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Risk and resilience profiles and their transition pathways in the ABCD Study

Published online by Cambridge University Press:  09 October 2024

Ruiyu Yang Open the ORCID record for Ruiyu Yang [Opens in a new window] ,
Sabrena Tuy ,
Lea Rose Dougherty Open the ORCID record for Lea Rose Dougherty [Opens in a new window]  and
Jillian Lee Wiggins
Ruiyu Yang*
Affiliation:
San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Department of Psychology, San Diego State University, San Diego, CA, USA
Sabrena Tuy
Affiliation:
Department of Psychology, San Diego State University, San Diego, CA, USA
Lea Rose Dougherty
Affiliation:
Department of Psychology, University of Maryland, College Park, MD, USA
Jillian Lee Wiggins
Affiliation:
San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA Department of Psychology, San Diego State University, San Diego, CA, USA
*
Corresponding author: Ruiyu Yang; Email: [email protected]
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Abstract

The transition from childhood to adolescence presents elevated risks for the onset of psychopathology in youth. Given the multilayered nature of development, the present study leverages the longitudinal, population-based Adolescent Brain Cognitive Development Study to derive ecologically informed risk/resilience profiles based on multilevel influences (e.g., neighborhood and family socioeconomic resources, parenting, school characteristics) and their transition pathways and examine their associations with psychopathology. Latent profile analysis characterized risk/resilience profiles at each time point (i.e., baseline, Year-1, Year-2); latent transition analysis estimated the most likely transition pathway for each individual. Analysis of covariance was used to examine associations between profile membership at baseline (i.e., ages 9–11) and psychopathology, both concurrently and at Year-2 follow-up. Further, we examined the associations between profile transition pathways and Year-2 psychopathology. Four distinct profiles emerged across time – High-SES High-Protective, High-SES Low-Protective, Low-SES High-Family-Risk, and Low-SES High-Protective. Despite reasonably high stability, significant transition over time among profiles was detected. Profile membership at baseline significantly correlated with concurrent psychopathology and predicted psychopathology 2 years later. Additionally, profile transition pathways significantly predicted Year-2 psychopathology, exemplifying equifinality and multifinality. Characterizing and tracing shifts in ecologically informed risk/resilience influences, our findings have the potential to inform more precise intervention efforts in youth.

Keywords

Developmental psychopathologyrisk and resilienceperson-centered approach
Type
Regular Article
Information
Development and Psychopathology , First View , pp. 1 - 10
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Introduction

The transition from late childhood to early adolescence, characterized by seismic changes occurring within multiple environmental contexts (e.g., school environment, peer interactions, family dynamics), as well as intraindividual changes (e.g., psychosocial functioning, neurocognitive development), creates a “perfect storm” for the development of psychopathology and, indeed, marks the peak of onset/worsening of many common psychopathologies (Casey et al., Reference Casey, Jones, Levita, Libby, Pattwell, Ruberry, Soliman and Somerville2010). Per an ecological model (Bronfenbrenner, Reference Bronfenbrenner1977), myriad proximal and distal factors (e.g., elements within the micro-, meso-, and exo-systems) work together to shape the profiles and trajectories of youth’s development within this critical window. Extensive research has examined associations between particular environmental factors at each ecological level and mental health outcomes in youth. For example, neighborhood and community resources (e.g., area unemployment rates and educational attainment at the neighborhood level) are negatively associated with the development of internalizing and externalizing symptoms (e.g., depression, anxiety, disruptive behaviors) above and beyond household socioeconomic resources (Okuzono et al., Reference Okuzono, Wilson and Slopen2023). In addition, researchers have established a positive link between family conflict and the development of internalizing and externalizing symptoms in children (Cummings et al., Reference Cummings, Koss and Davies2015). There is ample evidence that school experiences (e.g., sense of security, school resources, teacher–student relationships) (Aldridge & McChesney, Reference Aldridge and McChesney2018; Rakesh et al., Reference Rakesh, Zalesky and Whittle2023; Thijssen, Reference Thijssen2023) and peer relationships (e.g., quality of friendships) (Masten et al., Reference Masten, Eisenberger, Borofsky, Pfeifer, McNealy, Mazziotta and Dapretto2009; Sahi et al., Reference Sahi, Eisenberger and Silvers2023) play vital roles in shaping youth’s development during the critical transition from late childhood to early adolescence.

Nevertheless, piecemeal focus on the contributions of specific environmental elements to the development of psychopathology (i.e., variable-centered) and cross-sectional “snapshots” of a specific developmental time have limited most of this foundational research. Theoretical models of development’s multilayered and transactional nature posit that environmental and intraindividual forces coact to shape development cross-sectionally and longitudinally. In this light, longitudinal studies to trace development are necessary to elucidate equifinality (i.e., singular outcome from different originating points) and multifinality (i.e., differential outcomes from a single originating point) (Cicchetti & Rogosch, Reference Cicchetti and Rogosch1996; Handley et al., Reference Handley, Duprey, Russotti, Levin and Warmingham2024). So far, a handful of longitudinal studies have characterized individual profiles based on environmental elements and found that profile membership significantly predicts mental health outcomes (e.g., Christian et al., Reference Christian, Cole, McDade, Pachankis, Morgan, Strahm and Kamp Dush2021; Cooper et al., Reference Cooper, Di Biase, Bei, Quach and Cropley2023; Retzler et al., Reference Retzler, Hallam, Johnson and Retzler2023). These studies have primarily focused on a single area of environment or functioning (e.g., psychopathology outcome, emotion regulation, sleep problems) (Cooper et al., Reference Cooper, Di Biase, Bei, Quach and Cropley2023; Huffman & Oshri, Reference Huffman and Oshri2022; Retzler et al., Reference Retzler, Hallam, Johnson and Retzler2023), with little attention yet given to the ecological context of development. Further, most studies to date have examined developmental psychopathology primarily from a risk perspective by focusing on risk factors (e.g., low socioeconomic status, family conflict, adverse childhood experiences) associated with negative mental health outcomes to identify at-risk groups and guide intervention efforts (LeMoult et al., Reference LeMoult, Humphreys, Tracy, Hoffmeister, Ip and Gotlib2020). Meanwhile, emerging research is beginning to demonstrate the value of identifying protective factors (e.g., social support, school connectedness, educational attainment, positive childhood experiences; see Buchanan et al., Reference Buchanan, Walker, Boden, Mansoor and Newton-Howes2023 for a review), which may also inform intervention efforts by enhancing developmental environments and bolstering adaptive modes of emotional functioning and behaviors (Carr et al., Reference Carr, Cullen, Keeney, Canning, Mooney, Chinseallaigh and O’Dowd2021; Lerner et al., Reference Lerner, Phelps, Forman, Bowers, Lerner and Steinberg2009; Vanderbilt-Adriance & Shaw, Reference Vanderbilt-Adriance and Shaw2008). Critically, protective factors, often overlooked in existing empirical research on developmental psychopathology (Cicchetti & Rogosch, Reference Cicchetti and Rogosch2002), may buffer the effects of risk factors. For instance, taking a strength-based perspective and complementing the traditional examination of adverse childhood experiences, Morris et al. (Reference Morris, Hays-Grudo, Zapata, Treat and Kerr2021) found that cumulative protective and compensatory experiences (e.g., nurturing relationships, access to various resources) moderated the association between adverse childhood experiences and later parenting attitudes (Morris et al., Reference Morris, Hays-Grudo, Zapata, Treat and Kerr2021). Similarly, studies have shown that prosocial characteristics and behaviors of youth are linked with positive developmental outcomes and serve as a protective factor in face of life challenges and adversity (Collie, Reference Collie2020; Malti & Speidel, Reference Malti and Speidel2023); parental factors, such as perceived parental warmth and involvement, may also serve as a protective mechanism and demonstrate positive links with general well-being of the youth (Chen et al., Reference Chen, Adleman, Saad, Leibenluft and Cox2014; Lan, Reference Lan2022; Yap et al., Reference Yap, Pilkington, Ryan and Jorm2014).

