Gender differences in the mental symptom network of high school students in Shanghai, China: a network analysis

Abstract

Background

Adolescence is a critical period for the onset of mental health issues. In China, high school students face significant academic and social pressures, leading to high rates of mental health challenges. Gender differences in the manifestation of these symptoms have been observed, with boys and girls exhibiting distinct psychological profiles.

Objective

This study aims to explore the structure of psychological symptoms among Chinese high school students using network analysis, focusing on identifying core symptoms and gender differences in symptom networks. The key objectives are to: 1) identify the central psychological symptoms for boys and girls, and 2) uncover the interactions between symptoms to inform targeted interventions.

Methods

A cluster sampling method was used to recruit 3,769 high school students (2,206 males and 1,563 females) in Shanghai. The Middle School Students Mental Health Scale (MSSMHS) was administered, and network analysis was conducted using the R packages bootnet and qgraph to assess symptom network edges, centrality, and network strength. Comparisons between male and female networks were made.

Results

Network analysis showed tightly connected symptom networks for both genders, with 43 non-zero edges for boys (sparsity 0.04) and 39 for girls (sparsity 0.13). Depression was the core symptom for boys (centrality 1.20), while anxiety was central for girls (centrality 1.46). Boys showed a stronger link between interpersonal sensitivity and depression (edge value 0.20), while girls exhibited a stronger connection between anxiety and obsessive-compulsive symptoms (edge value 0.16). Network comparison tests revealed no significant differences in overall network strength between boys (4.625) and girls (4.660), with P-values greater than 0.05 across all comparisons.

Conclusion

This study highlights significant gender differences in the psychological symptom networks of Chinese high school students. Depression and anxiety emerged as core symptoms for boys and girls, respectively. These findings provide a foundation for developing gender-sensitive mental health interventions, emphasizing the need for tailored approaches based on gender-specific symptom profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-20130-7.

Highlights

Significant gender differences in the symptom networks of adolescents, with depression and anxiety as key central nodes.

A tight link between anxiety and depression in female students, whereas a strong connection exists between depression and interpersonal sensitivity in male students.

Emphasizes the importance of designing targeted educational and mental health interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-20130-7.

Introduction

Adolescence, encompassing ages 12 to 24, is a critical developmental period during which most mental disorders first emerge [1]. Epidemiological data indicate that approximately 20% of adolescents worldwide experience varying degrees of mental health issues, such as depression, anxiety, and behavioral disorders, during this stage [2]. In some regions, the prevalence is even higher; for instance, studies show that up to 25% of adolescents in high-income countries suffer from diagnosable mental health conditions [1]. These statistics underscore the widespread nature of adolescent mental health challenges, establishing them as a significant global public health concern. Adolescents face intrinsic challenges related to identity formation, making them particularly vulnerable to mental health issues [3]. In China, high school students aged 16 to 18 are in the later stages of adolescence, grappling not only with internal identity development but also with conflicts between self-identity and external factors such as family, school, and societal pressures [4]. Recent surveys in China reveal that the prevalence of depressive symptoms among high school students ranges from 15 to 30%, reflecting the intense psychological distress and pressure experienced by this population [5, 6].

Adolescent mental symptoms refer to atypical psychological and behavioral manifestations observed during adolescence, which may be associated with mental health issues or psychiatric disorders. Research shows that adolescent mental symptoms can lead to decreased academic performance, school dropout, increased engagement in high-risk behaviors, self-harm, and even suicide [7]. The World Health Organization reports that suicide is the fourth leading cause of death among 15- to 19-year-olds globally, further highlighting the severity of these issues [8]. These mental health challenges impose significant emotional and psychological burdens on individuals and families while also leading to substantial social and economic losses. Health economics research suggests that adolescent mental health problems contribute to increased healthcare expenditures, reduced productivity, and higher social service costs [9]. For example, untreated mental health disorders during adolescence are associated with an estimated 10–20% reduction in lifetime earnings, demonstrating their long-term economic impact [10–12]. Moreover, many mental disorders that first emerge during adolescence, if left untreated, may persist into adulthood, exacerbating the burden of disease and economic losses later in life [13].

