Problematic Internet Use Profiles: Identifying Social-Cultural Demographic Risk Factors and Predicting Longitudinal Effects on Psychopathology

Problematic Internet Use Profiles: Identifying Social-Cultural Demographic Risk Factors and Predicting Longitudinal Effects on Psychopathology | 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 Problematic Internet Use Profiles: Identifying Social-Cultural Demographic Risk Factors and Predicting Longitudinal Effects on Psychopathology Yi Wang, Brian Hall, Yuran Chen, Chun Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4740201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Problematic Internet Use (PIU) has many adverse effects on youth mental health and development. However, few studies have systematically investigated the internal heterogeneity of PIU symptoms among rural Chinese adolescents. This study collected two waves of data (T1: October 2022; T2: April 2023) from 5,271 rural Chinese adolescents from two secondary schools in Guizhou and Sichuan provinces. Methods A Latent Profile Analysis (LPA) was conducted to first identify PIU symptom profiles. Then, a “three-step” logistic regression mixed model was conducted to explore the association between PIU patterns and demographic correlates. Anxiety, depression, and stress symptoms collected at the second wave were compared across PIU profiles. Results The study found that (1) The patterns of PIU among rural adolescents could be divided into four subgroups: low PIU group (57.18%), medium PIU group (15.65%), high PIU group (9.01%), and self-blame group (18.16%), which is a uniquely identified group. (2) Being female, an ethnic minority, living off-campus, having left-behind experiences, and having fewer siblings were risk factors for high PIU group membership. (3) The order of severity for anxiety, depression, and stress was as follows: high PIU, medium PIU, self-blame, and low PIU groups. Conclusions Addressing the dimension of internet obsession of PIU is vital for rural Chinese adolescent mental health, necessitating tailored interventions involving families and schools. problematic internet use latent profile analysis anxiety depression stress Figures Figure 1 1. Introduction Problematic Internet use (PIU) is characterized by addictive and maladaptive internet use, which causes psychological, social, or academic difficulties in an individual’s life (Beard & Wolf, 2001 , p.378). The prevalence of PIU in China is reported to be notably higher compared to other countries in the world (Guo et al., 2018 ), with rates reaching up to 23% (Sun et al., 2020 ). Previous studies on PIU tend to assume PIU symptoms as homogenous among different individuals, primarily using cross-sectional data in a variable-centered approach. However, these approaches do not adequately consider individual differences in PIU symptom manifestation, and there is a lack of nuanced investigation on the internal heterogeneity of PIU symptoms within individuals. In light of these limitations, a person-centered approach, such as latent profile analysis (LPA), could examine the similarities and differences among individuals. Moreover, most PIU studies were conducted among the urban student population. However, PIU was found to be more severe among adolescents in rural China than their counterparts in urban areas (Li et al., 2014 ) due to its significant disparity in socioeconomic status and mental health resources (Tang et al., 2018 ). Therefore, the present study aimed to provide a person-centered examination of PIU among adolescents in rural China. The antecedent risk factors and the severity of mental health symptoms on specific profiles were also evaluated. 1.1. Understanding PIU from A Person-Centered Approach With numerous research investigating the characteristics of PIU from a holistic perspective through a variable-centered approach, the individual differences across PIU symptoms are overlooked, which is essential for clinical intervention and understanding the intrinsic traits of PIU. Person-centered approach attempts to identify heterogeneous subtypes of individuals through conducting LPA (Morin et al., 2011 ), which concentrates on how individuals differ based on the indicator variable rather than homogenizing the variable across participants (Abar, 2012 ) and measure the latent level for the probabilities of a person’s membership in various latent profiles (DiStefano & Kamphaus, 2006 ). This method captures interindividual variability in intraindividual changes in developmental processes by allowing outcomes to differ between participant subpopulations that are not observed, without presuming population homogeneity (Morin & Litalien, 2019 ). Some existing studies have used person-centered approaches to investigate PIU (e.g., Lee et al., 2018 ). However, to our knowledge, few research utilized LPA to explore various symptom patterns of PIU and its influencing factors among Chinese rural adolescents. The present study sought to provide a more nuanced investigation of the manifestation of PIU symptoms and to identify its demographic risk factors in a large group of rural Chinese adolescents. 1.2. Demographic Factors Associated with PIU Bronfenbrenner's ecological systems theory believes that child development is seen as a complex system of relationships including a variety of environmental factors, ranging from the immediate home and school environment to broader cultural values (Bronfenbrenner & Morris, 1998 ). To understand the mechanism of PIU, the current research would consider the demographic influence in each system. However, few studies have ever identified risk and resilience factors of PIU from a comprehensive range of social-ecological demographic variables. At the individual level, sex, affected by specific biological factors and social norms (Fattore et al., 2014 ), is an important demographic variable for PIU. For example, a representative study in Chinese elementary and middle schools found a higher prevalence rate among males (Li et al., 2014 ). As to the family level, apart from socioeconomic status, parental presence is also a critical consideration. Left behind students are defined as the children from rural backgrounds who were raised by grandparents or other family members while their parents moved to an urban location for at least six months (Cheng & Sun, 2015 ). Adolescents with left behind experiences had a greater prevalence of PIU than adolescents who were not left behind (e.g., Ren et al., 2017 ), which cannot be ignored when exploring PIU in rural China. For the school system, an important factor to consider is the policy of boarding schools in rural China, which brings both positive and negative effects on adolescent growth. Living on campus could benefit students through preventing the development of some serious problems, enhancing their competencies such as socializing and independence (Wang et al. 2017 ), and improving their academic performance and well-being (Liu & Villa, 2020 ). However, Chinese rural boarding schools frequently fall short of meeting students' requirements for health, nutrition, and emotional support (Luo et al., 2009 ). Such a lack of resources in rural schools (e.g., professional mental health staff) might result in more negative outcomes, such as higher depressive symptoms (Zhu et al., 2019 ), lower self-esteem (Hou et al., 2015 ) and higher likelihood of bullying (Zhang, 2020 ). Moreover, China is made up of 56 ethnic groups, including the Han ethnic group as the majority ethnic group comprising 91.11% (National Bureau of Statistics, 2021 ) of the Chinese population. Since Chinese ethnic groups are divided by social history, economic life, language, and religion (Maurer-Fazio & Hasmath, 2015 ), there might be cultural differences among these groups. Most previous PIU studies in China focused on students from the Han ethnic group, while existing yet limited research has found that minority ethnic status was a risk factor for PIU and found the prevalence rate of PIU in students from minority groups was much higher (Lu et al., 2018 ). 1.3. Association between PIU and Psychopathology Existing studies have consistently established a robust association between PIU and psychopathology (e.g., Tung et al., 2022 ; Younes et al., 2016 ). For instance, previous research found a positive relationship between PIU and quality of life among Chinese rural students (Guo et al., 2021 ). Furthermore, an LPA study also indicated that compared to adolescents in the normal-use category, adolescents in the high internet addiction and excessive online gaming categories typically had greater depressuon symptoms and problematic behaviors (Sun et al., 2022 ). While there have been some studies discussing PIU in rural areas, it still remains limited compared to those in urban areas (Pan et al., 2020 ). Meanwhile, although one study has examined the relationship between PIU and mental health in rural areas through LPA (Sun et al., 2022 ), it is generally not representative of all rural areas in China due to the difference in developmental level indices and network resource allocation (Li et al., 2014 ). 1.4. The Present Study The present study aimed to conduct a person-centered study to understand PIU patterns among adolescents in rural China, as well as the antecedent risks of PIU and its subsequent experiences with depression, anxiety, and stress. We aimed to (1) examine PIU latent profiles at the beginning of an academic year in 2022, (2) the association between demographic variables and PIU profiles, and (3) its association with subsequent psychopathology symptoms in 2023 among Chinese rural adolescents. 