Predicting Mental Health Trajectories After Potentially Traumatic Events: A Machine Learning Approach

Predicting Mental Health Trajectories After Potentially Traumatic Events: A Machine Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Mental Health Trajectories After Potentially Traumatic Events: A Machine Learning Approach Dunja Tutus, Tanmay Nayyar, Jörg Fegert, Ann-Christin Haag This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7048562/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2026 Read the published version in European Child & Adolescent Psychiatry → Version 1 posted 14 You are reading this latest preprint version Abstract Objective: This study aimed to investigate the trajectories of internalizing and externalizing problems following childhood potentially traumatic events (PTEs) and analyse a comprehensive set of baseline variables (PTEs, individual, environmental) to elucidate their predictive role as contributors to different mental health trajectories. Method: The sample consisted of 4141 participants ( M = 9.48, SD = 0.51 years at baseline; 48.7% girls; 72.1% White) from the Adolescent Brain Cognitive Development study who had experienced at least one PTE. Participants’ mental health problems were assessed using the Brief Problem Monitor self-report form. Latent Growth Mixture Modelling was used to identify trajectories of youth´s internalizing and externalizing problems across the six assessments. Extreme Gradient Boosting, a machine learning approach, was utilized to investigate 37 predictors of different trajectories. Results: Three distinct trajectories were identified: “Resilient”, “Mild stable” and “Moderate chronic increasing”, for internalizing and “Resilient”, “Mild increasing” and “Moderate chronic decreasing” for externalizing problems. Predictors of the “Moderate chronic” versus “Resilient” trajectories were identified using machine learning. The three most important predictors of the internalizing problems trajectory were: behavioural inhibition, female gender, and less parental monitoring, whereas predictors of the externalizing problems trajectory were family conflicts, screentime and behavioural inhibition. Conclusion: The findings can help characterize individual variation in mental health trajectories following childhood PTEs and provide potential targets for intervention to foster mental health. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Worldwide, approximately half of children/adolescents are exposed to potentially traumatic events (PTEs) [ 1 – 3 ]. Of these, 15.9% develop Posttraumatic Stress Disorder (PTSD) [ 4 ]. While the majority of individuals show resilience in adjusting to PTEs, maintaining a stable trajectory of healthy functioning, others experience mild disruptions or even severe and persistent mental health issues [ 5 ]. Research on symptom severity and change over time has identified four main trajectories: resilience, recovery (prolonged but ultimately decreasing disruption in functioning), delayed onset (disruptions that emerge after a significant delay), and chronic (continuing disruption), in both adult and child/adolescent samples [ 6 , 7 ]. Unresolved childhood trauma has been linked to academic problems, social withdrawal, delinquency, poor socioeconomic outcomes, a range of medical (e.g., cardiovascular and metabolic disorders, accelerated aging), and mental health adversities (including a wide range of internalizing and externalizing problems, as well as diagnoses like PTSD, depression, anxiety disorders, attention deficit hyperactivity disorder, and disruptive behaviour disorders), potentially leading to lifelong impairments [ 8 – 11 ]. However, many individuals with trauma-related sequelae are often not identified or treated promptly. A history of trauma is frequently discovered late in the treatment of other conditions [ 8 ]. To enhance healthcare and mitigate the long-term consequences of trauma, it is crucial to develop scalable prediction models based on a broad array of resilience and risk factors. Numerous protective and risk factors associated with trauma adjustment have been identified. For instance, poly-victimization and trauma type (e.g., interpersonal vs. non-interpersonal; threat vs. deprivation), negative coping strategies, low self-control, family psychiatric disorders and poor functioning, low social support, racial/ethnic minority status, low socioeconomic status and female gender have all been linked to poorer trauma recovery [ 2 , 4 , 7 , 12 – 15 ]. While family, community, and school connectedness and support, peer support, higher parental education, and physical activity have been identified as robust protective factors [ 7 , 9 , 15 – 18 ]. Many parents experience mental health difficulties following their child´s exposure to PTEs, which can contribute to the development and persistence of the child´s trauma-related problems [ 19 , 20 ]. PTEs are associated with excessive recreational screentime and exposed youth are more likely to experience internet-initiated victimizations, such as cyberbullying, highlighting the need to include screentime in models for risk estimation [ 7 , 21 , 22 ]. Most previous research has focused on only a few risk and protective factors, mainly due to limitations in sample size. Innovative approaches that utilize methods like artificial intelligence and machine learning have the capability to analyse large datasets and offer new insights to tackle longstanding research questions that were previously not adequately addressed due to small sample sizes and computational limitations [ 5 , 23 , 24 ]. To overcome these limitations, we analysed data from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term developmental study of child health in the United States (U.S.) [ 25 ]. Our first goal was to identify different trajectories of internalizing and externalizing problems among ABCD study participants who had experienced PTE(s). Building on existing research, we hypothesized four trajectories: chronic, emerging, recovering, and resilient. We anticipated that the majority of participants would exhibit a resilient trajectory. Our second objective was to examine a comprehensive set of risk and protective factors to distinguish between participants following chronic and resilient trajectories. We hypothesized that predictors indicating more severe PTEs and heightened psychosocial problems would increase the likelihood of classification in chronic trajectories compared to resilient trajectories. Method Participants The data from the ABCD study were utilized [ 25 ]. Starting in 2016, children aged 9–10, selected through probability sampling in U.S. schools, have been followed for a period of 10 years. Children were excluded if they had magnetic resonance imaging contraindications, lacked English proficiency, had severe sensory/neurological/medical/intellectual limitations, or were unwilling to complete assessments. All parents provided written informed consent and children provided assent. Institutional review board approval was obtained for each site before data collection, with central institutional review board approval granted by the University of California, San Diego [ 26 ]. We utilized data from the 5.1 release, which includes information on 11,868 participants collected between 2016 and 2022 from 21 data acquisition sites in the U.S [ 27 ]. Specifically, we focused on participants who had experienced at least one PTE, as reported at baseline using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K-SADS-PL) for DSM-5 [ 28 ]. This resulted in a final sample size of 4,141 youth (48.7% female) with an average age of 9.48 years ( SD = 0.51). The median household annual income ranged between $ 50,000 and $ 74,999. Participants identified themselves mostly as White (72.1%), Hispanic (19.3%), Black (18.3%), and Asian (1.4%). Measures and Variable Selection Independent Variables All independent variables utilized in this study were assessed at baseline and included in the analyses as either continuous or dichotomous predictors. They were categorized into three groups: individual characteristics, PTEs and other adverse childhood experiences and characteristics of the social environment. Individual Characteristics In addition to sociodemographic characteristics, the present study included the following variables related to the child: screentime, the Behavioural Inhibition System and the Behavioural Approach System (BIS/BAS) [ 29 ], prosocial behaviour, and physical activity. Screentime is a composite score that includes watching television, videos (e.g., YouTube), playing video games, texting on a cell phone/tablet/computer, visiting social networking sites, and video chatting. The BIS/BAS questionnaire consists of one BIS scale and three BAS subscales. Participants rated items based on how they typically think or feel. Total values were calculated by adding up all responses for each (sub)scale. Higher BIS and BAS (sub)scale scores indicated greater sensitivity to punishment and reward, respectively. Prosocial behaviour was assessed using a three-item subscale derived from the Strengths and Difficulties Questionnaire (SDQ) [ 30 ]. The subscale was built using the average item mean scores. Finally, participants were asked how many days they were physically active for a total of at least 60 minutes per day, and how many days they had done exercises to strengthen or tone their muscles, during the past week [ 31 ]. If both questions were answered, the mean value was used. If only one question was answered, that value was used as a measure of physical