Research consistently emphasizes significant gender differences in adolescent mental health issues, which are particularly relevant in the context of Chinese high school students. Female adolescents tend to exhibit higher levels of anxiety and depression, which can be attributed to a combination of heightened emotional reactivity, social sensitivity, and the intense academic pressures prevalent in Chinese society [14, 15]. In China, where academic achievement is often a central focus, female students may experience additional stress related to societal expectations, such as the pressure to balance academic success with traditional gender roles [16]. This pressure can exacerbate feelings of anxiety and depression, as these students struggle to meet high standards in both their academic and social lives. In contrast, male adolescents are more prone to behavioral problems and externalizing symptoms, such as impulsivity and antisocial behavior [17]. These tendencies may be linked to more frequent hormonal fluctuations during adolescence [18, 19], but they are also influenced by gender role expectations encountered during socialization [20]. In the Chinese context, traditional masculinity may emphasize traits like assertiveness and emotional restraint, making male students less likely to express feelings of anxiety or sadness openly. Instead, these emotions may manifest as anger, defiance, or risk-taking behaviors. The academic environment in China, which often emphasizes discipline and conformity, might further challenge male students who struggle with these externalizing behaviors, leading to conflicts with teachers and peers, and potentially exacerbating their psychological distress.

These gender differences affect not only the manifestation of individual symptoms but also the network relationships between symptoms [21]. For example, in female students, symptoms like anxiety and depression may be closely linked, forming a network that is reinforced by social and academic stressors. In male students, behavioral issues might form a different network, interconnected with stress, frustration, and the pressure to conform to masculine ideals [22]. Consequently, adolescents of different genders may require distinct intervention strategies when facing similar psychological issues [23]. By recognizing and addressing the unique challenges faced by male and female students, educators and mental health professionals can create more inclusive and effective support systems that cater to the diverse needs of Chinese high school students.

Previous studies suggest that symptoms of mental disorders often stem from common psychological factors [24]. However, traditional models typically focus only on these underlying single causes or linear relationships, which have shown significant limitations, particularly in addressing the complexity and heterogeneity of mental illnesses. Traditional models tend to view mental disorders as a series of symptoms triggered by a core cause, emphasizing diagnosis and treatment of this root cause [25]. This simplified perspective overlooks the complex interactions that may exist between different symptoms. For instance, the comorbidity of depression and anxiety is not merely the sum of two independent disorders but is driven by the intricate interplay between shared symptoms, complicating both diagnosis and treatment [26]. This complexity is especially pronounced in adolescents, who are in a critical developmental stage where psychological symptoms are diverse and deeply interconnected. Adolescents’ psychological states are influenced by a multitude of factors, including physiological changes, social pressures, and identity formation, which often interact in complex and dynamic ways, making it difficult for traditional models to effectively capture and explain the full scope of adolescent mental health issues [27]. Network analysis offers significant advantages for exploring the relationships between factors within a single construct, providing a detailed and dynamic representation of these interactions. Unlike traditional methods that may assume independence among factors, network analysis maps the complex web of direct connections, identifying central factors pivotal to the construct’s influence. This approach is particularly valuable as it accommodates non-linear and non-normal relationships, typical in psychological data, allowing for a more accurate modeling of real-world dynamics [28]. Additionally, its ability to depict changes within a construct over time offers insights into how alterations in one factor might impact others. Supported by modern psychological research, network analysis aligns with contemporary theories that view psychological phenomena as complex systems of interrelated components [29, 30], enhancing both the scientific rigor and theoretical depth of our study. By integrating these advantages, we provide a robust justification for using network analysis to examine intricate factor relationships within a single construct.

In contrast, network theory offers a more dynamic and holistic perspective. Unlike traditional models, network theory does not seek a single “root cause” but rather focuses on the interactions and feedback mechanisms between symptoms [31]. According to this theory, mental disorders are viewed as networks of interrelated symptoms, where certain key symptoms may act as central nodes, directly influencing the emergence and exacerbation of other symptoms. Through network analysis, researchers can identify these central nodes and develop targeted intervention strategies that more effectively alleviate symptoms and improve outcomes [29, 32]. Additionally, network theory can explain the heterogeneity of mental disorders, revealing why individuals with the same diagnosis may exhibit vastly different symptom profiles and trajectories [32]. This perspective allows for more comprehensive interventions that go beyond single symptoms or causes, aiming to alter the entire network by addressing its key nodes [33]. Overall, the advantage of network theory lies in its ability to better reflect the complexity and dynamism of mental disorders, particularly in adolescents—a group characterized by rapid change and high plasticity. This theory not only offers new insights into the study of mental disorders but also paves the way for the development of more precise and individualized treatment approaches. If we aim to measure the network of psychological health symptoms in adolescents, it is essential to use a scale that encompasses a wide range of symptoms. To this end, we selected the MSSMHS (Middle School Students Mental Health Scale) [34], which can comprehensively assess various mental health symptoms, thereby providing the necessary foundational data for network analysis. The use of MSSMHS ensures that we can capture the complexity of adolescent mental health and provides a rich and comprehensive set of symptom information for building the network model. This choice aligns with our research goal of gaining a deep understanding and analysis of the interrelationships and network structure of adolescent mental health symptoms.