To address these gaps, the present study examines multiple layers of the developmental context (e.g., neighborhood, family, school, peer, intraindividual) longitudinally during the critical transition from late childhood to early adolescence. We leverage data from the Adolescent Brain Cognitive Development (ABCD) Study® to derive ecologically informed risk/resilience profiles at preadolescence, which we expect to evolve over the transition to adolescence, and validate these transition profiles’ predictive power for adolescent psychopathology.

Method

Participants

The ABCD Study includes nationwide, population-based, diverse longitudinal psychosocial and neurobiological data on youth from preadolescence to early adulthood, with the recruitment procedures extensively described elsewhere (Garavan et al., Reference Garavan, Bartsch, Conway, Decastro, Goldstein, Heeringa, Jernigan, Potter, Thompson and Zahs2018). In the present study, we used Data Release 4.0, including three annual waves: baseline (N = 9,854), Year-1 (N = 9,275), and Year-2 (N = 8,399) (Table 1, S1). We randomly selected one child per family when twins/siblings were present.

Table 1. Demographic characteristics at baseline (N = 9,854)

Measures

Drawing from the ecological model (Bronfenbrenner, Reference Bronfenbrenner1977), we incorporated measures capturing different developmental contexts. See Supplement for more details on measures (Table S2 for measure content, Table S3 for measure availability).

Neighborhood

Growing evidence has demonstrated the effects of neighborhood environment on development, such as neighborhood socioeconomic resources and perceived neighborhood safety (Sripada et al., Reference Sripada, Gard, Angstadt, Taxali, Greathouse, McCurry, Hyde, Weigard, Walczyk and Heitzeg2022; Taylor et al., Reference Taylor, Cooper, Jackson and Barch2020). In the ABCD Study, the participants’ primary home addresses were utilized to generate the Area Deprivation Index (ADI) (Singh, Reference Singh2003) and the Child Opportunity Index (COI) (Acevedo-Garcia et al., Reference Acevedo-Garcia, McArdle, Hardy, Crisan, Romano, Norris, Baek and Reece2014), capturing neighborhood economic disadvantages and child opportunity (i.e., educational, health and environmental, and social and economic opportunities). Per ABCD recommendations, we used the ADI and COI scores collected at baseline and extended these scores to Year-1 and Year-2. Additionally, Neighborhood Crime/Safety Survey (Mujahid et al., Reference Mujahid, Diez Roux, Morenoff and Raghunathan2007) captured perceived neighborhood safety, reported by both parent (i.e., primary caregiver) and youth at all three time points – baseline, Year-1, and Year-2.

School environment and peer interactions

School plays an increasingly important role as youth transition into early adolescence. School Risk and Protective Factors Survey assessed the general school protective environment (e.g., positive relationships between youth and teachers) and was reported by youth at all three time points. School Attendance and Grades Questionnaire measured youth’s number of excused and unexcused school absences and was reported by parent at Year-2. Peer Behavioral Profile captured both prosocial (e.g., friends who are excellent students) and delinquent (e.g., friends who have skipped school or shoplifted occasionally) peer involvement, as reported by youth at Year-2. Lastly, the Peer Network Health Protective Scale captured youth’s close friends’ protective behaviors against substance use (e.g., advising against substance use) and support-providing behaviors (e.g., helping by talking through problems), as reported by youth at Year-2.

Family dynamics

Family dynamics can often serve as a “protective shield,” buffering adverse experiences outside of the home (Pynoos et al., Reference Pynoos, Steinberg and Piacentini1999); meanwhile, intrafamily conflict may elevate youth’s risk for psychopathology (Weymouth et al., Reference Weymouth, Fosco, Mak, Mayfield, LoBraico and Feinberg2019; Yang et al., Reference Yang, Anderson, Zhou, Liu, Haase and Qu2023). We used the Parental Behavioral Inventory for parental warmth/acceptance traits and behaviors (e.g., making youth feel better after discussing worries with them) and the Parental Monitoring Survey for parental monitoring behaviors (e.g., parents knowing the youth’s whereabouts). Both measures were reported by youth on the primary and secondary (if applicable) caregiver(s) at baseline and Year-1, with the Parental Monitoring Survey also collected at Year-2. Additionally, the family conflict subscale of the Family Environment Scale (Moos & Moos, Reference Moos and Moos2014) assessed intrafamily conflict and was reported by parent and youth at all three time points. Given the associations between parental psychopathology and youth’s mental health outcomes (Gureje et al., Reference Gureje, Oladeji, Hwang, Chiu, Kessler, Sampson, Alonso, Andrade, Beautrais, Borges, Bromet, Bruffaerts, De Girolamo, De Graaf, Gal, He, Hu, Iwata, Karam and Nock2011), we included parental internalizing and externalizing symptoms, measured by the Adult Self-Reported Scores, as reported by parent at baseline and Year-2.