2. Materials and Methods 2.1. Participants and Procedure After obtaining approval from the school administrator, the project was introduced to the students and their guardians by the school teachers. Students and their guardians were informed that the participation was voluntary. Guardians needed to provide informed consent. The digital survey was disseminated to students whose parents agreed them to participate during class on a regular school day via a web-based survey platform, and students needed to provide their assent on the first page of the survey before proceeding. A total of 5,447 middle and high school students from two boarding schools located in rural areas in Guizhou and Sichuan Provinces, China, were invited across both waves of the study in October 2022 and April 2023. After screening out invalid data, 176 questionnaires were excluded from the dataset, leaving 5,271 valid questionnaires, with an effective response rate of 96.76%. The sex and grade level distribution of the participants were relatively balanced. Among the participants, 1,773 (33.6%) were Han ethnicity (i.e., the largest ethnic group in China), 3,035 (57.6%) were the local minority ethnicities (i.e., Yi ethnicity in Sichuan and Dong ethnicity in Guizhou), and 463 (8.8%) were other ethnic minorities. The majority (94.5%) of the participants lived on the school campus. This study was reviewed and approved by the Ethics Committee of [blinded for review] (No. EF20220602002). 2.2. Measures 2.2.1. Problematic Internet Use Questionnaire Short Form (PIUQ-SF) The PIUQ has proven to be a valid and reliable instrument for measuring PIU symptoms (Laconi et al., 2019). A short version with nine items on three subscales, being obsession, neglect, and control disorder, was developed by Demetrovics et al. ( 2008 ) as a second-order three-factor model structure with three items within each factor. PIUQ-SF uses a 5-point Likert scale (range from "never" to "always/almost always") to estimate the frequency of PIU symptoms. The Chinese version of the PIUQ-SF was translated and validated by Koronczai et al. ( 2017 ). The Cronbach's alpha of the measure in the present sample is 0.87. 2.2.2. The Depression Anxiety Stress Scale 21 (DASS-21) The 21-item Depression Anxiety Stress Scale (DASS; Lovibond & Lovibond, 1995 ) version is a self-report measure of differentiation between depression, anxiety, and stress-related mood disorders. The DASS-21 is a 4-point Likert scale from "does not apply to me at all" to “completely applies to me”. The validity and reliability of the Chinese version of DASS-21 has been demonstrated in Chinese adolescents (Gong et al., 2010 ). Cronbach’s alpha was 0.90 for depression, 0.87 for anxiety, and 0.87 for stress. 2.2.3. Demographic Variables Participants were asked to answer demographic questions including age, gender, ethnicity, residency status, sibling numbers, SES, and left-behind status. Ethnicity was collected because the two counties are multi-ethnic areas. The residence status is to collect whether they live on campus or at home. The Family Affluence Scale (FAS; Hobza et al., 2017 ) questionnaire was administered to measure participants’ socioeconomic status (SES). The psychometric properties have been empirically validated among Chinese adolescents (Liu et al., 2012 ). Left-behind status was determined by asking if their father or mother had migrated to another city for work in the previous year and had not returned home for more than six months. 2.3. Statistical Analyses To determine the pattern of PIU among participants, we conducted a latent profile analysis (LPA) using Mplus 8 (Muthén & Muthén, 2017 ) to investigate heterogeneity and identify the most mutually exclusive classes in PIU. After identifying the best model, the next step was to see if the auxiliary variables (i.e., sex, age, ethnicity, staying on-campus or not, SES, left-behind status, and sibling numbers) and distal outcomes (depression, anxiety, and stress) differed significantly across the identified categories. To include auxiliary variables and distal outcomes (Asparouhov & Muthén, 2014 ), a three-step approach (Nylund-Gibson et al., 2019 ) was used. The three-step approach was preferred because it helped to ensure that auxiliary variables and distal outcomes did not influence the latent class (Asparouhov & Muthén, 2014 ). Auxiliary variables (i.e., age, gender, SES, residence) and distal outcomes (depression, anxiety, and stress) were included by using multivariable logistic regression. Missing data was handled using maximum likelihood (ML) estimates. Descriptive statistics are in Table 1 . This study was not preregistered. The data and analysis code that support the findings of the study are available on request from the corresponding author. Table 1 Correlation Coefficients Among Variables. Mean/Proportion SD 1 2 3 4 5 6 7 8 9 10 11 1. Gender .56 .496 1 2. Age 14.82 14.038 − .016 1 3. Ethnicity .75 .602 − .025 .020 1 4. Living on-campus or not .95 .227 .061** .002 .027 1 5. Sibling numbers 1.59 .890 .088** .004 .312** .064** 1 6. SES 2.59 1.639 .027* − .011 − .267** − .064** − .286** 1 7. Left behind .46 .746 .000 .027 .308** .045** .048** − .203** 1 8. Anxiety 9.33 10.023 .046** .016 .176** -0.006 − .085** − .031* .213** 1 9. Depression 9.17 10.111 .031* .010 .157** -0.008 − .077** − .050** .177** .893** 1 10. Stress 10.68 10.284 .072** .026 .200** -0.005 − .083** − .046** .232** .909** .888** 1 11. PIU 1.86 .73 .070** .015 .029* − .035* − .074** -0.008 .114** .322** .327** .347** 1 * p < 0.05, ** p < 0.01 3. Results 3.1. Latent Profile Analysis of PIU Considering the optimal fit statistics, parsimony, category significance, the minimum number of participants in the smallest class, and model comparison, the four-class model was selected based on a total of 9 items. as shown in Table 2 . The representation in the four-class were (1) low PIU (PIU mean = 1.36), accounting for 57.18% of the sample; students in this class scored relatively low on all items; (2) medium PIU (PIU mean = 2.21), accounting for 15.65% of the sample; students in this class scored in the middle of the range on all items; (3) self-blame group (PIU mean = 2.39), students who showed lower addiction score, medium neglect score, and higher control disorder score account for 18.16% of the total. The naming of “self-blame group” is because participants had higher self-blame scores on the dimension of control disorder, believing that they should reduce their online time but felt that they always failed to do so. (4) The last group was high PIU (PIU mean = 3.32), accounting for 9.01%; students in this class scored relatively high on all items, as shown in Fig. 1 . Table 2 Fit indices of LCA Class Models. Model (K-Class) LL npar AIC CAIC BIC saBIC AWE LRTS BF (K, K + 1) Number of minimal class/percentages 1-class -67827.111 18 135690.22 135826.48 135808.48 135751.28 136016.74 – 0.000 - 2-class -60453.979 28 120963.96 121175.92 121147.92 121058.94 121471.88 14746.26 0.000 1208/22.91% 3-class -58056.156 38 116188.31 116475.97 116437.97 116317.22 116877.63 4795.65 0.000 580/11% 4-class -56982.137 48 114060.27 114423.63 114375.63 114223.10 114930.99 2148.04 0.000 475/9.01% 5-class -52451.997 58 105019.99 105459.05 105401.05 105216.75 106072.11 9060.28 0.000 159/3.01% 3.2. Demographic Factors Associated with PIU Latent Profiles The likelihood of being placed in a particular class differed among different demographic variables. As shown in Table 3 , compared with the high PIU class, students who were Han ethnicity, living on campus, accompanied by both parents and more siblings, were more likely placed in the low PIU class (ethnicity: logit = -0.21, p < 0.05, OR = 0.81; staying on-campus or not: logit = 0.44, p < 0.05, OR = 1.55; left-behind: logit = -0.33, p < 0.01, OR = 0.72; sibling numbers: logit = 0.301, p < 0.01, OR = 1.35). Being male, living on campus, their parents not leaving them behind, and having more siblings were more likely to be in low PIU class compared with medium and self-blame groups (C2 VS C1: sex: logit = 0.18, p < 0.05, OR = 1.20; staying on-campus or not: logit = -0.37, p < 0.05, OR = 0.69; left-behind: logit = 0.40, p < 0.01, OR = 1.50; sibling numbers: logit = -0.14, p < 0.01, OR = 0.87; C3 VS C1: sex: logit = 0.36, p < 0.01, OR = 1.44; staying on-campus or not: logit = -0.42, p < 0.01, OR = 0.66; left-behind: logit = 0.28, p < 0.01, OR = 1.33; sibling numbers: logit = -0.13, p < 0.01, OR = 0.88). Table 3 Coefficients and Odds Ratio for the Four-Class Model with Demographic Variables Variables Gender (reference group = male) Age Ethnicity Living at school or not (reference group = not) SES Left-Behind Status Sibling Numbers C1vs C4 Logit -0.186 -0.004 -0.213* 0.435* 0.041 -0.327** 0.301** SE 0.102 0.017 0.091 0.206 0.032 0.069 0.06 Odds Ratio 0.83* 0.996 0.809** 1.545 1.042 0.721** 1.351** C2 vs C4 Logit -0.008 0 -0.191 0.068 0.048 0.078 0.163* SE 0.119 0 0.104 0.236 0.038 0.077 0.069 Odds Ratio 0.992 1 0.826* 1.07 1.049 1.081 1.176* C3 vs C4 Logit 0.178 0 -0.075 0.014 0.015 -0.044 0.176** SE 0.117 0 0.103 0.23 0.036 0.076 0.067 Odds Ratio 1.195 1 0.928 1.015 1.015 0.957 1.193* C2 vs C1 Logit 0.178* 0.003 0.022 -0.367* 0.007 0.404** -0.139** SE 0.081 0.017 0.075 0.17 0.027 0.055 0.049 Odds Ratio 1.195* 1.003 1.022 0.693** 1.007 1.499** 0.871** C3 vs C1 Logit 0.364** 0.004 0.138 -0.421** -0.026 0.283** -0.125** SE 0.078 0.017 0.073 0.162 0.025 0.054 0.046 Odds Ratio 1.44** 1.004 1.147 0.657** 0.974 1.327** 0.883** C3 vs C2 Logit 0.186 0.001 0.116 -0.053 -0.033 -0.122 0.014 SE 0.099 0 0.089 0.197 0.032 0.064 0.059 Odds Ratio 1.205 1.001 1.123 0.948 0.968 0.886* 1.014 **p < 0.01, *p < 0.05 3.3. PIU Latent Profiles Associated with Depression, Stress, and Anxiety Students in the high-level PIU group had the highest values of anxiety, stress, and depression. High PIU (mean = 16.46) and medium PIU (mean = 13.20) had higher anxiety scores than the self-blame class (with low addiction dimension score, medium neglect dimension score, and high control disorder dimension score) (mean = 10.38) and low PIU class (mean = 6.78; the difference between class 1 and class 2: p < 0.001, d = -6.42; the difference between class 1 and class 4: p < 0.001, d = -9.68; the difference between class 3 and class 4: p < 0.001, d = -6.09; the difference between class 2 and class 3: p < 0.001, d = 2.82). The self-blame class (mean = 10.377) had higher anxiety scores than the low PIU class (mean = 6.78; the difference between class 1 and class 3: p < 0.001, d = -3.60). The same situation as the high PIU and medium PIU group (difference between class 2 and class 4: p < 0.001, d = -3.26). Similar patterns of anxiety mean differences were observed in depression and stress in Table 4 . Table 4 Means of Anxiety, Depression, and Stress in Each Class. PIU Class Anxiety (Range 0–42) Depression (Range 0–42) Stress (Range 0–42) Class 1: Low PIU 6.778 a 6.647 a 7.878 a Class 2: Medium PIU 13.201 b 12.960 b 14.720 b Class 3: Self-Blame 10.377 c 9.958 c 12.190 c Class 4: High PIU 16.462 d 16.789 d 18.157 d Note . Means that do not share subscripts differ at p < 0.001. 