Longitudinal Network Analysis Research Articles

Traditional Chinese herbal formulas modulate gut microbiome and improve insomnia in patients with distinct syndrome types: insights from an interventional clinical study

BackgroundTraditional Chinese medicine (TCM) comprising herbal formulas has been used for millennia to treat various diseases, such as insomnia, based on distinct syndrome types. Although TCM has been proposed to be effective in insomnia through gut microbiota modulation in animal models, human studies remain limited. Therefore, this study employs machine learning and integrative network techniques to elucidate the role of the gut microbiome in the efficacies of two TCM formulas — center-supplementing and qi-boosting decoction (CSQBD) and spleen-tonifying and yin heat-clearing decoction (STYHCD) — in treating insomnia patients diagnosed with spleen qi deficiency and spleen qi deficiency with stomach heat.MethodsSixty-three insomnia patients with these two specific TCM syndromes were enrolled and treated with CSQBD or STYHCD for 4 weeks. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and Insomnia Severity Index (ISI) every 2 weeks. In addition, variations in gut microbiota were evaluated through 16S rRNA gene sequencing. Stress and inflammatory markers were measured pre- and post-treatment.ResultsAt baseline, patients exhibiting only spleen qi deficiency showed slightly lesser severe insomnia, lower IFN-α levels, and higher cortisol levels than those with spleen qi deficiency with stomach heat. Both TCM syndromes displayed distinct gut microbiome profiles despite baseline adjustment of PSQI, ISI, and IFN-α scores. The nested stratified 10-fold cross-validated random forest classifier showed that patients with spleen qi deficiency had a higher abundance of Bifidobacterium longum than those with spleen qi deficiency with stomach heat, negatively associated with plasma IFN-α concentration. Both CSQBD and STYHCD treatments significantly improved sleep quality within 2 weeks, which lasted throughout the study. Moreover, the gut microbiome and inflammatory markers were significantly altered post-treatment. The longitudinal integrative network analysis revealed interconnections between sleep quality, gut microbes, such as Phascolarctobacterium and Ruminococcaceae, and inflammatory markers.ConclusionThis study reveals distinct microbiome profiles associated with different TCM syndrome types and underscores the link between the gut microbiome and efficacies of Chinese herbal formulas in improving insomnia. These findings deepen our understanding of the gut-brain axis in relation to insomnia and pave the way for precision treatment approaches leveraging TCM herbal remedies.

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data

BackgroundImproving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal. MethodsIn this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx). ResultsA sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0–4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment. LimitationsThis study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration. ConclusionsAt-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

A longitudinal network analysis of suicide risk factors among service members and veterans sampled for suicidal ideation or attempt.

Suicidal thoughts and behaviors are elevated among active-duty service members (ADSM) and veterans compared to the general population. Hence, it is a priority to examine maintenance factors underlying suicidal ideation among ADSM and veterans to develop effective, targeted interventions. In particular, interpersonal risk factors, hopelessness, and overarousal have been robustly connected to suicidal ideation and intent. To identify the suicidal ideation risk factors that are most relevant, we employed network analysis to examine between-subjects (cross-sectional), contemporaneous (within seconds), and temporal (across four hours) group-level networks of suicidal ideation and related risk factors in a sample of ADSM and veterans (participant n = 92, observations n = 10 650). Participants completed ecological momentary assessment (EMA) surveys four times a day for 30 days, where they answered questions related to suicidal ideation, interpersonal risk factors, hopelessness, and overarousal. The between-subjects and contemporaneous networks identified agitation, not feeling close to others, and ineffectiveness as the most central symptoms. The temporal network revealed that feeling ineffective was most likely to influence other symptoms in the network over time. Our findings suggest that ineffectiveness, low belongingness, and agitation are important drivers of moment-to-moment and longitudinal relations between risk factors for suicidal ideation in ADSM and veterans. Targeting these symptoms may disrupt suicidal ideation.

