The impact of primary mental healthcare on core symptoms of depression among underrepresented adolescents: A network analysis perspective

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Despite the World Health Organization's promotion of primary mental healthcare as a critical solution, its real-world effectiveness in low- and middle-income countries remains debated due to high costs and insufficient follow-up. This study aimed to explore the impact of primary mental healthcare on the core symptoms of adolescent depression using network analysis, while examining the influence of demographic factors such as gender, age, and family support, to identify more precise and targeted intervention strategies, improving its effectiveness. Methods: A citywide, multi-center, longitudinal cohort study was conducted in Nanchong, Sichuan Province, China, involving 73,750 adolescents (34,606 girls and 39,144 boys) with median age of 14.00 years old. The Comprehensive Primary Healthcare for Adolescents Program (CPHG) involved two rounds of psychological screening and early intervention. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D). Network analysis was employed to map the interrelations between depressive symptoms and evaluate the intervention's impact. Results: The CPHG program significantly reduced CES-D median scores from 6.00 to 2.00 (p < 0.001). Network analysis revealed changes in the structure and centrality of depressive symptoms post-intervention, with specific symptoms such as sadness (C18) showing consistent reductions across subgroups. Gender disparities were evident, with female adolescents exhibiting stronger symptom interconnectivity. Junior high school students also demonstrated a more robust symptom network compared to senior high school students. Adolescents living in social welfare institutions exhibited higher global expected influence of depressive symptoms than those living with both parents. Conclusions: Primary mental healthcare interventions effectively modify the network structure of depressive symptoms in adolescents, with specific symptoms like sadness being critical targets for intervention. Gender and grade-level differences highlight the need for tailored mental healthcare strategies. The findings underscore the importance of addressing both core and peripheral symptoms to enhance treatment efficacy and reduce the severity and recurrence of depression among underrepresented adolescents. primary mental healthcare depression underrepresented adolescents network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Depression has emerged as one of the leading contributions of the global mental health burden, significantly threating to adolescent psychological well-being ( 1 – 4 ). According to the World Health Organization, depression is the primary cause of disability-adjusted life years (DALYs) in adolescents aged 10 to 19 ( 1 ). Furthermore, adolescence—critical period marked by significant physical and mental development—is characterized by profound changes in physiology, brain function, and social relationships, which increase the risk of instability and heighten the incidence of depression during this stage ( 5 – 8 ). Currently, the global prevalence of adolescent depression is rising at an alarming rate ( 2 , 9 ). The growing mental health crisis among adolescents requires strong policy support. In response, the World Health Organization (WHO) has advocated primary mental healthcare as a key strategy to address this issue ( 10 , 11 ). However, despite the decades of implementation, some studies indicate that the impact of primary mental healthcare has fallen short of expectations ( 12 , 13 ). One major factor is the severe shortage in supply-side resources, which significantly hampers the effectiveness of primary mental healthcare. According to the World Health Organization’s Mental Health Atlas, the ratio of psychiatrists or mental health professionals to the population is as low as one per 100,000 individuals ( 14 ). Furthermore, in many low- and middle-income countries, mental health expenditure constitutes less than 1% of the total health budget. This inadequate allocation of resources directly constrains the implementation of primary mental healthcare programs, significantly undermining their anticipated effectiveness ( 11 , 15 ). On the other hand, demand-side challenges, such as limited awareness of depression as a treatable condition, as well as stigma and social exclusion associated with reduced help-seeking behavior, have also contributed to the suboptimal effectiveness of primary mental healthcare ( 16 ). Patel emphasizes that in low- and middle-income countries, social exclusion and stigma not only reduce patients' willingness to seek treatment but also weaken their family and community support systems, further undermining the sustained effectiveness of primary mental healthcare ( 17 , 18 ). Moreover, the variations in the effectiveness of primary mental healthcare can be largely attributed to individual heterogeneity in treatment responses. For instance, while some symptoms, such as sleep problems, may improve, others, such as hopelessness, may worsen within the same patient, resulting in no observable change in overall depressive levels ( 19 , 20 ). Therefore, it is of significant interest to evaluate the real-world effectiveness of primary psychological healthcare and to explore the mechanisms underlying these heterogeneous treatment responses ( 11 , 21 ). Given the high interdependence and mutual influence of depressive symptoms ( 20 ), a network approach to analyzing intervention effects is particularly advantageous. Recent studies employing network analysis have uncovered complex relationships among core symptoms of depression, demonstrating how mental healthcare interventions can modify these networks and potentially inform more effective treatment strategies ( 22 , 23 ). These disparities in the real-world effectiveness of primary psychological healthcare may stem from variations in the core symptoms of depression associated with different demographic factors ( 24 ). Demographic differences, including gender ( 25 ), grade level ( 26 ), and parenting style ( 27 ), may read to distinct structural characteristics of depressive symptom networks. Fried et al. ( 28 ) found that in females, the depressive symptom network demonstrates stronger connections between affective symptoms (e.g., sadness and hopelessness) and somatic symptoms (e.g., fatigue and sleep disturbances). In contrast, in males, cognitive symptoms (e.g., self-criticism and feelings of worthlessness) tend to occupy a more central role within the network ( 29 ). Furthermore, research indicates that the network centrality of depressive symptoms in adolescents may vary with advancing grade levels ( 26 ). Parental and family factors also emerge as critical determinants of mental health outcomes among adolescents. A substantial body of evidence highlights the protective role of supportive family environments in reducing the risk of mental health disorders and improving the efficacy of therapeutic interventions ( 30 , 31 ). Adolescents who perceive strong familial support are more likely to utilize mental health services and adhere to treatment plans, leading to more favorable outcomes. Conversely, adolescents from dysfunctional or unsupportive family environments often encounter significant barriers to accessing and benefiting from mental healthcare. This study aims to examine the interplay of gender, age, and family support in shaping the effectiveness of primary mental healthcare for adolescents. Applying a comprehensive analytical framework, this study seeks to clarify the differential impacts of these factors and offer evidence-based recommendations to improve mental health service delivery for this vulnerable population. In summary, although primary psychological healthcare has been widely implemented, its efficacy in addressing the complex symptom structure of depression and the influence of key demographic factors among adolescents from low-income regions facing challenging circumstances remain underexplored. This study aims to address this gap by utilizing network analysis to investigate the complex relationships between core symptoms of depression and the effects of primary mental health interventions. By focusing on the unique challenges faced by adolescents, such as gender disparities, age, and family support, this study aims to enhance understanding of how these factors shape treatment outcomes. Ultimately, the study aims to offer insights that could inform more effective and sustainable mental healthcare strategies for adolescents. Materials and Methods Healthcare procedure The healthcare was conducted in Nanchong, Sichuan Province, China, as a citywide, multi-center, population-based longitudinal cohort study. The primary objective was to assess the real-world effects of the Comprehensive Primary Healthcare for Adolescents Program (CPHG) on mitigating the risk of depression in adolescents ( 32 ). Nanchong, a city with a mid-to-low economic profile in western China, was selected as the study site to represent the typical challenges faced by economically disadvantaged regions. The CPHG program ( 32 ) established 385 healthcare service centers and social welfare institutions across Nanchong to provide comprehensive primary mental healthcare coverage for middle and high school students. The program focused on depression screening to assess the mental health status of local adolescents. The screening process involved general testing followed by clinical assessments, psychological interventions, and medical treatments for those identified as at risk for severe depression. The CPHG system adopted multiple projects to ensure its implementation, including the "2 + 2” psychological healthcare practice, psychological healthcare education, psychological healthcare training, and psychological healthcare management ( 32 ). The core initiative was the "2 + 2” psychological healthcare practice, with two rounds of psychological screening and two rounds of early psychological care ( 32 ). All enrolled children and adolescents were initially screened for depressive symptoms by the Center for Epidemiological Studies-Depression Scale (CES-D) ( 33 ) as part of the first round of psychological screening. The second round of psychological screening focused on individuals identified as at risks for depressive symptoms during the first round. Following the two rounds of screening, individuals identified with severe depression received two rounds of specific psychological care (the latter "2” in the "2 + 2” workflow). In the subsequent psychological healthcare phase, children and adolescents identified with depression underwent the first round of psychological care, which was administered by qualified psychological healthcare specialists. A subset of these children and adolescents was referred to government-sponsored mental health center for clinical medical treatments based on specialist recommendations, constituting the second round of the latter “2”. Study design and participants Participants were selected from the CPHG program, which included a large-scale, multi-center cohort (n = 249,772) ( 32 ), to examine the effects of psychological care on depression among underrepresented