4. Discussion This study conducted LPA to investigate the PIU profiles and examined its relationship with a range of social-ecological demographic variables and psychopathology in rural Chinese students. A four-class PIU pattern was identified into low PIU, medium PIU, self-blame, and high PIU group. The findings also suggested that demographic variables played an essential role in distinguishing different patterns of PIU among rural Chinese adolescents. In addition, different PIU traits could predict anxiety, depression, and stress symptoms. The results revealed the heterogeneity of adolescent PIU in a rural Chinese context and identified risk and protective factors. 4.1. Latent Profiles of PIU The classifications of PIU identified were mostly consistent with previous studies of LPA in rural Chinese adolescent samples by Sun (2022), where they identified normal internet use, low internet addiction, high internet addiction, and overuse of online games. The distinction from previous findings is the identification of the special group, the self-blame group. The unique finding of the self-blame group in our study accounted for almost one in five students. This group of students demonstrated high self-expectation. Although the results showed that they wanted to reduce the frequency of their Internet access, their obsession scores were relatively low. This group performed well when they weren't online, but when they had the chance to use the Internet, they would be over-engaged and neglecting everything else and after online, they felt guilty and hoped that next time they could control their online time. It is possible that even though this group of adolescents wished to manage their online time, but in reality, rural adolescents had fewer extracurricular activities compared with urban counterparts (Zhang & Tang, 2017 ). Consequently, due to these factors, the Internet has become a medium that satisfies their experience and exploration needs and functions as an escape or avoidance mechanism (Lowry et al., 2015 ). At the same time, the socio-economic environment in rural areas was relatively restricted, students were taught to be self-disciplined from a young age to “escape” from rural areas, which might contribute to excessive self-blame about their lack of control online. 4.2. Demographic Factors Associated with PIU Latent Profiles Being female, an ethnic minority, living off-campus, having left-behind experiences, and having fewer siblings were risk factors for being in a higher PIU group. Consistent with some studies (e.g., Gansner et al., 2019 ), males were more likely to be in the low PIU group (versus medium and self-blame group), which means female students were more vulnerable to PIU and more likely to have control disorder problems. This could be explained by the fact that in rural areas, females had less safe space to outspeak their needs. They were more likely to be mistreated because of sex discrimination (Hannum et al., 2009 ). Therefore, the Internet gives them a place to escape, further leading to PIU and control issues. Ethnic minority students were more likely to be classified in the high PIU group, which might be explained by cultural differences. In Han ethnic families, parents tended to be relatively strict with their children as the Preferential Admission Policy gives bonus points in national college entrance exams to students with minority ethnicities (Wang, 2007 ). Moreover, with the Han culture being localized in different regions in China, it was likely that students from ethnic minority backgrounds would experience struggle in school settings as the main teaching language is Mandarin (Yang et al., 2015 ), further resulting in their seeking online spaces to avoid these frustrations (Li et al., 2015 ). Students who lived on campus were more represented in the Low PIU group than in the other three groups. This suggested that living on campus could effectively reduce PIU. In rural China, schools had a strict residential system with precise light-out times and internet and cellphone control. Hence, the strict on-campus schedule and cell phone ban policy prevented many students from obtaining higher PIU scores, suggesting that restrictions on Internet access time may effectively reduce PIU among rural adolescents. Moreover, parental presence was a protective factor. Compared with adolescents who had one or both parents left behind, children whose parents did not migrate were more likely to be classified in the low PIU group. Being at home, parents would be able to better manage their children's Internet problems. Having more emotional ties would also reduce the dependence on the Internet. The presented study also showed that the greater number of siblings was a protective factor against PIU. This was because more siblings could make up for the lack of parental companionship. Siblings in the same generation could confide in and help each other to effectively reduce PIU. 4.3. PIU Latent Profiles Associated with Psychopathology This study revealed that Chinese rural adolescents with distinct profiles of PIU exhibited significantly varied levels of anxiety, depression and stress symptoms. In particular, participants in the self-blame group, high PIU group, and medium PIU group displayed more severe anxiety, depression, and stress symptoms than those with low PIU. This result suggests that high PIU was accompanied by high psychological distress in adolescents from rural areas. This finding is consistent with prior research (e.g., Dalbudak et al., 2013 ). In addition, regular Internet users have less face-to-face communication; consequently, their interpersonal relationships and social support are diminished (Kim, 2017 ), resulting in potential anxiety, depression, and stress symptoms. In addition, this study revealed that participants in the self-blame group exhibited less anxiety, depression, and stress symptoms than those in the medium and high PIU groups. The self-blame group had the highest control disorder score and the second high neglect score, and the total PIU mean score was higher than the medium PIU group. The result implies that adolescents with less obsession with the Internet would exhibit less anxiety, depression, and stress symptoms. Therefore, the obsession was an essential factor leading to more psychological distress. 4.4. Limitations Some limitations of this study should be taken into consideration. Firstly, the data was collected through self-report questionnaires, which may have been affected by social expectations and recall biases. Future studies could consider multi-informants, such as parents, teachers, and peers. Secondly, the generalizability of the results to Chinese adolescents in rural areas was limited by the sample size, as all students were recruited from only two schools in Guizhou and Sichuan in Southwest China. Therefore, future studies should aim to replicate these findings using a more diverse and representative sample to improve generalization. 5. Conclusion In conclusion, this study sheds light on the latent profiles of PIU among rural Chinese adolescents, revealing four distinct subgroups with varying levels of severity across the three symptoms. Risk factors for PIU include being female, being an ethnic minority, living off-campus, having left-behind experience, and having fewer siblings. Importantly, higher levels of PIU were associated with increased psychopathology, underscoring the importance of addressing internet obsession for rural Chinese adolescent mental health. Tailored interventions involving families and schools are crucial in mitigating the adverse effects of PIU among rural Chinese adolescents. Declarations Human Ethical Approval. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was reviewed and approved by the Ethics Committee of Chinese University of Hong Kong, Shenzhen (No. EF20220602002). Consent to Participate . Informed consent was obtained from all individual participants and their guardians included in the study. Consent for Publication. Not applicable. Availability of data and materials. The datasets generated and/or analysed during the current study are not publicly available due to confidentiality of participants’ information but deidentified data are available from the corresponding author on reasonable request. Competing interests. The authors declare that they have no competing interests Funding . The study is funded by Pengcheng Peacock Matching Research Funding - Category C (No. 2024TC0135). Authors' Contributions. Y.W. conducted the formal analysis and wrote the original draft of the manuscript. B.H., Y.C., and C.C. reviewed and edited the manuscripts. C.C. and Y.W. conceptualized the study. C.C. supervised the study. All authors reviewed the manuscript and have approved the submitted version. Acknowledgement . We thank the teachers, students, and parents in the two participating schools in the present study as well as Zhejiang Xinhua Compassion Education Foundation. References Abar, C. C. (2012). Examining the relationship between parenting types and patterns of student alcohol-related behavior during the transition to college. Psychology of Addictive Behaviors, 26 (1), 20-29. https://doi.org/10.1037/a0025108 Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using M plus. 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Bulletin of the Seventh National Census. https://www.stats.gov.cn/english/PressRelease/202105/t20210510_1817192.html (in Chinese) Nylund-Gibson, K., Grimm, R. P., & Masyn, K. E. (2019). Prediction from latent classes: A demonstration of different approaches to include distal outcomes in mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 26 (6), 967-985. https://doi.org/10.1080/10705511.2019.1590146 Pan, Y. C., Chiu, Y. C., & Lin, Y. H. (2020). Systematic review and meta-analysis of epidemiology of internet addiction. Neuroscience & Biobehavioral Reviews , 118 , 612-622. https://doi.org/10.1016/j.neubiorev.2020.08.013 Ren, Y., Yang, J., & Liu, L. (2017). Social Anxiety and Internet Addiction among Rural Left-behind Children: The Mediating Effect of Loneliness. Iranian Journal of Public Health , 46 (12), 1659–1668. Sun, Y., Li, Y., Bao, Y., Meng, S., Sun, Y., Schumann, G., ... & Shi, J. (2020). Brief report: increased addictive internet and substance use behavior during the COVID‐19 pandemic in China. The American Journal on Addictions , 29 (4), 268-270. https://doi.org/10.1111/ajad.13066 Sun, Y., Shao, J., Li, J., & Jiang, Y. (2022). Internet addiction patterns of rural Chinese adolescents: Longitudinal predictive effects on depressive symptoms and problem behaviors. Journal of Pacific Rim Psychology, 16, 18344909221105351. https://doi.org/10.1177/18344909221105351 Tang, J., Li, G., Chen, B., Huang, Z., Zhang, Y., Chang, H., ... & Yu, Y. (2018). Prevalence of and risk factors for non-suicidal self-injury in rural China: results from a nationwide survey in China. Journal of Affective Disorders, 226, 188-195. https://doi.org/10.1016/j.jad.2017.09.051 Tung, S. E. H., Gan, W. Y., Chen, J. S., Kamolthip, R., Pramukti, I., Nadhiroh, S. R., ... & Lin, C. Y. (2022). Internet-related instruments (Bergen Social Media Addiction Scale, Smartphone Application-Based Addiction Scale, Internet Gaming Disorder Scale-Short Form, and Nomophobia Questionnaire) and their associations with distress among Malaysian university students. Healthcare. 2022; 10 (8): 1448. https://doi.org/10.3390/healthcare10081448 Wang, S., Dong, X., & Mao, Y. (2017). The impact of boarding on campus on the social-emotional competence of left-behind children in rural western China. Asia Pacific Education Review , 18 , 413-423. https://doi.org/10.1007/s12564-017-9476-7 Wang, Tiezhi (2007). Preferential policies for ethnic minority students in China's college/university admission. Asian Ethnicity, 8(2) , 149-163. https://doi.org/10.1080/14631360701406288 Yang, Y., Wang, H., Zhang, L., Sylvia, S., Luo, R., Shi, Y., ... & Rozelle, S. (2015). The Han-minority achievement gap, language, and returns to schools in rural China. Economic Development and Cultural Change, 63 (2), 319-359. https://doi.org/10.1086/679070 Younes, F., Halawi, G., Jabbour, H., El Osta, N., Karam, L., Hajj, A., & Rabbaa Khabbaz, L. (2016). Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: A cross-sectional designed study. PloS One , 11 (9), e0161126. https://doi.org/10.1371/journal.pone.0161126 Zhang, B. (2020). Four Types of School Bullying in Primary and Middle Schools and Related Factors. Journal of Education Studies, (03),70-79. https://doi.org/10.14082/j.cnki.1673-1298.2020.03.008. Zhang, D., & Tang, X. (2017). The influence of extracurricular activities on middle school students’ science learning in China. International Journal of Science Education, 39(10) , 1381-1402. https://doi.org/10.1080/09500693.2017.1332797 Zhu, Z., Li, Y., & Song, Y. (2019). Boarding education and children’s development: Evidence from 137 rural boarding schools. Educational Research , 40 , 79-91. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4740201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328584372,"identity":"9e89a1d9-db52-48e0-8a32-216afe56fdfd","order_by":0,"name":"Yi Wang","email":"","orcid":"","institution":"Chinese University of Hong Kong, Shenzhen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":328584374,"identity":"3ba47573-f6c0-4524-b771-74b7fd009e8e","order_by":1,"name":"Brian Hall","email":"","orcid":"","institution":"New York University Shanghai","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Hall","suffix":""},{"id":328584375,"identity":"168ed1d2-3c0b-4ebb-b104-db650b940012","order_by":2,"name":"Yuran Chen","email":"","orcid":"","institution":"Chinese University of Hong Kong, Shenzhen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuran","middleName":"","lastName":"Chen","suffix":""},{"id":328584377,"identity":"364c3f45-a734-4928-8ff4-77769e8384a1","order_by":3,"name":"Chun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCSBmbGBg4GdgYAPRIEykFskGkrUYHCBWC//s5mMPfu44nGd8/IzZwxkMNrIbDjA/e4DXkjvH0g17zxwuNjuTY264gSHNeMMBNnMDfFoMJHLMpBnbDiduu8FjJvmA4XDihgM8bBL4teR/A2vZPAOs5T8xWnLYwFo2SAC1bGA4QFiLxI00M8netvTEGWfSyiRnGCQbzzzMZoZXC/+M5GcSP9usE/vbD2+T7Kmwk+073vwMrxZ0dwIxMwnqR8EoGAWjYBRgBwCGCUlnYjFRMQAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese University of Hong Kong, Shenzhen","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-07-15 01:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4740201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4740201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62142344,"identity":"6301aea6-19d0-43c5-bd78-3b4a988ef781","added_by":"auto","created_at":"2024-08-09 17:42:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFour-Class Model.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. 1 = neglect household chores to spend more time online. 2 = decrease the amount of time spent online. 3 = spend time online when you’d rather sleep. 4 = wish to decrease the amount of time spent online but do not succeed. 5 =feel tense, irritated, or stressed if cannot use the internet. 6 = try to conceal the amount of time online. 7 =feel tense, irritated, or stressed if cannot use the internet for days. 8 = feel depressed when not on the internet and feelings top once back online. 9 =people complain about you spending too much time online.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4740201/v1/cc904b9a29fcc1f8ad9c8d3a.png"},{"id":69876062,"identity":"143a3fdf-0427-46f6-b6eb-b932e0ce3ae9","added_by":"auto","created_at":"2024-11-26 08:32:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":890234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4740201/v1/f915703f-e64a-4320-83e5-ab2dccae001e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Problematic Internet Use Profiles: Identifying Social-Cultural Demographic Risk Factors and Predicting Longitudinal Effects on Psychopathology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProblematic Internet use (PIU) is characterized by addictive and maladaptive internet use, which causes psychological, social, or academic difficulties in an individual\u0026rsquo;s life (Beard \u0026amp; Wolf, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, p.378). The prevalence of PIU in China is reported to be notably higher compared to other countries in the world (Guo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with rates reaching up to 23% (Sun et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous studies on PIU tend to assume PIU symptoms as homogenous among different individuals, primarily using cross-sectional data in a variable-centered approach. However, these approaches do not adequately consider individual differences in PIU symptom manifestation, and there is a lack of nuanced investigation on the internal heterogeneity of PIU symptoms within individuals. In light of these limitations, a person-centered approach, such as latent profile analysis (LPA), could examine the similarities and differences among individuals. Moreover, most PIU studies were conducted among the urban student population. However, PIU was found to be more severe among adolescents in rural China than their counterparts in urban areas (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) due to its significant disparity in socioeconomic status and mental health resources (Tang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the present study aimed to provide a person-centered examination of PIU among adolescents in rural China. The antecedent risk factors and the severity of mental health symptoms on specific profiles were also evaluated.