Another critical factor in shaping family dynamics is household socioeconomic status (SES), which affects multiple aspects and mechanisms of family life (e.g., resource availability and financial stress). We used the income-to-needs ratio, perceived/subjective family material hardship, and highest education of the parents as indicators of household SES. These indicators were reported by parent at all three time points.

Prosocial behavior, traumatic experiences, psychopathology

Positive social interactions with others are essential in attaining better developmental outcomes. Specifically, the tendency to support and benefit others (i.e., prosocial behavior) is associated with better psychosocial functioning (Hirani et al., Reference Hirani, Ojukwu and Bandara2022; Malti & Speidel, Reference Malti and Speidel2023). Prosocial Behavior Survey, reported by parent and youth at all three time points, captured youth’s prosocial behaviors, including being considerate of other people’s feelings and offering help to people in need. On the other hand, traumatic experiences pose a significant risk for psychopathology (Herringa, Reference Herringa2017; McLaughlin et al., Reference McLaughlin, Koenen, Hill, Petukhova, Sampson, Zaslavsky and Kessler2013), and we utilized the Traumatic Events scale embedded in the Kiddie Schedule for Affective Disorders and Schizophrenia interview (Kaufman et al., Reference Kaufman, Birmaher, Brent, Rao, Flynn, Moreci, Williamson and Ryan1997) to measure youth’s cumulative trauma exposure, reported by parent at baseline and Year-2.

Psychopathology was measured using the Internalizing and Externalizing Syndrome scales from the Child Behavior Checklist (Achenbach & Ruffle, Reference Achenbach and Ruffle2000) and was reported by parent at all three time points.

Dimensionality reduction

Given the number of measures and variables of interest, we conducted factor analyses to reduce the dimensionality of our data, identify the most representative variables for each developmental context, and facilitate interpretation. Specifically, we used exploratory factor analysis (EFA) for variable selection and confirmatory factor analysis (CFA) for validation. We randomly halved the sample for robustness and validation purposes, with one half as the test sample for EFA and the other half as the validation sample for CFA. First, EFA using direct oblimin rotation was conducted to explore the dimensionality of our data, utilizing the factanal function of R (version 4.2.2; maximum likelihood factor analysis). The variance accounted for by the solution, the variance accounted for by each factor, and the interpretability of the factors were all evaluated to determine the initial plausibility of the factor structure. Variables with factor loadings smaller than 0.35 were removed. Second, a corresponding model was tested using CFA to cross-validate the factor structure derived from EFA. We referenced commonly used model fit recommendations (Bentler, Reference Bentler2007), specifically (a) the comparative fit index (Bentler, Reference Bentler1990), with values greater than 0.95 indicating reasonable model fit and values greater than 0.90 indicating a plausible model; (b) the root mean square error of approximation (Steiger, Reference Steiger1990), an absolute index of overall model fit with values less than 0.08 indicative acceptable model fit and values less than 0.05 indicative of good model fit; and (c) the standardized root mean square residual (SRMR) (Hu & Bentler, Reference Hu and Bentler1999), an absolute index of overall model fit with values less than 0.08 indicative of acceptable model fit and values less than 0.05 indicative of good model fit. This procedure was repeated for each time point to identify key variables for inclusion in the next steps – deriving latent profiles using these key variables. See Table S4 for model fit indices and Table S5 for factor loadings of the key variables selected. Statistical analyses were performed in R (version 4.2.2), with the lavaan package.

Deriving groups from latent profiles and latent profile transition pathways

Key variables selected from 2.3 Dimensionality Reduction were utilized to illustrate risk/resilience profiles at each time point using latent profile analysis (LPA) (Spurk et al., Reference Spurk, Hirschi, Wang, Valero and Kauffeld2020). Latent transition analysis (Hickendorff et al., Reference Hickendorff, Edelsbrunner, McMullen, Schneider and Trezise2018) then examined profile transition pathways across time (i.e., from baseline to Year-2). Specifically, in LPA, the selection of the best fitting profile was guided by multiple fit indices, including entropy, Bayesian information criteria, sample-adjusted Bayesian information criteria, Akaike information criteria, bootstrapped likelihood ratio test, and adjusted Lo–Mendell–Rubin test, as well as theoretical interpretability (see Table S6 for model fit indices). After the best-fitting model was determined at each time point, latent transition analysis was performed to determine each individual’s most likely transition pathway (see Table S6 for model fit indices). Analyses were performed using Mplus (version 8.9).

Predicting psychopathology symptoms from profiles and pathways

Finally, analysis of covariance was used to examine the associations of profile membership and profile transition pathways with internalizing and externalizing psychopathology, both concurrently and at Year-2 follow-up (controlling for baseline psychopathology). Familywise error for post hoc comparisons was controlled using the Tukey Honest Significant Difference test; post hoc analyses controlled for age and biological sex.

Results

Deriving groups from latent profiles and latent profile transition pathways

Utilizing key variables identified in dimensionality reduction (see Supplement for details), we conducted LPA testing 2, 3, 4, 5, and 6 profiles at each time point. Model fit indices and theoretical interpretability indicated that a four-profile solution fits better than other solutions at every time point (Figure 1; see Supplement for Year-1, Year-2).

Figure 1. Latent profile characteristics at baseline. Family conflict (P) = parent-report family conflict; family conflict (Y) = youth-report family conflict.

Four distinct profiles emerged. The High-SES High-Protective Group (55.31%) was characterized by relatively high neighborhood and family SES, low family risk (i.e., less family conflict and fewer parental internalizing and externalizing symptoms), and greater protective factor (e.g., more protective school environment, more parental acceptance); the High-SES Low-Protective Group (10.15%) was characterized by relatively high neighborhood and family SES, high family risk, and low protective factor; the Low-SES High-Family-Risk Group (9.22%) was characterized by relatively low neighborhood and family SES, high family risk, and low protective factor; lastly, the Low-SES High-Protective Group (25.33%) was characterized by relatively low neighborhood and family SES, low family risk, and greater protective factor.