Trajectories and comorbid symptom networks of posttraumatic stress symptoms in frontline rescuers: A longitudinal study

BackgroundPrevious research has largely lacked studies that explore the trajectories of Posttraumatic stress symptoms (PTSS) and the structure of comorbid psychiatric symptom networks following traumatic event, while controlling for the severity of traumatic exposure. The present study aims to explore the characteristic trajectories of PTSS, in the context of ensuring controlled levels of traumatic exposure. Furthermore, the PTSS, depressive, and anxiety comorbid symptom networks of different PTSS trajectory subgroups are also investigated. MethodsA total of 296 frontline rescue personnel were enrolled into our study. In an effort to control for variations in traumatic exposure severity, this study ensured that all participants had same responsibilities and cumulative operational duration at the post-disaster rescue circumstance. Growth mixture models (GMMs) were employed to scrutinize the trajectories of PTSS. Additionally, network analysis was used to examine the comorbid symptom network of PTSS, depression, and anxiety. ResultsFour distinct PTSS trajectories were identified, namely Persisting Symptom, Gradual Recovery, Gradual Aggravation, and Asymptomatic. Although both the Persisting Symptom and Gradual Aggravation groups belong to the high-risk subgroups for persistent PTSS, they exhibit differences in core symptoms within their respective networks. The core symptom for the Persisting Symptom Network is flashbacks, while for the Gradual Aggravation Network, it is sleep disturbances. ConclusionTo the best of our knowledge, the present study represents the first research endeavor to integrate longitudinal trajectory analysis of PTSS with longitudinal symptom network analysis, clarifying the evolving features of PTSS but also offering valuable insights for early screening and intervention strategies.

The impact of long-term online learning on social anxiety and problematic smartphone use symptoms among secondary school students with different levels of fear of missing out: Evidence from a symptom network and longitudinal panel network analysis.

The advancement of communication technology and the impact of the COVID-19 pandemic have led to an increased reliance on online education. However, the effects of the long-term use of smart devices for online learning on students' social anxiety and problematic smartphone use (PSU) and the role of fear of missing out (FoMO) in this process have yet to be fully explored. This study analysed longitudinal data from 2,356 high school students (female = 1,137 (48.26%), meanage = 13.84, SD age = 1.37) in China, divided into high- and low-FoMO groups based on their scores on the FoMO scale, to examine the impact of four months of online learning on social anxiety and PSU. The Social Anxiety Scale (SAS) and Mobile Phone Addiction Index (MPAI) were used to assess socialanxiety and PSU symptoms. The undirected symptom networks revealed more bridge symptoms among the students in the high-FoMO group, although their overall symptom scores decreased. The results of the directed cross-lagged panel networks showed that "productivity loss" predicted other symptoms in the low-FoMO group but that "afraid of negative evaluation" was the predictor in the high-FoMO group. Meanwhile, "withdrawal/escape" and "productivity loss" were thesymptoms that were most affected by other symptoms in the high-FoMO and low-FoMO groups, respectively. The current study therefore sheds light on the changes in social anxiety and PSU symptoms among secondary school students during long-term online learning, as well as the moderating role of FoMO.

Building a communication and support network among quality improvement teams in nursing homes: a longitudinal study of the SCOPE trial

BackgroundWe applied a longitudinal network analysis approach to assess the formation of knowledge sharing and collaboration networks among care aide-led quality improvement (QI) teams in Canadian nursing homes participating in the Safer Care for Older Persons (in residential) Environments (SCOPE) trial which aimed to support unregulated front-line staff to lead unit-based quality improvement (QI) teams in nursing homes. We hypothesized that SCOPE's communicative and participatory nature would provide opportunities for peer support, knowledge sharing, and collaboration building among teams.MethodsFourteen QI teams in Alberta (AB) and seventeen QI teams in British Columbia (BC) participated in the study. Communications across nursing homes occurred through a series of 4 collaborative Learning Congresses (training sessions) over a 1-year period. The senior leaders of QI teams participated in two online network surveys about the communication/collaboration between teams in their province, 1 month after the first, and 6 months later, after the fourth Learning Congress. We developed communication and collaboration network maps pertaining to three time points: before SCOPE, at 2 months, and at 9 months.ResultsOver time, teams made significantly more new connections and strengthened existing ones, within and across regions. Geographic proximity and co-membership in organizational chains were important predictors of connectivity before and during SCOPE. Teams whose members were well connected at baseline disproportionately improved connectivity over time. On the other hand, teams that did not have prior opportunities to connect appeared to use SCOPE to build new ties.ConclusionsOur findings suggest the importance of network-altering activities to the formation of collaboration networks among QI teams across nursing homes. Active strategies could be used to better connect less connected teams and facilitate collaboration among geographically proximate teams. These findings may inform the development of interventions to leverage existing networks and provide new networking opportunities to develop and sustain organizational improvements.