children. For this analysis, data from the first and second rounds of screening were used for statistical analysis. After excluding participants with incomplete demographic information, the final matched dataset comprised 73,750 adolescents (34,606 girls and 39,144 boys). The median age of participants was 14.00 years (interquartile range [IQR]: 3.00 years). Measure Demographic information was collected as part of the mental health screening process. Depressive symptoms were assessed using the Chinese version of the Center for Epidemiologic Studies Depression Scale (CES-D) ( 33 ). The CES-D is a brief self-report scale developed to measure depressive symptoms in the general population ( 34 ). The scale comprises 20 items, each rated on a 4-point scale ranging from "0" (rarely or none of the time) to "3" (most or all of the time), with items 4, 8, 12, and 16 reverse-scored. Total scores range from 0 to 60, with higher scores indicating more severe depressive symptoms ( 33 ). In this study, the CES-D demonstrated excellent internal consistency, with a Cronbach's α coefficient of 0.95, and showed good construct validity. Statistical analyses Descriptive statistics For descriptive statistics, the median, interquartile range (IQR), kurtosis, and skewness were calculated for the CES-D. Based on the normal distribution of the variables, the Wilcoxon signed-rank test was employed to assess whether a significant overall change in depression scores occurred following the intervention. Demographic characteristics and descriptive statistics were analyzed using IBM SPSS Statistics 25.0 ( 35 ). Network estimation To explore the underlying mechanisms of depression and identify critical targets for intervention, network analysis was used to map the interrelationships between individual depressive symptoms ( 36 ). All network analyses were conducted using R scripts ( 37 ) in RStudio (Version 4.2.2) ( 38 ). The network was estimated using the Gaussian Graphical Model (GGM) and constructed with the Least Absolute Shrinkage and Selection Operator (LASSO) method, applying a penalty parameter to achieve sparsity and uses the Extended Bayesian Information Criterion (EBIC) to determine the optimal set of factors for each node ( 39 , 40 ). The network comprises "nodes" and "edges," with each symptom represented as a node and the correlation between two symptoms depicted as an edge ( 36 ). After connecting each node to several other nodes, the network is automatically constructed, displaying the strength of direct relationships between nodes. In the network graph, nodes that are more frequently and strongly associated with other nodes are positioned at the center, and the strength of associations between nodes is indicated by the thickness of the edges ( 41 ). Network centrality Following previous research, we mainly used the expected influence centrality indices to represent the importance of individual symptoms within the model. Expected Influence measures a node's overall impact in a network by summing the weights of its connections to other nodes, considering both positive and negative effects, with higher values indicating greater centrality within the network ( 42 ). This analysis was performed using the "qgraph" ( 41 ) package, and the results are visualized as standardized scores (z-scores). Centrality stability tests According to the recommendations of Bringmann et al. ( 43 ), we assessed the robustness of the network solution by estimating the accuracy of edge weights and the stability of centrality indices using the R-package "bootnet" ( 44 ). We employed non-parametric bootstrapping to calculate 95% confidence intervals for the accuracy of edge weights. Wider confidence intervals indicate lower precision in edge estimates, whereas narrower intervals suggest higher network reliability. Further, we conducted 1000 case-dropping bootstrap samples, calculating correlation stability coefficient (CS-C) to evaluate the stability of centrality indices through subset bootstrapping. If the centrality indices of a node do not change significantly after removing a subset of samples from the dataset, the network structure is considered stable ( 44 ). The CS-C should not fall below a certain threshold (i.e., 0.25) to ensure stability. Time-variance and subgroup analyses After confirming the network's stability, we performed time-variance and subgroup analyses to examine intervention effects and demographic disparities. We focused on changes in the expected influence of the depression symptom network before and after the primary mental healthcare intervention as well as the subgroup differences. Using the "Network Comparison Test" (NCT) ( 45 ) package in R, we applied a permutation test with 1,000 iterations to assess differences. We built and compared symptom networks through 1,000 bootstrap resamples to derive the null distribution of network differences, maintaining a significance level of 0.05 (corrected by false discovery rate (FDR) correction). The NCT evaluates within-participant differences across intervention in three main areas: ( 1 ) global expected influence, which is the total of nodes’ direct or potential indirect effects in the network, ( 2 ) structural invariance, which looks at significant changes in relationships between variables and nodes, and ( 3 ) edge and centrality invariance, which focuses on changes of specific edges or nodes centrality indices. However, as for subgroup network comparisons, we mainly concentrated on ( 1 ) global expected influence and ( 2 ) structural invariance. Results Sample characteristics All demographic variables are presented in Table 1 . Of these, 46,027 (62.4%) were junior high school students, and 27,723 (37.6%) were senior high school students. In terms of family support, 25,712 live with both parents, 23,743 live with one parent, 23,600 live with other relatives, and 695 live in a social welfare institute. Results of CHPG showed significant effects of practicing this system on preventing depression among individuals; the CES-D median score decreased from 6.00 to 2.00 (p < 0.001) compared to the first round. Table 1 Sociodemographic characteristics of the population enrolled in this study Variables N (73750) % Median IQR Age 73750 14.00 3.00 Gender female 34,606 46.9 male 39,144 53.1 Grade Junior students 46,027 62.4 Senior students 27,723 37.6 CES-D First round 6.00 14.00 Second round 2.00 8.00 Living status with parents Living with both parents 25,712 34.9 Living with one parent 23,743 32.2 Living with other relatives 23,600 32.0 Living in the social welfare institute 695 0.9 Note: N, number of valid samples; IQR, interquartile range; CES-D: Center for Epidemiologic Studies Depression Scale; SD, standard deviation. Network analysis Network estimation and visualization The network of depressive symptoms following two rounds across primary mental health interventions is displayed in Fig. 1 , with detailed edge weights provided in Supplementary Table 1S and 2S. Centrality indices, including strength, betweenness, closeness, and expected influence were calculated for the symptom networks at both time points, and comparisons of these values is illustrated in Fig. 2 . In alignment with previous research, which emphasizes the reliability of symptom rankings based on centrality measures, our analysis primarily focuses on symptom expected influence as the indicator of the symptom’s global importance within the network. Accordingly, interpretations of network structure and changes over time are centered on these centrality measures. Network structure and edge weight In the first round, the symptom network contained 145 non-zero edges out of 190 possible connections (Supplementary Table 1S). Notable connections included the association between C8 (Hopeful) and C9 (Feeling like a failure) with the strongest edge weight, followed by the connection between C15 (People unfriendly) and C19 (Feeling disliked by others) as well as the edge between C16 (Life is interesting) and C20 (Inability to get going) in weights. The network expanded slightly with structural changes (M = 0.037, p = 0.002) in the second round, of which 150 edges out of 190 possible connections were non-zero (Supplementary Table 2S). The strongest association in this round was between C15 (People unfriendly) and C19 (Feeling disliked by others). This was followed by the connections between C8 (Hopelessness) and C9 (Feeling like a failure) and between C16 (Lack of enjoyment) and C20 (Inability to get going). Network centrality Regarding node expected influence, there was no obvious shift in the centrality ranking of symptoms between the two rounds of primary mental healthcare interventions. However, the bootstrap difference test confirmed that the changes in global expected influence and node expected influence between the two-time points were statistically significant. Specifically, the global expected influence slightly expanded (S = 0.077, p < 0.001) after intervention, indicating that the symptoms affected each other more closely. Among the nodes, C2 (Appetite changes), C4 (Lack of feeling good), C5 (Difficult with concentrating), C7 (Everything was an effort), C11 (Sleep disturbances), and C15 (People unfriendly) increased obvious in expected influence after intervention, whereas C14 (Lonely), C16 (Lack of enjoyment), and C18 (Sadness) decreased (Fig. 2 . See Supplementary Table 3S for details). These results reinforced the relevance of these central symptoms in the progression or alleviation of depression, among which emotional related symptoms (C14, C16, and C18) played important roles in weakening the symptom connections along with the intervention period. Additionally, the nodes C2 (Appetite changes), C11 (Sleep disturbance), C13 (Taking less), and C20 (Inability to get going) stayed peripheral. This indicated that although there were some changes for centralities, these nodes demonstrated less importance within the networks across intervention. Among them, C20 (Inability to get going) only decreased on expected influence, suggesting that it may contributed to global weakening of the network rather than affected its local part. Network stability and accuracy Applying case-dropping bootstrap methods (n = 1,000), the stability analysis of the network demonstrated that the centrality indices (i.e., strength, betweenness, closeness, and expected influence) exhibited exceptionally high stability, with a correlation stability (CS) coefficient of 0.75. This indicated that the centrality measures remained highly consistent even when up to 75% of the sample data was removed (Fig. 3 ). We used the nonparametric bootstrapping (n = 1,000) method to calculate the edge weight accuracy. Figure 4 demonstrated the close alignment of the bootstrapping mean with the original sample, which indicated high accuracy across intervention. Subgroup analysis Finally, we conducted network comparison tests to compare the global expected influence of depressive symptoms among adolescents of different genders, grades, and parenting styles before and after the intervention. In the gender-based analysis, the results illustrated significant differences on network structure across time (Time1: M = 0.060, p < 0.001; Time2: M = 0.067, p < 0.001), and that the global expected influence of depressive symptoms was