In this study, we hypothesize the presence of an interactive symptom network connecting mental disorders and psychological symptoms among high school students. By analyzing the interactions among these psychological symptoms—each captured by the MSSMHS—we aim to develop a clear and intuitive understanding of the mental health conditions prevalent in this population. This network-based approach not only helps identify key central nodes within the symptom network but also lays the groundwork for developing targeted intervention strategies specifically tailored to high school students.

Method

Participants

Participants were recruited using the cluster sampling method from a variety of high schools in Shanghai, encompassing both public and private institutions, as well as those located in urban and rural areas. Prior to the assessment, informed consent was obtained from the parents of participants through WeChat groups. Trained professionals conducted the psychological assessment. Participants were instructed to review the assessment instructions to ensure comprehension of the assessment content prior to commencement. Subsequently, mental health professionals saved and analyzed the assessment data to identify students at higher risk of mental disorders and to prepare for intervention. All participants provided written informed consent at the survey outset, with the option to withdraw at any time without penalty. The study enrolled a total of 3769 eligible participants, comprising 2206 males and 1563 females, with an average age of 16.41 ± 1.67 years.

Measurement

Middle school students mental health scale (MSSMHS)

The Middle School Students Mental Health Scale (MSSMHS), developed by Chinese psychologist Wang Jisheng, aims to assess the psychological status of middle and high school students [35]. The scale comprises 60 items categorized into 10 factors, including obsessive-compulsive disorder, paranoia, hostility, interpersonal sensitivity, depression, anxiety, academic pressure, maladaptation, emotional instability, and psychological imbalance, with 6 items allocated to each factor. Participants rated all items on a 5-point Likert scale. Participants’ psychological status was assessed using both the total score and individual factor scores derived from the MSSMHS. Widely utilized in the Chinese education sector, this scale offers the advantages of fewer items, comprehensive symptom coverage, suitability for large-scale screening, and high reliability and validity. In this study, the reliability coefficient of the scale was 0.9.

Statistical analysis

Data compilation was conducted using SPSS 21.0, and subjects with missing values were excluded from the analysis. R packages utilized in this study encompassed bootnet, qgraph, networktools, NetworkComparisonTest, psychTools [36–39]. Network analysis measurements comprised network edge, network centrality, and network robustness. The network edge diagram was employed to illustrate the interconnections among symptoms. Various centrality measures were employed to address distinct symptoms. Closeness centrality measured the average distance between a node and others, indicating the rapidity and efficiency of information transmission. Strength centrality measured the connection strength between a node and others, indicating its importance and contribution to information transmission. Betweenness centrality measured the nodes’ ability to act as intermediaries, referring to their role as bridges in information transmission. Expected influence measured a node’s influence on others in information transmission. Given the study’s focus on the overall symptom influence, intensity centrality elucidated the contribution of individual symptoms in the network.

Results

Utilizing descriptive statistics and ANOVA (Analysis of Variance) on a sample of 3769 participants, statistically significant differences emerged between male and female students across various parameters including obsessive-compulsive disorder, paranoia, hostility, interpersonal sensitivity, depression, anxiety, academic pressure, and the total MSSMHS score (F = 9.708, 4.619, 9.689, 9.203, 61.485, 29.307, 16.415, and 17.637, respectively; all P < 0.05). Nevertheless, there were no significant differences observed regarding psychological imbalance and maladaptation (P = 0.116 and 0.123, respectively) (Table 1).

Table 1.

