariance test were performed in 5000 permutations to assess whether the two networks differed in weight of edges and global strength.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Common method bias test\u003c/h2\u003e \u003cp\u003eAs all data in this study were collected using participant self-report, there may be common methodological bias. The Harman one-way method of testing was used in this study, which showed that there were a total of 12 factors with eigenvalues greater than 1, the first of which had a percentage of the variance of 23.4%, which did not exceed the critical value of 50% [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Therefore, common method bias had little effect on the results of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Descriptive statistics\u003c/h2\u003e \u003cp\u003eOf the total of 2,100 adolescent secondary school students included in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 905 (43.1%) were boys, and 1,195 (56.9%) were girls, with an average age of 15.60 years (age 12 to 20, SD\u0026thinsp;=\u0026thinsp;1.78); the urban population was 386 (18.4%), the township population was 528 (25.1%), and the rural population was 1,186 (56.5%). An analysis of the respondents' left-behind status shows that 78 (3.7%) live with their fathers but cannot be under their guardianship or care, 75 (3.6%) have fathers who are away for less than three months a year, 415 (19.8%) have fathers who are away from three to six months a year, 722 (34.4%) have fathers who are away for more than six months a year, and 754 (35.9%) have fathers who are absent for almost all of the year (35.9%), 56 (2.7%) whose fathers had passed away; 135 (6.4%) who lived with their mothers but could not be under their guardianship or care, 103 (4.9%) whose mothers were away for less than three months a year, 391 (18.6%) whose mothers were away for three to six months a year, 691 (32.9%) whose mothers were away for more than six months a year and 737 (35.9%) whose mothers were away almost all the year. 737 (35.1%), and 43 (2.0%) whose mothers had passed away.\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\u003eBasic information of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNumber (%)/M(SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynthesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-level Parent-Child Conflict Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-level Parent-Child Conflict Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e905 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,195 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e348 (61.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e277 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.60 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.94 (1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.30 (1.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,186 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e342 (60.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving with my father or not\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with my father lack of his custody and care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 3 months of my father's absence annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 3\u0026ndash;6 months of my father's absence annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 6 months of my father's absence annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e722 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost a whole year of my father's absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e754 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMy father has passed away.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving with my mother or not\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with my mother lack of his custody and care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 3 months of my mother's absence annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 3\u0026ndash;6 months of my mother's absence annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 6 months of my father's absence annually\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e691 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost a whole year of my mother's absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e737 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMy mother has passed away.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe high level of parent-child conflict group consisted of 567 individuals, 219 males (38.6%) and 348 females (61.4%), with a mean age of 15.94 years (age 12 to 20, SD\u0026thinsp;=\u0026thinsp;1.81); the low level of parent-child conflict group consisted of 567 individuals, 290 males (51.1%) and 277 females (48.9%), with a mean age of 15.30 years (age 12 to 20, SD\u0026thinsp;=\u0026thinsp;1.83). Comparing the differences between the high level of parent-child conflict group and the low level of parent-child conflict group on some demographic variables, the descriptive statistics showed that the two groups were essentially similar on the variables of gender, age, place of birth, and retention (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Through t-tests, there were no statistically significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in demographic variables other than age, township, and rural birthplace, suggesting that in most respects the two groups were essentially equivalent.\u003c/p\u003e \u003cp\u003eThe overall sample mean total score for the BSI was 33.29 (SD\u0026thinsp;=\u0026thinsp;10.70), the MPAI mean total score was 48.67 (SD\u0026thinsp;=\u0026thinsp;12.18), the RSCA mean total score was 87.43 (SD\u0026thinsp;=\u0026thinsp;14.61), and the Parent-Child Conflict Scale mean total score was 9.66 (SD\u0026thinsp;=\u0026thinsp;3.42). The mean and standard deviation of each dimension of the scale, i.e., network nodes, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean of the total score of the summary symptom scale for the high level of parent-child conflict group was 41.72 (SD\u0026thinsp;=\u0026thinsp;10.53), the mean of the total score of the index of mobile phone dependence scale was 52.60 (SD\u0026thinsp;=\u0026thinsp;11.19), the mean of the total score of the resilience scale for adolescents was 77.99 (SD\u0026thinsp;=\u0026thinsp;12.29), and the mean of the total score of the parent-child conflict scale was 14.38 (SD\u0026thinsp;=\u0026thinsp;2.54). The low level of parent-child conflict group had a total score mean of 26.58 (SD\u0026thinsp;=\u0026thinsp;7.28) for the Brief Symptoms Scale, 42.37 (SD\u0026thinsp;=\u0026thinsp;11.53) for the Mobile Phone Dependence Index Scale, 97.57 (SD\u0026thinsp;=\u0026thinsp;13.97) for the Adolescent resilience Scale, and 6.38 (SD\u0026thinsp;=\u0026thinsp;0.49) for the Parent-Child Conflict Scale.