Profile membership was fairly stable over time. Overall, 96.26% of the High-SES High-Protective Group, 76.40% of the High-SES Low-Protective Group, 73.83% of the Low-SES High-Family-Risk Group, and 95.88% of the Low-SES High-Protective Group stayed in the same group over time. However, significant change over time was detected: 3.74% of the individuals in the High-SES High-Protective Group transitioned to the High-SES Low-Protective Group at Year-1, 23.60% of the individuals in the High-SES Low-Protective Group transitioned to the High-SES High-Protective Group at Year-1, 13.09% of the individuals in the Low-SES High-Family-Risk Group transitioned to the High-SES Low-Protective Group, and another 13.09% transitioned to the Low-SES High-Protective Group, at Year-2; lastly, 4.12% of the individuals in the Low-SES High-Protective Group transitioned to the Low-SES High-Family-Risk Group at Year-2 (Figure 2).

Figure 2. Latent profile transition pathways.

Predicting psychopathology symptoms from profiles and pathways

Profile membership at baseline was significantly associated with concurrent internalizing (Cohen’s f 2 = 0.15) and externalizing (f 2 = 0.16) symptoms, also predicting Year-2 internalizing (f 2 = 0.12) and externalizing (f 2 = 0.16) symptoms (Figure 3). Further, transition pathways of profiles over time significantly predicted Year-2 internalizing (f 2 = 0.22) and externalizing symptoms (f 2 = 0.27) (Figure 4). According to guidelines (Cohen, Reference Cohen1988), Cohen’s f 2 values >= 0.02, ≳ 0.15, and ≳ 0.35 indicate small, medium, and large effect sizes, respectively.

Figure 3. Baseline profiles and psychopathology. (a) Latent profiles at baseline with concurrent internalizing and externalizing symptoms. (b) Latent profiles at baseline predicted Year-2 internalizing and externalizing symptoms (controlling for baseline). Compact letter display (cld) illustrates pairwise comparisons (Tukey HSD); if a group shares >= 1 letter(s) with any other group(s), this group does not statistically differ from the other group(s).

Figure 4. Profile transition pathways and psychopathology.

At baseline, the Low-SES High-Family-Risk Group demonstrated the most concurrent internalizing and externalizing symptoms, followed by the High-SES Low-Protective Group, Low-SES High-Protective Group, and High-SES High-Protective Group (Low-SES High-Protective = High-SES High-Protective for internalizing symptoms; Low-SES High-Protective > High-SES High-Protective for externalizing symptoms) (Figure 3a). Predicting Year-2 internalizing and externalizing symptoms while controlling for baseline psychopathology, the Low-SES High-Family-Risk Group predicted the most internalizing and externalizing symptoms, significantly more than the High-SES High-Protective and Low-SES High-Protective Groups, while comparable to the High-SES Low-Protective Group (Figure 3b).

Further, profile transition pathways significantly predicted Year-2 psychopathology while controlling for baseline psychopathology. For internalizing symptoms (Figure 4a), individuals starting in the same profile but ending in different profiles presented divergent psychopathology outcomes, indicating developmental multifinality. Individuals who remained in the High-SES High-Protective Group showed significantly fewer internalizing symptoms than those who started in the same group but transitioned to the High-SES Low-Protective Group; individuals who remained in the Low-SES High-Family-Risk Group showed significantly more internalizing symptoms than those who transitioned to a different group later; lastly, individuals who remained in the Low-SES High-Protective Group showed significantly fewer internalizing symptoms than those who later transitioned to the Low-SES High-Family-Risk Group.

For externalizing symptoms (Figure 4b), developmental multifinality and equifinality were observed. Individuals who remained in the High-SES High-Protective Group showed significantly fewer externalizing symptoms than those who started in the same group but later transitioned to the High-SES Low-Protective Group; individuals who remained in the Low-SES High-Family-Risk Group showed significantly more externalizing symptoms than those who transitioned out of this group later; lastly, individuals who remained in the Low-SES High-Protective Group showed significantly fewer externalizing symptoms than those who later transitioned to the Low-SES High-Family-Risk Group. Conversely, individuals who ended in the High-SES Low-Protective Group showed fewer externalizing symptoms when they transitioned from a Low-SES High-Family-Risk Group, compared with those who started in the High-SES High-Protective Group and transitioned to the High-SES Low-Protective Group, while comparable to those who remained in the High-SES Low-Protective Group over time

Discussion

Developmental theoretical work (Bronfenbrenner, Reference Bronfenbrenner1977; Cicchetti & Rogosch, Reference Cicchetti and Rogosch2002) and empirical evidence support how different factors in the multilayered ecological context shape youth’s development cross-sectionally and shift over time. In alignment with this framework, the present study identified salient developmental forces within multiple ecological contexts (e.g., neighborhood, family, school, peer network) that combined to shape distinct risk/resilience profiles characterized by socioeconomic resources within both neighborhood and family, family risk (e.g., family conflict and parental psychopathology), and protective factors spanning multiple domains (e.g., parenting, school, peer interactions, prosociality). Groupwise comparison among profiles further illustrated the crucial roles of protective factors and SES as critical differentiators for developmental psychopathology in the present study. Indeed, for youth with similarly high SES and low family risk, protective factors emerged as a critical differentiator for developmental psychopathology, such that greater protective resources concurred with fewer internalizing and externalizing symptoms. However, such differences diminished (i.e., became comparable) two years later. Meanwhile, for youth with similarly low family risk and adequate protective resources, lower SES was linked with comparable internalizing symptoms concurrently and even fewer internalizing problems after 2 years. In contrast, it concurred with more externalizing symptoms, which diminished after two years.

