Symptom network differences in school adjustment and anxiety-depression-stress in adolescents: a gender-based perspective

Abstract

Objective

To investigate the characteristics of adolescents’ school adjustment and their associations with anxiety, depression, and stress, respectively, as well as their differences by gender.

Methods

A convenience sampling method was used to measure 3,922 secondary school students from 10 secondary schools in five locations in the Xinjiang Uygur Autonomous Region using the Anxiety-Depression-Stress Scale and the School Adjustment Scale, and latent profile analysis was used to identify the subjects’ school adjustment categories, and network analysis was subsequently used to explore the relationship between different school adjustment categories and anxiety, depression, and stress, respectively, as well as their gender characteristics.

Results

In the symptom network of adolescents at risk for school maladjustment, “school emotions and attitudes” was the core symptom (Expected Impact Index: 0.86); in the co-morbidity network of school adjustment and anxiety, depression, and stress in adolescents at risk for school maladjustment, the core symptom was “uneasiness” (Expected Impact Index: 0.86); and “stress” (Expected Impact Index: 0.86) was the core symptom. “(EI: 1.12), “difficulty relaxing” (EI: 1.14) in males, and “depression” (EI: 1.06) in females, all with significant gender differences. Significant gender differences were found.

Conclusion

Adolescents’ school adjustment was strongly associated with symptoms of anxiety, depression and stress, with significant gender differences in the structure of the network of co-morbid symptoms.

Introduction

School adjustment is an important indicator of students’ mental health, but there are different understandings of the concept of school adjustment in the academic community. For example, Ladd et al. defined school adjustment as the degree to which students feel comfortable and successful during their interaction with the school environment [1].Perry and Weinstein defined school adjustment in terms of academic, social-emotional, and behavioral aspects [2]. Due to the differences in research purposes and perspectives, there are differences in the measurement indicators of school adjustment among different scholars.In line with the trend of diversity, this study evaluated adolescents’ school adjustment in five dimensions: academic adjustment, peer relationships, routine adjustment, teacher-student relationships, and school emotions and attitudes.

Academic adjustment mainly measures students’ competence in academic life and their interest in learning, etc., while academic achievement is one of the indicators of students’ academic adjustment [3]. Academic achievement can have an impact on adolescents’ mental health through their sense of self-worth and self-evaluation [4], e.g., lower academic achievement can make students tend to evaluate themselves more negatively, leading to a higher risk of depression [5]. Especially in the Chinese cultural context, academic achievement is given a very high value, and poor academic performance will not only bring a lot of pressure to adolescents, but also more likely to be considered as a failure and will face many difficulties in later life [6].

Peer relationships mainly measure whether adolescents have good relationships with their classmates. The increase in adolescents’ need for peer relationships makes adolescents sensitive to peer evaluations, and the desire to be recognized by their peers also receives varying degrees of hurt and the formation of poor peer relationships [7]. While adolescence is a critical period for the development and maturation of the individual’s brain emotion regulation function, exposure to a poor peer relationship environment will limit the development of the individual’s emotion regulation function and increase the risk of depression [8].

Routine adaptation mainly refers to students’ adaptation to school discipline and rules, and adolescents’ routine maladaptation is mainly manifested in a series of externalized problems, such as smoking, fighting, drinking, and absenteeism [9]. According to the self-determination theory [10], an individual’s behavior is the result of a combination of the external environment, the individual’s internal characteristics and past and present behaviors. As the most important external microenvironment for adolescents other than family, school, in addition to peer relationships, teacher-student relationships also have a significant impact on adolescents’ routine adaptation. Good teacher-student relationships can enhance students’ self-efficacy, self-esteem, and sense of belonging to the school, thus reducing students’ problematic behaviors [11] and achieving the purpose of enhancing students’ routine adaptation, but dependence on the teacher to a certain extent is also not conducive to students’ development of independence and autonomy [12], which in turn increases the probability of mental health problems.

School emotions and attitudes refer to students’ evaluations of school life and the emotional reactions caused by school life. It has been found that human emotional temperament, especially negative emotions, is partially hereditary and also related to an individual’s social experiences, and that students’ social experiences at school affect their emotional expression [13]. Making students more exposed to the risk of depression.

The above studies have indirectly demonstrated the link between school adjustment and depression, mainly through various measures of school adjustment, while others have directly shown the relationship between students’ school adjustment status and the subsequent development of depressive symptoms [14]. Both indicate that school maladjustment is a risk factor for depression, so do anxiety and stress, the factors most directly linked to depression, also have an impact on adolescents’ school adjustment? Are there gender differences in these traits?