activity. PTEs and other Adverse Childhood Experiences PTEs meeting DSM-5 criteria were assessed using the K-SADS-PL [ 28 ]. All confirmed PTEs were counted (number of PTEs). Additionally, the PTEs were categorized into poly-victimization, accidents/natural disasters/fire, terrorism/war/community violence, physical abuse, domestic violence, sexual abuse, and traumatic loss of loved ones. The Parental Monitoring Scale (PMS) measures caregiver awareness of their child’s whereabouts, both at home and when they are not [ 32 ]. Low scores on the PMS may indicate physical neglect [ 33 ]. Mean values were used for analyses. Financial struggles within the immediate family over the past year, including difficulties with food, medical, or other expenses (utilities), or facing eviction due to unpaid rent/mortgage, were also included as dichotomous indicators of physical neglect [ 33 ]. The Acceptance subscale from the Revised Child's Report of Parental Behaviour Inventory (CRPBI) was utilized to assess parenting/acceptance by the primary caregiver [ 34 ]. This subscale contains items related to comfort, positive emotions, and open communication. Low scores on the CRPBI may suggest emotional neglect [ 33 ]. A mean value was computed for analysis purposes. Bullying was evaluated using the K-SADS-PL and categorized as a dichotomous variable [ 28 ]. Characteristics of the Social Environment Parental externalizing and internalizing problems were assessed using the available t-scores from the Adult Self Report (ASR), which consists of 120 items [ 35 ]. When completing the ASR, participants report their behaviour, thoughts, and feelings over the previous six months by rating how applicable the items are. A modified version of the Family History Assessment from the National Consortium on Alcohol and Neurodevelopment in Adolescence was utilized to operationalize current parental alcohol and drug problems [ 36 ]. If at least one parent had an issue with either alcohol or drugs, the item was coded as one. The Family Conflict subscale from the Family Environment Scale (FES) addresses topics such as fighting, anger, criticism, yelling, and loss of temper within the family [ 37 ]. Participants were required to indicate whether each item was applicable or not applicable. Scales measuring School Environment, School Involvement and School Disengagement are derived from the School Risk and Protective Factors (SRPF) survey [ 38 ]. The scores for each item were added up to build scale scores. Additionally, we included the item “My neighbourhood is safe from crime” [ 38 ]. Dependent Variables The Internalizing and Externalizing Problem scores obtained from the Brief Problem Monitor youth form (BPM-Y) served as dependent variables [ 39 ]. Participants rated their behaviour, thoughts, and feelings over the previous six months by indicating the applicability of the items. The BPM-Y was completed six months after the baseline assessment for the first time in the ABCD study. All subsequent follow-up assessments up to a 3-year follow-up (six measurement time points in total) were included. A detailed description of the measures is provided in Table 1 . Table 1 Measures of risk and protective factors and dependent variables in the total sample Construct Informant Measure Number of items Min-Max/Answer options M (SD)/n (%) α Individual characteristics Child´s age Caregiver Demographics 1 8–11 years 9.48 (0.51) Child´s gender Caregiver Demographics 1 Male (1) female (0) 2123 (51.3) 2018 (48.7) Child´s race Caregiver Demographics 1 White/Non-White 2985 (72.1) 1150 (27.8) Child´s ethnicity Caregiver Demographics 1 Hispanic/Non-Hispanic 799 (19.3) 3302 (79.7) Caregiver´s education Caregiver Demographics 1 3 (the third grade completed at school)-21 (Doctoral degree) 16.40 (2.56) Caregiver´s employment Caregiver Demographics 1 Employed/non-employed 2835 (68.5) 1300 (31.4) Family income (past year) Caregiver Demographics 1 1 (< $ 5,000)-10 (≥ $ 200,000) 6.80 (2.48) Average screentime (workday) Child Youth Screen Time 6 0–24 hours 3.85 (3.30) BIS (anticipation of punishment) Child BIS/BAS 7 Items: 0–3; Scale: 0–21 9.62 (3.82) .62 BAS: Drive (intensity of goal directed behaviour) Child BIS/BAS 4 0–3; 0–12 4.26 (3.11) .77 BAS: Fun Seeking (willingness to approach a potentially rewarding event) Child BIS/BAS 4 0–3; 0–12 5.86 (2.72) .66 BAS: Reward Responsiveness (anticipation of reward) Child BIS/BAS 5 0–3; 0–15 11.10 (2.94) .73 Prosocial behaviour Child SDQ 3 0–2 1.69 (0.37) .58 Physical activity Child Risk Behaviour 2 0–7 days 2.69 (1.76) PTEs and other adverse childhood experiences PTEs (number) Caregiver K-SADS-PL 17 1–17 1.42 (1.06) Poly-victimization Caregiver K-SADS-PL 17 Yes/No 1147 (27.7) 2994 (72.3) Accidents/natural disasters/fire Caregiver K-SADS-PL 4 Yes/No 1260 (30.4) 2881 (69.6) Terrorism/war/community violence Caregiver K-SADS-PL 3 Yes/No 141 (3.4) 4000 (96.6) Physical abuse Caregiver K-SADS-PL 3 Yes/No 109 (2.6) 4032 (97.4) Domestic violence Caregiver K-SADS-PL 1 Yes/No 929 (22.4) 3212 (77.6) Sexual abuse Caregiver K-SADS-PL 3 Yes/No 222 (5.4) 3919 (94.6) Traumatic loss Caregiver K-SADS-PL 1 Yes/No 2778 (67.1) 1363 (32.9) Parental monitoring Child PMS 5 1–5; Mean: 1.80-5 4.39 (0.52) .45 Struggling expenses: food Caregiver Demographics 1 Yes/No 518 (12.5) 3590 (86.7) Evicted from home Caregiver Demographics 1 Yes/No 81 (2.0) 4047 (97.7) Struggling expenses: medical Caregiver Demographics 2 Yes/No 673 (16.3) 3451 (83.3) Struggling expenses: other Caregiver Demographics 3 Yes/No 881 (21.3) 3231 (78.0) Parenting/acceptance Child CRPBI 5 1–3 2.78 (0.29) .68 Bullying Caregiver K-SADS-PL 1 Yes/No 871 (21.0) 3267 (78.9) Characteristics of the social environment Caregiver: internalizing problems Caregiver ASR 39 0–2; t-scores: 30–95 51.01 (10.98) .92 Caregiver: externalizing problems Caregiver ASR 35 0–2; t-scores: 30–90 48.20 (9.96) .87 Parents: alcohol/drug problems Caregiver Family History 4 Yes/No 1156 (27.9) 2797 (67.5) Family conflicts Child FES 9 0–9 2.17 (2.01) .68 School: environment Child SRPF 6 1–4; 6–24 19.89 (2.91) .61 School: involvement Child SRPF 4 1–4; 4–16 13.03 (2.42) .65 School: disengagement Child SRPF 2 1–4; 2–8 3.80 (1.50) Neighbourhood Child Toolkit 1 1–5 3.93 (1.15) Dependent variables Internalizing problems Child BPM-Y 6 0–2; 0–12 1.98 (2.11) .70 Externalizing problems Child BPM-Y 7 0–2; 0–14 2.13 (2.07) .68 Note . Alphas were calculated for all scales containing at least three items at baseline for all measures, except for BPM-Y. All data reported for BPM-Y are from the 6-month follow-up assessment. BIS/BAS = the Behavioural Inhibition System and the Behavioural Approach System; SDQ = the Strengths and Difficulties Questionnaire; PTEs = Potentially Traumatic Events; K-SADS-PL = the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version; PMS = The Parental Monitoring Scale; CRPBI = the Revised Child's Report of Parental Behaviour Inventory; ASR = the Adult Self Report; FES = the Family Environment Scale; SRPF = the School Risk and Protective Factors; BPM-Y = the Brief Problem Monitor youth form. Statistical Analysis Statistical analyses were conducted using IBM SPSS Statistics 29 and R Version 4.4.0, utilizing the lcmm [ 40 ], xgboost [ 41 ], and SHAPforxgboos t [ 42 ] packages. The significance levels for all statistical tests were set to p < .05 (two-tailed). Data on the two dependent variables, assessed at six time points using the BPM-Y, were missing completely at random (MCAR; Little’s MCAR test: ꭓ² (35) = 0.104, p = 1.000; 9.0% missing data). This allowed us to impute the data using expectation-maximization [ 43 ] and include all 4141 participants in Latent Growth Mixture Modelling (LGMM). LGMM was used to identify trajectories of children’s internalizing and externalizing problems across all measurement time points. Intercepts and slopes were examined as random effects. Model solutions ranging from one to five classes were compared using model fit indices: the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-Size Adjusted BIC (SSABIC), entropy, and Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT). Solutions with fewer than 5.0% of participants in one class were rejected. Once the optimal number of trajectory classes was determined, participants were assigned to classes based on posterior class probabilities to ensure that each participant was placed in the most probable latent class identified by the LGMM. Extreme Gradient Boosting (XGBoost) [ 24 ], a machine learning approach, was utilized to identify key predictors of either the chronic problems or resilient trajectory group. All 37 predictors described in Table 1 were included in the analyses. Two XGBoost models were developed: one for internalizing and another for externalizing problems. The models were trained and tested for binary classification (resilient vs. chronic problems). The data was split into training (80.0%) and testing (20.0%) sets. XGBoost was chosen due to its built-in regularization, efficient handling of missing data, and scalability to large datasets, making it ideal for predictive modelling with structured data. Traditional decision trees, though simple and interpretable, often suffer from high variance and overfitting. Ensemble methods like bagging (combining multiple trees) and boosting (iteratively training trees to address prior weaknesses) mitigate these issues by combining weak learners into a stronger model. Gradient Boosting Machines improve on this further by using gradients and second-order derivatives of the loss function to guide tree construction. However, they can be slow, prone to