significantly higher in girls compared to boys at both time points (Time1: S = 0.090, p < 0.001; Time2: S = 0.076, p < 0.001), suggesting stronger interconnectivity of depressive symptoms in female adolescents before and after mental healthcare. Next, we analyzed the depressive symptom networks by grade level. The network demonstrated significant variance between high and low grades both before (M = 0.052, p = 0.003) and after (M = 0.092, p < 0.001) primary healthcare. The global expected influence was higher in the junior high school group than in the senior high school group across the two time points (Time1: S = 0.041, p = 0.005; Time2: S = 0.043, p = 0.004). Then, we examined the depressive symptom networks for adolescents under different living statuses with parents. Network variance was only found between those living with both parents and living in the social welfare institute. There was significant difference (M = 0.252, p = 0.049) on network structure after healthcare intervention, while no structural difference (M = 0.178, p = 0.326) was found before intervention. However, adolescents living with both parents demonstrated lower global expected influence than those living in the social welfare institute before and after intervention (Time1: S = 0.200, p = 0.017; Time2: S = 0.180, p = 0.043). These results indicate that adolescents living in the social welfare institute exhibit stronger depressive symptoms connections than those who live in normal environment. Additionally, we tested the within-participant changes of each subgroup followed by the primary healthcare. Participants living in the social welfare institute showed significant (S = 0.134, p = 0.043) elevation on global expected influence of the depressive symptoms network after healthcare intervention. Moreover, other subgroups (i.e., female, male, senior students, junior students, living with both parents, living with one parent, and living with other relatives) also demonstrated significant while more similar increase on expected influence (S: 0.057–0.089, p = 0.001) aligning with the total. Specifically, the node C18 (Sadness), which is the core emotional symptom of depression, decreased in expected influence after intervention across the participants classified as “female” (p = 0.011), “junior students” (p = 0.011), “living with one parent” (p = 0.010), and “living with both parents” (p = 0.040). This result indicated that C18 (Sadness) might be regarded as a key useful target in interventions among these subgroups. Discussion This study employed network analysis to examine the impact of primary mental healthcare interventions on the core symptoms of adolescent depression. By focusing on gender, grade, and parenting style, we sought to provide a deeper understanding of how these demographic factors influence treatment outcomes and assess the overall efficacy and specific targets of primary mental healthcare. The results offer several important insights into the relationships between depressive symptoms and the effects of primary healthcare interventions, which could inform future mental healthcare strategies. Effectiveness of primary mental healthcare Our findings illustrate that primary mental healthcare interventions significantly modify the network structure of depressive symptoms in adolescents, totally enhanced the inner influence, which indicates that the interventions do not effectively treat depression as a symptom network compared to a mental problem as a whole. However, the observed shift in centrality of the symptoms like C14 (Lonely), C16 (Lack of enjoyment), and C18 (Sadness) after intervention highlights the potential amendable targets in reducing the interconnectedness of specific depressive symptoms. Among these, C18 (Sadness) demonstrates stable changes by intervention across different subgroups, reveal that it is critical in controlling the expanding of depressive symptoms network, thereby mitigating the severity and recurrence of depression. These results align with existing research emphasizing the evolving nature of depression during adolescence, where emotional regulation and self-perception become increasingly central as individuals mature ( 46 , 47 ). The finding is also supported by recent studies indicating that reducing symptom connectivity can interpret treatment outcomes ( 48 , 49 ). However, the persistence of peripheral but indirectly affective symptoms such as C2 (Appetite changes), C11 (Sleep disturbance), and C13 (Talking less) which may even increase the severity of depressive network indicates that while interventions effectively diminish the impact of some symptoms, less prominent symptoms may necessitate additional, targeted treatment approaches. This suggests a need for comprehensive strategies that address both critical and peripheral symptoms to enhance overall treatment efficacy. Gender disparities Two significant findings from this study are that ( 1 ) “sadness” may be a critical symptom for weakening of the network among girls rather than boys, and that ( 2 ) the consistently higher average network expected influence of depressive symptoms in girls compared to boys, both before and after mental healthcare interventions. This more robust interconnectivity of symptoms among female adolescents aligns with previous studies, which have demonstrated that girls are more likely to experience emotional co-activation ( 50 , 51 ). These results suggest that primary mental health interventions for girls may need to address not only the core symptom of depression but also the heightened emotional reactivity that tends to sustain symptom networks. Meanwhile, research indicates that hormonal and social factors may contribute to this increased emotional reactivity in girls, necessitating tailored intervention strategies ( 52 ). Therefore, future research should focus on gender-specific adaptations to mental healthcare interventions to improve treatment efficacy for female adolescents. Such adaptations could include techniques to enhance emotional regulation and resilience, effectively reducing symptom interconnectivity ( 53 , 54 ). Age and grade-level differences Our analysis also reveals significant differences in the global expected influence of depressive symptoms between junior and senior high school students. The symptom network is more robust in the junior high group across the two rounds of care, which aligns with a similar previous study that junior grade students, especially those at the first grade demonstrate high level of depression ( 55 ). This may attribute to the difficult adjustment of new environment from primary school to middle school ( 55 ). Research suggests that as adolescents entering new environment may encounter unfamiliar inter-personal relationship and poor social support, potentially exacerbating stress and emotional challenges ( 56 , 57 ). These pressures can intensify the interconnectedness of depressive symptoms, highlighting the need for age-specific interventions that address these unique stressors. Understanding these age-related shifts is crucial for tailoring interventions to the specific needs of adolescents at different developmental stages, ensuring that mental healthcare strategies are both relevant and practical. Influence of family support The results of our subgroup analysis based on parenting style provide further insight into the role of family support in adolescent mental health. The significantly lower network global expected influence observed in both-parents children compared to children living in social welfare across the intervention suggests that absence of parenting may lead to severe depression, which agrees with previous studies ( 58 , 59 ). Meanwhile, the global expected influence of depressive network for children living in social welfare increases slightly after intervention, there is no changes on any of the symptom’s expected influence. However, although the global expected influence for parenting style subgroups like “living with one parent” and “living with both parents” increases obviously, the symptom “Sadness” plays an important role in reduce the network connectivity, which agrees with previous study indicating that sadness is among a causal chain of feelings and emotions triggered by a stress life event ( 60 ), while there is no direct evidence that intervention on sadness can mitigate the depressive symptoms. These groups' adverse total changes underscore the importance of advancing family-centered approaches into mental healthcare strategies, especially focusing on specific key symptoms that directly linked with distress emotion itself. This highlights the need for more comprehensive mental health policies that incorporate family-centered approaches, particularly for adolescents in high-risk family situations. Additionally, more innovative and effective intervention approaches need to be considered for children with no parents. Implications for mental healthcare strategies The network analysis conducted in this study provides a unique perspective on how primary mental healthcare interventions affect the relationships between depressive symptoms over time. By identifying the core symptoms that remain central to adolescent depression networks, our results offer valuable insights for designing more targeted and effective interventions. Moreover, the demographic disparities observed across gender, age, and family support underscore the importance of personalized care strategies that address the unique challenges faced by different subgroups of adolescents. In conclusion, this study underscores the significant contributions of network analysis in elucidating the complex interplay of depressive symptoms among adolescents undergoing primary mental healthcare interventions. Our findings offer a nuanced understanding of how these interventions can be optimized for greater efficacy by pinpointing core emotional symptoms and highlighting demographic disparities. The potential value of this research lies in its ability to inform the development of more direct, precise and personalized treatment strategies. Limitation While this study provides valuable insights into the impact of primary mental healthcare on adolescent depression, several limitations should be acknowledged. First, the absence of a standard randomized controlled trial (RCT) because of ethical constraints limits our ability to establish causality. Future research with RCT designs focused on specific population would strengthen the evidence. Second, we lacked clear metrics to assess the quality of care provided. Including such measures in future studies would better understand intervention effectiveness. Lastly, our sample was drawn from a provincial population, which may limit the broader applicability of our findings. Further research in diverse regions is needed to confirm these results. Conclusion This study utilized network analysis to explore the impact of primary mental healthcare on core symptoms of depression among adolescents. By examining the relationships between depressive symptoms and considering vital demographic factors such as gender, age, and family support, we gained valuable insights into the effectiveness of these interventions. Our findings revealed significant changes in the structure of symptom networks, highlighting the dynamic