A Virtual Battlefield for Embassies: Longitudinal Network Analysis of Competing Mediated Public Diplomacy on Social Media

ABSTRACT Mediated public diplomacy is a critical way of strategic communication for governments to influence global public opinion, especially during international conflicts. Based on the theories of mediatization and digitalization as well as the layered model of communicative space, this study investigates how the United States and Iranian embassies competed on the Chinese social media platform Sina Weibo over 13 months. Following a network ethnography approach, this study conducts longitudinal network analysis and textual analysis. The findings suggest that the macro-layer function (i.e. hashtag) facilitated horizontal dissemination of news about the US-Iran conflict from mainly China’s state-media accounts, increasing public awareness of the issue within a short time. The meso-layer (repost and follower network) and micro-layer (mention and comment) functions helped establish vertical communication facilitated by opinion leaders and crowd-enabled leaders that perpetuated public attention on relevant issues. The two embassies engaged with the Chinese public in different communicative layers. The US embassy interacted with the Chinese publics through reposts and replies, while the Iranian embassy constructed their posts in the form of replies. The Iranian embassy leveraged China’s state media agendas as narrative agency to conduct diplomatic communication by incorporating relevant hashtags in their posts. A layered model of mediated public diplomacy is proposed. The findings deepen our understanding of competing mediated public diplomacy, and contribute to the field of international communication of international conflicts.

Interactive changes in depression and loneliness symptoms prior to and during the COVID-19 pandemic: A longitudinal network analysis

ObjectivesDepression and loneliness co-occur frequently. This study examined interactive changes between depression and loneliness among older adults prior to and during the COVID-19 pandemic from a longitudinal network perspective. MethodsThis network study was based on data from three waves (2016–2017, 2018–2019, and 2020) of the English Longitudinal Study of Ageing (ELSA). Depression and loneliness were measured with the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8) and three item version of the University of California Los Angeles (UCLA) Loneliness Scale, respectively. A network model was constructed using an Ising Model while network differences were assessed using a Network Comparison Test. Central symptoms were identified via Expected Influence (EI). ResultsA total of 4,293 older adults were included in this study. The prevalence and network of depression and loneliness did not change significantly between the baseline and pre-pandemic assessments but increased significantly from the pre-pandemic assessment to during COVID-19 assessment. The central symptom with the strongest increase from pre-pandemic to pandemic assessments was "Inability to get going" (CESD8) and the edge with the highest increase across depression-loneliness symptom communities was “Lack companionship” (UCLA1) - "Inability to get going" (CESD8). Finally, “Feeling depressed” (CESD1) and “Everything was an effort" (CESD2) were the most central symptoms over the three assessment periods. ConclusionsThe COVID-19 pandemic was associated with significant changes in the depression-loneliness network model. The most changed symptoms and edges could be treatment targets for reducing the risk of depression and loneliness in older adults.

Elevated depression and anxiety predict future patterns of individualistic and collectivistic cultural values: A cross-lagged longitudinal network analysis

BackgroundSpecific components of independent and interdependent self-construal have been associated with psychopathology. However, most studies on this topic have been cross-sectional, precluding causal inferences. We used contemporaneous and temporal cross-lagged network analysis to establish weak causal effects in understanding the association between self-construal and psychopathology components. MethodsMiddle-aged and older community-dwelling adults (n = 3294) participated in the Midlife Development in the United States study across two time-points, spaced nine years apart. Six self-construal (interdependence: connection to others, commitment to others, receptiveness to influence; independence: behavioral consistency, sense of difference from others, self-reliance) and three psychopathology nodes (major depressive disorder (MDD), generalized anxiety disorder (GAD), and panic disorder (PD) symptom severity) were examined. All network analyses controlled for age, sex, race, and number of chronic illnesses as covariates. ResultsContemporaneous and temporal networks yielded relations between elevated MDD and PD and increased receptiveness to influence. Heightened GAD symptom severity was associated with future increased difference from others and decreased connection to others, commitment to others, and receptiveness to influence. Higher MDD, GAD, and PD severity were associated with future lower self-reliance. Network comparison tests revealed no consistent network differences across sex and race. LimitationsDSM-III-R measures of MDD, GAD, and PD were used. Results may not generalize to culturally diverse racial groups. ConclusionsChanges in self-construal may result from increased MDD, GAD, and PD severity. Findings suggest the importance of targeting common mental health symptoms to positively influence how individuals view the self and others in various social contexts.