overfitting, and less effective with large or sparse datasets. XGBoost addresses these challenges through key innovations: built-in regularization adds penalties to the loss function to control model complexity and reduce overfitting; parallelized operations optimize the identification of the best splits during tree construction; and efficient handling of missing data allows the algorithm to learn optimal splits even with incomplete inputs. Features like column subsampling (considering subsets of features during training) and post-pruning (removing unhelpful branches after tree construction) further enhance performance and prevent overfitting. A considerable class imbalance in the dataset was addressed by computing sample weights for chronic problems outcomes, a crucial XGBoost hyperparameter that reduces penalties for the minority class, improving detection of underrepresented outcomes. The SHapley Additive exPlanations (SHAP) value summary bee swarm plots are provided to showcase the influence of each feature on trajectory prediction for both internalizing and externalizing problems. SHAP values consider the effect of each feature across all predictions, revealing complex interactions and non-linear effects [ 44 ]. Each row in the bee swarm plot represents a single predictor ordered by its mean absolute SHAP values. Predictors with larger absolute SHAP values hold more significance than those with lower values. SHAP values can be negative or positive, providing information about the distribution of the impact of each predictor for both trajectories. Each dot represents a SHAP value, and the dots stack up to show density, color-coded according to the magnitude of the value´s contribution to the model impact (violet-high, orange-low). The colour represents the actual predictor value in the data set. Jittered overlapping points are shifted towards the y-axis to display the distribution of the SHAP values for each predictor. Results Identification of Internalizing and Externalizing Problem Trajectories The fit indices for various class solutions are provided in Supplementary 1. For internalizing problems, a 3-class model was chosen. Although AIC, BIC, SABIC and LMR-LRT indicated a better fit for the 4-class model, entropy indicated that the 3-class model provided the best overall fit to the data. Importantly, the 4-class model suggested resilient, moderate and two very similar trajectories characterized by mild problem levels. Since those two trajectories seemed redundant and were not of any particular theoretical interest, the 3-class model was retained instead. The three identified trajectories were labelled as follows: 1. “Resilient” ( n = 2104, 50.8% of the sample), characterized by low internalizing problems (intercept = 0.77, SE = 0.04, p < .001) that slightly increased over time but still remained minimal (slope = 0.02, SE = 0.01, p = .037); 2. “Mild stable” ( n = 1544, 37.3%), characterized by initially mild problems (intercept = 2.53, SE = 0.09, p < .001) that slightly, but not significantly improved over time (slope = − 0.01, SE = 0.02, p = .496); and 3. “Moderate chronic increasing” ( n = 493, 11.9%), characterized by moderate problems at baseline (intercept = 3.86, SE = 0.19, p < .001) that significantly worsened over time (slope = 0.25, SE = 0.05, p < .001). The mean posterior probabilities were adequate for the “Resilient” (.89), “Mild stable” (.79), and “Moderate chronic increasing” (.88) trajectories. The observed means and 95% CIs of the three trajectories across the six assessments are presented in Fig. 1 . For externalizing problems, most model fit indices pointed towards choosing between the 4-class model and the 5-class model. LMR-LRT did not show a preference for any of the solutions. However, similar to internalizing problems, the 4-class model indicated resilient, moderate, and two very similar trajectories characterized by mild problem levels. The 5-class model was not favoured due to one class containing only 2.8% of the participants. The derived 3-class model is easily interpretable and was ultimately retained. The trajectories were labelled as follows: 1. “Resilient” ( n = 1434, 34.6%), characterized by initially low externalizing problems (intercept = 0.70, SE = 0.04, p < .001) that increased over time but remained minimal (slope = 0.03, SE = 0.01, p = .009); 2. “Mild increasing” ( n = 1907, 46.1%), characterized by mild problems at baseline (intercept = 1.99, SE = 0.07, p < .001) that significantly increased over time (slope = 0.06, SE = 0.01, p < .001); and 3. “Moderate chronic decreasing” ( n = 800, 19.3%), characterized by initially moderate problems (intercept = 4.33, SE = 0.14, p < .001) that significantly improved over time (slope = − 0.06, SE = 0.03, p = .011). The mean posterior probabilities were adequate for “Resilient” (.82), “Mild increasing” (.77), and “Moderate chronic decreasing” (.87) trajectories. The observed means of the three trajectories of externalizing problems across time are presented in Fig. 2 . Predictors of Internalizing and Externalizing Problem Trajectories The XGBoost model demonstrated good predictive accuracy when testing predictors for the “Moderate chronic increasing” versus the “Resilient” trajectory of internalizing problems (AUC = .84; 95% CI .81-.87). Out of 37 predictors, 36 contributed to the model. The three most important predictors of the “Moderate chronic increasing” trajectory, in descending order of importance, were pronounced BIS, female gender, and lower parental monitoring (indicating physical neglect). Furthermore, more screentime, family conflicts, caregiver mental health problems, fun seeking, and ethnic minority status were associated with the “Moderate chronic increasing” trajectory, serving as risk factors. Conversely, engagement and a positive attitude towards school, parental acceptance (as opposed to emotional neglect), neighbourhood safety, and partially physical activity and drive served as protective factors, associated with the “Resilient” trajectory. The SHAP values for all predictors are depicted in Fig. 3 . For externalizing problems, the XGBoost model demonstrated good accuracy in predicting risk and protective factors associated with the “Moderate chronic decreasing” versus “Resilient” trajectory, with an AUC of .79 (95% CI .75-.83). Similar to the previous model, the SHAP values suggest that individual and social environment characteristics are crucial, while PTEs and other adversities play a smaller role in distinguishing the two trajectories. Out of 37 predictors, 36 contributed to the model. The two most important predictors of the “Moderate chronic decreasing” problems trajectory, noted in descending order of importance, were family conflicts and increased screentime, followed by higher levels of BIS, caregiver mental health problems, school disengagement, and pronounced BAS. Higher levels of prosocial behaviour, having more educated caregivers, higher family income, engaging in physical activity, enhanced parental acceptance and monitoring, and perceiving school as a resource all played a protective role. SHAP values of all predictors are depicted in Fig. 4 . Discussion The present study aimed to investigate the courses of adjustment following PTEs and to identify the most important risk and protective factors contributing to “Resilient” and “Moderate chronic” trajectories, utilizing a representative community sample derived from the ABCD study [ 25 ]. Mental Health Trajectories Concerning internalizing problems, most youth followed a “Resilient” trajectory, demonstrating healthy functioning across the six assessments. This aligns with a previous study on posttraumatic stress symptoms in children/adolescents who experienced childhood sexual abuse, as well as a meta-analysis including various childhood PTEs [ 6 , 7 ]. The percentage of trauma-exposed individuals following a chronic trajectory in the meta-analysis aligns with our results. The increasing trend of the “Moderate chronic increasing” trajectory in our study may be due to age effects. The onset of adolescence, which can bring reduced well-being and increased vulnerability to internalizing mental health problems, may contribute to this rise [ 45 ]. This trend could also be a result of ongoing stressors over time, as seen in previous research and exacerbated by the COVID-19 pandemic [ 6 , 25 , 27 ]. In contrast to the commonly identified trajectories of recovery and delayed onset, the third trajectory of internalizing problems in our study was “Mild stable”. Due to missing information on the timing of PTEs, one possible explanation for this could be that the events occurred long ago, and our study did not capture the early trauma adjustment stages, potentially involving recovery and delayed symptom onset. Furthermore, the differing findings may be due to the fact that we focused on internalizing problems, which may offer a broader perspective on mental health, rather than solely examining trauma adjustment. It is noteworthy that all three trajectories of externalizing problems show relatively low average means, which may be the result of a tendency for children/adolescents to underestimate their level of externalizing problems as shown in previous research [ 46 ]. Furthermore, internalizing problems, such as depression or anxiety, have been more frequently associated with trauma exposure than externalizing problems [ 8 ]. In summary, our first hypothesis was partially confirmed as the majority of participants followed the “Resilient” trajectory only concerning internalizing problems. The four expected trajectories - chronic, emerging, recovering, and resilient - were not clearly distinguished in the present study. One possible explanation could be that the PTEs occurred a long time ago, preventing the present study from capturing early trauma adjustment stages involving recovery and delayed symptom onset. Predictors of the “Resilient” versus “Moderate Chronic” Trajectories Psychological characteristics, increased screentime, sociodemographic variables, family, social environment, and physical activity were identified as the most influential predictors of trajectories for both internalizing and externalizing problems. These findings align with a meta-analysis that identified psychological, environmental, individual and social characteristics, as the most frequently identified predictors of outcome trajectories [ 6 ]. Regarding PTE characteristics, the meta-analysis found mixed results, such as high rates of resilience among samples with high trauma exposure, leading to the conclusion that the severity and types of PTEs are not key contributors to the trajectories [ 6 ]. The substantial contribution of BIS is not surprising, given the strong positive associations between BIS sensitivity and various psychological difficulties, including anxiety, depression, stress sensitivity, and emotion dysregulation [ 47 ]. However, the identification of screentime as one of the most important features of “Moderate chronic” trajectories is remarkable, considering the inconsistent findings of meta-analyses and reviews on the relationship between screentime and youth mental health [ 48 ]. One recent study in U.S. adolescents found an association between elevated smartphone use and escalating symptom trajectories, which aligns with our findings [ 49 ]. Exposure to PTEs, especially cumulative stressors, can facilitate problematic social media use as a coping mechanism and, in turn, foster mental health problems [ 50 ]. Research on adolescents exposed to PTEs has shown that they are more likely to engage in high-risk internet and social media activities, e.g., heightened usage, encountering sexual content and being victims of cyberbullying, which can significantly impact their well-being [ 7 , 22 ]. Further clarification is needed on the intricate relationship between PTEs and screentime, and how they affect youth´s mental health. It is crucial to take into account factors such as excessive and problematic screen media usage, use of various types of media, specific online behaviours, motivations for use, household rules regarding screen time, the screen habits of other family members, and experiences of online victimization. The predominance of girls in the “Moderate chronic increasing” internalizing problems trajectory is consistent with previous research that has found females, particularly during adolescence, to be more vulnerable to developing trauma-related symptoms and internalizing problems [ 2 , 4 , 12 – 15 , 45 ]. Consistent with existing literature, various family factors such as family conflicts, parental mental health, acceptance and monitoring appear to play a crucial role in trauma adjustment. A child´s exposure to PTEs may trigger trauma-related symptoms in their caregivers, impairing their ability to effectively address the child´s needs [ 19 ]. These symptoms can negatively impact parenting strategies (e.g., becoming overprotective), contribute to the development of unhelpful trauma-related beliefs (e.g., blaming the child for the PTE), and result in maladaptive coping mechanisms (e.g., avoidance) [ 20 ]. Research on parent-child relationships in the face of adversity has shown that children’s symptoms can influence parental symptoms and vice versa. The bidirectional influences, shared genetic and environmental factors, parental role as a caregiver and role model, as well as parental history of PTEs and their involvement in the current PTE need to be considered when addressing childhood trauma [ 19 , 20 ]. PTEs and other adversities, although playing a smaller role compared to individual and environmental factors in differentiating between the two trajectories, primarily served as risk factors, aligning with expectations based on the current literature [ 2 , 4 , 6 , 12 – 14 ]. Taken together, our hypothesis that predictors indicating more severe PTEs and heightened psychosocial problems would increase the likelihood of “Moderate chronic” trajectories has been confirmed. Limitations Information on PTEs was collected through caregiver reports, which raises the possibility that caregivers may not have disclosed all PTEs the child experienced or that their own history of PTEs may have influenced their reporting. The history of PTEs was assessed using a basic checklist, therefore specific details such as timing, description, impact, or severity of the events were not available, even though they are crucial to consider. Additionally, there was no assessment with the BPM-Y before the PTEs occurred, preventing us from comparing problem levels before and after the events. Conclusions and Clinical Implications Given the high frequency of PTEs and their potential long-term consequences, it is crucial to implement regular trauma screening into clinical practice when working with children/adolescents. When a history of trauma is identified, it is vital to consider both individual (e.g., psychological characteristics and screentime) and social environmental factors (e.g., family conflicts) to predict the course of psychological adjustment. Our findings emphasize the significance of monitoring screen media usage in individual risk estimation and considering it in treatment. Finally, addressing parental symptoms following a child´s PTE is crucial to empower parents to actively participate in their child´s trauma recovery. Declarations Author Contribution D.T. and A.H. designed the present study. D.T. conducted data analyses and prepared figures with support from A.H. and T.N. D.T. wrote the main manuscript text. A.H., T.N., and J.F. helped in drafting the manuscript. A.H. provided supervision. All authors reviewed the manuscript. Data Availability One goal of the ABCD Study is to create a unique data resource for the entire scientific community by embracing an open science model. The ABCD Study releases curated, anonymized data annually and makes it available to the research community. Information on how to access ABCD data is available at: https://abcdstudy.org/scientists/data-sharing/. References American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th edn. Washington DC Landolt MA, Schnyder U, Maier T et al (2013) Trauma exposure and posttraumatic stress disorder in adolescents: A national survey in Switzerland. J Trauma Stress 26(2):209–216. https://doi.org/10.1002/jts.21794 McLaughlin KA, Koenen KC, Hill ED et al (2013) Trauma exposure and posttraumatic stress disorder in a national sample of adolescents. 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Consulting Psychologists, Palo Alto, CA Zucker RA, Gonzales R, Feldstein Ewing SW et al (2018) Assessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data. Dev Cogn Neurosci 32:107–120. https://doi.org/10.1016/j.dcn.2018.03.004 Achenbach TM, McConaughy SH, Ivanova MY, Rescorla LA (2011) Manual for the ASEBA brief problem monitor (BPM). Burlington, VT Proust-Lima C, Philipps V, Liquet B (2017) Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. J Stat Softw 78:1–56. https://doi.org/10.18637/jss.v078.i02 Chen T, He T, Benesty M, Khotilovich V (2019) Package ‘xgboost’. R version 90:1–66 Yang L, Allan J (2023) SHAPforxgboost: SHAP Plots for ‘XGBoost’ Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser BStat Method 39(1):1–22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x Lundberg S (2018) SHAP https://shap.readthedocs.io/en/latest/example_notebooks/tabular_examples/tree_based_models/Census%20income%20classification%20with%20XGBoost.html . Accessed 26 April 2025 Solmi M, Radua J, Olivola M et al (2022) Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry 27(1):281–295. https://doi.org/10.1038/s41380-021-01161-7 De Los Reyes A, Augenstein TM, Wang M et al (2015) The validity of the multi-informant approach to assessing child and adolescent mental health. Psychol Bull 141(4):858–900. https://doi.org/10.1037/a0038498 Markarian SA, Pickett SM, Deveson DF, Kanona BB (2013) A model of BIS/BAS sensitivity, emotion regulation difficulties, and depression, anxiety, and stress symptoms in relation to sleep quality. Psychiatry Res 210(1):281–286. https://doi.org/10.1016/j.psychres.2013.06.004 Orben A, Meier A, Dalgleish T, Blakemore S-J (2024) Mechanisms linking social media use to adolescent mental health vulnerability. Nat Rev Psychol 3:407–423. https://doi.org/10.1038/s44159-024-00307-y Haag A-C, Nick EA, Chen MS et al (2025) Investigating risk profiles of smartphone activities and psychosocial factors in adolescents during the COVID-19 pandemic. J Res Adolesc. https://doi.org/10.1111/jora.13045 Pazdur M, Tutus D, Haag A-C (2025) Risk factors for problematic social media use in youth: A systematic review of longitudinal studies. Adolesc Res Rev 10:237–253. https://doi.org/10.1007/s40894-025-00264-4 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7048562","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485371480,"identity":"87f12228-9d49-4121-9a73-7e3a96cc7200","order_by":0,"name":"Dunja