response of core depressive symptoms to mental healthcare. Gender disparities were evident, with girls exhibiting more robust symptom interconnectivity than boys, while age-related differences indicated that lower grade demonstrated more stronger symptom connectivity. The influence of family support emerged as a critical factor in sustaining the care benefits, particularly for vulnerable adolescents. Additionally, targeted intervention on sadness directly may be the key to reduce the severity of the symptom network for specific subgroups of children. Future research should focus on broadening the geographic and socio-economic scope, integrating and precise healthcare measures, and utilizing more rigorous study designs to validate these findings further. Declarations Ethics approval and consent to participate This study is not a randomized controlled trial (clinical trial number: not applicable), but an observed design along with necessary primary healthcare measures, which was conducted in accordance with the Declaration of Helsinki. The study has been officially approved by the Internal Review Board (IRB) of Nanchong Psychosomatic Hospital (No. NCPP 2022002). Informed consent was obtained from all participants and their legal guardian online. Consent for publication Not applicable. Availability of data and material The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request. Funding This study is supported by the National Natural Science Foundation of China (32300907) and the “Incubating Talent” Project of Army Medical University. Competing interests The authors declare that they have no competing interests. Authors' contributions Qianyu Zhang and Li Ran contribute equally to this manuscript: Data handling; Formal analysis; Methodology; Original draft; Revision; Editing. Wei Li: Data curation; Visualization; Methodology. Xuerong Liu: Data curation; Formal analysis; Revision. Jie Gong: Revision; Validation. Xianyong An: Data curation; Validation. Zhengzhi Feng: Validation. Zhiyi Chen: Conceptualization; Funding acquisition. Jingxuan Zhang: Conceptualization; Funding acquisition; Critical review; Editing; Supervision. 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What do centrality measures measure in psychological networks? J Abnorm Psychol. 2019;128(8):892–903. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. van Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, et al. Comparing network structures on three aspects: A permutation test. Psychol Methods. 2023;28(6):1273–85. De Castella K, Goldin P, Jazaieri H, Ziv M, Dweck CS, Gross JJ. Beliefs about emotion: Links to emotion regulation, well-being, and psychological distress. Basic Appl Soc Psychol. 2013;35(6):497–505. Dunkley DM, Starrs CJ, Gouveia L, Moroz M. Self-critical perfectionism and lower daily perceived control predict depressive and anxious symptoms over four years. J Couns Psychol. 2020;67(6):736–46. Beard C, Millner AJ, Forgeard MJ, Fried EI, Hsu KJ, Treadway MT, et al. Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychol Med. 2016;46(16):3359–69. Fried EI, van Borkulo CD, Cramer AO, Boschloo L, Schoevers RA, Borsboom D. Mental disorders as networks of problems: a review of recent insights. Soc Psychiatry Psychiatr Epidemiol. 2017;52(1):1–10. Liu S, Ren H, Li Y, Liu Y, Fu S, Han ZR. Gender difference in the onset of adolescent depressive symptoms: A cross-lagged panel network analysis. Res Child Adolesc Psychopathol. 2025;53(1):113–23. Su Z, Yang X, Hou J, Liu S, Wang Y, Chen Z. Gender differences in the co-occurrence of anxiety and depressive symptoms among early adolescents: A network approach. J Psychiatr Res. 2024;179:300–5. Hyde JS, Mezulis AH. Gender differences in depression: Biological, affective, cognitive, and sociocultural factors. Harv Rev Psychiatry. 2020;28(1):4–13. Rodrigues AR, Castro D, Cardoso J, Ferreira F, Serrao C, Coelho CM, et al. A network approach to emotion regulation and symptom activation in depression and anxiety. Front Public Health. 2024;12:1362148. Zhou Y, Gao W, Li H, Yao X, Wang J, Zhao X. Network analysis of resilience, anxiety and depression in clinical nurses. BMC Psychiatry. 2024;24(1):719. Zhang J, Liu D, Ding L, Du G. Prevalence of depression in junior and senior adolescents. Front Psychiatry. 2023;14:1182024. Yu Y, Peng L, Mo PKH, Yang X, Cai Y, Ma L, et al. Association between relationship adaptation and Internet gaming disorder among first-year secondary school students in China: Mediation effects via social support and loneliness. Addict Behav. 2022;125:107166. Pellegrini AD, Bartini M. A longitudinal study of bullying, victimization, and peer affiliation during the transition from primary school to middle school. 2000;37(3):699–725. Fu M, Xue Y, Zhou W, Yuan TF. Parental absence predicts suicide ideation through emotional disorders. PLoS ONE. 2017;12(12):e0188823. Yu Z, Du Y, Hu N, Zhang Y, Li J. Association between parental absence and depressive symptoms in adolescence: Evidence from a national household longitudinal survey. Child Psychiatry Hum Dev. 2024;55(2):405–14. van Borkulo C, Boschloo L, Borsboom D, Penninx BW, Waldorp LJ, Schoevers RA. Association of symptom network structure with the course of depression [corrected]. JAMA Psychiatry. 2015;72(12):1219–26. Additional Declarations No competing interests reported. Supplementary Files SupplementaryV1.0.docx Cite Share Download PDF Status: Published Journal Publication published 24 May, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 09 Apr, 2025 Reviews received at journal 08 Apr, 2025 Reviews received at journal 05 Apr, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers agreed at journal 25 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor invited by journal 21 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 07 Mar, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6176772","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435916384,"identity":"6a633d16-1270-40b9-b367-0f2c9407f09a","order_by":0,"name":"Qianyu Zhang","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qianyu","middleName":"","lastName":"Zhang","suffix":""},{"id":435916385,"identity":"8c72d45e-8b6e-4a7e-8ec9-aed84e94e1aa","order_by":1,"name":"Li Ran","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Ran","suffix":""},{"id":435916386,"identity":"843b5ace-35d7-4095-acd6-862d796061df","order_by":2,"name":"Wei Li","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Li","suffix":""},{"id":435916387,"identity":"00a0f6fd-ed8a-4069-b5b9-3fd651031bb7","order_by":3,"name":"Xuerong Liu","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuerong","middleName":"","lastName":"Liu","suffix":""},{"id":435916388,"identity":"917e8ce5-2eb6-4da7-a0be-1103674c19c1","order_by":4,"name":"Jie Gong","email":"","orcid":"","institution":"Nanchong Psychosomatic Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Gong","suffix":""},{"id":435916389,"identity":"834c9d12-e297-4088-8d04-d0bbe46e14e3","order_by":5,"name":"Xianyong An","email":"","orcid":"","institution":"Nanchong Psychosomatic Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianyong","middleName":"","lastName":"An","suffix":""},{"id":435916390,"identity":"0394601e-fd95-4c64-8d21-8ae1c699ac9c","order_by":6,"name":"Zhengzhi Feng","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhengzhi","middleName":"","lastName":"Feng","suffix":""},{"id":435916391,"identity":"dd9e05aa-07c2-4c5e-9218-e48f5cee3a93","order_by":7,"name":"Zhiyi Chen","email":"","orcid":"","institution":"Army Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyi","middleName":"","lastName":"Chen","suffix":""},{"id":435916392,"identity":"f07ee49e-c80f-4e09-bed6-bda53d89e6cb","order_by":8,"name":"Jingxuan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACCRDBIwHlVUBpHuK1nDEgVgsMMLYRoUV+dvOzh19kLPLkI3IMPxfO+xOtOyOB8cHbNgZ5cxxaGOccMzeW4ZEoNjxzxlh65jaD3G03EpgN57YxGO5swK6FWSLBTFqCRyJxY3uPgTQvRAubNG8bQ4LBAexa2CTSv0G0NPMY/+adA9bC/hufFh6JHDPJD0At89l7zKR5GyC2MOPTIiGRUyYN1Ji4gedYmTXPMePcbWceNkvOOSdhuAGHFvkZ6dskf/bUJc6fkbz5Nk+NXO6248kHP7wps5HHZQs4CHh7GBiQFDA2MKDFFwZg/PEDaF0DXjWjYBSMglEwkgEAXiFWX7V9ikYAAAAASUVORK5CYII=","orcid":"","institution":"Army Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jingxuan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-07 09:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6176772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6176772/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-025-06992-0","type":"published","date":"2025-05-24T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79843509,"identity":"a7b000e0-5ff6-4b5c-a57d-c664c2e8c5f3","added_by":"auto","created_at":"2025-04-03 13:11:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":795251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated network model for adolescent depressive symptoms (N = 73,750)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (a) the first round; (b) the second round. Red edges represent negative connections.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6176772/v1/02e8c9af737522a2691ccf4c.png"},{"id":79843503,"identity":"87768fa4-e86f-4a2c-8ea1-a5d0432a7b75","added_by":"auto","created_at":"2025-04-03 13:11:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":269827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentrality indices of depressive symptoms, shown as standardized values z-scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote:\u003cstrong\u003e \u003c/strong\u003eThe red line indicates the first round intervention of CPHG and the green line indicates the second round.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6176772/v1/5431903a90da8fb68c73707d.jpg"},{"id":79844102,"identity":"aa274780-1bbf-48f5-85c2-db79724d36f5","added_by":"auto","created_at":"2025-04-03 13:19:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage correlations with the original sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (a) the first round; (b) the second round. The X-axis represents the percentage of the original sample used in each subset. The Y-axis depicts the average correlation between centrality indices in the original network and those in the re-estimated networks after excluding the corresponding percentage of cases.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6176772/v1/55b68a8c56e06abfb7852952.png"},{"id":79843505,"identity":"b115f6d1-48f5-4d82-b477-ea05674be987","added_by":"auto","created_at":"2025-04-03 13:11:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":509873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBootstrap means of the edge weights\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: (a) the first round; (b) the second round.