Search Results (15)

This study used a Cross-Lagged Panel Network model to examine prospective longitudinal associations among dimensions of adolescent mental health and differences in these associations across gender and grade levels. A total of 3610 Chinese adolescents completed the Middle School Student Mental Health Scale at two time points, with an interval of approximately six months between assessments. In the overall network, interpersonal sensitivity had the strongest out-expected influence, indicating the strongest outgoing predictive associations with other mental health dimensions. Depression ranked second and showed a significant bidirectional prospective association with interpersonal sensitivity. Emotional instability had the strongest in-expected influence, suggesting that it was the dimension most strongly predicted by other domains. Subgroup analyses revealed that interpersonal sensitivity showed the strongest outgoing predictive associations in the male network, whereas depression played this role in the female network. In the junior high school network, depression showed the strongest outgoing predictive associations, whereas interpersonal sensitivity was the most central predictive domain in the senior high school network. These findings may inform gender- and grade-sensitive screening and monitoring strategies and provide preliminary evidence for future intervention research. Full article

Emotional problems have become increasingly prevalent among university students, underscoring the importance of identifying protective psychological capacities that are linked to lower vulnerability to emotional problems. However, prior research has largely relied on cross-sectional designs and conventional statistical approaches, which limit the ability to clarify the temporal associations among multiple variables. To address this gap, we recruited 950 undergraduate students (61.6% female; M_age = 19.26, SD = 1.18) from 20 universities and conducted a two-wave longitudinal study. Cross-lagged panel network analysis was applied to examine the prospective associations linking positive psychological capacities (e.g., resilience, mindfulness) with emotional outcomes (e.g., negative affect, depression). Results revealed that positive affect and the acceptance dimension of mindfulness were among the most influential nodes within the network and exhibited stronger prospective associations with other positive psychological capacities. Based on the pathways identified in the network analysis, a half-longitudinal mediation model was further estimated to examine whether acceptance and awareness were prospectively associated with lower depressive symptoms through optimism. Together, these findings further clarified the temporal associations among positive psychological capacities and identified a prospective association linking mindfulness and depressive symptoms. These findings suggest that future mental health interventions for university students may benefit from incorporating strategies that promote positive affect and optimism within mindfulness practices. Full article

Background: Loneliness and depression are interrelated constructs that significantly impact adolescents’ mental health. Understanding their interplay, particularly at the symptom level, is critical for developing effective interventions. Objective: To examine longitudinal relationships between loneliness and depressive symptoms during late childhood, aiming to identify symptom-level interactions and directional effects. Participants and Setting: A total of 4333 children (M_age = 11.06, SD = 0.73; 50.8% girls) from the NLSY79 Children and Young Adults survey participated, with data collected over two years. Methods: A cross-lagged panel network (CLPN) model was employed to analyze symptom-level associations between loneliness and depressive symptoms. This approach combines network analysis and cross-lagged panel modeling, allowing for the estimation of both autoregressive effects (stability of symptoms over time) and cross-lagged effects (directional relationships between symptoms across time points). Results: The longitudinal network suggests the following: (1) a reciprocal link between loneliness and both sadness and parental pressure; (2) a forward effect of loneliness on anxiety and being busy; (3) the loneliness-reducing effect of prior happiness and loneliness-increasing effect of boredom. Conclusions: The findings highlight the complex interplay between loneliness and depressive symptoms, emphasizing reciprocal and unidirectional effects at the symptom level. These insights underscore the need for targeted, symptom-focused interventions to address loneliness and its impact on adolescent mental health. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

The global transport sector stands at a critical juncture where economic growth imperatives intersect with urgent environmental sustainability challenges. This paper investigates the impact of air freight transport, digitalisation, energy consumption, economic growth, and regulatory quality on CO₂ emissions in Gulf Cooperation Council (GCC) countries. Despite the region’s strategic importance in global air freight networks and rapid digital transformation, empirical evidence on how these factors collectively influence environmental sustainability remains limited. GCC countries provide a unique context for examining the digitalisation–transport–environment nexus. Using panel data from six GCC member states spanning 1999–2022, this study employs a second-generation autoregressive distributed lag (CS-ARDL) model to analyse short- and long-run relationships while accounting for cross-sectional dependence and heterogeneity. The empirical model designates CO₂ emissions as the dependent variable, while the digitalisation indicator, air freight transport, and energy consumption serve as principal explanatory variables. The empirical findings indicate that energy consumption and economic growth are significant drivers of CO₂ emissions in GCC countries, while digitalisation is associated with lower emissions. Regulatory quality exhibits a weaker but non-negligible negative influence. Moreover, air freight transport does not display a significant long-run effect on emission in the GCC context. These findings are robust across multiple panel estimators. The research provides evidence-based guidance for GCC national vision programmes, green aviation initiatives, and digital transformation strategies, contributing to a sustainable development discourse in resource-rich economies. Full article