Tutus","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABHklEQVRIie3RMUvDQBQH8CcH53KJ6wVqv4HwwsFT0A+TTlmiCFkEQQpCugRcu/gd7OZmQocu+QCBLunSKULdOhQxlwRdLp0d7j89ePfjf8cB2Nj8x2QA2I8nFYBElwHkU4AR/G1MBEEAMNSE90QcI9ATLvXEdd8x4pa32f0eDpMPGW4fnfcrxU+dPJ/DUlxM2aIyEK+8C/wUcJLKiNZOIYkzN8jfGkIZj001WEaIoiN87STyhjOB+eZbE0FygPiHloTb+JdUbYu43A8Q1bUExBpCLekuJsj0fK+oUY1QqbSolfeaSNWSOYSCljw2XcxdReTXD+Pz2Szc7D6TJ//lrFBfKVyPafW82Jlqmu/AgS9j5vN6Uw2ubGxsbGx0fgBb9VrFm0ipQQAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital Ulm","correspondingAuthor":true,"prefix":"","firstName":"Dunja","middleName":"","lastName":"Tutus","suffix":""},{"id":485371481,"identity":"6999017c-c810-46b7-8a9b-dca1d30a42f9","order_by":1,"name":"Tanmay Nayyar","email":"","orcid":"","institution":"University Hospital Ulm","correspondingAuthor":false,"prefix":"","firstName":"Tanmay","middleName":"","lastName":"Nayyar","suffix":""},{"id":485371482,"identity":"77ef0397-92cc-47d5-9f22-19c9ad524be9","order_by":2,"name":"Jörg Fegert","email":"","orcid":"","institution":"University Hospital Ulm","correspondingAuthor":false,"prefix":"","firstName":"Jörg","middleName":"","lastName":"Fegert","suffix":""},{"id":485371483,"identity":"3119746a-f824-4fe1-bf6a-9297e9398b78","order_by":3,"name":"Ann-Christin Haag","email":"","orcid":"","institution":"University Hospital Ulm","correspondingAuthor":false,"prefix":"","firstName":"Ann-Christin","middleName":"","lastName":"Haag","suffix":""}],"badges":[],"createdAt":"2025-07-04 16:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7048562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7048562/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00787-026-03022-6","type":"published","date":"2026-04-01T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87039161,"identity":"2592a2e7-5914-4ff7-b3d2-e937834b4707","added_by":"auto","created_at":"2025-07-18 13:36:50","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174977,"visible":true,"origin":"","legend":"\u003cp\u003eInternalizing problems: The observed means and 95% CIs of the three trajectories across the six assessments\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7048562/v1/b8a69d7e725e6a971514c12a.jpeg"},{"id":87038476,"identity":"ce54f3a2-1345-45b8-929b-3515143c63f0","added_by":"auto","created_at":"2025-07-18 13:28:50","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171663,"visible":true,"origin":"","legend":"\u003cp\u003eExternalizing problems: The observed means and 95% CIs of the three trajectories across the six assessments\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7048562/v1/b7ef37b1f8a2a7e4ccf88bb4.jpeg"},{"id":87039164,"identity":"35c32b2a-4ab3-485d-8b45-ca0947550b9d","added_by":"auto","created_at":"2025-07-18 13:36:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":341379,"visible":true,"origin":"","legend":"\u003cp\u003eInternalizing problems:\u003cstrong\u003e \u003c/strong\u003eSHAP Summary Bee Swarm plot\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. SHAP values for all predictors in the XGBoost machine learning model used to classify between the “Moderate chronic increasing” and the “Resilient” trajectory of internalizing problems. Positive values of the bars filled in violet indicate predictors that serve as risk factors, contributing to the “Moderate chronic increasing” trajectory. Conversely, negative values of the violet bars signify that a predictor acts as a protective factor, contributing to the “Resilient” trajectory. SHAP = SHapley Additive exPlanations; XGBoost = Extreme Gradient Boosting; BIS = the Behavioural Inhibition System; PTEs = Potentially Traumatic Events.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7048562/v1/3139c7dd1d07c6810521e9c9.png"},{"id":87038478,"identity":"697a5d73-e5f3-4eca-8248-8c310ae1a4dd","added_by":"auto","created_at":"2025-07-18 13:28:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":389530,"visible":true,"origin":"","legend":"\u003cp\u003eExternalizing problems: SHAP Summary Bee Swarm plot\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. SHAP values for all predictors in the XGBoost machine learning model used to classify between the “Moderate chronic decreasing” problems and the “Resilient” trajectory of externalizing problems. Positive values of the bars filled in violet indicate predictors that serve as risk factors, contributing to the “Moderate chronic decreasing” trajectory. Conversely, negative values of the violet bars signify that a predictor acts as a protective factor, contributing to the “Resilient” trajectory. SHAP = SHapley Additive exPlanations; XGBoost = Extreme Gradient Boosting; BIS = the Behavioural Inhibition System; PTEs = Potentially Traumatic Events.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7048562/v1/5af2f47083fe9866d5788291.png"},{"id":106343577,"identity":"82848abd-9add-4917-8f13-b78fe30e0838","added_by":"auto","created_at":"2026-04-07 16:06:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1836207,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7048562/v1/e57283d2-efce-4cf7-a339-d3a708098ad7.pdf"},{"id":87038470,"identity":"ef0c93d3-e8b5-486f-b23f-8495b4aff891","added_by":"auto","created_at":"2025-07-18 13:28:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14864,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7048562/v1/9b23b103235a4d958c8a164e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Mental Health Trajectories After Potentially Traumatic Events: A Machine Learning Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWorldwide, approximately half of children/adolescents are exposed to potentially traumatic events (PTEs) [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Of these, 15.9% develop \u003cem\u003ePosttraumatic Stress Disorder\u003c/em\u003e (PTSD) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While the majority of individuals show resilience in adjusting to PTEs, maintaining a stable trajectory of healthy functioning, others experience mild disruptions or even severe and persistent mental health issues [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research on symptom severity and change over time has identified four main trajectories: resilience, recovery (prolonged but ultimately decreasing disruption in functioning), delayed onset (disruptions that emerge after a significant delay), and chronic (continuing disruption), in both adult and child/adolescent samples [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnresolved childhood trauma has been linked to academic problems, social withdrawal, delinquency, poor socioeconomic outcomes, a range of medical (e.g., cardiovascular and metabolic disorders, accelerated aging), and mental health adversities (including a wide range of internalizing and externalizing problems, as well as diagnoses like PTSD, depression, anxiety disorders, attention deficit hyperactivity disorder, and disruptive behaviour disorders), potentially leading to lifelong impairments [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, many individuals with trauma-related sequelae are often not identified or treated promptly. A history of trauma is frequently discovered late in the treatment of other conditions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To enhance healthcare and mitigate the long-term consequences of trauma, it is crucial to develop scalable prediction models based on a broad array of resilience and risk factors.\u003c/p\u003e\u003cp\u003eNumerous protective and risk factors associated with trauma adjustment have been identified. For instance, poly-victimization and trauma type (e.g., interpersonal vs. non-interpersonal; threat vs. deprivation), negative coping strategies, low self-control, family psychiatric disorders and poor functioning, low social support, racial/ethnic minority status, low socioeconomic status and female gender have all been linked to poorer trauma recovery [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While family, community, and school connectedness and support, peer support, higher parental education, and physical activity have been identified as robust protective factors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Many parents experience mental health difficulties following their child´s exposure to PTEs, which can contribute to the development and persistence of the child´s trauma-related problems [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. PTEs are associated with excessive recreational screentime and exposed youth are more likely to experience internet-initiated victimizations, such as cyberbullying, highlighting the need to include screentime in models for risk estimation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMost previous research has focused on only a few risk and protective factors, mainly due to limitations in sample size. Innovative approaches that utilize methods like artificial intelligence and machine learning have the capability to analyse large datasets and offer new insights to tackle longstanding research questions that were previously not adequately addressed due to small sample sizes and computational limitations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo overcome these limitations, we analysed data from the \u003cem\u003eAdolescent Brain Cognitive Development\u003c/em\u003e (ABCD) study, the largest long-term developmental study of child health in the United States (U.S.) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our first goal was to identify different trajectories of internalizing and externalizing problems among ABCD study participants who had experienced PTE(s). Building on existing research, we hypothesized four trajectories: chronic, emerging, recovering, and resilient. We anticipated that the majority of participants would exhibit a resilient trajectory. Our second objective was to examine a comprehensive set of risk and protective factors to distinguish between participants following chronic and resilient trajectories. We hypothesized that predictors indicating more severe PTEs and heightened psychosocial problems would increase the likelihood of classification in chronic trajectories compared to resilient trajectories.