Using cross-sectional data, this study investigated the relationship between school adjustment and anxiety, depression, and stress among adolescents in Xinjiang Uygur Autonomous Region, China. Most of the earlier studies measured adolescents’ school adjustment and their anxiety, depression, and stress status separately and did not take into account the internal heterogeneity and gender differences in adolescents’ school adjustment characteristics. Therefore, the present study used latent profile analysis to discover internal differences in adolescents’ school adjustment status and identify populations at risk for school maladjustment, and secondly, network analysis was used to establish network structure maps of school adjustment and anxiety, depression, and stress across genders separately, and to synthesize the associations between adolescents’ school adjustment and emotional symptoms, which could provide functional roles and the importance of specific symptoms in the maintenance of the disease provide new insights [15].

Research objects and methods

Research subjects

Multistage stratified cluster sampling method was used to select 4310 students from 10 middle schools in 5 places in Xinjiang Uygur Autonomous Region as the research subjects, and 3922 valid questionnaires were recovered, with an effective rate of 91.67%. Age 16.06 ± 0.982 years, of which 1884 (48.0%) were male and 2038 (52.0%) were female. The questionnaire distribution, recovery and data processing were in accordance with the principles of informed consent and data confidentiality of the subjects. All subjects participated in this study voluntarily, and in order to ensure the quality of the questionnaires, the subjects were assisted by the graduate students of the group and the teachers of the class in the process of filling out the questionnaires, and the process of administering the test was in accordance with the Declaration of Helsinki on the Ethical Principles of Human Subjects in Medical Research. This study was approved by the Ethics Committee of Xinjiang Medical University (approval number: XJYKDXR20240724003) in accordance with the principles of Helsinki Declaration.

Inclusion criteria

(1) secondary school students aged 12–17 years old; (2) no serious physical illnesses that prevented them from completing the questionnaire; (3) able to understand and complete the questionnaire; (4) agreed to participate and signed the informed consent form.

Exclusion criteria

(1) people who have been clearly diagnosed with mental disorders or who are undergoing psychotherapy; (2) people who refuse or ask to quit in the middle of the process; (3) people who fail to fill in the questionnaire data, including incomplete filling in, duplication of filling in, and careless filling in (contradictory choices and a large number of repeated choices, etc.).

Measurement tools

Depression-anxiety-stress simplified scale (DASS-21)

This study used the Depression-Anxiety-Stress Simplified Chinese version of the Depression-Anxiety-Stress Scale developed by Lovibond et al. in Australia in 1995 [16]. The scale consists of 21 questions and contains three dimensions: depression, anxiety, and stress. It has good reliability and validity in the child and adolescent population [17, 18]. The questionnaire adopts a 4-point scale, with 0 ~ 3 representing from “not conforming” to “very conforming”, and the level of score reflects the individual’s emotional level in terms of state and trait. The Cronbach’s alpha coefficient in this study was 0.940.

School adaptation scale

This study used the School Adaptation Questionnaire developed by China’s Cui Na in 2008 [19]. The scale consists of 27 questions containing five dimensions: teacher-student and peer relationships, school attitudes and emotions, academics, and routine adaptation. The questionnaire was scored on a 5-point Likert scale, with 1–5 representing “not at all” to “completely”, and a higher score of 0 indicating a higher level of school adjustment. The Cronbach’s alpha coefficient for this study was 0.922.

Data processing

First, to ensure the quality of the survey, all completed questionnaires were screened for inappropriate responses and missing information. Demographic information was analyzed descriptively using SPSS 27.0 software. Next, potential profile analysis was conducted using Mplus 8.3 software to identify potential categories of school adjustment in adolescents using the 27 questions of the School Adjustment Scale as exogenous variables in order to identify populations at risk for school maladjustment. Based on the initial model, the number of models was increased sequentially, and one to six potential category models were extracted for comparison. The evaluation indexes of potential profile models included Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Adjusted Bayesian Information Criterion (aBIC).The lower values of AIC, BIC, and aBIC indicated a better model fit. In addition, an information entropy > 0.8 (0 to 1) indicates a classification accuracy of more than 90%. Bootstrap Likelihood Ratio Test (BLRT) and Lo-Mendell-Rubin Likelihood Ratio Test (LMRT) were used to compare the fit of different classes of models. The class K model was considered superior to the class K-1 model if the p-value corresponding to the LMRT and BLRT reached the significant level. In addition, the mean attribution probability matrix was examined, and if all values on the diagonal were higher than 0.7, the classification of the model was indicated as acceptable [20].

Network analysis was then performed using Rstudio software in the R4.3.2 environment. The partial correlation network approach was used to estimate the full symptom network, with edges in the network representing symptom-adjusted associations. For the estimation of each partially correlated network, a Gaussian graphical model (GGMS) was first used to estimate the correlation coefficients between nodes. Since parameter estimation for all edges may result in an increased probability of Type 1 error, a graphical lasso [21] was used to create a cleaner network by reducing weak correlations to 0, providing network stability. The network graph was also constructed using the Fruchterman-Reingold algorithm, placing nodes with more or stronger connections in the center of the network graph and nodes with fewer connections at the outer edges of the network graph. The network was estimated and visualized using the R software “qgraph” package [22].