The Connections among Problematic Usage of the Internet, Psychological Distress, and Eating Disorder Symptoms: A Longitudinal Network Analysis in Chinese Adolescents

The Connections among Problematic Usage of the Internet, Psychological Distress, and Eating Disorder Symptoms: A Longitudinal Network Analysis in Chinese Adolescents

A Longitudinal Network of Borderline-Related Trait Vulnerabilities from Childhood to Adolescence.

Early maladaptive traits are predictive of later borderline personality pathology (BPP), but little is known about their dynamic interplay over time. This is an important issue to address, however, as significant differences in the 'clinical weight' of various traits constituting the early BPP trait phenotype may inform the field on important target constructs from an early intervention perspective. Therefore, the current study aims to uncover the complex dependencies between BPP traits across the crucial developmental period of childhood and adolescence, by using longitudinal network analysis. Both between- and within-person networks were constructed to identify how early mother-reported borderline-related traits are connected across a timespan of six years (ntime 1 = 718, Mtime1 = 10.73years, SDtime1 = 1.39, 55.1% girls). Overall, the temporal network suggested various trait interdependencies, with internalizing traits being particularly influential in the development of the BPP trait network structure. At the same time, externalizing traits likely inhibit the negative effects of these core traits. In addition, results also revealed that internalizing and externalizing clusters of early borderline-related traits are linked through emotional lability. Implications of these findings are discussed in view of the change mechanisms at play and potential targets for early intervention.

Adolescents’ Drinking Behavior Off- and Online: Disentangling Exposure Effects and Selective Exposure in Networked Communication Structures

This article applies a network approach to better understand the often-demonstrated link between adolescents’ drinking behavior and their exposure to alcohol-related content on social media. Focusing on social dynamics among adolescents and their peers, we investigate the causes and consequences of exposure to individual peers who share alcohol-related content online. Drawing on social network literature and the perspective of networked communication online, we distinguish between exposure effects and selective exposure as the two core dynamics that can explain the association between drinking behavior and exposure to alcohol-related content online. Based on a two-wave network survey among adolescents aged 14 to 17 (n = 277), we applied a longitudinal network analysis to test both dynamics simultaneously. The findings indicate no exposure effects but robust evidence for selective exposure. This means that drinking adolescents are more likely to become exposed to peers who post alcohol-related content. The stochastic actor-oriented model hereby controls for rivaling explanations, such as the tendency to be exposed to friends, classmates, and peers of the same gender. In addition to these empirical findings, we discuss the value of the network approach, outlining the implications for future research and prevention strategies.

Hydrographr: An R package for scalable hydrographic data processing

Abstract Freshwater ecosystems are considered biodiversity hotspots, but assessing the spatial distribution of species remains challenging. One major obstacle lies in the complex geospatial processing of large amounts of data, such as stream network, sub‐catchment and basin data, that are necessary for addressing the longitudinal connectivity among water bodies. Workflows thus need to be scalable, especially when working across large spatial extents and at high spatial resolution. This in turn requires advanced command‐line GIS skills and programming language integration, which often poses a challenge for freshwater researchers. To address this challenge, we developed the package hydrographr that provides scalable hydrographic data processing in R. The package contains functions for downloading data of the high‐resolution Hydrography90m dataset, processing, reading and extracting information, as well as assessing network distances and connectivity. While the functions are, by default, tailored toward the Hydrography90m data, they can also be generalised toward other data and purposes, such as efficient cropping and merging of raster and vector data, point‐raster extraction, raster reclassification and data aggregation. The package depends on the open‐source software GDAL/OGR, GRASS‐GIS and the AWK programming language in the Linux environment, allowing a seamless language integration. Since the data is processed outside R, hydrographr allows creating scalable geo‐processing workflows. We illustrate the hydrographr functions using two workflows that focus on (i) a freshwater species distribution modelling approach, and (ii) assessing stream connectivity given the fragmentation by dams. We also provide a detailed guide for the initial installation of the required software. Windows users need to first enable the Windows Subsystem for Linux (WSL) feature, and can then follow the same software installation as Linux users. hydrographr is maintained on GitHub at https://github.com/glowabio/hydrographr. hydrographr provides a set of key functions for processing freshwater geospatial data. We expect that the package will support the freshwater‐related research communities given the easy‐to‐use wrapper functions that allow capitalizing on powerful open‐source command‐line software, which may otherwise require a steep learning curve. Users can thus perform large‐scale freshwater‐specific longitudinal connectivity and network analyses across large geographic extents while staying within the R environment.