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6176772/v1/3e67b171535221848a9ca09d.png"},{"id":83460194,"identity":"29040acf-290e-4a05-8f74-dc7f913c002a","added_by":"auto","created_at":"2025-05-26 16:11:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2568728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6176772/v1/827264ed-2935-4b63-b909-ee0211cef55a.pdf"},{"id":79844959,"identity":"1f42033d-cad1-4eb4-8eff-8683bc85a155","added_by":"auto","created_at":"2025-04-03 13:27:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":41850,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryV1.0.docx","url":"https://assets-eu.researchsquare.com/files/rs-6176772/v1/588e86117c613455c04be37c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of primary mental healthcare on core symptoms of depression among underrepresented adolescents: A network analysis perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression has emerged as one of the leading contributions of the global mental health burden, significantly threating to adolescent psychological well-being (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). According to the World Health Organization, depression is the primary cause of disability-adjusted life years (DALYs) in adolescents aged 10 to 19 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Furthermore, adolescence\u0026mdash;critical period marked by significant physical and mental development\u0026mdash;is characterized by profound changes in physiology, brain function, and social relationships, which increase the risk of instability and heighten the incidence of depression during this stage (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Currently, the global prevalence of adolescent depression is rising at an alarming rate (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe growing mental health crisis among adolescents requires strong policy support. In response, the World Health Organization (WHO) has advocated primary mental healthcare as a key strategy to address this issue (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, despite the decades of implementation, some studies indicate that the impact of primary mental healthcare has fallen short of expectations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). One major factor is the severe shortage in supply-side resources, which significantly hampers the effectiveness of primary mental healthcare. According to the World Health Organization\u0026rsquo;s Mental Health Atlas, the ratio of psychiatrists or mental health professionals to the population is as low as one per 100,000 individuals (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Furthermore, in many low- and middle-income countries, mental health expenditure constitutes less than 1% of the total health budget. This inadequate allocation of resources directly constrains the implementation of primary mental healthcare programs, significantly undermining their anticipated effectiveness (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, demand-side challenges, such as limited awareness of depression as a treatable condition, as well as stigma and social exclusion associated with reduced help-seeking behavior, have also contributed to the suboptimal effectiveness of primary mental healthcare (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Patel emphasizes that in low- and middle-income countries, social exclusion and stigma not only reduce patients' willingness to seek treatment but also weaken their family and community support systems, further undermining the sustained effectiveness of primary mental healthcare (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, the variations in the effectiveness of primary mental healthcare can be largely attributed to individual heterogeneity in treatment responses. For instance, while some symptoms, such as sleep problems, may improve, others, such as hopelessness, may worsen within the same patient, resulting in no observable change in overall depressive levels (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Therefore, it is of significant interest to evaluate the real-world effectiveness of primary psychological healthcare and to explore the mechanisms underlying these heterogeneous treatment responses (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the high interdependence and mutual influence of depressive symptoms (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), a network approach to analyzing intervention effects is particularly advantageous. Recent studies employing network analysis have uncovered complex relationships among core symptoms of depression, demonstrating how mental healthcare interventions can modify these networks and potentially inform more effective treatment strategies (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese disparities in the real-world effectiveness of primary psychological healthcare may stem from variations in the core symptoms of depression associated with different demographic factors (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Demographic differences, including gender (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), grade level (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), and parenting style (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), may read to distinct structural characteristics of depressive symptom networks. Fried et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) found that in females, the depressive symptom network demonstrates stronger connections between affective symptoms (e.g., sadness and hopelessness) and somatic symptoms (e.g., fatigue and sleep disturbances). In contrast, in males, cognitive symptoms (e.g., self-criticism and feelings of worthlessness) tend to occupy a more central role within the network (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, research indicates that the network centrality of depressive symptoms in adolescents may vary with advancing grade levels (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Parental and family factors also emerge as critical determinants of mental health outcomes among adolescents. A substantial body of evidence highlights the protective role of supportive family environments in reducing the risk of mental health disorders and improving the efficacy of therapeutic interventions (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Adolescents who perceive strong familial support are more likely to utilize mental health services and adhere to treatment plans, leading to more favorable outcomes. Conversely, adolescents from dysfunctional or unsupportive family environments often encounter significant barriers to accessing and benefiting from mental healthcare. This study aims to examine the interplay of gender, age, and family support in shaping the effectiveness of primary mental healthcare for adolescents. Applying a comprehensive analytical framework, this study seeks to clarify the differential impacts of these factors and offer evidence-based recommendations to improve mental health service delivery for this vulnerable population.\u003c/p\u003e \u003cp\u003eIn summary, although primary psychological healthcare has been widely implemented, its efficacy in addressing the complex symptom structure of depression and the influence of key demographic factors among adolescents from low-income regions facing challenging circumstances remain underexplored. This study aims to address this gap by utilizing network analysis to investigate the complex relationships between core symptoms of depression and the effects of primary mental health interventions. By focusing on the unique challenges faced by adolescents, such as gender disparities, age, and family support, this study aims to enhance understanding of how these factors shape treatment outcomes. Ultimately, the study aims to offer insights that could inform more effective and sustainable mental healthcare strategies for adolescents.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHealthcare procedure\u003c/h2\u003e \u003cp\u003eThe healthcare was conducted in Nanchong, Sichuan Province, China, as a citywide, multi-center, population-based longitudinal cohort study. The primary objective was to assess the real-world effects of the Comprehensive Primary Healthcare for Adolescents Program (CPHG) on mitigating the risk of depression in adolescents (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Nanchong, a city with a mid-to-low economic profile in western China, was selected as the study site to represent the typical challenges faced by economically disadvantaged regions. The CPHG program (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) established 385 healthcare service centers and social welfare institutions across Nanchong to provide comprehensive primary mental healthcare coverage for middle and high school students. The program focused on depression screening to assess the mental health status of local adolescents. The screening process involved general testing followed by clinical assessments, psychological interventions, and medical treatments for those identified as at risk for severe depression.\u003c/p\u003e \u003cp\u003eThe CPHG system adopted multiple projects to ensure its implementation, including the \"2\u0026thinsp;+\u0026thinsp;2\u0026rdquo; psychological healthcare practice, psychological healthcare education, psychological healthcare training, and psychological healthcare management (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The core initiative was the \"2\u0026thinsp;+\u0026thinsp;2\u0026rdquo; psychological healthcare practice, with two rounds of psychological screening and two rounds of early psychological care (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). All enrolled children and adolescents were initially screened for depressive symptoms by the Center for Epidemiological Studies-Depression Scale (CES-D) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) as part of the first round of psychological screening. The second round of psychological screening focused on individuals identified as at risks for depressive symptoms during the first round. Following the two rounds of screening, individuals identified with severe depression received two rounds of specific psychological care (the latter \"2\u0026rdquo; in the \"2\u0026thinsp;+\u0026thinsp;2\u0026rdquo; workflow). In the subsequent psychological healthcare phase, children and adolescents identified with depression underwent the first round of psychological care, which was administered by qualified psychological healthcare specialists. A subset of these children and adolescents was referred to government-sponsored mental health center for clinical medical treatments based on specialist recommendations, constituting the second round of the latter \u0026ldquo;2\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and participants\u003c/h3\u003e\n\u003cp\u003eParticipants were selected from the CPHG program, which included a large-scale, multi-center cohort (n\u0026thinsp;=\u0026thinsp;249,772) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), to examine the effects of psychological care on depression among underrepresented children. For this analysis, data from the first and second rounds of screening were used for statistical analysis. After excluding participants with incomplete demographic information, the final matched dataset comprised 73,750 adolescents (34,606 girls and 39,144 boys). The median age of participants was 14.00 years (interquartile range [IQR]: 3.00 years).