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean, standard deviation, predictability, and centrality indicators for each node\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePredictability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDissociation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBridge Strength\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomatisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInability to control cravings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeeling anxious and lost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPAI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithdrawal and escape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPAI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProductivity loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoal planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSCA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffect control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSCA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSCA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSCA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHelp-seeking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Network structure\u003c/h2\u003e \u003cp\u003eThe network structure of the left-behind adolescents' mental health, mobile phone addiction, and resilience is demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. There are 12 nodes in the network, and a total of 45 non-zero edges actually exist, including 20 negative edges and 25 positive edges, accounting for 68.18% of the number of possible connected edges. The proportion of the circle around a node that is filled represents the predictability of that node, with a larger proportion of the filled portion indicating a higher predictability of that node, with an average predictability of 0.44 (range 0.23 to 0.68, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe network module analysis displayed that the nodes of mental health, mobile phone addiction, and resilience of the left-behind children clustered with each other to form three node communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), which was consistent with the three research variables and their dimensions. The communities for mental health and mobile phone addiction were more strongly connected internally; whereas the communities for resilience were weaker except for RSCA1 (goal planning) and RSCA3 (positive thinking) which were deeply associated. The links between the three communities were also stronger, with the dimensional nodes interacting with each other. The strongest connections were between BSI2 (depression) and RSCA4 (family support), BSI3 (anxiety) with RSCA2 (affect control) and MPAI2 (the feeling anxious and lost), and MPAI1 (inability to control cravings) with RSCA4 (family support) and RSCA1 (goal planning) directly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Indicators of centrality\u003c/h2\u003e \u003cp\u003eThe results of the centrality index of psychological status, mobile phone addiction and resilience network of left-behind adolescents are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the specific values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Node BSI3 (anxiety, Strength\u0026thinsp;=\u0026thinsp;1.23) has the highest strength centrality, and node MPAI1 (inability to control cravings, Strength\u0026thinsp;=\u0026thinsp;1.21) is the second highest. In terms of bridge strength centrality, nodes MPAI1 (inability to control carving, Bridge Strength\u0026thinsp;=\u0026thinsp;0.64) and RSCA4 (family support, Bridge Strength\u0026thinsp;=\u0026thinsp;0.60) were significantly stronger than the other nodes. The results of the variability test for the centrality index also indicate that the high centrality nodes are stable and reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Accuracy and stability of the network\u003c/h2\u003e \u003cp\u003eThe results of the edge weight bootstrap procedure are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, where the network estimation is moderately accurate and there is a partial overlap between the 95% CI of the edge weights. The results of the excluded cases bootstrap method are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, with CS coefficients of 0.75 for strength, bridge strength, closeness, and edges, and 0.594 for betweenness, which are greater than 0.5, saying that the network estimation has good stability. The result of bootstrapped difference tests is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, nodes and edges with strong centrality in the network are statistically more strongly different than other nodes in the network, further indicating that the results of centrality analysis are stable and generalisable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Comparison of networks\u003c/h2\u003e \u003cp\u003eNetwork Comparison Test (NCT) was performed on the Parent-Child Conflict High-Level Group and Low-Level Group. The results show that both networks contain 12 nodes, and the result of the parent-child conflict high-level group contains 47 edges, while the parent-child conflict low-level group contains 42 edges, and the visualisation of the network is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. By centrality analysis, in the parent-child conflict high-level group, the core nodes and core bridge nodes are BSI3 (anxiety) and MPAI1 (inability to control cravings). In the low-level group of parent-child conflict, the core nodes were BSI3 (anxiety), MPAI2 (the feeling anxious and lost), and the core bridge nodes were MPAI1 (inability to control cravings), and RSCA4 (family support).