Strength, proximity and median were chosen for centrality analysis to quantify the characteristics of the nodes. “Strength” represents the total weight of node connections. “Proximity” is defined as the reciprocal of the sum of the shortest distances from a particular node to all other nodes in the network. “Median” is the number of times the shortest path between any two symptoms passes through another symptom. The “EBICglasso” and “qgraph” packages were used for the analysis.

Network Accuracy and Stability Estimates Edge accuracy and stability estimates for the network were calculated using bootnet [23]with 1000 iterations. Edge accuracy was checked using 95% confidence intervals (CIs) for bootstrap edge weights, with narrower edge weight CIs indicating higher accuracy. We then tested the stability of centrality using the correlation between the centrality index for the entire sample and the network index for the reduced number of cases, with a higher correlation between the original index and the index obtained from the downsampling indicating a greater tendency towards stability. The centrality stability coefficient (CS coefficient) was calculated as a reference index.CS coefficients [24] below 0.25 indicate a high degree of instability and are recommended to be greater than or equal to 0.5.

Results

General demographic information

Among the 3,922 adolescents who met the criteria included in this study, 1,884 were males, 1,597 were adolescents with urban household registration, 1,340 were adolescents with total monthly household income between 3,000 and 5,000 yuan, 3,190 were adolescents who were not only children, 1,557 were adolescents whose mothers’ education level was junior high school, and 1,607 were adolescents whose fathers’ education level was junior high school. See Table 1 for details.

Table 1.

Demographics of adolescents (n = 3922)

Variable		Number of examples	Component ratio
Age	—	16.06 ± 0.982	—
Genders	Male	1884	48.0
	Female	2038	52.0
Gross monthly household income	≦ 3000yuan	565	14.4
	3001-5000yuan	1340	34.2
	5001-8000yuan	1062	27.1
	8001-12000yuan	666	17.0
	> 12000yuan	289	7.3
Only child or not	Yes	732	18.7
	No	3190	81.3
Educational level of mothers	No education	58	1.5
	Secondary schools	608	15.5
	Junior high school	1557	39.7
	Secondary/Technical schools	287	7.3
	Vocational/High school	594	15.1
	University college	350	8.9
	University undergraduate course	440	11.2
	Graduate students and above	28	0.7
Educational level of fathers	No education	37	0.9
	Secondary schools	570	14.5
	Junior high school	1607	41.0
	Secondary/Technical schools	266	6.8
	Vocational/High school	720	18.4
	University college	339	8.6
	University undergraduate course	346	8.8
	Graduate students and above	37	0.9

Identification of adolescent school adjustment risk groups

A total of 4 potential categories were fitted in this study, and the model fitting indexes are shown in Table 2. aIC, BIC, and aBIC showed a decreasing trend, and both LMR and BLRT were significant, and the category with a sample size of 5% existed in the 4 categories, so it was excluded. Meanwhile, combining the practical significance and interpretability of the classification, the 3-category model was finally selected as the best fitting model. According to the classification characteristics, the three categories can be named as C1 poor school adjustment type (636, 16.22%), C2 average school adjustment type (1303, 33.22%), and C3 good school adjustment type (1983, 50.56%), and in the present study, the poor school adjustment type and the average school adjustment type were combined into the school maladjustment risk population, which consisted of 1,939 people, of which boys 957 and 982 girls.

Table 2.

Adolescent school adaptation potential profile model fit information

Categories	AIC	BIC	aBIC	Entropy	LMR(P)	BLRT(P)	Category probability (%)
1	341188.58	341527.39	341355.81	—	—	—	—
2	312204.30	312718.80	312458.25	0.949	P < 0.05	P < 0.05	36.59%/63.41%
3	304643.50	305333.68	304984.15	0.936	P < 0.05	P < 0.05	16.22%/33.22%/50.56%
4	301246.85	302112.71	301674.21	0.931	P < 0.05	P < 0.05	3.21%/46.48%/20.25%/30.06%

Common method bias test

Validation factor analysis was conducted using Mplus 8.3, and the results showed that the results of scale fitting indicators were χ2/df = 6.617, RMSEA = 0.054, 90% CI = 0.052–0.056, CFI = 0.917, TLI = 0.907, and SRMR = 0.042. The scale fitting indicators were all in accordance with the requirements, which indicated that the overall fit of the scale to the data was Good.

Statistical description and correlation analysis of adolescent school adjustment and anxiety-depression-stress

Adolescents’ school affect and attitudes, routine adjustment, academic adjustment, peer relationships, and teacher-student relationships were negatively related to anxiety, depression, and stress, respectively. See Table 3.