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Understanding PIU from A Person-Centered Approach\u003c/h2\u003e \u003cp\u003eWith numerous research investigating the characteristics of PIU from a holistic perspective through a variable-centered approach, the individual differences across PIU symptoms are overlooked, which is essential for clinical intervention and understanding the intrinsic traits of PIU. Person-centered approach attempts to identify heterogeneous subtypes of individuals through conducting LPA (Morin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which concentrates on how individuals differ based on the indicator variable rather than homogenizing the variable across participants (Abar, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and measure the latent level for the probabilities of a person\u0026rsquo;s membership in various latent profiles (DiStefano \u0026amp; Kamphaus, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This method captures interindividual variability in intraindividual changes in developmental processes by allowing outcomes to differ between participant subpopulations that are not observed, without presuming population homogeneity (Morin \u0026amp; Litalien, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome existing studies have used person-centered approaches to investigate PIU (e.g., Lee et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, to our knowledge, few research utilized LPA to explore various symptom patterns of PIU and its influencing factors among Chinese rural adolescents. The present study sought to provide a more nuanced investigation of the manifestation of PIU symptoms and to identify its demographic risk factors in a large group of rural Chinese adolescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Demographic Factors Associated with PIU\u003c/h2\u003e \u003cp\u003eBronfenbrenner's ecological systems theory believes that child development is seen as a complex system of relationships including a variety of environmental factors, ranging from the immediate home and school environment to broader cultural values (Bronfenbrenner \u0026amp; Morris, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). To understand the mechanism of PIU, the current research would consider the demographic influence in each system. However, few studies have ever identified risk and resilience factors of PIU from a comprehensive range of social-ecological demographic variables.\u003c/p\u003e \u003cp\u003eAt the individual level, sex, affected by specific biological factors and social norms (Fattore et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), is an important demographic variable for PIU. For example, a representative study in Chinese elementary and middle schools found a higher prevalence rate among males (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As to the family level, apart from socioeconomic status, parental presence is also a critical consideration. Left behind students are defined as the children from rural backgrounds who were raised by grandparents or other family members while their parents moved to an urban location for at least six months (Cheng \u0026amp; Sun, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Adolescents with left behind experiences had a greater prevalence of PIU than adolescents who were not left behind (e.g., Ren et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which cannot be ignored when exploring PIU in rural China. For the school system, an important factor to consider is the policy of boarding schools in rural China, which brings both positive and negative effects on adolescent growth. Living on campus could benefit students through preventing the development of some serious problems, enhancing their competencies such as socializing and independence (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and improving their academic performance and well-being (Liu \u0026amp; Villa, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, Chinese rural boarding schools frequently fall short of meeting students' requirements for health, nutrition, and emotional support (Luo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Such a lack of resources in rural schools (e.g., professional mental health staff) might result in more negative outcomes, such as higher depressive symptoms (Zhu et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), lower self-esteem (Hou et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and higher likelihood of bullying (Zhang, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, China is made up of 56 ethnic groups, including the Han ethnic group as the majority ethnic group comprising 91.11% (National Bureau of Statistics, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) of the Chinese population. Since Chinese ethnic groups are divided by social history, economic life, language, and religion (Maurer-Fazio \u0026amp; Hasmath, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), there might be cultural differences among these groups. Most previous PIU studies in China focused on students from the Han ethnic group, while existing yet limited research has found that minority ethnic status was a risk factor for PIU and found the prevalence rate of PIU in students from minority groups was much higher (Lu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Association between PIU and Psychopathology\u003c/h2\u003e \u003cp\u003eExisting studies have consistently established a robust association between PIU and psychopathology (e.g., Tung et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Younes et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For instance, previous research found a positive relationship between PIU and quality of life among Chinese rural students (Guo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, an LPA study also indicated that compared to adolescents in the normal-use category, adolescents in the high internet addiction and excessive online gaming categories typically had greater depressuon symptoms and problematic behaviors (Sun et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile there have been some studies discussing PIU in rural areas, it still remains limited compared to those in urban areas (Pan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, although one study has examined the relationship between PIU and mental health in rural areas through LPA (Sun et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it is generally not representative of all rural areas in China due to the difference in developmental level indices and network resource allocation (Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4. The Present Study\u003c/h2\u003e \u003cp\u003eThe present study aimed to conduct a person-centered study to understand PIU patterns among adolescents in rural China, as well as the antecedent risks of PIU and its subsequent experiences with depression, anxiety, and stress. We aimed to (1) examine PIU latent profiles at the beginning of an academic year in 2022, (2) the association between demographic variables and PIU profiles, and (3) its association with subsequent psychopathology symptoms in 2023 among Chinese rural adolescents.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants and Procedure\u003c/h2\u003e \u003cp\u003eAfter obtaining approval from the school administrator, the project was introduced to the students and their guardians by the school teachers. Students and their guardians were informed that the participation was voluntary. Guardians needed to provide informed consent. The digital survey was disseminated to students whose parents agreed them to participate during class on a regular school day via a web-based survey platform, and students needed to provide their assent on the first page of the survey before proceeding. A total of 5,447 middle and high school students from two boarding schools located in rural areas in Guizhou and Sichuan Provinces, China, were invited across both waves of the study in October 2022 and April 2023. After screening out invalid data, 176 questionnaires were excluded from the dataset, leaving 5,271 valid questionnaires, with an effective response rate of 96.76%. The sex and grade level distribution of the participants were relatively balanced. Among the participants, 1,773 (33.6%) were Han ethnicity (i.e., the largest ethnic group in China), 3,035 (57.6%) were the local minority ethnicities (i.e., Yi ethnicity in Sichuan and Dong ethnicity in Guizhou), and 463 (8.8%) were other ethnic minorities. The majority (94.5%) of the participants lived on the school campus. This study was reviewed and approved by the Ethics Committee of [blinded for review] (No. EF20220602002).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Problematic Internet Use Questionnaire Short Form (PIUQ-SF)\u003c/h2\u003e \u003cp\u003eThe PIUQ has proven to be a valid and reliable instrument for measuring PIU symptoms (Laconi et al., 2019). A short version with nine items on three subscales, being obsession, neglect, and control disorder, was developed by Demetrovics et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) as a second-order three-factor model structure with three items within each factor. PIUQ-SF uses a 5-point Likert scale (range from \"never\" to \"always/almost always\") to estimate the frequency of PIU symptoms. The Chinese version of the PIUQ-SF was translated and validated by Koronczai et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Cronbach's alpha of the measure in the present sample is 0.87.