Moral disengagement (MD) typically peaks during adolescence. While the Dark Triad (DT) traits—Machiavellianism, psychopathy, and narcissism—are broadly linked to MD, the specific prospective pathways through which individual DT components predict distinct MD strategies remain unclear, particularly with respect to gender-specific variations in these influences among adolescents. To systematically investigate these temporal associations, this study employed Cross-Lagged Panel Network (CLPN) modeling on a sample of 1410 Chinese adolescents (M_age = 16.95, SD = 0.75) surveyed across three waves at three-month intervals. Results revealed a hierarchical pattern of DT influence: Machiavellianism exerted the strongest predictive effect on the MD system, followed by psychopathy, while narcissism showed negligible or even negative effects. Among MD strategies, euphemistic labelling, advantageous comparison and displacement of responsibility were the most responsive to DT traits. Bridge centrality analysis confirmed Machiavellianism as the primary cross-domain connector linking DT traits to MD. Weak but significant reciprocal effects were observed: MD slightly fed back onto later Machiavellianism and psychopathy, supporting a partially bidirectional process. Gender-separated networks revealed divergent pathways: Machiavellianism served as the key DT-MD bridge for males, whereas psychopathy fulfilled this role for females. These findings refine the understanding of the “dark side” of moral development by highlighting mechanism-specific MD vulnerabilities and demonstrating that the primary socio-cognitive pathway to MD is gender-contingent, thereby advancing developmental models of MD. Full article

Objective: We aimed to examine the longitudinal associations between lifestyle behaviors, academic achievement, and physical diseases in adolescents. Study Design: Longitudinal cohort study. Methods: We recruited participants (n = 4330; mean age of 14.0 (SD = 1.51) years at the first time point and 16.0 (1.51) years at the second time point) from 16 districts in Shanghai, China, who completed a survey in 2021 (T1) and 2023 (T2). We employed a cross-lagged panel network model to explore the interconnected relationships among lifestyle behaviors, academic achievement, and physical condition (i.e., obesity, high blood pressure, high myopia, depressive symptoms). Results: Among the cross-lagged associations, the predictive effects of T1 obesity on T2 high blood pressure (OR = 2.39), T1 breakfast skipping on T2 TV screen time (OR = 1.49), (in cross-domain relationships) T1 symptoms of depression on T2 low fruit and vegetable consumption (OR = 2.43), T1 obesity on T2 TV screen time (OR = 1.53), and T1 computer time on T2 high BP (OR = 1.31) were particularly prominent. Nonetheless, the observed cross-lagged effect sizes were small. Based on the sum of expected influence on their connecting nodes, obesity, depressive symptoms, and breakfast skipping demonstrated their paramount roles in the network metrics. We found breakfast skipping showed the strongest bridging effect among all factors in association with coexisting conditions and academic performance in children. Conclusions: Our findings identified breakfast skipping as the pivotal bridge node with the highest centrality within the network of modifiable lifestyle factors. Although this does not imply direct causality, its prominent bridge effect highlights its essential role in maintaining network stability and mediating interactions across distinct variable clusters. Full article

The aim of this study was to explore the longitudinal relationship between death anxiety (DA) and fear of cancer recurrence (FCR) in women newly diagnosed with breast cancer at baseline and 3 months post-discharge. A total of 426 women with breast cancer completed the Templer’s Death Anxiety Scale and the Fear of Cancer Recurrence Inventory at hospital discharge and 3 months later. Cross-lagged panel analysis (CLPA) was used to describe the relationship of the two variables (DA and FCR) over time and identify the optimal intervention symptom nodes for breast cancer patients in different stages. The findings suggest that the specific symptoms of DA, known as “cognition”, predict the subsequent symptom development for a variety of mental health problems in the network structure. The “Psychological distress” symptom in FCR is the most susceptible to other symptoms. In addition, death-related cognition may be a bridge symptom that connects the co-occurrence of DA and FCR. Death-related “time awareness” is the optimal symptom node for intervention in early-stage breast cancer patients, while it is “cognition” in advanced patients. The death-related cognition and emotional regulation of death may be the best target for interventions among breast cancer patients, considering their DA coincides with FCR. The best intervention for patients with early-stage breast cancer may be the time awareness of death, while it may be more effective for patients with advanced cancer to be educated about disease and death, as well as to enhance correct perception. Full article