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data from the ABCD study were utilized [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Starting in 2016, children aged 9–10, selected through probability sampling in U.S. schools, have been followed for a period of 10 years. Children were excluded if they had magnetic resonance imaging contraindications, lacked English proficiency, had severe sensory/neurological/medical/intellectual limitations, or were unwilling to complete assessments. All parents provided written informed consent and children provided assent. Institutional review board approval was obtained for each site before data collection, with central institutional review board approval granted by the University of California, San Diego [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe utilized data from the 5.1 release, which includes information on 11,868 participants collected between 2016 and 2022 from 21 data acquisition sites in the U.S [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Specifically, we focused on participants who had experienced at least one PTE, as reported at baseline using the \u003cem\u003eKiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version\u003c/em\u003e (K-SADS-PL) for DSM-5 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This resulted in a final sample size of 4,141 youth (48.7% female) with an average age of 9.48 years (\u003cem\u003eSD\u003c/em\u003e = 0.51). The median household annual income ranged between \u003cspan\u003e$\u003c/span\u003e50,000 and \u003cspan\u003e$\u003c/span\u003e74,999. Participants identified themselves mostly as White (72.1%), Hispanic (19.3%), Black (18.3%), and Asian (1.4%).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasures and Variable Selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIndependent Variables\u003c/p\u003e\u003cp\u003eAll independent variables utilized in this study were assessed at baseline and included in the analyses as either continuous or dichotomous predictors. They were categorized into three groups: individual characteristics, PTEs and other adverse childhood experiences and characteristics of the social environment.\u003c/p\u003e\u003cp\u003eIndividual Characteristics\u003c/p\u003e\u003cp\u003eIn addition to sociodemographic characteristics, the present study included the following variables related to the child: screentime, the \u003cem\u003eBehavioural Inhibition System and the Behavioural Approach System\u003c/em\u003e (BIS/BAS) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], prosocial behaviour, and physical activity. Screentime is a composite score that includes watching television, videos (e.g., YouTube), playing video games, texting on a cell phone/tablet/computer, visiting social networking sites, and video chatting. The BIS/BAS questionnaire consists of one BIS scale and three BAS subscales. Participants rated items based on how they typically think or feel. Total values were calculated by adding up all responses for each (sub)scale. Higher BIS and BAS (sub)scale scores indicated greater sensitivity to punishment and reward, respectively. Prosocial behaviour was assessed using a three-item subscale derived from the \u003cem\u003eStrengths and Difficulties Questionnaire\u003c/em\u003e (SDQ) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The subscale was built using the average item mean scores. Finally, participants were asked how many days they were physically active for a total of at least 60 minutes per day, and how many days they had done exercises to strengthen or tone their muscles, during the past week [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. If both questions were answered, the mean value was used. If only one question was answered, that value was used as a measure of physical activity.\u003c/p\u003e\u003cp\u003ePTEs and other Adverse Childhood Experiences\u003c/p\u003e\u003cp\u003ePTEs meeting DSM-5 criteria were assessed using the K-SADS-PL [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. All confirmed PTEs were counted (number of PTEs). Additionally, the PTEs were categorized into poly-victimization, accidents/natural disasters/fire, terrorism/war/community violence, physical abuse, domestic violence, sexual abuse, and traumatic loss of loved ones. The \u003cem\u003eParental Monitoring Scale\u003c/em\u003e (PMS) measures caregiver awareness of their child’s whereabouts, both at home and when they are not [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Low scores on the PMS may indicate physical neglect [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Mean values were used for analyses. Financial struggles within the immediate family over the past year, including difficulties with food, medical, or other expenses (utilities), or facing eviction due to unpaid rent/mortgage, were also included as dichotomous indicators of physical neglect [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The Acceptance subscale from the \u003cem\u003eRevised Child's Report of Parental Behaviour Inventory\u003c/em\u003e (CRPBI) was utilized to assess parenting/acceptance by the primary caregiver [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This subscale contains items related to comfort, positive emotions, and open communication. Low scores on the CRPBI may suggest emotional neglect [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A mean value was computed for analysis purposes. Bullying was evaluated using the K-SADS-PL and categorized as a dichotomous variable [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCharacteristics of the Social Environment\u003c/p\u003e\u003cp\u003eParental externalizing and internalizing problems were assessed using the available t-scores from the \u003cem\u003eAdult Self Report\u003c/em\u003e (ASR), which consists of 120 items [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. When completing the ASR, participants report their behaviour, thoughts, and feelings over the previous six months by rating how applicable the items are. A modified version of the \u003cem\u003eFamily History Assessment\u003c/em\u003e from the \u003cem\u003eNational Consortium on Alcohol and Neurodevelopment in Adolescence\u003c/em\u003e was utilized to operationalize current parental alcohol and drug problems [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. If at least one parent had an issue with either alcohol or drugs, the item was coded as one. The Family Conflict subscale from the \u003cem\u003eFamily Environment Scale\u003c/em\u003e (FES) addresses topics such as fighting, anger, criticism, yelling, and loss of temper within the family [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Participants were required to indicate whether each item was applicable or not applicable. Scales measuring School Environment, School Involvement and School Disengagement are derived from the \u003cem\u003eSchool Risk and Protective Factors\u003c/em\u003e (SRPF) survey [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The scores for each item were added up to build scale scores. Additionally, we included the item “My neighbourhood is safe from crime” [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDependent Variables\u003c/p\u003e\u003cp\u003eThe Internalizing and Externalizing Problem scores obtained from the \u003cem\u003eBrief Problem Monitor youth\u003c/em\u003e form (BPM-Y) served as dependent variables [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Participants rated their behaviour, thoughts, and feelings over the previous six months by indicating the applicability of the items. The BPM-Y was completed six months after the baseline assessment for the first time in the ABCD study. All subsequent follow-up assessments up to a 3-year follow-up (six measurement time points in total) were included. A detailed description of the measures is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasures of risk and protective factors and dependent variables in the total sample\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin-Max/Answer options\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eM (SD)/n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eα\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eIndividual characteristics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild´s age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8–11 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.48 (0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild´s gender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMale (1) female (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2123 (51.3)\u003c/p\u003e\u003cp\u003e2018 (48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild´s race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWhite/Non-White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2985 (72.1)\u003c/p\u003e\u003cp\u003e1150 (27.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild´s ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHispanic/Non-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e799 (19.3)\u003c/p\u003e\u003cp\u003e3302 (79.