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. The Depression Anxiety Stress Scale 21 (DASS-21)\u003c/h2\u003e \u003cp\u003eThe 21-item Depression Anxiety Stress Scale (DASS; Lovibond \u0026amp; Lovibond, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) version is a self-report measure of differentiation between depression, anxiety, and stress-related mood disorders. The DASS-21 is a 4-point Likert scale from \"does not apply to me at all\" to \u0026ldquo;completely applies to me\u0026rdquo;. The validity and reliability of the Chinese version of DASS-21 has been demonstrated in Chinese adolescents (Gong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Cronbach\u0026rsquo;s alpha was 0.90 for depression, 0.87 for anxiety, and 0.87 for stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Demographic Variables\u003c/h2\u003e \u003cp\u003eParticipants were asked to answer demographic questions including age, gender, ethnicity, residency status, sibling numbers, SES, and left-behind status. Ethnicity was collected because the two counties are multi-ethnic areas. The residence status is to collect whether they live on campus or at home. The Family Affluence Scale (FAS; Hobza et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) questionnaire was administered to measure participants\u0026rsquo; socioeconomic status (SES). The psychometric properties have been empirically validated among Chinese adolescents (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Left-behind status was determined by asking if their father or mother had migrated to another city for work in the previous year and had not returned home for more than six months.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical Analyses\u003c/h2\u003e \u003cp\u003eTo determine the pattern of PIU among participants, we conducted a latent profile analysis (LPA) using Mplus 8 (Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to investigate heterogeneity and identify the most mutually exclusive classes in PIU. After identifying the best model, the next step was to see if the auxiliary variables (i.e., sex, age, ethnicity, staying on-campus or not, SES, left-behind status, and sibling numbers) and distal outcomes (depression, anxiety, and stress) differed significantly across the identified categories. To include auxiliary variables and distal outcomes (Asparouhov \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), a three-step approach (Nylund-Gibson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used. The three-step approach was preferred because it helped to ensure that auxiliary variables and distal outcomes did not influence the latent class (Asparouhov \u0026amp; Muth\u0026eacute;n, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Auxiliary variables (i.e., age, gender, SES, residence) and distal outcomes (depression, anxiety, and stress) were included by using multivariable logistic regression. Missing data was handled using maximum likelihood (ML) estimates. Descriptive statistics are in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study was not preregistered. The data and analysis code that support the findings of the study are available on request from the corresponding author.\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\u003e\u003cem\u003eCorrelation Coefficients Among Variables.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean/Proportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Living on-campus or not\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.061**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Sibling numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.088**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.312**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.064**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.267**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.286**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Left behind\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.308**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.045**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.048**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.203**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.176**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.085**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.213**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.157**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.050**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.177**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.893**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.072**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.200**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.083**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.046**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.232**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.909**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.888**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. PIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.070**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.029*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.035*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.074**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.114**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.322**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.327**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.347**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e*\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Latent Profile Analysis of PIU\u003c/h2\u003e \u003cp\u003eConsidering the optimal fit statistics, parsimony, category significance, the minimum number of participants in the smallest class, and model comparison, the four-class model was selected based on a total of 9 items. as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The representation in the four-class were (1) low PIU (PIU mean\u0026thinsp;=\u0026thinsp;1.36), accounting for 57.18% of the sample; students in this class scored relatively low on all items; (2) medium PIU (PIU mean\u0026thinsp;=\u0026thinsp;2.21), accounting for 15.65% of the sample; students in this class scored in the middle of the range on all items; (3) self-blame group (PIU mean\u0026thinsp;=\u0026thinsp;2.39), students who showed lower addiction score, medium neglect score, and higher control disorder score account for 18.16% of the total. The naming of \u0026ldquo;self-blame group\u0026rdquo; is because participants had higher self-blame scores on the dimension of control disorder, believing that they should reduce their online time but felt that they always failed to do so. (4) The last group was high PIU (PIU mean\u0026thinsp;=\u0026thinsp;3.32), accounting for 9.01%; students in this class scored relatively high on all items, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eFit indices of LCA Class Models.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (K-Class)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enpar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003esaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAWE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLRTS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBF (K, K\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNumber of minimal class/percentages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-67827.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135690.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135826.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e135808.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e135751.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e136016.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-60453.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120963.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121175.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e121147.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e121058.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e121471.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14746.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1208/22.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58056.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116188.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116475.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e116437.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e116317.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e116877.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4795.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e580/11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-56982.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114060.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114423.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114375.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e114223.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e114930.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2148.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e475/9.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-52451.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105019.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105459.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105401.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e105216.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e106072.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9060.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e159/3.01%\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Demographic Factors Associated with PIU Latent Profiles\u003c/h2\u003e \u003cp\u003eThe likelihood of being placed in a particular class differed among different demographic variables. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, compared with the high PIU class, students who were Han ethnicity, living on campus, accompanied by both parents and more siblings, were more likely placed in the low PIU class (ethnicity: logit = -0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, OR\u0026thinsp;=\u0026thinsp;0.81; staying on-campus or not: logit\u0026thinsp;=\u0026thinsp;0.