Digital skills represent a key dimension of digital transformation, shaping the innovation potential, competitiveness, and long-term sustainability of the European economy. The aim of this paper is to compare the development of digital skills in EU-27 countries from 2018 to 2024 and identify the strengths and weaknesses within the European context. The analysis is based on secondary data from the Digital Economy and Society Index (DESI). From the total of 36 indicators included in DESI, 12 variables were selected, with an emphasis on 3 core digital-skills metrics: Internet use, ICT specialists, and ICT graduates. To assess their interrelationships and linkages with overall digital transformation performance, non-parametric correlation analyses (Kendall’s Tau and Spearman’s rank correlation) were applied. Furthermore, across-year nonparametric tests (Friedman ANOVA with Kendall’s coefficient of concordance, W) were used to evaluate year-to-year differences and the stability of country rankings over 2018–2024. The empirical results confirmed that higher levels of digital skills are associated with stronger digital transformation performance among EU member states, while significant cross-country disparities persist. Germany and the Nordic economies (Finland, Sweden, and Denmark) achieved the best results, while Southern and Eastern European countries such as Bulgaria, Portugal, and Greece lagged behind. These findings highlight the strategic role of digital education, ICT specialization, and lifelong learning initiatives in promoting sustainable digital transformation and competitiveness across Europe. In addition, panel regression analysis confirmed that digital infrastructure, particularly FTTP coverage and Very High Capacity Networks, is a key driver of digital skills development, whereas the effects of business digitalization appear indirect or delayed. The outcomes provide relevant implications for broadband deployment and user-centric digital public services to support the objectives of the EU Digital Decade 2030. The study contributes to a deeper understanding of the determinants of digital skills and digital transformation performance, providing evidence-based guidance for targeted digital policies aimed at reducing the digital divide and strengthening digital transformation performance within the European Union. Full article

Depression is prevalent and may be transmitted within the family. However, whether and how gender influences the interaction of depressive symptoms between parents and adolescents remains largely unclear. The current study used a cross-lagged panel network (CLPN) analysis to examine the gender-specific transmission of depressive symptoms in representative Chinese families from the China Family Panel Studies. The participants included 1469 adolescents (48.3% girls) and their parents, with depressive symptoms assessed by the epidemiological studies depression scale in 2020 (T1; M_age = 13.80) and 2022 (T2; M_age = 15.62), respectively. The gender-specific CLPNs (i.e., boy–father, boy–mother, girl–father, and girl–mother CLPNs) showed that the “loneliness” at T1 repeatedly exhibited higher impacts on the other symptoms at T2 across networks. Furthermore, the symptoms of girls at T1 were more likely to influence their parents at T2, while the symptoms of boys at T2, especially the “sleep restlessness”, were susceptible to parental influence at T1. These findings provide deeper insights into the development of mental health policies, and future studies are needed to explore the mediating mechanisms of such transmission. Full article

Background/Objectives: The study investigated the dynamic interrelations of both positive and negative self-esteem with depression among immigrant adolescents. Methods: Longitudinal data from the Children of Immigrants Longitudinal Study (CILS) were analyzed using a Cross-Lagged Panel Network (CLPN) model. Results: The results showed strong autoregressive effects; both the positive and negative dimensions of self-esteem and symptoms of depression were fairly stable across the two measurement times. Cross-lagged effects indicated that higher levels of positive self-esteem predicted reduced depressive symptoms; for example, higher self-worth at Time 1 was associated with a lower lack of motivation at Time 2. However, some components, for instance, positive self-attitude, predicted in greater sadness from Time 1 to Time 2. On the other hand, certain dimensions of negative self-esteem, such as feeling useless at Time 1, were related to decreases in depressive symptoms at Time 2, which points to complex and bidirectional effects that challenge traditional hypotheses on how self-esteem may affect mental health. Conclusions: The current study teases apart sub-components of self-esteem and, in doing so, demonstrates how different facets uniquely predict depression over time and inform nuanced mental health trajectories among immigrant youth. The findings indicate that selective self-esteem interventions should be carried out to enhance resilience and mental well-being in adolescents from diverse backgrounds. Full article