Descriptive statistics and analysis of variance (N = 3769)

	Overall (N = 3769)	Male (N = 2206)	Female (N = 1563)	F	P	η²	McDonald’s ω
OCS	1.877 ± 0.616	1.851 ± 0.609	1.914 ± 0.624	9.708	0.002	0.003	0.703
PAR	1.652 ± 0.651	1.632 ± 0.648	1.679 ± 0.654	4.619	0.032	0.001	0.787
HOS	1.605 ± 0.687	1.575 ± 0.677	1.646 ± 0.698	9.689	0.002	0.003	0.811
IS	1.783 ± 0.711	1.754 ± 0.715	1.825 ± 0.704	9.203	0.002	0.002	0.805
DEP	1.738 ± 0.730	1.661 ± 0.689	1.848 ± 0.770	61.485a	0.001	0.016	0.839
ANX	1.842 ± 0.802	1.783 ± 0.772	1.926 ± 0.835	29.307a	0.001	0.008	0.874
AP	1.857 ± 0.724	1.817 ± 0.713	1.913 ± 0.734	16.415	0.001	0.004	0.841
PI	1.528 ± 0.520	1.517 ± 0.524	1.544 ± 0.514	2.467	0.116	0.000	0.740
EI	1.723 ± 0.656	1.704 ± 0.661	1.750 ± 0.647	4.493	0.034	0.001	0.778
MAL	1.678 ± 0.663	1.664 ± 0.672	1.697 ± 0.651	2.381	0.123	0.000	0.737
T	1.733 ± 0.582	1.700 ± 0.572	1.780 ± 0.593	17.637	0.001	0.005	0.959

Network analysis

Network edge structure and density

The network edge structure graph (Fig. 1) illustrates that the mental symptom networks are tightly interconnected among both male and female high school students. Among male students, the network consisted of 43 non-zero edges (out of 45), resulting in a sparsity of 0.04, indicating a high network density. Conversely, the network of female students exhibited 39 non-zero edges (out of 45), resulting in a sparsity of 0.13, which indicates a lower density compared to the male network. The specific values of the network edge weights are placed in the supplementary tables.

Fig. 1.

Network edge structure of Chinese high school students: Comparison between genders. Male (left panel), Female (right panel).

Note: Obsessive-Compulsive Symptoms (OCS), Paranoia (PAR), Hostility (HOS), Interpersonal Sensitivity (IS), Depression (DEP), Anxiety (ANX), Academic Pressure (AP), Maladaptation (MAL), Emotional Instability (EI), and Psychological Imbalance (PI)

Key connections between symptoms

In terms of the variance in the connection between interpersonal sensitivity and depression, the edge value was 0.20 for males and 0.14 for females, suggesting a stronger link between these nodes in the male network. For the connection between anxiety and obsessive-compulsive disorder, the marginal value was 0.1 for males and 0.16 for females, indicating a stronger association between these nodes in the female network (Fig. 1).

Centrality measures

As depicted in the centrality plot (Fig. 2), the intensity and expected influence of all factors were relatively consistent between males and females. Depression, anxiety, and paranoia showed higher centrality for both male and female students, signifying their central role in the psychopathological symptoms of high school students. Notably, paranoia, emotional instability, and interpersonal sensitivity also demonstrated significant contributions to the symptoms observed in Chinese high school students. In terms of centrality (reflected by intensity), depression was the core disorder for males with a value of 1.20 while anxiety was the core disorder for females, registering at 1.46 The centrality of other factors remained relatively consistent between the genders. Specific numerical values of the network centrality are placed in the supplementary tables.

Fig. 2.

Intensity and expected influence of factors in the network

Network comparison testing

Using independent group Gaussian network comparison tests, we first examined the overall invariance of the network. The results indicated no significant differences in the edge weights of the entire network between males and females (M = 0.066, P = 0.896). Additionally, tests for the invariance of overall network strength also revealed no significant differences, with the overall network strength for male and female students being 4.625 and 4.660, respectively, suggesting no significant difference in the network connectivity strength of psychological symptoms between the two groups (S = 0.035, P = 0.149). Further permutation test results showed no significant differences in the strength of individual edge weights and network centrality indices between male and female students. Specifically, none of the edge weights had a P-value less than 0.05, and similarly, the tests for differences in the strength of centrality indices did not reveal any significance (all P > 0.05). In summary, these results suggest that there may be no significant gender differences in the network structure and connectivity strength of psychological symptoms among high school students.

Network robustness testing

The robustness analysis of network edges and centralities indicates that the structure of psychological symptom networks is highly stable and consistent across both genders. This suggests that the associations and interactions among symptoms maintain a certain pattern regardless of gender, although central symptoms may differ by gender. Moreover, this stability implies that the network model can be reliably used for psychological health analysis and prediction across different gender groups. (Fig. 3).

Fig. 3.