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, OR\u0026thinsp;=\u0026thinsp;1.55; left-behind: logit = -0.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;0.72; sibling numbers: logit\u0026thinsp;=\u0026thinsp;0.301, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;1.35). Being male, living on campus, their parents not leaving them behind, and having more siblings were more likely to be in low PIU class compared with medium and self-blame groups (C2 VS C1: sex: logit\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, OR\u0026thinsp;=\u0026thinsp;1.20; staying on-campus or not: logit = -0.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, OR\u0026thinsp;=\u0026thinsp;0.69; left-behind: logit\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;1.50; sibling numbers: logit = -0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;0.87; C3 VS C1: sex: logit\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;1.44; staying on-campus or not: logit = -0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;0.66; left-behind: logit\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;1.33; sibling numbers: logit = -0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, OR\u0026thinsp;=\u0026thinsp;0.88).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCoefficients and Odds Ratio for the Four-Class Model with Demographic Variables\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender (reference group\u0026thinsp;=\u0026thinsp;male)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLiving at school or not (reference group\u0026thinsp;=\u0026thinsp;not)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeft-Behind Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSibling Numbers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1vs C4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.213*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.435*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.327**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.301**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.721**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.351**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2 vs C4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.163*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.826*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.176*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 vs C4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.176**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.193*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2 vs C1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.178*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.367*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.404**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.139**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.195*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.693**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.499**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.871**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 vs C1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.421**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.283**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.125**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.657**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.327**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.883**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3 vs C2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.886*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. PIU Latent Profiles Associated with Depression, Stress, and Anxiety\u003c/h2\u003e \u003cp\u003eStudents in the high-level PIU group had the highest values of anxiety, stress, and depression. High PIU (mean\u0026thinsp;=\u0026thinsp;16.46) and medium PIU (mean\u0026thinsp;=\u0026thinsp;13.20) had higher anxiety scores than the self-blame class (with low addiction dimension score, medium neglect dimension score, and high control disorder dimension score) (mean\u0026thinsp;=\u0026thinsp;10.38) and low PIU class (mean\u0026thinsp;=\u0026thinsp;6.78; the difference between class 1 and class 2: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d = -6.42; the difference between class 1 and class 4: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d = -9.68; the difference between class 3 and class 4: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d = -6.09; the difference between class 2 and class 3: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d\u0026thinsp;=\u0026thinsp;2.82). The self-blame class (mean\u0026thinsp;=\u0026thinsp;10.377) had higher anxiety scores than the low PIU class (mean\u0026thinsp;=\u0026thinsp;6.78; the difference between class 1 and class 3: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d = -3.60). The same situation as the high PIU and medium PIU group (difference between class 2 and class 4: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, d = -3.26). Similar patterns of anxiety mean differences were observed in depression and stress in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMeans of Anxiety, Depression, and Stress in Each Class.\u003c/em\u003e\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIU Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003cp\u003e(Range 0\u0026ndash;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003cp\u003e(Range 0\u0026ndash;42)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003cp\u003e(Range 0\u0026ndash;42)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1: Low PIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.778\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.647\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.878\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2: Medium PIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.201\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.960\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.720\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3: Self-Blame\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.377\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.958\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.190\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 4: High PIU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.462\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.789\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.157\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e. Means that do not share subscripts differ at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study conducted LPA to investigate the PIU profiles and examined its relationship with a range of social-ecological demographic variables and psychopathology in rural Chinese students. A four-class PIU pattern was identified into low PIU, medium PIU, self-blame, and high PIU group. The findings also suggested that demographic variables played an essential role in distinguishing different patterns of PIU among rural Chinese adolescents. In addition, different PIU traits could predict anxiety, depression, and stress symptoms. The results revealed the heterogeneity of adolescent PIU in a rural Chinese context and identified risk and protective factors.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Latent Profiles of PIU\u003c/h2\u003e \u003cp\u003eThe classifications of PIU identified were mostly consistent with previous studies of LPA in rural Chinese adolescent samples by Sun (2022), where they identified normal internet use, low internet addiction, high internet addiction, and overuse of online games. The distinction from previous findings is the identification of the special group, the self-blame group. The unique finding of the self-blame group in our study accounted for almost one in five students. This group of students demonstrated high self-expectation. Although the results showed that they wanted to reduce the frequency of their Internet access, their obsession scores were relatively low. This group performed well when they weren't online, but when they had the chance to use the Internet, they would be over-engaged and neglecting everything else and after online, they felt guilty and hoped that next time they could control their online time. It is possible that even though this group of adolescents wished to manage their online time, but in reality, rural adolescents had fewer extracurricular activities compared with urban counterparts (Zhang \u0026amp; Tang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consequently, due to these factors, the Internet has become a medium that satisfies their experience and exploration needs and functions as an escape or avoidance mechanism (Lowry et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). At the same time, the socio-economic environment in rural areas was relatively restricted, students were taught to be self-disciplined from a young age to \u0026ldquo;escape\u0026rdquo; from rural areas, which might contribute to excessive self-blame about their lack of control online.