Table 3.

Descriptive statistics and correlation analysis of school adjustment and anxiety-depression-stress in adolescents

	M(P25,P75)	School Attitudes and Emotions	Routine adaptation	Academic adaptation	Peer relation	Teacher-student relation	Stress	Anxiety	Depression
School Attitudes and Emotions	28(23,32)	1
Routine adaptation	17(14,19)	0.670**	1
Academic adaptation	14(12,16)	0.589**	0.483**	1
Peer relation	22(18,25)	0.703**	0.649**	0.435**	1
Teacher-student relation	20(16,23)	0.713**	0.670**	0.570**	0.659**	1
Stress	5(2,8)	0-0.459**	-0.356**	-0.376**	-0.404**	-0.450**	1
Anxiety	4(1,7)	-0.433**	-0.350**	-0.349**	-0.403**	-0.442**	0.817**	1
Depression	4(1,8)	-0.510**	-0.390**	-0.434**	-0.459**	-0.502**	0.812**	0.760**	1

** Significant correlation at the 0.01 level (two-tailed)

Symptom network analysis of adolescent school maladjustment risk groups

The results of the network analysis are shown in Fig. 1. The results showed that the school adjustment network of the adolescent school maladjustment risk group had the greatest weight of connections between SA2 (Routine Adjustment) and SA4 (Peer Relationships) (0.27), followed by SA1 (School Emotions and Attitudes) and SA4 (Peer Relationships) (0.25). In the results of centrality analysis, school adaptation SA1 (school emotions and attitudes) had the largest expected impact index (0.88) and the highest proximity to centrality (0.88), suggesting that SA1 (school emotions and attitudes) was the core symptom of the network.

Fig. 1.

Network diagram of school adjustment symptoms for school maladjustment risk groups. Note: each node in the graph represents a dimension; each edge represents the association of the node edges, the thickness of the edge is the strength of the association; the color of the edge, blue represents the positive correlation of two continuity variables, and the red dotted line represents the negative correlation of two continuity variables

Centrality analyses revealed that in the symptom network of the male school maladjustment risk group, SA1 had the highest expected impact index (0.86), intensity (0.86), and proximity to centrality (0.05), and was the core symptom of the network. Similarly, in the symptom network of the female school maladjustment risk group, SA1 was the core symptom with the highest expected impact index (0.86) and had the highest degree of centrality (0.86) and proximity to centrality (0.05). Finally, in terms of overall network structure, the school maladjustment risk group (mean weight of 0.164), the male school adjustment risk group (mean weight of 0.167), and the female school adjustment risk group (mean weight of 0.161) had essentially the same network structure, with item 1 of school adjustment (school emotions and attitudes) being the core symptom common to all three networks, but there were differences in the strength of connectivity among nodes in the three networks. there were differences in the strength of connections between the three network nodes, see Fig. 2.

Fig. 2.

School Adaptation Centrality Map for School Maladaptation Risk Groups. Note: Centrality indicators are presented as standard Z-scores, with expected impacts listed in descending order of degree centrality

In the symptom network of the school adjustment risk group of male adolescents, the connection weight between SA2 (routine adjustment) and SA5 (teacher-student relationship) was the largest (0.29), followed by SA1 (school emotions and attitudes) and SA2 (0.26), see Fig. 3. In the symptom network of the school adjustment risk group of female adolescents, the connection weight between SA2 and SA4 (peer relationship) was the largest ( 0.28), followed by SA1 vs. SA4 (0.25), see Fig. 4.

Fig. 3.

Network diagram of school adjustment symptoms in the male school maladjustment risk group

Fig. 4.

School adjustment symptom network diagram for female school maladjustment risk groups

Symptom network analysis of school adjustment with anxiety, depression and stress in adolescent school maladjustment risk group

The results of the symptom network analysis of school adjustment with anxiety, depression and stress in the adolescent school maladjustment risk group respectively showed that in the symptom network of school adjustment with anxiety, the connection weight between SA2 (routine adjustment) and AN4 (social anxiety) was the largest (0.07), and there was a positive correlation between the two, whereas the connection weights of SA4 (peer relations) and AN7 (fear without reason) in their negative correlation networks connection weight (-0.08); in the symptom network of school adjustment and depression, SA3 (academic adjustment) and DE3 (hopelessness) of school adjustment had the largest connection weight (-0.07), followed by SA5 (teacher-student relationship) and DE2 (motivational deficits) (-0.06); and in the symptom network of the negative correlation of school adjustment and stress, SA3 (academic adjustment) had the largest connection weight (-0.07) with ST1 (activity excess), SA1 (school emotions and attitudes) with ST6 (lack of patience), and SA2 (routine adaptation) with ST2 (sensitivity) had the largest connection weights (all − 0.04), while in its positive correlation network, SA4 (peer relationships) had the largest connection weight with ST6 (lack of patience) (0.03), see Figs. 5 and 6.