The implications are twofold. First, cross-sectionally, our findings align with existing literature supporting the buffering effect of protective factors, shedding light on intervention efforts by identifying both patterns of risk to intervene (e.g., intrafamily conflict) and patterns of resilience to harness (e.g., cultivating more prosocial behaviors in youth, investing more in school facilities and teacher–student relationships). Our findings also support the link between lower socioeconomic resources, both family- and neighborhood-wise, and the emergence of externalizing symptoms, demonstrated in several studies (Evans, Reference Evans2016; Peverill et al., Reference Peverill, Dirks, Narvaja, Herts, Comer and McLaughlin2021; Taylor & Barch, Reference Taylor and Barch2022), including studies leveraging the ABCD Study (Kim et al., Reference Kim, McLaughlin, Chibnik, Koenen and Tiemeier2022; Maxwell et al., Reference Maxwell, Taylor and Barch2023). Further, the observed association between lower SES and externalizing symptoms, but not internalizing symptoms, may reflect divergent etiological pathways for internalizing and externalizing psychopathology (Peverill et al., Reference Peverill, Dirks, Narvaja, Herts, Comer and McLaughlin2021; Ramphal et al., Reference Ramphal, Whalen, Kenley, Yu, Smyser, Rogers and Sylvester2020). For instance, a few studies suggest that the association between low SES and externalizing behaviors may be uniquely mediated by reductions in cortical surface areas indicated in various domains of functioning (e.g., executive functioning) (Kim et al., Reference Kim, McLaughlin, Chibnik, Koenen and Tiemeier2022), reduced intracranial volume (Maxwell et al., Reference Maxwell, Taylor and Barch2023), as well as poorer inhibitory control (Taylor & Barch, Reference Taylor and Barch2022). These findings, including ours, underscore how external factors such as poverty cast a significant influence on developmental psychopathology, calling for meso-system changes and policies addressing broader societal resource and opportunity inequalities. Second, longitudinally, the diminishing effect of protective factors on internalizing and externalizing psychopathology and the differential influences of SES on internalizing versus externalizing psychopathology illustrate the dynamic interplay between environmental forces and developmental psychopathology over time. The buffering effects of protective factors, while observed initially, did not endure after 2 years after controlling for psychopathology at baseline. Speculatively, other experiences in the ecological context (e.g., SES, family dynamics) may arise and temporarily overshadow the “protective shield”; indeed, studies have shown that resilience may reemerge later in life (i.e., “late bloomers”) ( Masten & Tellegen, Reference Masten and Tellegen2012). On the other hand, the opposition direction of change for internalizing versus externalizing symptoms suggests that a singular, deficit-only model of SES tends to oversimplify the picture and miss the nuances, as other processes within the ecological context (e.g., family risk, protective factors), in combination with SES, lead to different risk/resilience profiles and project divergent psychopathology outcomes. Additionally, the finding that lower SES was linked with comparable concurrent and even fewer Year-2 internalizing symptoms, while more concurrent yet comparable Year-2 externalizing symptoms, may account for mixed findings in existing literature on the association between SES and psychopathology (Vollebergh et al., Reference Vollebergh, Van Dorsselaer, Monshouwer, Verdurmen, Van Der Ende and Ter Bogt2006; Wight et al., Reference Wight, Botticello and Aneshensel2006), emphasizing the importance of considering other developmental forces, as well as when the “snapshot” is taken.

The longitudinal, population-based design of the ABCD Study allows for characterizing risk/resilience profiles and examining their associations with concurrent and future psychopathology; it is also a well-suited avenue for observing developmental continuity versus discontinuity (i.e., stable vs. shifting profiles), as well as equifinality versus multifinality, contributing to more precise intervention efforts. Both continuity and discontinuity were observed, such that youth in the High-SES High-Protective Group and Low-SES High-Protective Group showed the most stable profile membership. In contrast, individuals in the other two groups (i.e., Low-SES High-Family-Risk, High-SES Low-Protective) showed greater transition probabilities, indicating differences in the overall stability of the ecological context for different risk/resilience profiles. Further, transition pathways and their associations with developmental psychopathology exemplify the experience-dependent nature of development, echoing developmental equifinality and multifinality.

Overall, youth who started with the same profile membership but followed different pathways demonstrated different internalizing and externalizing psychopathology (i.e., multifinality). Notably, the effects are bidirectional. On the one hand, youth who followed pathways that were characterized by increasing risk or decreasing protective resources showed more psychopathology than peers who stayed in the same group. For example, youth who started with lower risk levels and adequate protective resources (e.g., High-SES High-Protective) demonstrated a significant increase in internalizing and externalizing psychopathology when they transitioned into a greater-risk or lower-protective environment. On the other hand, youth who started with greater risk levels and fewer protective resources (e.g., Low-SES High-Family-Risk Group) demonstrated a significant decrease in psychopathology when they transitioned into a lower-risk or higher-protective environment. In a similar vein, equifinality manifested in the present study, as youth who ended with the same profile membership (e.g., High-SES Low-Protective) showed fewer psychopathology when they transitioned from environments marked by more risk or fewer protective resources (e.g., Low-SES High-Family-Risk), again illustrating experience-dependent development. These patterns demonstrated heightened sensitivity to ecological risk/resilience forces across this critical transition to early adolescence. However, they also, and more importantly, signal opportunities for practical intervention efforts (Lee et al., Reference Lee, Heimer, Giedd, Lein, Estan, Weinberger and Casey2014) to reduce risk and harness resilience. From an assessment perspective, our findings emphasize the need to consider the dynamic evolvement of the developmental context, characterized by changes in multiple ecological domains, as focusing on one single domain or taking a “snapshot” of a specific developmental time may “miss the boat” and misguide intervention efforts.

A few limitations shall be considered. First, the present study draws from the ecological model and focuses on the relatively inner layers (i.e., micro-, meso-, exo-systems). While we recognize the significant influences of broader sociocultural layers (e.g., laws, political climates) upon youth’s development, including broader sociocultural forces is beyond the scope of the present study and is not well supported by available assessments from the ABCD Study. Second, per the ABCD Study’s recommendations and guidelines, we utilized the baseline ADI and COI scores (i.e., residential history-derived scores in the ABCD Study) and extended them to later time points. This decision may have limited the transition probability of the risk/resilience profiles, as it reduces the heterogeneity in our data.

To conclude, the present study, both theory- and data-driven, presented ecologically derived youth’s risk/resilience profiles at different time points; it also characterized the transition pathways among profiles during the critical transition from late childhood to early adolescence and linked the profiles and transition pathways with developmental psychopathology. Our findings reflected developmental equifinality and multifinality and echoed the experience-dependent nature of development, presenting a more integral illustration of the ecological context and shedding light on more precise intervention efforts in youth.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0954579424001603.