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the network invariance test showed a significant difference in structure between the high and low-level groups of parent-child conflict (M\u0026thinsp;=\u0026thinsp;0.265, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the results of the global strength invariance test did not find a significant difference in the global strength of the network (high-level group: 5.526 vs. low-level group: 4.952; S\u0026thinsp;=\u0026thinsp;0.566, p\u0026thinsp;=\u0026thinsp;0.162). Tests of centrality invariance revealed significant differences in both intensity centrality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, cohen's d\u0026thinsp;=\u0026thinsp;0.456) and bridge intensity centrality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, cohen's d\u0026thinsp;=\u0026thinsp;0.828). A total of 6 node centrality of BSI1 (somatization), RSCA5 (interpersonal assistance), and BSI2 (depression) were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accounting for 50% of the total. The results of the borderline weight invariance test showed that a total of 15 borderlines such as RSCA4 (family support) differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from BSI2 (depression), MPAI1 (inability to control cravings), and RSCA2 (affect control), and BSI1 (somatization) differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from BSI2 (depression), and MPAI4 (productivity loss), which accounted for about 28% of the total number of borderlines.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we used network analysis to explore in depth the associations between the dimensions of resilience and mental health and mobile phone addiction among Chinese left-behind adolescents, and to further compare the core dimensions and network structure differences in the networks of resilience and mental health and mobile phone addiction among left-behind adolescents with different levels of parent-child conflict. The results of the study found that: (1) there exists a structurally stable network relationship between resilience, psychological health, and mobile phone addiction among left-behind adolescents; (2) BSI3 (anxiety) is the most central node in the network model, followed by MPAI1 (inability to control cravings); (3) MPAI1 (inability to control cravings) and RSCA4 (family support) are the most central bridge nodes connecting resilience, psychological health and mobile phone addiction in the study sample; (4) there were significant differences in the network structure between the high and low-level groups of parent-child conflict, specifically no significant differences in the global strength of the network, and significant differences in both strength centrality and bridge strength centrality.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1 Network structure and its core dimensions of resilience, mental health, and mobile phone addiction of left-behind adolescents\u003c/b\u003e,\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.1.1 Network structure\u003c/h2\u003e \u003cp\u003eThe study showed that there were three relatively independent clusters in the networks of resilience and mental health and mobile phone addiction among left-behind adolescents. The mental health and mobile phone addiction communities are more closely connected internally, in line with psychopathology network theory [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], i.e. some symptoms are more closely connected to each other than others, and the clusters form manifestations of mental disorders. Connections within the resilience community are looser overall, with only a strong positive correlation shown between goal planning and positive thinking, with the five dimensions representing different dimensions of the individual, the environment, and so on, which work together to contribute to the overall resilience of the individual. Looking at the network as a whole, mental health and mobile phone addiction in general have strong negative associations with resilience, while a positive correlation was shown between mental health and mobile phone addiction. Enhancing resilience may help to reduce the risk of mobile phone addiction and promote mental health among left-behind adolescents.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Network core dimensions\u003c/h2\u003e \u003cp\u003eThis study found that inability to control carving occupies a crucial position in the network of left-behind adolescents' resilience and mental health, and mobile phone addiction. is both a core node and a core bridge node, which has a profound impact on the overall network structure. Inability to control carving is manifested in the individual's difficulty in self-regulation, investing too much time in mobile phone use without being able to manage it effectively [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The results of this study are similar to those of existing network analysis studies [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], and are also consistent with the Interaction of Person-Affect-Cognition-Executionmodel (I-PACE) proposed by Brand [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], diminished control over decision-making can be transferred to behavioural addictions and specific Internet-use disorders. Network visualisation results showed a stronger direct relationship between inability to control carving and goal planning and family support in resilience, possibly because the inability to control carving creates an attention bias toward the automation of addictions [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] that affects addicts' attention allocation and cognitive resource use. According to the social displacement hypothesis, addiction to smartphones can neglect face-to-face interactions with friends and family members [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] and lack of real-world social support [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] and left-behind adolescents are already more deprived of parental companionship, leading to lower levels of family support and interpersonal assistance. For mental health, uncontrollability was only