A robustness test of Chinese high school students’ social networks. Male (A, B,C), Female (D, E,F)

Discussion

This study employs network analysis to explore the structure and key manifestations of psychological symptoms among high school students in the context of academic stress. Network analysis, which assumes dynamic interactions among psychological symptoms, offers insights into how stress and emotional distress influence each other and activate these processes [24]. Compared to traditional causal models, network analysis provides a more nuanced understanding of the complex relationships between symptoms, thereby supporting the development of more targeted intervention strategies. The results indicate that depression and anxiety occupy central positions within the network, highlighting their critical role in adolescent mental health. These findings contribute to a deeper understanding of the complexity of psychological issues among adolescents, offering a solid foundation for optimizing intervention measures.

In this study, the psychological symptoms exhibited by high school students include obsessive-compulsive behaviors, paranoia, hostility, interpersonal sensitivity, depression, anxiety, academic pressure, maladjustment, emotional instability, and psychological imbalance. Network analysis reveals that these symptoms form a closely interconnected network, indicating mutual influence among them. Depression and anxiety are identified as central to this network, further underscoring their pivotal role in adolescent mental health. This is particularly evident in environments where academic pressure and social relationships are frequently in flux, as depression and anxiety are easily triggered and can exacerbate each other [40]. Additionally, interpersonal sensitivity and obsessive-compulsive symptoms emerge as important nodes within the network, reflecting the challenges adolescents face in managing peer relationships, parental expectations, and self-perception. For instance, under significant academic or social stress, adolescents may exhibit excessive self-demand or hypersensitivity to others’ evaluations, leading to obsessive-compulsive behaviors or hostile responses [41, 42]. These findings underscore the multifaceted and complex nature of adolescent mental health issues and suggest that factors such as academic pressure, social challenges, and self-identity may play significant roles in the development of psychological symptoms [43, 44]. Understanding and addressing these interactive mechanisms are crucial for effectively supporting adolescent mental health.

Network analysis reveals significant gender differences in emotional disorders among adolescents. The study finds that, among boys, there is a strong correlation between depressive symptoms and interpersonal sensitivity. This link may stem from the unique challenges boys face in managing interpersonal relationships during adolescence [45, 46]. Boys are often influenced by societal expectations to appear “strong” and “independent,” which may discourage them from openly expressing emotional distress [47] When they encounter stress or conflict in peer relationships, they are likely to internalize these negative emotions, thereby exacerbating depressive symptoms [48]. Moreover, boys may experience emotional distress in their relationships with parents, particularly if they feel they are failing to meet parental expectations or struggle with communication, further intensifying the connection between depression and interpersonal sensitivity [49, 50]. In contrast, the symptom network for girls is more focused on the interaction between anxiety and depression. Girls tend to exhibit higher emotional sensitivity when dealing with academic and social pressures, making them more prone to worry about upcoming exams, subtle changes in peer relationships, or conflicts within social circles [51–53]. This heightened sensitivity often leads to a vicious cycle of anxiety and depression. For example, when girls fear underperforming academically or being excluded socially, their anxiety may escalate rapidly, eventually resulting in depressive symptoms [54, 55].

Regarding gender differences in core network symptoms, anxiety emerges as a major factor among female high school students, reflecting their increased vulnerability to anxiety when faced with academic and social pressures [55]. This can be linked to the expectations placed on girls in terms of academic performance, peer relationships, and social interactions. Girls often place greater importance on their image and behavior in the eyes of others, which can lead to heightened anxiety in the face of academic competition, maintaining friendships, and social media interactions. This pressure often manifests as anxiety, particularly when facing exams or social challenges, and can accumulate over time, negatively impacting their mental health. Additionally, girls are more likely to internalize negative emotions, explaining the close relationship between obsessive-compulsive symptoms and anxiety among them [56]. For instance, when girls worry about meeting academic or social standards, they may exhibit excessive self-demand or obsessive behaviors, such as repeatedly checking their work or social media interactions, as a way to cope with anxiety. These compulsive behaviors not only reflect their anxiety but can also exacerbate it, leading to a vicious cycle.