\u003c/p\u003e\n\u003ch3\u003eMeasure\u003c/h3\u003e\n\u003cp\u003eDemographic information was collected as part of the mental health screening process. Depressive symptoms were assessed using the Chinese version of the Center for Epidemiologic Studies Depression Scale (CES-D) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The CES-D is a brief self-report scale developed to measure depressive symptoms in the general population (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The scale comprises 20 items, each rated on a 4-point scale ranging from \"0\" (rarely or none of the time) to \"3\" (most or all of the time), with items 4, 8, 12, and 16 reverse-scored. Total scores range from 0 to 60, with higher scores indicating more severe depressive symptoms (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In this study, the CES-D demonstrated excellent internal consistency, with a Cronbach's α coefficient of 0.95, and showed good construct validity.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eFor descriptive statistics, the median, interquartile range (IQR), kurtosis, and skewness were calculated for the CES-D. Based on the normal distribution of the variables, the Wilcoxon signed-rank test was employed to assess whether a significant overall change in depression scores occurred following the intervention. Demographic characteristics and descriptive statistics were analyzed using IBM SPSS Statistics 25.0 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNetwork estimation\u003c/h2\u003e \u003cp\u003eTo explore the underlying mechanisms of depression and identify critical targets for intervention, network analysis was used to map the interrelationships between individual depressive symptoms (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). All network analyses were conducted using R scripts (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) in RStudio (Version 4.2.2) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The network was estimated using the Gaussian Graphical Model (GGM) and constructed with the Least Absolute Shrinkage and Selection Operator (LASSO) method, applying a penalty parameter to achieve sparsity and uses the Extended Bayesian Information Criterion (EBIC) to determine the optimal set of factors for each node (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe network comprises \"nodes\" and \"edges,\" with each symptom represented as a node and the correlation between two symptoms depicted as an edge (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). After connecting each node to several other nodes, the network is automatically constructed, displaying the strength of direct relationships between nodes. In the network graph, nodes that are more frequently and strongly associated with other nodes are positioned at the center, and the strength of associations between nodes is indicated by the thickness of the edges (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNetwork centrality\u003c/h3\u003e\n\u003cp\u003eFollowing previous research, we mainly used the expected influence centrality indices to represent the importance of individual symptoms within the model. Expected Influence measures a node's overall impact in a network by summing the weights of its connections to other nodes, considering both positive and negative effects, with higher values indicating greater centrality within the network (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This analysis was performed using the \"qgraph\" (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) package, and the results are visualized as standardized scores (z-scores).\u003c/p\u003e\n\u003ch3\u003eCentrality stability tests\u003c/h3\u003e\n\u003cp\u003eAccording to the recommendations of Bringmann et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), we assessed the robustness of the network solution by estimating the accuracy of edge weights and the stability of centrality indices using the R-package \"bootnet\" (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). We employed non-parametric bootstrapping to calculate 95% confidence intervals for the accuracy of edge weights. Wider confidence intervals indicate lower precision in edge estimates, whereas narrower intervals suggest higher network reliability. Further, we conducted 1000 case-dropping bootstrap samples, calculating correlation stability coefficient (CS-C) to evaluate the stability of centrality indices through subset bootstrapping. If the centrality indices of a node do not change significantly after removing a subset of samples from the dataset, the network structure is considered stable (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The CS-C should not fall below a certain threshold (i.e., 0.25) to ensure stability.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTime-variance and subgroup analyses\u003c/h2\u003e \u003cp\u003eAfter confirming the network's stability, we performed time-variance and subgroup analyses to examine intervention effects and demographic disparities. We focused on changes in the expected influence of the depression symptom network before and after the primary mental healthcare intervention as well as the subgroup differences. Using the \"Network Comparison Test\" (NCT) (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) package in R, we applied a permutation test with 1,000 iterations to assess differences. We built and compared symptom networks through 1,000 bootstrap resamples to derive the null distribution of network differences, maintaining a significance level of 0.05 (corrected by false discovery rate (FDR) correction).\u003c/p\u003e \u003cp\u003eThe NCT evaluates within-participant differences across intervention in three main areas: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) global expected influence, which is the total of nodes\u0026rsquo; direct or potential indirect effects in the network, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) structural invariance, which looks at significant changes in relationships between variables and nodes, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) edge and centrality invariance, which focuses on changes of specific edges or nodes centrality indices. However, as for subgroup network comparisons, we mainly concentrated on (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) global expected influence and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) structural invariance.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003eSample characteristics\u003c/h2\u003e\n \u003cp\u003eAll demographic variables are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Of these, 46,027 (62.4%) were junior high school students, and 27,723 (37.6%) were senior high school students. In terms of family support, 25,712 live with both parents, 23,743 live with one parent, 23,600 live with other relatives, and 695 live in a social welfare institute. Results of CHPG showed significant effects of practicing this system on preventing depression among individuals; the CES-D median score decreased from 6.00 to 2.00 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the first round.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic characteristics of the population enrolled in this study\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN (73750)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46,027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27,723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCES-D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst round\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecond round\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving status with parents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with both parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with one parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with other relatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23,600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving in the social welfare institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: N, number of valid samples; IQR, interquartile range; CES-D: Center for Epidemiologic Studies Depression Scale; SD, standard deviation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eNetwork analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003eNetwork estimation and visualization\u003c/h2\u003e\n \u003cp\u003eThe network of depressive symptoms following two rounds across primary mental health interventions is displayed in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, with detailed edge weights provided in Supplementary Table 1S and 2S. Centrality indices, including strength, betweenness, closeness, and expected influence were calculated for the symptom networks at both time points, and comparisons of these values is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. In alignment with previous research, which emphasizes the reliability of symptom rankings based on centrality measures, our analysis primarily focuses on symptom expected influence as the indicator of the symptom\u0026rsquo;s global importance within the network. Accordingly, interpretations of network structure and changes over time are centered on these centrality measures.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eNetwork structure and edge weight\u003c/h2\u003e\n \u003cp\u003eIn the first round, the symptom network contained 145 non-zero edges out of 190 possible connections (Supplementary Table\u0026nbsp;1S). Notable connections included the association between C8 (Hopeful) and C9 (Feeling like a failure) with the strongest edge weight, followed by the connection between C15 (People unfriendly) and C19 (Feeling disliked by others) as well as the edge between C16 (Life is interesting) and C20 (Inability to get going) in weights. The network expanded slightly with structural changes (M\u0026thinsp;=\u0026thinsp;0.037, p\u0026thinsp;=\u0026thinsp;0.002) in the second round, of which 150 edges out of 190 possible connections were non-zero (Supplementary Table\u0026nbsp;2S). The strongest association in this round was between C15 (People unfriendly) and C19 (Feeling disliked by others). This was followed by the connections between C8 (Hopelessness) and C9 (Feeling like a failure) and between C16 (Lack of enjoyment) and C20 (Inability to get going).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eNetwork centrality\u003c/h2\u003e\n \u003cp\u003eRegarding node expected influence, there was no obvious shift in the centrality ranking of symptoms between the two rounds of primary mental healthcare interventions. However, the bootstrap difference test confirmed that the changes in global expected influence and node expected influence between the two-time points were statistically significant. Specifically, the global expected influence slightly expanded (S\u0026thinsp;=\u0026thinsp;0.077, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after intervention, indicating that the symptoms affected each other more closely. Among the nodes, C2 (Appetite changes), C4 (Lack of feeling good), C5 (Difficult with concentrating), C7 (Everything was an effort), C11 (Sleep disturbances), and C15 (People unfriendly) increased obvious in expected influence after intervention, whereas C14 (Lonely), C16 (Lack of enjoyment), and C18 (Sadness) decreased (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. See Supplementary Table 3S for details). These results reinforced the relevance of these central symptoms in the progression or alleviation of depression, among which emotional related symptoms (C14, C16, and C18) played important roles in weakening the symptom connections along with the intervention period. Additionally, the nodes C2 (Appetite changes), C11 (Sleep disturbance), C13 (Taking less), and C20 (Inability to get going) stayed peripheral. This indicated that although there were some changes for centralities, these nodes demonstrated less importance within the networks across intervention. Among them, C20 (Inability to get going) only decreased on expected influence, suggesting that it may contributed to global weakening of the network rather than affected its local part.