With the implementation of the rural revitalization strategy, rural e-commerce has become an essential means of promoting rural economic development and increasing farmers’ income. However, the development of rural e-commerce varies significantly among different regions. Based on the perspective of “three rural areas”, this study constructs a rural e-commerce development capability measurement system centered on readiness, utilization, and influence. It adopts a panel vector autoregressive model to identify key influencing factors. Through the exploratory spatiotemporal data analysis (ESTDA) method, the spatiotemporal dynamic characteristics of rural e-commerce development capacity and the interaction relationship between provinces and regions are revealed. The study shows that (1) China’s rural e-commerce development capacity gained significant improvement from 2011 to 2022, but provincial polarization is evident, with eastern and central provinces leading and western and marginal provinces lagging; the rural e-commerce development capacity shows a decreasing dynamic pattern from the east to the central and western to the northeastern regions. (2) The eastern region has active rural e-commerce development, stable spatial structure, and provincial solid correlation, which creates a significant linkage effect. The western region shows strong internal spatial dependence, the district cross-regional interaction and linkage effect are beginning to emerge, and the northeastern low-development provinces are challenging to leap to a higher level in the short term; (3) the spatiotemporal interaction network of rural e-commerce development among several provinces and regions shows a positive synergistic relationship, and it is an essential consideration for the high-quality development of rural e-commerce to strengthen regional cooperation and realize complementary advantages. The study results provide a theoretical basis for formulating differentiated regional e-commerce development policies, which can help enhance regional synergy and narrow the regional development gap. Full article

This article introduces the R package pspatreg, which is publicly available for download from the Comprehensive R Archive Network, for estimating semiparametric spatial econometric penalized spline (P-Spline) models. These models can incorporate a nonparametric spatiotemporal trend, a spatial lag of the dependent variable, independent variables, noise, and time-series autoregressive noise. The primary functions in this package cover the estimation of P-Spline spatial econometric models using either Restricted Maximum Likelihood (REML) or Maximum Likelihood (ML) methods, as well as the computation of marginal impacts for both parametric and nonparametric terms. Additionally, the package offers methods for the graphical display of estimated nonlinear functions and spatial or spatiotemporal trend maps. Applications to cross-sectional and panel spatial data are provided to illustrate the package’s functionality. Full article

Background: The COVID-19 pandemic and the shift to online learning have increased the risk of Internet addiction (IA) among adolescents, especially those who are depressed. This study aims to identify the core symptoms of IA among depressed adolescents using a cross-lagged panel network framework, offering a fresh perspective on understanding the interconnectedness of IA symptoms. Methods: Participants completed the Internet addiction test and the Patient Health Questionnaire-9. A total of 2415 students were initially included, and after matching, only 342 students (a cutoff score of 8) were retained for the final data analysis. A cross-lagged panel network analysis was conducted to examine the autoregressive and cross-lagged trajectories of IA symptoms over time. Results: The incidence rate of depression rose remarkably from 14.16% (N = 342) to 17.64% (N = 426) after the four-month online learning. The symptom of “Anticipation” exhibited the highest out-expected influence within the IA network, followed by “Stay online longer” and “Job performance or productivity suffer”. Regarding the symptom network of depression, “Job performance or productivity suffer” had the highest in-expected influence, followed by “Life boring and empty”, “Snap or act annoyed if bothered”, “Check email/SNS before doing things”, and “School grades suffer”. No significant differences were found in global network strength and network structure between waves 1 and 2. Conclusion: These findings prove the negative effects of online learning on secondary students’ mental health and have important implications for developing more effective interventions and policies to mitigate IA levels among depressed adolescents undergoing online learning. Full article

Loneliness and depression are significant mental health challenges among college students; however, the intricate relationship between these phenomena remains unclear, particularly in the context of self-compassion. In this comprehensive study, we employ a cross-lagged panel network (CLPN) analysis to investigate the symptom-level association between depression and loneliness while exploring the potential moderating influence of self-compassion. Our sample consisted of 2785 college students, who were categorized into high- and low-self-compassion groups based on scores from the Self-Compassion Scale. Depressive symptoms were assessed using the Patient Health Questionnaire-9, while the UCLA Loneliness Scale-8 measured loneliness expressions. Our findings indicate that self-compassion plays a crucial role in the relationship between depression and loneliness. Specifically, we observed distinctive patterns within the high and low-self-compassion groups. In the low-self-compassion group, “energy” emerged as the most influential symptom, whereas “motor function” exhibited the highest influence in the high-self-compassion group. Furthermore, among individuals with high self-compassion, the pathway from depression to loneliness was characterized by “guilt—being alone when desired,” while the reverse path from loneliness to depression encompassed “left out—feeling sad” and “left out—anhedonia.” Conversely, in the low-self-compassion group, depression and loneliness demonstrated a more intricate mutual triggering relationship, suggesting that self-compassion effectively moderates the association between these variables. This study provides valuable insights into the underlying mechanisms driving the interplay between depression and loneliness, shedding light on the pivotal role of self-compassion in this intricate dynamic. Full article