While depression is generally more prevalent among girls, the expression of depressive symptoms in boys may differ, often manifesting as atypical symptoms such as irritability or behavioral issues [57, 58]. For example, when boys feel overwhelmed or helpless, they may not show overt sadness like girls but rather express their inner pain through irritability, defiance, or withdrawal [59]. This behavioral manifestation often leads to under-recognition and misdiagnosis of depression in boys. Furthermore, biological factors play a crucial role in these gender differences [37]. Hormonal fluctuations during puberty and the menstrual cycle, such as changes in estrogen and progesterone levels, may make girls more susceptible to anxiety and depression [60]. In contrast, higher levels of testosterone in boys may offer some protective effects, contributing to greater emotional stability under stress [61]. However, this does not imply that boys do not require attention to their mental health; rather, appropriate support and intervention are still necessary, particularly in coping with social pressures and emotional challenges.

Implications

The findings of this study offer valuable insights for educators, parents, and students in addressing adolescent mental health issues. For teachers, it’s essential to recognize that girls are more prone to anxiety under academic and social pressures. One successful example is Finland’s School Mental Health Program, which integrates school counseling services and mental health education to help students cope with academic stress [62]. Teachers can draw on this experience to provide emotional support, especially during high-stress periods. Additionally, attention should be given to boys who exhibit irritability or behavioral problems. Canada’s Youth Mental Health and Addiction Strategy serves as a useful model, emphasizing the importance of building trust and offering psychological counseling to help boys express their emotions [63].

Parents should understand the importance of gender differences in emotional expression. Open communication is key, especially with daughters, to address anxiety and help them develop coping mechanisms [64]. Australia’s “KidsMatter” initiative encourages parents to actively listen and provide emotional support to help children manage their emotions [65]. For sons, parents should be attentive to signs of hidden emotional distress manifested through behavior and engage in non-confrontational discussions to help them manage their emotions in a healthy way [66].

Finally, students themselves should be encouraged to recognize and understand their emotional responses, particularly when facing academic and social pressures. Girls should be encouraged to seek help actively, rather than allowing anxiety to dominate their lives. Boys, on the other hand, should learn that expressing emotions is a vital aspect of mental health, not a sign of weakness. Schools can implement gender-sensitive mental health education, similar to the UK’s “Healthy Schools Programme,” to help students better understand and cope with psychological challenges during adolescence, ultimately improving overall mental health outcomes [67].

Limitations and practical considerations

Despite the significant methodological strengths and findings of this study, several limitations exist. First, the study is based on cross-sectional data, limiting our ability to explore causal relationships between symptoms. Future research should adopt longitudinal designs to better understand the dynamic changes in these relationships. Second, the sample was primarily drawn from specific provinces, with a relatively small sample size, which may not represent the overall adolescent population nationwide. Thus, broader, nationally representative studies are needed to validate these findings. Lastly, this study relied solely on the MSSMHS questionnaire, which may limit the scope of our understanding of adolescent mental health. Future research should incorporate multiple assessment tools and qualitative interviews to gain a more comprehensive understanding of adolescents’ psychological states in various life contexts. For example, in addition to academic stress, attention should also be given to adolescents’ experiences in social media, family dynamics, and future planning, as these factors significantly impact their mental health.

Conclusion

The present study has discerned gender disparities in various factors contributing to mental disorders among high school students. Depression emerges as the central factor for male high school students, tightly linked with relationship tension and sensitivity. Conversely, anxiety emerges as the central factor for female students, closely intertwined with obsessive-compulsive symptoms/disorder. The gender disparities in factors contributing to mental disorders identified in this study hold promise for informing the development of preventive and intervention strategies tailored for high school students.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviations

MSSMHS

Middle School Students Mental Health Scale

ANOVA

Analysis of Variance

SPSS

Statistical Product and Service Solutions

MAL

Maladaptation

RAR

Paranoia

EI

Emotional Instability

HOS

Hostility

ANX

Anxiety

AP

Academic Pressure

PI

Psychological Imbalance

DEP

Depression

OCS

Obsessive-Compulsive Symptoms

IS

Interpersonal Sensitivity

Author contributions

XB Z, RH L conceived and designed the research and revised the manuscript. ZZ contributed to the first draft, data collection and analysis. XY Z, AD Q wrote the first draft of the introduction and revised the first draft.YX Z, L Y, J Y, Q Z and HD L contributed to the data collection. All authors have approved the final manuscript.

Funding

This research was funded by the Hunan Provincial Natural Science Foundation of China (2020JJ4828) and Hunan Provincial Social Science Foundation of China (23WTB07).

Data availability

The data presented in this study are available from the corresponding author. The data are not publicly available due to our laboratory’s policies.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee, School of Psychology, Jiangxi Normal University (JXNU-SOP-20221111). Informed consent was obtained from all participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflicts of interest.