directly related to depression. This may be due to the fact that both mobile phone addiction and depression are related to the dopamine system in the brain[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], and both show similar symptoms such as loss of interest, social withdrawal, and mood swings [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. In contrast, adolescent somatization and anxiety are influenced by a wide range of factors, including genetics, environment, past experiences, psychological states, and psychological changes [\u003cspan additionalcitationids=\"CR97\" citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], and so were only indirectly affected by runaway sex in this network. Notably, the results of the present study showed that inability to control carving in mobile phone addiction was positively associated with positive perceptions of resilience, in contrast to existing research where hopeful attitudes may reduce adolescents' dependence on smartphones [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] The results are not consistent with the results of Left-behind adolescents may need to take on family responsibilities earlier because of the unique nature of their home environment, an experience that may allow them to hone their independence and coping strategies [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] and thus be able to adapt to adversity more quickly in certain situations, and to make self-determination, self-planning and problem-solving with a more optimistic and positive attitude. This also suggests that left-behind adolescents may realise the seriousness of the problem after experiencing uncontrolled mobile phone addiction, and may engage in self-reflection and seek adjustment, in which their positive thinking may be enhanced.\u003c/p\u003e \u003cp\u003eIn addition, similar to previous network analysis studies [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], anxiety is also one of the most central nodes of the network model in this study. Anxiety is the brain's response to danger, stimuli, and is a state that an organism will actively try to avoid [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. In this network, anxiety mainly affects withdrawal or escape in mobile phone addiction. the over-comfort pathway in the pathway theory of problematic mobile phone use proposed by Billieux [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e] states that individuals with increased anxiety contribute to their mobile phone dependence addiction out of needs such as comforting reassurance. Meanwhile, according to the Attention Gate Model (AGM), anxiety overestimates the time interval of negative stimuli [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e] by paying attention to them [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. When addicts are in withdrawal, their attention is more likely to be focused on the time they are waiting to use their mobile phones, leading to distorted perceptions of time and making withdrawal more difficult for addicts by making it feel more difficult. Emotional control and family support in the resilience of left-behind adolescents are directly and negatively affected by anxiety. Relevant studies have shown that chronic anxiety leads to a more sensitive response to external stimuli [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e] which leads to difficulties in regulating negative emotions [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Due to parental absence and limited resources, left-behind adolescents have difficulties in obtaining immediate emotional support and proper guidance, and lack effective strategies for emotional expression and control. Anxiety may affect individuals' perception and utilisation of family support [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e], or due to communication barriers [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], the inability to effectively access needed support from family members.\u003c/p\u003e \u003cp\u003eFamily support is another one of the core bridge nodes in the network structure of this study, connecting depression, anxiety, somatization in left-behind adolescents' mental health and uncontrollability and ineffectiveness in mobile phone addiction. It refers to the tolerant, respectful and supportive attitudes of family members [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], an important external factor in resilience [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. Increased family support can enhance family members' psychological well-being [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. In the present study, the family support of the left-behind adolescents also had direct effects on their depression, anxiety and somatization to varying degrees. Among them, the effect on depression was the most significant, and lack of parental care leading to depression was one of the most prominent problems among left-behind children [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. The lack of direct parent-child interactions and emotional communication, and the sense of stress caused by the impairment of parental care resources or unmet needs of left-behind adolescents have both immediate and delayed negative predictive effects on depression [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. The effect of family support on anxiety was also more significant, consistent with previous research findings [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. The relatively weak effect on somatization, on the other hand, may be related to the indirect nature of somatization symptoms and the multiple influencing factors [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] of interference. Existing research suggests that family support significantly and negatively predicts adolescent mobile phone addiction [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], but in the network structure of this study, family support was negatively associated with inability to control carving and showed positive results with inefficacy. Inefficacy refers to excessive mobile phone use resulting in lower academic or work productivity [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] Ineffectiveness Left-behind