Acknowledgments

This work was supported by the National Institute of Mental Health (NIMH) grant (5R01MH122487) to JLW and LRD.

Competing interests

The authors declare no conflict of interest.

References

Acevedo-Garcia, D., McArdle, N., Hardy, E. F., Crisan, U. I., Romano, B., Norris, D., Baek, M., & Reece, J. (2014). The child opportunity index: Improving collaboration between community development and public health. Health Affairs, 33(11), 1948–1957. https://doi.org/10.1377/hlthaff.2014.0679.CrossRefGoogle ScholarPubMed
Achenbach, T. M., & Ruffle, T. M. (2000). The child behavior checklist and related forms for assessing behavioral/emotional problems and competencies. Pediatrics In Review, 21(8), 265–271. https://doi.org/10.1542/pir.21.8.265.CrossRefGoogle ScholarPubMed
Aldridge, J. M., & McChesney, K. (2018). The relationships between school climate and adolescent mental health and wellbeing: A systematic literature review. International Journal of Educational Research, 88, 121–145. https://doi.org/10.1016/j.ijer.2018.01.012.CrossRefGoogle Scholar
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238.CrossRefGoogle ScholarPubMed
Bentler, P. M. (2007). On tests and indices for evaluating structural models. Personality and Individual Differences, 42(5), 825–829. https://doi.org/10.1016/j.paid.2006.09.024.CrossRefGoogle Scholar
Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513–531. https://doi.org/10.1037/0003-066X.32.7.513.CrossRefGoogle Scholar
Buchanan, M., Walker, G., Boden, J. M., Mansoor, Z., & Newton-Howes, G. (2023). Protective factors for psychosocial outcomes following cumulative childhood adversity: Systematic review. BJPsych Open, 9(6), e197. https://doi.org/10.1192/bjo.2023.561.CrossRefGoogle ScholarPubMed
Carr, A., Cullen, K., Keeney, C., Canning, C., Mooney, O., Chinseallaigh, E., & O’Dowd, A. (2021). Effectiveness of positive psychology interventions: A systematic review and meta-analysis. The Journal of Positive Psychology, 16(6), 749–769. https://doi.org/10.1080/17439760.2020.1818807.CrossRefGoogle Scholar
Casey, B. J., Jones, R. M., Levita, L., Libby, V., Pattwell, S. S., Ruberry, E. J., Soliman, F., & Somerville, L. H. (2010). The storm and stress of adolescence: Insights from human imaging and mouse genetics. Developmental Psychobiology, 52(3), 225–235. https://doi.org/10.1002/dev.20447.CrossRefGoogle ScholarPubMed
Chen, G., Adleman, N. E., Saad, Z. S., Leibenluft, E., & Cox, R. W. (2014). Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model. NeuroImage, 99, 571–588. https://doi.org/10.1016/j.neuroimage.2014.06.027.CrossRefGoogle ScholarPubMed
Christian, L. M., Cole, S. W., McDade, T., Pachankis, J. E., Morgan, E., Strahm, A. M., & Kamp Dush, C. M. (2021). A biopsychosocial framework for understanding sexual and gender minority health: A call for action. Neuroscience & Biobehavioral Reviews, 129, 107–116. https://doi.org/10.1016/j.neubiorev.2021.06.004.CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8(4), 597–600. https://doi.org/10.1017/S0954579400007318.CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (2002). A developmental psychopathology perspective on adolescence. Journal of Consulting and Clinical Psychology, 70(1), 6–20. https://doi.org/10.1037/0022-006X.70.1.6.CrossRefGoogle ScholarPubMed
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.Google Scholar
Collie, R. J. (2020). The development of social and emotional competence at school: An integrated model. International Journal of Behavioral Development, 44(1), 76–87. https://doi.org/10.1177/0165025419851864.CrossRefGoogle Scholar
Cooper, R., Di Biase, M. A., Bei, B., Quach, J., & Cropley, V. (2023). Associations of changes in sleep and emotional and behavioral problems from late childhood to early adolescence. JAMA Psychiatry, 80(6), 585–596. https://doi.org/10.1001/jamapsychiatry.2023.0379.CrossRefGoogle ScholarPubMed
Cummings, E. M., Koss, K. J., & Davies, P. T. (2015). Prospective relations between family conflict and adolescent maladjustment: Security in the family system as a mediating process. Journal of Abnormal Child Psychology, 43(3), 503–515. https://doi.org/10.1007/s10802-014-9926-1.CrossRefGoogle ScholarPubMed
Evans, G. W. (2016). Childhood poverty and adult psychological well-being. Proceedings of the National Academy of Sciences of the United States of America, 113(52), 14949–14952. https://doi.org/10.1073/pnas.1604756114.CrossRefGoogle ScholarPubMed
Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018). Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience, 32, 16–22. https://doi.org/10.1016/j.dcn.2018.04.004.CrossRefGoogle ScholarPubMed
Gureje, O., Oladeji, B., Hwang, I., Chiu, W. T., Kessler, R. C., Sampson, N. A., Alonso, J., Andrade, L. H., Beautrais, A., Borges, G., Bromet, E., Bruffaerts, R., De Girolamo, G., De Graaf, R., Gal, G., He, Y., Hu, C., Iwata, N., Karam, E. G.…Nock, M. K. (2011). Parental psychopathology and the risk of suicidal behavior in their offspring: Results from the World Mental Health surveys. Molecular Psychiatry, 16(12), 1221–1233. https://doi.org/10.1038/mp.2010.111.CrossRefGoogle ScholarPubMed
Handley, E. D., Duprey, E. B., Russotti, J., Levin, R. Y., & Warmingham, J. M. (2024). Person-centered methods to advance developmental psychopathology. Development and Psychopathology, 1–9. https://doi.org/10.1017/S0954579424000282.CrossRefGoogle ScholarPubMed
Herringa, R. J. (2017). Trauma, PTSD, and the developing brain. Current Psychiatry Reports, 19(10), 69. https://doi.org/10.1007/s11920-017-0825-3.CrossRefGoogle ScholarPubMed