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eNetwork stability and accuracy\u003c/h2\u003e\n \u003cp\u003eApplying case-dropping bootstrap methods (n\u0026thinsp;=\u0026thinsp;1,000), the stability analysis of the network demonstrated that the centrality indices (i.e., strength, betweenness, closeness, and expected influence) exhibited exceptionally high stability, with a correlation stability (CS) coefficient of 0.75. This indicated that the centrality measures remained highly consistent even when up to 75% of the sample data was removed (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWe used the nonparametric bootstrapping (n\u0026thinsp;=\u0026thinsp;1,000) method to calculate the edge weight accuracy. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrated the close alignment of the bootstrapping mean with the original sample, which indicated high accuracy across intervention.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup analysis\u003c/h2\u003e\n \u003cp\u003eFinally, we conducted network comparison tests to compare the global expected influence of depressive symptoms among adolescents of different genders, grades, and parenting styles before and after the intervention. In the gender-based analysis, the results illustrated significant differences on network structure across time (Time1: M\u0026thinsp;=\u0026thinsp;0.060, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Time2: M\u0026thinsp;=\u0026thinsp;0.067, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and that the global expected influence of depressive symptoms was significantly higher in girls compared to boys at both time points (Time1: S\u0026thinsp;=\u0026thinsp;0.090, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Time2: S\u0026thinsp;=\u0026thinsp;0.076, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting stronger interconnectivity of depressive symptoms in female adolescents before and after mental healthcare.\u003c/p\u003e\n \u003cp\u003eNext, we analyzed the depressive symptom networks by grade level. The network demonstrated significant variance between high and low grades both before (M\u0026thinsp;=\u0026thinsp;0.052, p\u0026thinsp;=\u0026thinsp;0.003) and after (M\u0026thinsp;=\u0026thinsp;0.092, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) primary healthcare. The global expected influence was higher in the junior high school group than in the senior high school group across the two time points (Time1: S\u0026thinsp;=\u0026thinsp;0.041, p\u0026thinsp;=\u0026thinsp;0.005; Time2: S\u0026thinsp;=\u0026thinsp;0.043, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\n \u003cp\u003eThen, we examined the depressive symptom networks for adolescents under different living statuses with parents. Network variance was only found between those living with both parents and living in the social welfare institute. There was significant difference (M\u0026thinsp;=\u0026thinsp;0.252, p\u0026thinsp;=\u0026thinsp;0.049) on network structure after healthcare intervention, while no structural difference (M\u0026thinsp;=\u0026thinsp;0.178, p\u0026thinsp;=\u0026thinsp;0.326) was found before intervention. However, adolescents living with both parents demonstrated lower global expected influence than those living in the social welfare institute before and after intervention (Time1: S\u0026thinsp;=\u0026thinsp;0.200, p\u0026thinsp;=\u0026thinsp;0.017; Time2: S\u0026thinsp;=\u0026thinsp;0.180, p\u0026thinsp;=\u0026thinsp;0.043). These results indicate that adolescents living in the social welfare institute exhibit stronger depressive symptoms connections than those who live in normal environment.\u003c/p\u003e\n \u003cp\u003eAdditionally, we tested the within-participant changes of each subgroup followed by the primary healthcare. Participants living in the social welfare institute showed significant (S\u0026thinsp;=\u0026thinsp;0.134, p\u0026thinsp;=\u0026thinsp;0.043) elevation on global expected influence of the depressive symptoms network after healthcare intervention. Moreover, other subgroups (i.e., female, male, senior students, junior students, living with both parents, living with one parent, and living with other relatives) also demonstrated significant while more similar increase on expected influence (S: 0.057\u0026ndash;0.089, p\u0026thinsp;=\u0026thinsp;0.001) aligning with the total. Specifically, the node C18 (Sadness), which is the core emotional symptom of depression, decreased in expected influence after intervention across the participants classified as \u0026ldquo;female\u0026rdquo; (p\u0026thinsp;=\u0026thinsp;0.011), \u0026ldquo;junior students\u0026rdquo; (p\u0026thinsp;=\u0026thinsp;0.011), \u0026ldquo;living with one parent\u0026rdquo; (p\u0026thinsp;=\u0026thinsp;0.010), and \u0026ldquo;living with both parents\u0026rdquo; (p\u0026thinsp;=\u0026thinsp;0.040). This result indicated that C18 (Sadness) might be regarded as a key useful target in interventions among these subgroups.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed network analysis to examine the impact of primary mental healthcare interventions on the core symptoms of adolescent depression. By focusing on gender, grade, and parenting style, we sought to provide a deeper understanding of how these demographic factors influence treatment outcomes and assess the overall efficacy and specific targets of primary mental healthcare. The results offer several important insights into the relationships between depressive symptoms and the effects of primary healthcare interventions, which could inform future mental healthcare strategies.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEffectiveness of primary mental healthcare\u003c/h2\u003e \u003cp\u003eOur findings illustrate that primary mental healthcare interventions significantly modify the network structure of depressive symptoms in adolescents, totally enhanced the inner influence, which indicates that the interventions do not effectively treat depression as a symptom network compared to a mental problem as a whole. However, the observed shift in centrality of the symptoms like C14 (Lonely), C16 (Lack of enjoyment), and C18 (Sadness) after intervention highlights the potential amendable targets in reducing the interconnectedness of specific depressive symptoms. Among these, C18 (Sadness) demonstrates stable changes by intervention across different subgroups, reveal that it is critical in controlling the expanding of depressive symptoms network, thereby mitigating the severity and recurrence of depression. These results align with existing research emphasizing the evolving nature of depression during adolescence, where emotional regulation and self-perception become increasingly central as individuals mature (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The finding is also supported by recent studies indicating that reducing symptom connectivity can interpret treatment outcomes (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). However, the persistence of peripheral but indirectly affective symptoms such as C2 (Appetite changes), C11 (Sleep disturbance), and C13 (Talking less) which may even increase the severity of depressive network indicates that while interventions effectively diminish the impact of some symptoms, less prominent symptoms may necessitate additional, targeted treatment approaches. This suggests a need for comprehensive strategies that address both critical and peripheral symptoms to enhance overall treatment efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGender disparities\u003c/h2\u003e \u003cp\u003eTwo significant findings from this study are that (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u0026ldquo;sadness\u0026rdquo; may be a critical symptom for weakening of the network among girls rather than boys, and that (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the consistently higher average network expected influence of depressive symptoms in girls compared to boys, both before and after mental healthcare interventions. This more robust interconnectivity of symptoms among female adolescents aligns with previous studies, which have demonstrated that girls are more likely to experience emotional co-activation (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). These results suggest that primary mental health interventions for girls may need to address not only the core symptom of depression but also the heightened emotional reactivity that tends to sustain symptom networks. Meanwhile, research indicates that hormonal and social factors may contribute to this increased emotional reactivity in girls, necessitating tailored intervention strategies (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Therefore, future research should focus on gender-specific adaptations to mental healthcare interventions to improve treatment efficacy for female adolescents. Such adaptations could include techniques to enhance emotional regulation and resilience, effectively reducing symptom interconnectivity (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eAge and grade-level differences\u003c/h2\u003e \u003cp\u003eOur analysis also reveals significant differences in the global expected influence of depressive symptoms between junior and senior high school students. The symptom network is more robust in the junior high group across the two rounds of care, which aligns with a similar previous study that junior grade students, especially those at the first grade demonstrate high level of depression (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). This may attribute to the difficult adjustment of new environment from primary school to middle school (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Research suggests that as adolescents entering new environment may encounter unfamiliar inter-personal relationship and poor social support, potentially exacerbating stress and emotional challenges (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). These pressures can intensify the interconnectedness of depressive symptoms, highlighting the need for age-specific interventions that address these unique stressors. Understanding these age-related shifts is crucial for tailoring interventions to the specific needs of adolescents at different developmental stages, ensuring that mental healthcare strategies are both relevant and practical.