Fig. 5.

School Adjustment Centrality Maps for School Maladjustment Risk Groups by Gender

Fig. 6.

Network diagram of school adjustment and anxiety, depression, and stress symptoms in school maladjustment risk groups. Note: Each orange node in the graph represents a dimension; each node of the other colors represents the corresponding symptom respectively, each edge represents the association of the node edges, and the thickness of the edge is the strength of the association; the color of the edges, blue represents the positive correlation of the two continuity variables, and the red dotted line represents the negative correlation of the two continuity variables

Centrality analysis revealed that ST4 (Restlessness) of stress in the Anxiety-Depression-Stress scale had the greatest intensity (1.13) with the highest expected impact index (1.12) and was the core symptom of the entire network, see Fig. 7.

Fig. 7.

Centrality plot of school adjustment with anxiety, depression, and stress for school maladjustment risk groups

After grouping by gender, the results of comparative analysis of school adjustment and anxiety, depression and stress symptom networks of school maladjustment risk groups of different genders showed that both networks of men and women formed two node clusters, and that there were multiple connectivity lines between different types of school maladjustment risk groups and different symptoms of anxiety-depression-stress. In the positively correlated symptom network of school adjustment and anxiety in the male school maladjustment risk group, the greatest weight of the connection was between SA2 (routine adjustment) and AN4 (social anxiety) (0.01), and in the negatively correlated network, the greatest weight of the connection was between SA4 (peer relationships) and AN7 (fear for no reason) (-0.07); similarly, in the positively correlated symptom network of school adjustment and anxiety in the female school maladjustment risk group, there were multiple connections between the different types of school adjustment and anxiety-depression-stress. correlation symptom network, the largest connection weight was between SA2 (Routine Adjustment) and AN4 (Social Anxiety) (0.04), and in the negative correlation network, SA4 (Peer Relationships) was connected to AN7 (Fear for No Reason) (-0.07), as shown in Figs. 8 and 9.

Fig. 8.

Network diagram of school adjustment and anxiety, depression, and stress symptoms in the male school maladjustment risk group

Fig. 9.

Network diagram of school adjustment and anxiety, depression, and stress symptoms in the female school maladjustment risk group

In the school adjustment and depressive symptoms network of the male school maladjustment risk group, SA4 (peer relationships) had the largest connection weight with DE6 (self-depreciation) (-0.08), followed by SA5 (teacher-student relationships) and DE5 (loss of interest) (-0.06); whereas in the school adjustment and depressive symptoms network of the female school maladjustment risk group, SA3 (academic adjustment) had the largest connection weight with DE2 (motivational deficits) connections had the greatest weight (-0.08), followed by SA4 (peer relationships) with DE6 self-depreciation) (-0.07). In the school adjustment and stress symptom network of the male school adjustment risk group, SA2 (Routine Adaptation) had the largest connection weight with ST7 (Irritability) (-0.05), whereas in the school adjustment and depression symptom network of the female school adjustment risk group, SA3 (Academic Adaptation) had the largest connection weight with ST7 (Irritability) (-0.08).

Centrality analysis revealed that ST5 (Difficulty relaxing) was the core symptom in the symptom network of school adjustment and anxiety, depression and stress in the male school maladjustment risk group, which had the highest index of expected impact (1.14) with the greatest intensity (1.16). In women, DE4 (depression) had the highest expected impact index (1.06) and the highest intensity (1.10) and was the core symptom of the total symptom network in women, as shown in Fig. 10.

Fig. 10.

Centrality plot of school adjustment and anxiety, depression, and stress for school maladjustment risk groups by gender

Discussion

In recent years, research on school adjustment and adolescent anxiety, depression and stress has been growing year by year, providing valuable theoretical and empirical research experience. However, there are still some shortcomings in the research at this stage, which is limited to the relationship between school adjustment and two of the three variables of anxiety, depression and stress, and fails to consider these three variables together. In this study, using the adolescent school maladjustment risk population as the research population, we explored the relationship within school adjustment and between it and anxiety, depression and stress respectively, as well as gender differences through network analysis.