Hickendorff, M., Edelsbrunner, P. A., McMullen, J., Schneider, M., & Trezise, K. (2018). Informative tools for characterizing individual differences in learning: Latent class, latent profile, and latent transition analysis. Learning and Individual Differences, 66, 4–15. https://doi.org/10.1016/j.lindif.2017.11.001.CrossRefGoogle Scholar
Hirani, S., Ojukwu, E., & Bandara, N. A. (2022). Prosocial behavior and youth mental health outcomes: A scoping review protocol. PLoS One, 17(6), e0270089. https://doi.org/10.1371/journal.pone.0270089.CrossRefGoogle ScholarPubMed
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.CrossRefGoogle Scholar
Huffman, L. G., & Oshri, A. (2022). Continuity versus change in latent profiles of emotion regulation and working memory during adolescence. Developmental Cognitive Neuroscience, 58, 101177. https://doi.org/10.1016/j.dcn.2022.101177.CrossRefGoogle ScholarPubMed
Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., Williamson, D., & Ryan, N. (1997). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child & Adolescent Psychiatry, 36(7), 980–988. https://doi.org/10.1097/00004583-199707000-00021.CrossRefGoogle ScholarPubMed
Kim, H. H., McLaughlin, K. A., Chibnik, L. B., Koenen, K. C., & Tiemeier, H. (2022). Poverty, cortical structure, and psychopathologic characteristics in adolescence. JAMA Network Open, 5(11), e2244049. https://doi.org/10.1001/jamanetworkopen.2022.44049.CrossRefGoogle ScholarPubMed
Lan, X. (2022). Perceived parental warmth, emotional awareness, and positive adjustment profiles in Chinese and Italian early adolescents: A person-centered approach. Journal of Adolescence, 94(2), 206–223. https://doi.org/10.1002/jad.12018.CrossRefGoogle ScholarPubMed
Lee, F. S., Heimer, H., Giedd, J. N., Lein, E. S., Estan, N., Weinberger, D. R., & Casey, B. J. (2014). Adolescent mental health—Opportunity and obligation. Science, 346(6209), 547–549. https://doi.org/10.1126/science.1260497.CrossRefGoogle ScholarPubMed
LeMoult, J., Humphreys, K. L., Tracy, A., Hoffmeister, J.-A., Ip, E., & Gotlib, I. H. (2020). Meta-analysis: Exposure to early life stress and risk for depression in childhood and adolescence. Journal of the American Academy of Child & Adolescent Psychiatry, 59(7), 842–855. https://doi.org/10.1016/j.jaac.2019.10.011.CrossRefGoogle Scholar
Lerner, J. V., Phelps, E., Forman, Y. E., & Bowers, E. P. (2009). Positive youth development. In Lerner, R. M., & Steinberg, L. (Eds.), Handbook of adolescent psychology (1st ed.). Wiley, https://doi.org/10.1002/9780470479193.adlpsy001016.CrossRefGoogle Scholar
Malti, T., & Speidel, R. (2023). Prosocial cascades: Understanding and nurturing the potential for positive developmental trajectories. Advances in child development and behavior, 64, 189–216. https://doi.org/10.1016/bs.acdb.2022.10.002.CrossRefGoogle ScholarPubMed
Masten, A. S., & Tellegen, A. (2012). Resilience in developmental psychopathology: Contributions of the project competence longitudinal study. Development and Psychopathology, 24(2), 345–361. https://doi.org/10.1017/S095457941200003X.CrossRefGoogle ScholarPubMed
Masten, C. L., Eisenberger, N. I., Borofsky, L. A., Pfeifer, J. H., McNealy, K., Mazziotta, J. C., & Dapretto, M. (2009). Neural correlates of social exclusion during adolescence: Understanding the distress of peer rejection. Social Cognitive and Affective Neuroscience, 4(2), 143–157. https://doi.org/10.1093/scan/nsp007.CrossRefGoogle ScholarPubMed
Maxwell, M. Y., Taylor, R. L., & Barch, D. M. (2023). Relationship between neighborhood poverty and externalizing symptoms in children: Mediation and moderation by environmental factors and brain structure. Child Psychiatry & Human Development, 54(6), 1710–1722. https://doi.org/10.1007/s10578-022-01369-w.CrossRefGoogle ScholarPubMed
McLaughlin, K. A., Koenen, K. C., Hill, E. D., Petukhova, M., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2013). Trauma exposure and posttraumatic stress disorder in a national sample of adolescents. Journal of the American Academy of Child & Adolescent Psychiatry, 52(8), 815–830.e14. https://doi.org/10.1016/j.jaac.2013.05.011.CrossRefGoogle Scholar
Moos, R. H., & Moos, B. S. (2014). Encyclopedia of quality of life and well-being research. Springer.Google Scholar
Morris, A. S., Hays-Grudo, J., Zapata, M. I., Treat, A., & Kerr, K. L. (2021). Adverse and protective childhood experiences and parenting attitudes: The role of cumulative protection in understanding resilience. Adversity and Resilience Science, 2(3), 181–192. https://doi.org/10.1007/s42844-021-00036-8.CrossRefGoogle ScholarPubMed
Mujahid, M. S., Diez Roux, A. V., Morenoff, J. D., & Raghunathan, T. (2007). Assessing the measurement properties of neighborhood scales: From psychometrics to ecometrics. American Journal of Epidemiology, 165(8), 858–867. https://doi.org/10.1093/aje/kwm040.CrossRefGoogle Scholar
Okuzono, S. S., Wilson, J., & Slopen, N. (2023). Resilience in development: Neighborhood context, experiences of discrimination, and children’s mental health. Development and Psychopathology, 35(5), 1–9. https://doi.org/10.1017/S0954579423001025.CrossRefGoogle ScholarPubMed
Peverill, M., Dirks, M. A., Narvaja, T., Herts, K. L., Comer, J. S., & McLaughlin, K. A. (2021). Socioeconomic status and child psychopathology in the United States: A meta-analysis of population-based studies. Clinical Psychology Review, 83, 101933. https://doi.org/10.1016/j.cpr.2020.101933.CrossRefGoogle ScholarPubMed
Pynoos, R. S., Steinberg, A. M., & Piacentini, J. C. (1999). A developmental psychopathology model of childhood traumatic stress and intersection with anxiety disorders. Biological Psychiatry, 46(11), 1542–1554. https://doi.org/10.1016/S0006-3223(99)00262-0.CrossRefGoogle ScholarPubMed