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Demographic Factors Associated with PIU Latent Profiles\u003c/h2\u003e \u003cp\u003eBeing female, an ethnic minority, living off-campus, having left-behind experiences, and having fewer siblings were risk factors for being in a higher PIU group. Consistent with some studies (e.g., Gansner et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), males were more likely to be in the low PIU group (versus medium and self-blame group), which means female students were more vulnerable to PIU and more likely to have control disorder problems. This could be explained by the fact that in rural areas, females had less safe space to outspeak their needs. They were more likely to be mistreated because of sex discrimination (Hannum et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, the Internet gives them a place to escape, further leading to PIU and control issues. Ethnic minority students were more likely to be classified in the high PIU group, which might be explained by cultural differences. In Han ethnic families, parents tended to be relatively strict with their children as the Preferential Admission Policy gives bonus points in national college entrance exams to students with minority ethnicities (Wang, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Moreover, with the Han culture being localized in different regions in China, it was likely that students from ethnic minority backgrounds would experience struggle in school settings as the main teaching language is Mandarin (Yang et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), further resulting in their seeking online spaces to avoid these frustrations (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Students who lived on campus were more represented in the Low PIU group than in the other three groups. This suggested that living on campus could effectively reduce PIU. In rural China, schools had a strict residential system with precise light-out times and internet and cellphone control. Hence, the strict on-campus schedule and cell phone ban policy prevented many students from obtaining higher PIU scores, suggesting that restrictions on Internet access time may effectively reduce PIU among rural adolescents. Moreover, parental presence was a protective factor. Compared with adolescents who had one or both parents left behind, children whose parents did not migrate were more likely to be classified in the low PIU group. Being at home, parents would be able to better manage their children's Internet problems. Having more emotional ties would also reduce the dependence on the Internet. The presented study also showed that the greater number of siblings was a protective factor against PIU. This was because more siblings could make up for the lack of parental companionship. Siblings in the same generation could confide in and help each other to effectively reduce PIU.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. PIU Latent Profiles Associated with Psychopathology\u003c/h2\u003e \u003cp\u003eThis study revealed that Chinese rural adolescents with distinct profiles of PIU exhibited significantly varied levels of anxiety, depression and stress symptoms. In particular, participants in the self-blame group, high PIU group, and medium PIU group displayed more severe anxiety, depression, and stress symptoms than those with low PIU. This result suggests that high PIU was accompanied by high psychological distress in adolescents from rural areas. This finding is consistent with prior research (e.g., Dalbudak et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, regular Internet users have less face-to-face communication; consequently, their interpersonal relationships and social support are diminished (Kim, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), resulting in potential anxiety, depression, and stress symptoms. In addition, this study revealed that participants in the self-blame group exhibited less anxiety, depression, and stress symptoms than those in the medium and high PIU groups. The self-blame group had the highest control disorder score and the second high neglect score, and the total PIU mean score was higher than the medium PIU group. The result implies that adolescents with less obsession with the Internet would exhibit less anxiety, depression, and stress symptoms. Therefore, the obsession was an essential factor leading to more psychological distress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Limitations\u003c/h2\u003e \u003cp\u003eSome limitations of this study should be taken into consideration. Firstly, the data was collected through self-report questionnaires, which may have been affected by social expectations and recall biases. Future studies could consider multi-informants, such as parents, teachers, and peers. Secondly, the generalizability of the results to Chinese adolescents in rural areas was limited by the sample size, as all students were recruited from only two schools in Guizhou and Sichuan in Southwest China. Therefore, future studies should aim to replicate these findings using a more diverse and representative sample to improve generalization.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study sheds light on the latent profiles of PIU among rural Chinese adolescents, revealing four distinct subgroups with varying levels of severity across the three symptoms. Risk factors for PIU include being female, being an ethnic minority, living off-campus, having left-behind experience, and having fewer siblings. Importantly, higher levels of PIU were associated with increased psychopathology, underscoring the importance of addressing internet obsession for rural Chinese adolescent mental health. Tailored interventions involving families and schools are crucial in mitigating the adverse effects of PIU among rural Chinese adolescents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethical Approval.\u003c/strong\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was reviewed and approved by the Ethics Committee of Chinese University of Hong Kong, Shenzhen (No. EF20220602002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e. Informed consent was obtained from all individual participants and their guardians included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication.\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u003c/strong\u003e The datasets generated and/or analysed during the current study are not publicly available due to confidentiality of participants’ information but deidentified data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e. The study is funded by Pengcheng\u0026nbsp;Peacock Matching Research Funding - Category C\u0026nbsp;(No.\u0026nbsp;2024TC0135).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions.\u003c/strong\u003e Y.W. conducted the formal analysis and wrote the original draft of the manuscript. B.H., Y.C., and C.C. reviewed and edited the manuscripts. C.C. and Y.W. conceptualized the study. C.C. supervised the study. All authors reviewed the manuscript and have approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e. We thank the teachers, students, and parents in the two participating schools in the present study as well as Zhejiang Xinhua Compassion Education Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbar, C. C. (2012). 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Boarding education and children\u0026rsquo;s development: Evidence from 137 rural boarding schools. \u003cem\u003eEducational Research\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e, 79-91. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"problematic internet use, latent profile analysis, anxiety, depression, stress","lastPublishedDoi":"10.21203/rs.3.rs-4740201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4740201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eProblematic Internet Use (PIU) has many adverse effects on youth mental health and development. However, few studies have systematically investigated the internal heterogeneity of PIU symptoms among rural Chinese adolescents. This study collected two waves of data (T1: October 2022; T2: April 2023) from 5,271 rural Chinese adolescents from two secondary schools in Guizhou and Sichuan provinces.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA Latent Profile Analysis (LPA) was conducted to first identify PIU symptom profiles. Then, a \u0026ldquo;three-step\u0026rdquo; logistic regression mixed model was conducted to explore the association between PIU patterns and demographic correlates. Anxiety, depression, and stress symptoms collected at the second wave were compared across PIU profiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study found that (1) The patterns of PIU among rural adolescents could be divided into four subgroups: low PIU group (57.18%), medium PIU group (15.65%), high PIU group (9.01%), and self-blame group (18.16%), which is a uniquely identified group. (2) Being female, an ethnic minority, living off-campus, having left-behind experiences, and having fewer siblings were risk factors for high PIU group membership. (3) The order of severity for anxiety, depression, and stress was as follows: high PIU, medium PIU, self-blame, and low PIU groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAddressing the dimension of internet obsession of PIU is vital for rural Chinese adolescent mental health, necessitating tailored interventions involving families and schools.\u003c/p\u003e","manuscriptTitle":"Problematic Internet Use Profiles: Identifying Social-Cultural Demographic Risk Factors and Predicting Longitudinal Effects on Psychopathology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 17:42:30","doi":"10.21203/rs.3.rs-4740201/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c96b0c2-1ee4-4286-89c1-75a6e7fa1f18","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-26T08:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 17:42:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4740201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4740201","identity":"rs-4740201","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}