Interconnections between symptoms and gender differences in adolescent school maladjustment risk groups

First, this study found that “school emotions and attitudes” (Expected Impact Index: 0.86) was the core symptom in the symptom network of the adolescent school maladjustment risk group, and that the structure of the symptom network differed by gender. The affective domain is recognized as an important component of student literacy and individual sustainability in the Program for International Student Assessment test [25]. When students hold better attitudes and stronger emotions toward school, they will participate more actively in learning and activities, take the initiative to establish good relationships with teachers and classmates, and in the process gradually form a sense of identity with the school and school culture, which is conducive to enhancing the level of adolescents’ school adjustment, thus better promoting their own development. Some studies have confirmed that students who feel close to school are more adjusted to school life [26], which in turn plays a proximate and long-term role in adolescents’ mental health [27]. Therefore, this study suggests that schools and teachers should strive to “educate without traces” and “emotionally penetrate”, and if they focus on cultivating students’ emotions, attitudes, and values, and stimulating students’ inner motivation, they may be able to adapt their psychological state to the school environment through students’ actions. Environment.

Second, the highest correlation was found between “routine adaptation” and “peer relationship” (linkage weight: 0.27), which may indicates that adolescents’ adaptation to school routines and peer relationships have a bi-directional connection. Numerous studies have shown that good school adjustment helps adolescents to establish positive and friendly peer relationships and social skills [28], which enables them to better cope with challenges and difficulties in learning and life, and reduces psychological pressure [29]. In contrast, poor peer relationships have been shown to not only increase adolescents’ problematic behaviors, such as rule-breaking behaviors [30], which are strongly associated with many psychopathological symptoms. This result suggests that negative peer relationships may are an important stressor leading to maladaptive outcomes [31]. When individuals face stress or threats, well peer relationships may provide individuals with some moral or material resources [32], which can help individuals buffer their bad feelings and reduce their rule violations.

In addition, the correlation between the core symptom “school emotions and attitudes” and “peer relationships” (linkage weight: 0.25) was also strong. Peer relationships influence adolescents’ psychological and behavioral characteristics, and good peer relationships help individuals build strong social networks, provide a supportive environment for emotion regulation, and promote the use of positive emotion regulation strategies and the resolution of negative emotions [33]. This suggests that adolescents may be largely influenced by their peer relationships, and therefore adolescents’ establishment of good peer relationships may enable them to hold positive feelings and attitudes towards school, thereby increasing their level of school adjustment.

Finally, in the male symptom network, “routine adaptation” was most strongly correlated with “teacher-student relationships (connection weight: 0.29)”, whereas in the female symptom network, “routine adaptation” was most strongly correlated with “peer relationships” (connection weight: 0.28). " had the strongest correlation with “peer relationships” (connection weight: 0.28). It can be seen that the female and overall symptom profiles are closest to each other, with differences in the male, female and overall network profiles. This is consistent with the findings of developmental psychology showing that boys mature later both physically and psychologically compared to girls [34]; in interpersonal interactions, girls are more likely to express emotions, while boys are impulsive and prone to interpersonal problems. In the teacher-student relationship, teachers’ gender stereotypes lead to differences in their management of students [35], with teachers more likely to use gentle management for girls who make mistakes, while teachers may be more inclined to take a tougher approach to boys’ mistakes, and internal imbalance may be the reason for boys’ behavior of ignoring school rules and regulations [36]. Meanwhile, there are also studies confirming that males have a higher detection rate of behavioral problems than females [37], and gender may affect adolescents’ behavioral problems [38]. Boys’ level of school adjustment may increase if they have good teacher-student relationships with their teachers and are more compliant with school rules and regulations. Girls, on the other hand, are more emotionally sensitive and delicate compared to boys, which may suggest that girls are more prone to indiscipline and other undesirable behaviors when their peer relationships are strained.

Interconnections between school adjustment and symptoms of anxiety, depression and stress in adolescents at risk of school maladjustment, and gender differences

First, the core symptom in the symptom network of school adjustment and anxiety, depression, and stress among adolescents at risk for school maladjustment was “uneasiness” (expected impact index: 1.12). Several studies have pointed out that students’ feelings of uneasiness and tension due to stress at school [39] may lead to serious negative psychological problems [40], which in turn are closely related to students’ school adjustment. Some studies have pointed out that group activities can help individuals gain a sense of security [41], which can reduce their emotions such as uneasiness. Therefore, it is recommended that schools strive to improve students’ knowledge, ability, and literacy through various group activities, which may achieve the purpose of improving students’ school adjustment. When grouped by gender, the structure of the symptom network differed between genders. Among males, “difficulty relaxing” (expected impact index: 1.14) was the core symptom; among females, “depression and frustration” (expected impact index: 1.06) was the core symptom. The reason for this differential result may be the early neurological differences between boys and girls [42]. It has been noted that boys suffer from more severe emotional problems such as withdrawal, depression, and obsessive-compulsive symptoms compared to girls [43]. Inhibitory temperament in boys is further manifested by behavioral restraint, withdrawal and avoidance of novel stimuli. This may suggest that symptoms of relaxation difficulties in boys are critical for their school adjustment.