With a Little Help from My Friends? A Longitudinal Network Analysis on Fiscal Stress and Collaboration for Public Service Delivery

Local governments face revenue constraints and increasing demands for public service delivery. Confronted with fiscal pressures, cities, and counties engage in collaborative arrangements to save costs or improve service levels. However, the same pressures can also prevent them from seeking collaboration. Two distinguishable arguments in tension can be identified: fiscal stress as a driver or as a deterrent for collaboration. This study reconciles these contrasting views using longitudinal network analysis to examine how fiscal stress affects the likelihood of collaboration in four critical service areas. Results point toward fiscal stress negatively affecting collaboration in service areas when economies of scale are not achievable, and increasing the likelihood of collaboration in service domains where these scales are possible. Understanding when fiscal stress affects collaboration is particularly relevant given resource disparities at the local level. Such inequality can create reinforcing cycles of fiscal stress and reduced opportunities to collaborate in service delivery.

Who will participate in online collaborative problem solving? A longitudinal network analysis

ABSTRACT Community-based courses, driven by connectivist learning theory, provide learners with a social environment that supports collaborative problem solving (CPS). The purpose of this study is to investigate the characteristics of potential collaborators before they engage in CPS. This study analyzed a community-based course, a cMOOC with a total of 145 learners participated in the thematic learning stage and 52 of the 145 chose to participate in the CPS. The interaction pattern of the collaborators and the indirect effects of individual attribute-based (exogenous) effects and network structure-based (endogenous) effects on the interaction pattern were examined using the quadratic assignment procedure (QAP) and the stochastic actor-oriented model (SAOM). Results suggest that substantial interaction activeness and interaction attractiveness are key patterns of the collaborators’ social interactions before CPS, and there is some interaction homogeneity among the collaborators. Some individual attribute-based effects, such as age, gender, and occupation, will reduce the early interaction attractiveness of the collaborators. The reciprocity, transitivity, and cumulative advantages of the network structure-based effects will gradually increase collaborators’ interaction attractiveness; in contrast, the network structure-based effects simultaneously limit collaborators’ interaction activeness. This study provides a theoretical foundation and practical experience regarding encouraging learners’ deep collaboration in community-based courses.

The socialization of perceived discrimination in ethnic minority groups.

Contact with members of one's own group (ingroup) and other groups (outgroups) shapes individuals' beliefs about the world, including perceptions of discrimination against one's ingroup. Research to date indicates that, among members of disadvantaged groups, contact with an advantaged outgroup is associated with less perceived discrimination, while contact with the disadvantaged ingroup is associated with more perceived discrimination. Past studies, however, considered ingroup and outgroup contact in isolation and overlooked the various processes that could explain these associations. We addressed these issues by examining whether disadvantaged-group members' perceptions of discrimination are shaped by how much contact they have with ingroup and outgroup members (contact effects) or by those ingroup and outgroup members' perceptions of discrimination (socialization effects) while controlling for their tendency to affiliate with similar others (selection effects). Three studies (total N = 5,866 ethnic minority group members) assessed participants' positive contact, friendships, and perceived discrimination and applied longitudinal and social network analyses to separate and simultaneously test contact, socialization, and selection processes. In contrast to previous studies, we found no evidence that contact with members of the advantaged outgroup precedes perceived discrimination. Instead, we found that friendships with members of the disadvantaged ingroup longitudinally predict perceived discrimination through the process of socialization-disadvantaged-group members' perceptions of discrimination became more similar to their ingroup friends' perceptions of discrimination over time. We conclude that perceptions of discrimination should be partly understood as a socialized belief about a shared reality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The structure stability of negative symptoms: longitudinal network analysis of the Brief Negative Symptom Scale in people with schizophrenia.