Rakesh, D., Zalesky, A., & Whittle, S. (2023). The role of school environment in brain structure, connectivity, and mental health in children: A multimodal investigation. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8(1), 32–41. https://doi.org/10.1016/j.bpsc.2022.01.006.Google ScholarPubMed
Ramphal, B., Whalen, D. J., Kenley, J. K., Yu, Q., Smyser, C. D., Rogers, C. E., & Sylvester, C. M. (2020). Brain connectivity and socioeconomic status at birth and externalizing symptoms at age 2 years. Developmental Cognitive Neuroscience, 45, 100811. https://doi.org/10.1016/j.dcn.2020.100811.CrossRefGoogle ScholarPubMed
Retzler, C., Hallam, G., Johnson, S., & Retzler, J. (2023). Person-centred approaches to psychopathology in the ABCD study: Phenotypes and neurocognitive correlates. Research On Child and Adolescent Psychopathology, 51(8), 1195–1212. https://doi.org/10.1007/s10802-023-01065-w.CrossRefGoogle ScholarPubMed
Sahi, R. S., Eisenberger, N. I., & Silvers, J. A. (2023). Peer facilitation of emotion regulation in adolescence. Developmental Cognitive Neuroscience, 62, 101262. https://doi.org/10.1016/j.dcn.2023.101262.CrossRefGoogle ScholarPubMed
Singh, G. K. (2003). Area deprivation and widening inequalities in US mortality, 1969–1998. American Journal of Public Health, 93(7), 1137–1143. https://doi.org/10.2105/AJPH.93.7.1137.CrossRefGoogle ScholarPubMed
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and, how to, guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/10.1016/j.jvb.2020.103445.CrossRefGoogle Scholar
Sripada, C., Gard, A. M., Angstadt, M., Taxali, A., Greathouse, T., McCurry, K., Hyde, L. W., Weigard, A., Walczyk, P., & Heitzeg, M. (2022). Socioeconomic resources are associated with distributed alterations of the brain’s intrinsic functional architecture in youth. Developmental Cognitive Neuroscience, 58, 101164. https://doi.org/10.1016/j.dcn.2022.101164.CrossRefGoogle ScholarPubMed
Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173–180. https://doi.org/10.1207/s15327906mbr2502_4.CrossRefGoogle ScholarPubMed
Taylor, R. L., & Barch, D. M. (2022). Inhibitory control within the context of early life poverty and implications for outcomes. Neuroscience & Biobehavioral Reviews, 140, 104778. https://doi.org/10.1016/j.neubiorev.2022.104778.CrossRefGoogle ScholarPubMed
Taylor, R. L., Cooper, S. R., Jackson, J. J., & Barch, D. M. (2020). Assessment of neighborhood poverty, cognitive function, and prefrontal and hippocampal volumes in children. JAMA Network Open, 3(11), e2023774. https://doi.org/10.1001/jamanetworkopen.2020.23774.CrossRefGoogle ScholarPubMed
Thijssen, S. (2023). More than a learning environment: School climate as a protective factor for child neurodevelopment and mental health? Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8(1), 6–8. https://doi.org/10.1016/j.bpsc.2022.10.002.Google Scholar
Vanderbilt-Adriance, E., & Shaw, D. S. (2008). Protective factors and the development of resilience in the context of neighborhood disadvantage. Journal of Abnormal Child Psychology, 36(6), 887–901. https://doi.org/10.1007/s10802-008-9220-1.CrossRefGoogle ScholarPubMed
Vollebergh, W. A. M., Van Dorsselaer, S., Monshouwer, K., Verdurmen, J., Van Der Ende, J., & Ter Bogt, T. (2006). Mental health problems in early adolescents in the Netherlands: Differences between school and household surveys. Social Psychiatry and Psychiatric Epidemiology, 41(2), 156–163. https://doi.org/10.1007/s00127-005-0979-x.CrossRefGoogle Scholar
Weymouth, B. B., Fosco, G. M., Mak, H. W., Mayfield, K., LoBraico, E. J., & Feinberg, M. E. (2019). Implications of interparental conflict for adolescents’ peer relationships: A longitudinal pathway through threat appraisals and social anxiety symptoms. Developmental Psychology, 55(7), 1509–1522. https://doi.org/10.1037/dev0000731.CrossRefGoogle ScholarPubMed
Wight, R. G., Botticello, A. L., & Aneshensel, C. S. (2006). Socioeconomic context, social support, and adolescent mental health: A multilevel investigation. Journal of Youth and Adolescence, 35(1), 109–120. https://doi.org/10.1007/s10964-005-9009-2.CrossRefGoogle Scholar
Yang, B., Anderson, Z., Zhou, Z., Liu, S., Haase, C. M., & Qu, Y. (2023). The longitudinal role of family conflict and neural reward sensitivity in youth’s internalizing symptoms. Social Cognitive and Affective Neuroscience, 18(1). https://doi.org/10.1093/scan/nsad037.CrossRefGoogle ScholarPubMed
Yap, M. B. H., Pilkington, P. D., Ryan, S. M., & Jorm, A. F. (2014). Parental factors associated with depression and anxiety in young people: A systematic review and meta-analysis. Journal of Affective Disorders, 156, 8–23. https://doi.org/10.1016/j.jad.2013.11.007.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Demographic characteristics at baseline (N = 9,854)

Figure 1

Figure 1. Latent profile characteristics at baseline. Family conflict (P) = parent-report family conflict; family conflict (Y) = youth-report family conflict.

Figure 2

Figure 2. Latent profile transition pathways.

Figure 3

Figure 3. Baseline profiles and psychopathology. (a) Latent profiles at baseline with concurrent internalizing and externalizing symptoms. (b) Latent profiles at baseline predicted Year-2 internalizing and externalizing symptoms (controlling for baseline). Compact letter display (cld) illustrates pairwise comparisons (Tukey HSD); if a group shares >= 1 letter(s) with any other group(s), this group does not statistically differ from the other group(s).

Figure 4

Figure 4. Profile transition pathways and psychopathology.

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