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver´s education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (the third grade completed at school)-21 (Doctoral degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.40 (2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver´s employment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEmployed/non-employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2835 (68.5)\u003c/p\u003e\u003cp\u003e1300 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily income (past year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (\u0026lt;\u003cspan\u003e$\u003c/span\u003e5,000)-10 (≥\u003cspan\u003e$\u003c/span\u003e200,000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.80 (2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage screentime (workday)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYouth Screen Time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–24 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.85 (3.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBIS (anticipation of punishment)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIS/BAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eItems: 0–3; Scale: 0–21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.62 (3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAS: Drive (intensity of goal directed behaviour)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIS/BAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–3; 0–12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.26 (3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAS: Fun Seeking (willingness to approach a potentially rewarding event)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIS/BAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–3; 0–12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.86 (2.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBAS: Reward Responsiveness (anticipation of reward)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIS/BAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–3; 0–15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.10 (2.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProsocial behaviour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSDQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69 (0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRisk Behaviour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.69 (1.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ePTEs and other adverse childhood experiences\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTEs (number)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.42 (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoly-victimization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1147 (27.7)\u003c/p\u003e\u003cp\u003e2994 (72.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccidents/natural disasters/fire\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1260 (30.4)\u003c/p\u003e\u003cp\u003e2881 (69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerrorism/war/community violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e141 (3.4)\u003c/p\u003e\u003cp\u003e4000 (96.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical abuse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e109 (2.6)\u003c/p\u003e\u003cp\u003e4032 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomestic violence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e929 (22.4)\u003c/p\u003e\u003cp\u003e3212 (77.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSexual abuse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e222 (5.4)\u003c/p\u003e\u003cp\u003e3919 (94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2778 (67.1)\u003c/p\u003e\u003cp\u003e1363 (32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParental monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–5; Mean: 1.80-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.39 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStruggling expenses: food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e518 (12.5)\u003c/p\u003e\u003cp\u003e3590 (86.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEvicted from home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81 (2.0)\u003c/p\u003e\u003cp\u003e4047 (97.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStruggling expenses: medical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e673 (16.3)\u003c/p\u003e\u003cp\u003e3451 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStruggling expenses: other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e881 (21.3)\u003c/p\u003e\u003cp\u003e3231 (78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParenting/acceptance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCRPBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.78 (0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBullying\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eK-SADS-PL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e871 (21.0)\u003c/p\u003e\u003cp\u003e3267 (78.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eCharacteristics of the social environment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver: internalizing problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–2; t-scores: 30–95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.01 (10.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaregiver: externalizing problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–2; t-scores: 30–90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.20 (9.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParents: alcohol/drug problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCaregiver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFamily History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes/No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1156 (27.9)\u003c/p\u003e\u003cp\u003e2797 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily conflicts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.17 (2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool: environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRPF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–4; 6–24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.89 (2.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool: involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRPF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–4; 4–16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.03 (2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool: disengagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRPF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–4; 2–8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.80 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeighbourhood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eToolkit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1–5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.93 (1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eDependent variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternalizing problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBPM-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–2; 0–12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.98 (2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExternalizing problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBPM-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0–2; 0–14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.13 (2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e. Alphas were calculated for all scales containing at least three items at baseline for all measures, except for BPM-Y. All data reported for BPM-Y are from the 6-month follow-up assessment. BIS/BAS = the Behavioural Inhibition System and the Behavioural Approach System; SDQ = the Strengths and Difficulties Questionnaire; PTEs = Potentially Traumatic Events; K-SADS-PL = the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version; PMS = The Parental Monitoring Scale; CRPBI = the Revised Child's Report of Parental Behaviour Inventory; ASR = the Adult Self Report; FES = the Family Environment Scale; SRPF = the School Risk and Protective Factors; BPM-Y = the Brief Problem Monitor youth form.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using IBM SPSS Statistics 29 and R Version 4.4.0, utilizing the \u003cem\u003elcmm\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], \u003cem\u003exgboost\u003c/em\u003e [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and \u003cem\u003eSHAPforxgboos\u003c/em\u003et [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] packages. The significance levels for all statistical tests were set to \u003cem\u003ep\u003c/em\u003e \u0026lt; .05 (two-tailed).\u003c/p\u003e\u003cp\u003eData on the two dependent variables, assessed at six time points using the BPM-Y, were missing completely at random (MCAR; Little’s MCAR test: \u003cem\u003eꭓ²\u003c/em\u003e(35) = 0.104, \u003cem\u003ep\u003c/em\u003e = 1.000; 9.0% missing data). This allowed us to impute the data using expectation-maximization [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and include all 4141 participants in \u003cem\u003eLatent Growth Mixture Modelling\u003c/em\u003e (LGMM).