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of family support\u003c/h2\u003e \u003cp\u003eThe results of our subgroup analysis based on parenting style provide further insight into the role of family support in adolescent mental health. The significantly lower network global expected influence observed in both-parents children compared to children living in social welfare across the intervention suggests that absence of parenting may lead to severe depression, which agrees with previous studies (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Meanwhile, the global expected influence of depressive network for children living in social welfare increases slightly after intervention, there is no changes on any of the symptom\u0026rsquo;s expected influence. However, although the global expected influence for parenting style subgroups like \u0026ldquo;living with one parent\u0026rdquo; and \u0026ldquo;living with both parents\u0026rdquo; increases obviously, the symptom \u0026ldquo;Sadness\u0026rdquo; plays an important role in reduce the network connectivity, which agrees with previous study indicating that sadness is among a causal chain of feelings and emotions triggered by a stress life event (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), while there is no direct evidence that intervention on sadness can mitigate the depressive symptoms. These groups' adverse total changes underscore the importance of advancing family-centered approaches into mental healthcare strategies, especially focusing on specific key symptoms that directly linked with distress emotion itself. This highlights the need for more comprehensive mental health policies that incorporate family-centered approaches, particularly for adolescents in high-risk family situations. Additionally, more innovative and effective intervention approaches need to be considered for children with no parents.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eImplications for mental healthcare strategies\u003c/h2\u003e \u003cp\u003eThe network analysis conducted in this study provides a unique perspective on how primary mental healthcare interventions affect the relationships between depressive symptoms over time. By identifying the core symptoms that remain central to adolescent depression networks, our results offer valuable insights for designing more targeted and effective interventions. Moreover, the demographic disparities observed across gender, age, and family support underscore the importance of personalized care strategies that address the unique challenges faced by different subgroups of adolescents.\u003c/p\u003e \u003cp\u003eIn conclusion, this study underscores the significant contributions of network analysis in elucidating the complex interplay of depressive symptoms among adolescents undergoing primary mental healthcare interventions. Our findings offer a nuanced understanding of how these interventions can be optimized for greater efficacy by pinpointing core emotional symptoms and highlighting demographic disparities. The potential value of this research lies in its ability to inform the development of more direct, precise and personalized treatment strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eWhile this study provides valuable insights into the impact of primary mental healthcare on adolescent depression, several limitations should be acknowledged. First, the absence of a standard randomized controlled trial (RCT) because of ethical constraints limits our ability to establish causality. Future research with RCT designs focused on specific population would strengthen the evidence. Second, we lacked clear metrics to assess the quality of care provided. Including such measures in future studies would better understand intervention effectiveness. Lastly, our sample was drawn from a provincial population, which may limit the broader applicability of our findings. Further research in diverse regions is needed to confirm these results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study utilized network analysis to explore the impact of primary mental healthcare on core symptoms of depression among adolescents. By examining the relationships between depressive symptoms and considering vital demographic factors such as gender, age, and family support, we gained valuable insights into the effectiveness of these interventions. Our findings revealed significant changes in the structure of symptom networks, highlighting the dynamic response of core depressive symptoms to mental healthcare. Gender disparities were evident, with girls exhibiting more robust symptom interconnectivity than boys, while age-related differences indicated that lower grade demonstrated more stronger symptom connectivity. The influence of family support emerged as a critical factor in sustaining the care benefits, particularly for vulnerable adolescents. Additionally, targeted intervention on sadness directly may be the key to reduce the severity of the symptom network for specific subgroups of children. Future research should focus on broadening the geographic and socio-economic scope, integrating and precise healthcare measures, and utilizing more rigorous study designs to validate these findings further.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not a randomized controlled trial (clinical trial number: not applicable), but an observed design along with necessary primary healthcare measures, which was conducted in accordance with the Declaration of Helsinki. The study has been officially approved by the Internal Review Board (IRB) of Nanchong Psychosomatic Hospital (No. NCPP 2022002). Informed consent was obtained from all participants and their legal guardian online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the National Natural Science Foundation of China (32300907) and the \u0026ldquo;Incubating Talent\u0026rdquo; Project of Army Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQianyu Zhang\u003c/strong\u003e and \u003cstrong\u003eLi Ran\u003c/strong\u003e contribute equally to this manuscript: Data handling; Formal analysis; Methodology; Original draft; Revision; Editing. \u003cstrong\u003eWei Li:\u003c/strong\u003e Data curation; Visualization; Methodology. \u003cstrong\u003eXuerong Liu:\u003c/strong\u003e Data curation; Formal analysis; Revision. \u003cstrong\u003eJie Gong:\u003c/strong\u003e Revision; Validation. \u003cstrong\u003eXianyong An: \u003c/strong\u003eData curation; Validation. \u003cstrong\u003eZhengzhi Feng:\u003c/strong\u003e Validation. \u003cstrong\u003eZhiyi Chen:\u003c/strong\u003e Conceptualization; Funding acquisition. \u003cstrong\u003eJingxuan Zhang:\u003c/strong\u003e Conceptualization; Funding acquisition; Critical review; Editing; Supervision. \u003cstrong\u003eCorresponding at:\u003c/strong\u003e Jingxuan Zhang ([email protected]; TEL: +86 0 68771774) or Zhiyi Chen ([email protected]; TEL: +86 0 68771767)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Xiaolin Zhang for language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld-Health-Organization. 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JAMA Psychiatry. 2015;72(12):1219\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"primary mental healthcare, depression, underrepresented adolescents, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-6176772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6176772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eDepression has emerged as a leading contribution of the global mental health burden, particularly among underrepresented adolescents. Despite the World Health Organization's promotion of primary mental healthcare as a critical solution, its real-world effectiveness in low- and middle-income countries remains debated due to high costs and insufficient follow-up. This study aimed to explore the impact of primary mental healthcare on the core symptoms of adolescent depression using network analysis, while examining the influence of demographic factors such as gender, age, and family support, to identify more precise and targeted intervention strategies, improving its effectiveness.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA citywide, multi-center, longitudinal cohort study was conducted in Nanchong, Sichuan Province, China, involving 73,750 adolescents (34,606 girls and 39,144 boys) with median age of 14.00 years old. The Comprehensive Primary Healthcare for Adolescents Program (CPHG) involved two rounds of psychological screening and early intervention. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D). Network analysis was employed to map the interrelations between depressive symptoms and evaluate the intervention's impact.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe CPHG program significantly reduced CES-D median scores from 6.00 to 2.00 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Network analysis revealed changes in the structure and centrality of depressive symptoms post-intervention, with specific symptoms such as sadness (C18) showing consistent reductions across subgroups. Gender disparities were evident, with female adolescents exhibiting stronger symptom interconnectivity. Junior high school students also demonstrated a more robust symptom network compared to senior high school students. Adolescents living in social welfare institutions exhibited higher global expected influence of depressive symptoms than those living with both parents.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003ePrimary mental healthcare interventions effectively modify the network structure of depressive symptoms in adolescents, with specific symptoms like sadness being critical targets for intervention. Gender and grade-level differences highlight the need for tailored mental healthcare strategies. The findings underscore the importance of addressing both core and peripheral symptoms to enhance treatment efficacy and reduce the severity and recurrence of depression among underrepresented adolescents.\u003c/p\u003e","manuscriptTitle":"The impact of primary mental healthcare on core symptoms of depression among underrepresented adolescents: A network analysis perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 13:11:42","doi":"10.21203/rs.3.rs-6176772/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-09T06:25:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T05:34:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-05T09:12:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179390425569690777605139410212136070911","date":"2025-03-26T01:01:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144168242655720935473126026916658954626","date":"2025-03-26T00:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-25T12:22:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-21T19:12:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T10:01:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T09:59:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2025-03-07T09:05:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"207a9e2e-66d2-4a92-92a6-342570724d59","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:06:04+00:00","versionOfRecord":{"articleIdentity":"rs-6176772","link":"https://doi.org/10.1186/s12888-025-06992-0","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2025-05-24 15:57:49","publishedOnDateReadable":"May 24th, 2025"},"versionCreatedAt":"2025-04-03 13:11:42","video":"","vorDoi":"10.1186/s12888-025-06992-0","vorDoiUrl":"https://doi.org/10.1186/s12888-025-06992-0","workflowStages":[]},"version":"v1","identity":"rs-6176772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6176772","identity":"rs-6176772","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}