adolescents usually grow up with their grandparents, who are more lenient than their parents based on their love for their grandchildren, and do not know whether to stop, encourage, or ignore adolescents' problematic behaviours due to their lack of knowledge and backwardness [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]. Parents who work outside the home tend to feel indebted to their children and indulge them completely, and lack proper guidance and supervision of their children's learning in education management. Such intergenerational education is often prone to spoiling, and although a certain degree of family support is provided for children, improper discipline in life also leads to problematic behaviours of excessive mobile phone use and affects learning efficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2 Differences in parent-child conflict levels between resilience, mental health, and mobile phone addiction networks in adolescents who are left behind\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, it was found that the differences in network invariance test at different levels of parent-child conflict were significant, and the differences in the global strength of the networks were not significant. It indicated that the two networks had similar levels of connectivity overall and maintained some stability at different levels of parent-child conflict. Families with high levels of parent-child conflict were more connected within the mental health and mobile phone addiction communities, and distant within the resilience community, and even showed a significant negative internal correlation. This result reveals that parent-child conflict may undermine left-behind adolescents' resilience [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] to further disintegrate mental health and lead to mobile phone addiction problems. In addition, the network structure of the high-level parent-child conflict group had stronger direct associations among the three associations, the correlations among the nodes were more disordered, and changes in individual dimensions were more likely to spread across different associations. This suggests that intense parent-child conflict may lead to a rapid spread of risks or negative effects and cause individuals to show a high degree of flexibility and diversity in their psychological adaptations [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e] that can cope with different situations through multiple psychological mechanisms. In contrast, in the group with low levels of parent-child conflict, inter-community connections were relatively looser, intro-community associations within resilience were stronger, and the direct link between mental health and mobile phone addiction communities was significantly reduced, suggesting that resilience effectively buffers the interplay between mental health and mobile phone addiction problems [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], playing a better protective role which played a better protective role.\u003c/p\u003e \u003cp\u003eSomatization was the node with the most significant central difference in intensity, and was significantly more associated with depression, positive thinking, and inefficacy in left-behind adolescents with high levels of parent-child conflict than in the group with low levels of parent-child conflict. Somatization is a unique response to psychosocial stresses [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] and parent-adolescent conflict can exacerbate adolescent somatization symptoms [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e].. Parent-child conflict is one of the stressors for adolescents in puberty [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e], prolonged exposure to high-pressure and stressful environments increases the risk of mental health problems in adolescents [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e] and is more likely to lead to multiple co-morbidities of psychological problems [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. In the present study, somatization showed a stronger negative correlation with inefficacy in the high levels of parent-child conflict group, which may be due to the physical discomfort of somatization [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e] and intense parent-child conflict [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e] are the more dominant causes of academic inefficiency among left-behind adolescents. It is also possible that the separation anxiety of parents of left-behind adolescents (Scharf \u0026amp; Goldner, 2018) and severe family conflict [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e] lead to stricter parental psychological and behavioural control, which prevents excessive mobile phone use from affecting the adolescents' efficiency.\u003c/p\u003e \u003cp\u003ePositive thinking was the node with the most significant difference in bridge strength centrality. In the network structure of high parent-child conflict, positive thinking increased the direct positive correlation with lack of self-control, avoidance, and inefficiency in the mobile phone addiction community. This implies that more severe smartphone addiction problems such as lack of self-control, escapism, and inefficiency are intertwined with more optimistic attitudes, which is inconsistent with existing research [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. In addition to the previously mentioned ability of left-behind adolescents to adapt more quickly to adversity, make positive self-decisions and plans, and potentially reflect deeply and seek change, it is also possible that negative cognition allows them to feel the realities of the situation such as family conflict more acutely, rather than being fully immersed in the world of mobile phones, which reduces performance in areas such as loss of control [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. However, at the same time, according to the \"loss of compensation\" hypothesis [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], their addiction to mobile phones may be more of an emotional attachment, and their inner dependence on mobile phones will be strongly manifested during withdrawal.