Second, in the symptom network of school adjustment and anxiety in adolescents at risk for school maladjustment, the strongest positive correlations were found between “routine adjustment” and “social anxiety” (linkage weight: 0.07), whereas “peer relationships” and “anxiety” were the most strongly correlated. " and “fear for no reason” (connection weight: -0.08) had the strongest correlation in their negative network. Several studies have shown that social anxiety is one of the main stressors that contribute to the development of depressive symptoms in adolescents [44]. The present study indicated that students with social anxiety may have a direct impact on the way they perceive themselves or others and the way they cope with school rules and regulations, and students with routine maladjustment may also develop social anxiety. In addition, it has been suggested that adolescents’ frustration and unexplained fear arising from the fear of failing others are caused by adolescents’ internal problems [45], and in particular may be caused by adolescents’ self-esteem, and that the more strained an individual’s peer relationships are likely to be when their level of self-esteem has declined [46], which is consistent to some extent with the results of the present study. When grouped by gender, the positive correlations between the symptom networks of school adjustment and anxiety and the total symptom network were strongest for “routine adjustment” and “social anxiety” for adolescents at risk for school maladjustment by gender, but were more pronounced in the symptom networks of females. The aforementioned social anxiety of students at school is one of the hallmarks of school maladjustment, and its may also be one of the causes of their routine maladjustment, and social anxiety has been shown to be significantly higher in females than in males [47], further validating the results of this study.

Then, in the symptom network of school adjustment and depression in the adolescent school maladjustment risk group, the strongest correlation was between “academic adjustment” and “hopelessness” (connection weight: -0.07), and the second strongest correlation was between “teacher-student relationship” and “lack of motivation” (connection weight: -0.06), both of which were negatively correlated. " had the second strongest correlation with “lack of motivation” (connection weight: -0.06), both of which were negatively correlated. Students who are underperforming academically are more likely to be deep in hopelessness or despair, which can lead to more complex mental health problems [48], including depressive symptoms, high anxiety, irritability, or apathy [49]. In a study of children in grades three to six, it was found [50] that poor teacher-student relationships were associated with children’s internalization problems, which, in turn, can lead to problems such as lack of motivation to learn, difficulty concentrating on class, and reluctance to learn. Therefore, at the individual, family, and school levels, attention should be paid to adolescents’ do-it-yourself education, personality education, and mental health education to enhance their academic adjustment. After grouping by gender, the symptom networks of school adjustment and depression in adolescent school maladjustment risk groups of different genders differed significantly from the total symptom network, each with different characteristics. In the male symptom network, “peer relationships” had the strongest negative correlation with “self-depreciation” (connection weight: -0.08), whereas in the female symptom network, “school adjustment” had the strongest negative correlation with In the female symptom network, the strongest negative correlation was found between “academic adjustment” and “motivation deficit” (linkage weight: -0.08), followed by “peer relationships” and “self-depreciation” (linkage weight: -0.07). It can be seen that both “peer relationships” and “self-depreciation” are significantly present in both male and female symptom networks. The results of this study are supported by the fact that the quality of students’ peer relationships can effectively predict their level of school adjustment, and that individuals with low levels of school adjustment are prone to low self-esteem and the tendency to use self-depreciating strategies [51]. In the female symptom network, the stronger correlation between “school adjustment” and “motivation deficit” may be related to the fact that females amplify their negative emotions due to negative events compared to males, which may lead to a decrease in their motivation to learn, and thus to a decrease in academic progress, grades, and academic performance. Leading to a decline in academic progress, grades, and learning outcomes.

Finally, in the network of negatively correlated symptoms of school adjustment and stress, “academic adjustment” was associated with “hyperactivity,” “school emotions and attitudes” was associated with “lack of patience,” and “lack of motivation. Patience” and “Routine Adaptation” and “Sensitivity” (all with a connection weight of -0.04) had the strongest negative correlations, while in their positive correlation networks, “Peer Relationships” and “Patience” had the strongest negative correlations. In the positive network, “peer relationships” and “lack of patience” (connection weight: 0.03) had the strongest correlation. Individuals who hold negative feelings and attitudes toward school are prone to exhibit poor academic performance, anxiety, nervousness or restlessness, lack of patience, depression, conflicting behaviors, and hyperactivity symptoms [52]. Studies have shown that both environmental and anxiety sensitivities [53] are associated with strong reactions to routine adaptation and that individuals with higher sensitivities are more negatively affected by adverse environments, thus students’ routine adaptation may is strongly negatively correlated with sensitivities. In contrast, adolescents with strong school adaptation may were more likely to develop good habits in peer relationships, academic and learning environments, were less prone to negative emotions such as anxiety, stress, and lack of patience, and had higher levels of overall mental health. When grouped by gender, “routine adaptation” had the strongest negative correlation with “irritability” in the male symptom network, while “academic adaptation” had the strongest negative correlation with “irritability” in the female symptom network. In the female symptom network, “academic adjustment” had the strongest negative correlation with “irritability”. In a study of adolescents with disorders involving irritability, males and females differed in prevalence, with gender differences usually occurring at specific developmental stages, with females experiencing higher rates of first mood disorders during adolescence [54], and females having higher levels of irritability than males in late adolescence. Female irritability may be more likely to lead to deterioration in academic progress, grades, and learning outcomes, which may account for the strongest correlation between “academic adjustment” and “irritability” in females. It has been reported that emotional dysregulation is a stronger predictor of behavioral problems [55], that there is a significant negative correlation between emotion regulation and externalizing behavioral problems, and that the association between irritability and externalizing symptoms is twice as strong as the correlation between irritability and internalizing symptoms [56]. The aforementioned more pronounced externalizing emotional traits in males compared to females and the fact that males are more likely to experience routine maladjustment problems such as ignoring school rules and regulations may explain the differences in the symptom network between males and females.