The structure of negative symptoms of schizophrenia is still a matter of controversy. Although a two-dimensional model (comprising the expressive deficit dimension and the motivation and pleasure dimension) has gained a large consensus, it has been questioned by recent investigations. To investigate the latent structure of negative symptoms and its stability over time in people with schizophrenia using network analysis. Negative symptoms were assessed in 612 people with schizophrenia using the Brief Negative Symptom Scale (BNSS) at baseline and at 4-year follow-up. A network invariance analysis was conducted to investigate changes in the network structure and strength of connections between the two time points. The network analysis carried out at baseline and follow-up, supported by community detection analysis, indicated that the BNSS's items aggregate to form four or five distinct domains (avolition/asociality, anhedonia, blunted affect and alogia). The network invariance test indicated that the network structure remained unchanged over time (network invariance test score 0.13; P = 0.169), although its overall strength decreased (6.28 at baseline, 5.79 at follow-up; global strength invariance test score 0.48; P = 0.016). The results lend support to a four- or five-factor model of negative symptoms and indicate overall stability over time. These data have implications for the study of pathophysiological mechanisms and the development of targeted treatments for negative symptoms.

Understanding teacher network formation amidst reform: A mixed-methods study on collaborative structure, network intentionality, closeness, and school climates

Teacher networks are essential mechanisms driving school reform, yet there is limited research on the development of these networks from a structural dynamics perspective. This mixed-methods study examined collaborative relationships among teachers and factors affecting their formation in the context of reform initiatives at a public middle school in Taiwan. Longitudinal network analysis (ERGM and STERGMs) supported by interview data suggests the social, relational, cognitive, and organizational conditions that impact the establishment, preservation, and dissolution of professional ties. The study also uncovers perspectives on how teachers shape their professional communities. The findings carry implications for policy and practice.

Hope, career competency, and social well-being among non-engaged youth in Hong Kong: a longitudinal network analysis

Although it has been widely acknowledged that hope, career competency, and outcomes of social well-being (i.e., social integration, social contribution, and civic engagement) are key components of youth’s career and life development (CLD), those interrelationships, their predictive relationships, and the relative importance of the variables in the relationships remain poorly understood. To fill that gap, we conducted two network analyses on three waves of data collected in the CLAP@JC Community Intervention Project. A total of 4,220 non-engaged youth (NEY) from Hong Kong participated in the project, all of whom were 13–29 years old (M = 18.66, SD = 3.20) and 53.3% of whom were boys and young men. The first network analysis, conducted on the data at baseline, revealed a moderate correlation between career competency and hope and a strong correlation between civic engagement and social contribution. The second network analysis, conducted on the aggregated three-wave data, showed that career competency could predict all other variables and that only hope could positively impact career competency over time. We also observed that social contribution can positively influence hope. Overall, our findings highlight the central role of career competency in networks while also revealing the different roles of the examined variables within the networks. Our new findings on the functions of and the relationships among those variables provide insights for further theorizing CLD, while the information obtained can also help practitioners to develop effective intervention strategies for promoting CLD among NEY.

Impulsivity as key bridge symptoms in cross-sectional and longitudinal networks of ADHD and ODD.

Impulsivity is viewed as key to attention-deficit/hyperactivity disorder (ADHD) and disruptive behavior disorders (DBD). Yet, to date, no work has provided an item-level analysis in longitudinal samples across the critical developmental period from childhood into adolescence, despite prior work suggesting items exhibit differential relevance with respect to various types of impairment. The current study conducted a novel longitudinal network analysis of ADHD and oppositional defiant disorder (ODD) symptoms between childhood and adolescence, with the important applied prediction of social skills in adolescence. Participants were 310 children over-recruited for clinical ADHD issues followed longitudinally for six years in total with gold standard diagnostic procedures and parent and teacher ratings of symptoms and social outcomes. Findings from baseline, Year 3, and Year 6 suggested Difficulty waiting turn, Blurts, and Interrupts/intrudes were key bridge items across cross-sectional and longitudinal parent-reported DBD networks. Furthermore, shortened symptom lists incorporating these symptoms were stronger predictors of teacher-rated social skills 5 years later compared to total DBD scores. Such findings are consistent with the trait impulsivity theory of DBD and ADHD and may inform useful screening tools and personalized intervention targets for children at risk for DBD during adolescence.