\u003c/p\u003e \u003cp\u003eIn addition, The connecting line between inability to control carving and family support is borderline with the most significant difference in weights and is strongly negatively correlated in the low-level group. Family members in families with low parent-child conflict tend to use positive coping strategies [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e], and family support may be more accessible and effective. In contrast, if the parent-child conflict is high, although family support may be present, its effectiveness may be diminished by the conflict, and adolescents may seek other forms of social support to cope with the conflict, thus attenuating the direct effect of family support on mobile phone addiction loss of control, and instead having interpersonal assistance directly associated with loss of control.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Research limitations and future research perspectives\u003c/h2\u003e \u003cp\u003eThis study used a network analysis model to examine the relationship between psychological variables, providing a multidimensional understanding of left-behind adolescents' resilience in relation to mental health, mobile phone addiction, and parent-child conflict from a cross-sectional perspective for psychological research, as well as expanding and deepening the theory of resilience. This study reveals that teachers and clinical interveners can maximise psychosocial interventions by focusing on the high centrality dimensions such as anxiety, inability to control carving, and family support when confronting left-behind adolescents' mental health and mobile phone addiction issues [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] The However, this study still has some limitations. First, this study only used cross-sectional data to construct a partial correlation network, and was unable to infer causal relationships. Although the important role of core nodes can be affirmed based on the network model centrality feature [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], it should be verified by longitudinal or experimental design in the future. Second, the average predictability of the nodes of the network analysis model in this study was not high, indicating that the networks could not predict each other well internally and were influenced by factors outside the network (e.g., environmental, biological factors, other psychological variables) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Future research can collect more data with more representativeness and accuracy, identify and control for external variables that may affect the predictability of the model, and also incorporate multimodal indicators to construct the network based on relevant theories. Third, mobile phone addiction measured through self-report may be affected by social expectation bias. Some experiments have found that self-reported mobile phone use does not match the actual situation [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e], future research should be cautious in interpreting estimated smartphone use with more objective metrics or a combination of personal interviews and guardian observations for evaluation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e(1) The network structure of left-behind adolescents' resilience, mental health, and mobile phone addiction is stable, in which anxiety and inability to control cravings are core nodes, and controlling inability and family support are core bridge nodes. Practitioners should focus on the high centrality dimension for effective intervention for left-behind adolescents.\u003c/p\u003e \u003cp\u003e(2) There are significant differences in the network structure of resilience, psychological health, and mobile phone addiction among left-behind adolescents at different levels of parent-child conflict. In families with higher levels of parent-child conflict, the network structure is more complex, and the resilience of left-behind adolescents is undermined, with risks and negative effects spreading faster; while in the lower counterpart, resilience has a protective effect.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in strict accordance with the Declaration of Helsinki and received ethical approval from the Institutional Committee of Law School, Southwest University of Science and Technology in Mianyang, China (No. LL23001). Informed consent was signed by each adult participant, or their parent(s) or legal guardian(s) on behalf of adolescent participants.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author, upon reasonable request, immediately following publication and no end date. We can share individual participant data that underlie the results reported in this article, after deidentification (text, tables, figures and appendices).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the General Project of Sichuan Philosophy and Social Science Planning Fund of Sichuan Province [Project No. SCIJ23ND226], Steering Committee for Teaching Psychology in Higher Education, Ministry of Education [Project No. 20232010], and Institute of Psychology, Chinese Academy of Sciences [Project No.GJ202003].\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. XY curated and analyzed the questionnaire data, visualized the results, interpreted the results of the network analysis model, and was the main contributor to writing the first draft of the manuscript. YM curated the data, conducted formal analyses, reviewed and edited the manuscript. XX investigated, curated the data and conducted a formal analysis. RH translated, reviewed and edited the manuscript. YW investigated and formally analyzed the data. MT investigated and formally analyzed the data. GD investigated and formally analyzed the data. XT investigated and formally analyzed the data. HF\u0026nbsp;proposed the conceptualization and methodology and performed the result validation. XZ proposed the conceptualization and methodology, performed supervision, reviewed and edited the manuscript. GZ performed supervision, provided related resources, reviewed and edited the manuscript. BW proposed the conceptualization and methodology, provided funding for the project, provided related resources, performed supervision, and was the project administrator. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the participants in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eState Council of the People\u0026rsquo;s Republic of China. State council on strengthening rural left-behind children advice on care and protection work [Internet]. 2016 [cited 2024 Apr 25]. Available from: https://www.gov.cn/zhengce/content/2016-02/14/content_5041066.htm\u003c/li\u003e\n\u003cli\u003eNational Bureau of Statistics of China, UNICEF China, UNFPA China. What the 2020 Census Can Tell Us About Children in China: Facts and Figures [Internet]. 2023 [cited 2024 Apr 24]. Available from: https://www.unicef.cn/en/reports/population-status-children-china-2020-census\u003c/li\u003e\n\u003cli\u003eYao YS, Kang YW, Jin YL, Chen Y, Gong WZ, Zheng L, An Z, Tao FB, Hao JH. Analysis on physical and mental health and related influential factors among those \u0026ldquo;left behind\u0026rdquo; adolescents in Anhui province. Zhonghua Liu Xing Bing Xue Za Zhi. 2012 Jul;33(7):681\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eWang F, Lin L, Xu M, Li L, Lu J, Zhou X. Mental Health among Left-Behind Children in Rural China in Relation to Parent-Child Communication. Int J Environ Res Public Health. 2019 May 2;16(10):1855.\u003c/li\u003e\n\u003cli\u003eZhang X, Dai Z, Antwi CO, Ren J. A Cross-Temporal Meta-Analysis of Changes in Left-Behind Children\u0026rsquo;s Mental Health in China. 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BMC Public Health. 2024 Apr 2;24(1):940.\u003c/li\u003e\n\u003cli\u003eYang C, Gao H, Li Y, Wang E, Wang N, Wang Q. Analyzing the role of family support, coping strategies and social support in improving the mental health of students: Evidence from post COVID-19. Front Psychol. 2022 Dec 23;13:1064898.\u003c/li\u003e\n\u003cli\u003eChang B, Wei Y, Fang J. Lack of parental care increases depression of rural left-behind children in China: a moderated mediating effects*. Curr Psychol. 2024 Jul 1;43(25):21830\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eFAN X, FANG X, HUANG Y, CHEN F, YU S. The influence mechanism of parental care on depression among left-behind rural children in China: A longitudinal study. Acta Psychologica Sinica. 50(9):1029\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eWu X, Tang L, Gong J. Correlation analysis of mental toughness, family social support, and anxiety of nursing staff. 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PLOS ONE. 2015 Oct 28;10(10):e0139004.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Left-behind adolescents, Resilience, Mental health, Mobile phone addiction, Parent-child conflict, Network analysis","lastPublishedDoi":"10.21203/rs.3.rs-5063332/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5063332/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003emobile phone addiction and mental health problems have become increasingly prominent among left-behind adolescents in China. In recent years, some studies have focused on the important role of parent-child relationship and psychological resilience. Therefore, this study aims to explore the multidimensional relationships among resilience, mental health, and mobile phone addiction among left-behind adolescents, and to assess the impact of parent-child conflict level on these relationships.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Brief Symptom Inventory (BSI-18), the Chinese version of the Mobile Phone Addiction Index (MPAI), the Resilience Scale for Children and Adolescents (RSCA), and the Parent-Child Conflict Scale were used to investigate 2,100 left-behind adolescents in Sichuan Province, and R was run to make network analysis and network comparison.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(1) A structurally stable network relationship exists between left-behind adolescents' resilience, mental health, and mobile phone addiction; (2) BSI3 (Anxiety) is the most important node of the network model, followed by MPAI1 (the inability to control cravings subscale); (3) MPAI1 (the inability to control cravings subscale) and RSCA4 (family support) are key to connect resilience, mental health, and smartphone addiction in the study sample; (4) There was a significant difference in the network structure between the high- and low-level groups of parent-child conflict, no significant difference in the global strength of the network, and a significant difference in the centrality of strength and the centrality of bridge strength.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eChinese left-behind adolescents' resilience and mental health, mobile phone addiction are both independent and interact with each other to some extent. Specifically, high centrality dimensions such as anxiety, the inability to control cravings, and family support can be prioritised for intervention in related treatments, or reducing parent-child conflict and enhancing resilience to mitigate mobile phone addiction among left-behind adolescents, thus improving their mental health.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Resilience and Mental Health, Mobile Phone Addiction and Its Differences Across Levels of Parent-Child Conflict Among Left-Behind Adolescents: A Cross-Sectional Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 23:32:44","doi":"10.21203/rs.3.rs-5063332/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-17T07:19:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-17T05:55:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-13T07:37:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-09-10T09:11:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a55921f-de80-4bf5-9bc2-c5b91dd5a297","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T15:59:55+00:00","versionOfRecord":{"articleIdentity":"rs-5063332","link":"https://doi.org/10.1186/s12889-025-22105-8","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-03-10 15:57:09","publishedOnDateReadable":"March 10th, 2025"},"versionCreatedAt":"2024-12-03 23:32:44","video":"","vorDoi":"10.1186/s12889-025-22105-8","vorDoiUrl":"https://doi.org/10.1186/s12889-025-22105-8","workflowStages":[]},"version":"v1","identity":"rs-5063332","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5063332","identity":"rs-5063332","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}