Theoretical and practical implications

The results of this study support not only the psychological stress theory and the cognitive theory of depression, but also the interpersonal model of school adjustment and the theoretical model of school adjustment. The relationship between school adjustment and symptoms of anxiety, depression and stress, respectively, in different gender adolescent school maladjustment risk groups was revealed.

Based on the results of this study, the following three aspects can be considered in future intervention practices for adolescents with school maladjustment. First, “restlessness” may be the most common anxiety disorder among adolescents, thus suggesting that cognitive, psychological, and adolescent emotional counseling be provided and that teachers or peers be included in the process to provide symptomatic treatment for the causes of “restlessness”. Secondly, for male adolescents, the focus should be on the causes of the “disturbed” mood disorder. Secondly, for male adolescents, the focus can be on traits related to externalizing emotions, especially “difficulty relaxing”, while for female adolescents, the focus can be on traits related to internalizing emotions, especially “depression”. The present study suggests that a comprehensive psychometric evaluation should be considered for adolescents of different genders, and then differentiated interventions may be more effective.

Limitations and prospects

There are a number of limitations to this study. First, the present study explored the interrelationships between school adjustment and symptoms of anxiety, depression, and stress in only a subset of adolescents (i.e., middle school students at risk for school maladjustment), but rarely explored how these symptoms interacted with each other. Second, due to the size and representativeness of the sample, the applicability of the findings to other environmental and cultural groups is also an important issue; third, the present study was cross-sectional and could not draw a causal relationship, and future research should use longitudinal tracing to explore the process of symptom development in school adjustment. Fourth, the adolescents’ symptoms of anxiety, depression, and stress in this study were based on self-report data, which may be subject to recall bias and social desirability bias. Finally, although the present study explored the symptom network of anxiety, depression, and stress in a group at risk for school maladjustment, individual moods of anxiety, depression, and stress were similarly affected by a variety of variables, and future research should continue to delve in this direction.

Author contributions

Study design and manuscript drafting by Sufeila Shalayiding and Meng Weicui . Data collection by Sufeila Shalayiding, Meng Weicui, Xiaoting Wang, and Bahedana Sailike. Data analysis and interpretation: Sufeila Shalayiding, Meng Weicui, Xiaoting Wang, and Bahedana Sailike. Critical revision of the manuscript by Ting Jiang. All authors approved the final version for publication.

Funding

This study was supported by the Xinjiang Uygur Autonomous Region “14th Five-Year Plan” Higher Education School Characteristic Discipline of Public Health and Preventive Medicine, and the National Natural Science Foundation of China (82360669) and the Key Laboratory of Population Health and Eugenics of Anhui Province (JKYS20231).

Data Sharing and Declaration.

Due to the privacy of the adolescents involved in the data of this study and the related confidentiality agreements that have been signed with the schools, we may not be able to provide the raw data. We have fully described the study design, analyses and results, as well as the process of data analysis and processing. If readers have questions about specific data, we will endeavor to provide more detailed explanations and descriptions.

Data availability

Thank you very much for the editor’s attention and recognition of our research work, we appreciate the BMC Public Health journal’s requirements for data sharing, however, due to the fact that our data involves the privacy of adolescents and we have entered into a relevant confidentiality agreement with the university, we may not be able to provide the raw data. We have fully described the study design, analyses and results, as well as the process of data analysis and processing. If editors and reviewers have questions about specific data, we will endeavor to provide more detailed explanations and clarifications.

Declarations

Human ethics and consent to participate

Informed consent was obtained from the parent or legal guardian of each participant under 16 years of age. And in accordance with the principles of the Declaration of Helsinki, this study was approved by the Ethics Committee of Xinjiang Medical University (approval number: XJYKDXR20240724003).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.