\u003c/p\u003e\u003cp\u003eLGMM was used to identify trajectories of children’s internalizing and externalizing problems across all measurement time points. Intercepts and slopes were examined as random effects. Model solutions ranging from one to five classes were compared using model fit indices: the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-Size Adjusted BIC (SSABIC), entropy, and Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT). Solutions with fewer than 5.0% of participants in one class were rejected. Once the optimal number of trajectory classes was determined, participants were assigned to classes based on posterior class probabilities to ensure that each participant was placed in the most probable latent class identified by the LGMM.\u003c/p\u003e\u003cp\u003e\u003cem\u003eExtreme Gradient Boosting\u003c/em\u003e (XGBoost) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], a machine learning approach, was utilized to identify key predictors of either the chronic problems or resilient trajectory group. All 37 predictors described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were included in the analyses. Two XGBoost models were developed: one for internalizing and another for externalizing problems. The models were trained and tested for binary classification (resilient vs. chronic problems). The data was split into training (80.0%) and testing (20.0%) sets. XGBoost was chosen due to its built-in regularization, efficient handling of missing data, and scalability to large datasets, making it ideal for predictive modelling with structured data. Traditional decision trees, though simple and interpretable, often suffer from high variance and overfitting. Ensemble methods like bagging (combining multiple trees) and boosting (iteratively training trees to address prior weaknesses) mitigate these issues by combining weak learners into a stronger model. Gradient Boosting Machines improve on this further by using gradients and second-order derivatives of the loss function to guide tree construction. However, they can be slow, prone to overfitting, and less effective with large or sparse datasets. XGBoost addresses these challenges through key innovations: built-in regularization adds penalties to the loss function to control model complexity and reduce overfitting; parallelized operations optimize the identification of the best splits during tree construction; and efficient handling of missing data allows the algorithm to learn optimal splits even with incomplete inputs. Features like column subsampling (considering subsets of features during training) and post-pruning (removing unhelpful branches after tree construction) further enhance performance and prevent overfitting. A considerable class imbalance in the dataset was addressed by computing sample weights for chronic problems outcomes, a crucial XGBoost hyperparameter that reduces penalties for the minority class, improving detection of underrepresented outcomes.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eSHapley Additive exPlanations\u003c/em\u003e (SHAP) value summary bee swarm plots are provided to showcase the influence of each feature on trajectory prediction for both internalizing and externalizing problems. SHAP values consider the effect of each feature across all predictions, revealing complex interactions and non-linear effects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Each row in the bee swarm plot represents a single predictor ordered by its mean absolute SHAP values. Predictors with larger absolute SHAP values hold more significance than those with lower values. SHAP values can be negative or positive, providing information about the distribution of the impact of each predictor for both trajectories. Each dot represents a SHAP value, and the dots stack up to show density, color-coded according to the magnitude of the value´s contribution to the model impact (violet-high, orange-low). The colour represents the actual predictor value in the data set. Jittered overlapping points are shifted towards the y-axis to display the distribution of the SHAP values for each predictor.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIdentification of Internalizing and Externalizing Problem Trajectories\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe fit indices for various class solutions are provided in Supplementary 1. For internalizing problems, a 3-class model was chosen. Although AIC, BIC, SABIC and LMR-LRT indicated a better fit for the 4-class model, entropy indicated that the 3-class model provided the best overall fit to the data. Importantly, the 4-class model suggested resilient, moderate and two very similar trajectories characterized by mild problem levels. Since those two trajectories seemed redundant and were not of any particular theoretical interest, the 3-class model was retained instead. The three identified trajectories were labelled as follows: 1. \u0026ldquo;Resilient\u0026rdquo; (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2104, 50.8% of the sample), characterized by low internalizing problems (intercept\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) that slightly increased over time but still remained minimal (slope\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.037); 2. \u0026ldquo;Mild stable\u0026rdquo; (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1544, 37.3%), characterized by initially mild problems (intercept\u0026thinsp;=\u0026thinsp;2.53, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) that slightly, but not significantly improved over time (slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.01, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.496); and 3. \u0026ldquo;Moderate chronic increasing\u0026rdquo; (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;493, 11.9%), characterized by moderate problems at baseline (intercept\u0026thinsp;=\u0026thinsp;3.86, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) that significantly worsened over time (slope\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The mean posterior probabilities were adequate for the \u0026ldquo;Resilient\u0026rdquo; (.89), \u0026ldquo;Mild stable\u0026rdquo; (.79), and \u0026ldquo;Moderate chronic increasing\u0026rdquo; (.88) trajectories. The observed means and 95% CIs of the three trajectories across the six assessments are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor externalizing problems, most model fit indices pointed towards choosing between the 4-class model and the 5-class model. LMR-LRT did not show a preference for any of the solutions. However, similar to internalizing problems, the 4-class model indicated resilient, moderate, and two very similar trajectories characterized by mild problem levels. The 5-class model was not favoured due to one class containing only 2.8% of the participants. The derived 3-class model is easily interpretable and was ultimately retained. The trajectories were labelled as follows: 1. \u0026ldquo;Resilient\u0026rdquo; (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1434, 34.6%), characterized by initially low externalizing problems (intercept\u0026thinsp;=\u0026thinsp;0.70, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) that increased over time but remained minimal (slope\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009); 2. \u0026ldquo;Mild increasing\u0026rdquo; (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1907, 46.1%), characterized by mild problems at baseline (intercept\u0026thinsp;=\u0026thinsp;1.99, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) that significantly increased over time (slope\u0026thinsp;=\u0026thinsp;0.06, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001); and 3. \u0026ldquo;Moderate chronic decreasing\u0026rdquo; (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;800, 19.3%), characterized by initially moderate problems (intercept\u0026thinsp;=\u0026thinsp;4.33, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) that significantly improved over time (slope\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.06, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011). The mean posterior probabilities were adequate for \u0026ldquo;Resilient\u0026rdquo; (.82), \u0026ldquo;Mild increasing\u0026rdquo; (.77), and \u0026ldquo;Moderate chronic decreasing\u0026rdquo; (.87) trajectories. The observed means of the three trajectories of externalizing problems across time are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictors of Internalizing and Externalizing Problem Trajectories\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe XGBoost model demonstrated good predictive accuracy when testing predictors for the \u0026ldquo;Moderate chronic increasing\u0026rdquo; versus the \u0026ldquo;Resilient\u0026rdquo; trajectory of internalizing problems (AUC\u0026thinsp;=\u0026thinsp;.84; 95% CI .81-.87). Out of 37 predictors, 36 contributed to the model. The three most important predictors of the \u0026ldquo;Moderate chronic increasing\u0026rdquo; trajectory, in descending order of importance, were pronounced BIS, female gender, and lower parental monitoring (indicating physical neglect). Furthermore, more screentime, family conflicts, caregiver mental health problems, fun seeking, and ethnic minority status were associated with the \u0026ldquo;Moderate chronic increasing\u0026rdquo; trajectory, serving as risk factors. Conversely, engagement and a positive attitude towards school, parental acceptance (as opposed to emotional neglect), neighbourhood safety, and partially physical activity and drive served as protective factors, associated with the \u0026ldquo;Resilient\u0026rdquo; trajectory. The SHAP values for all predictors are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor externalizing problems, the XGBoost model demonstrated good accuracy in predicting risk and protective factors associated with the \u0026ldquo;Moderate chronic decreasing\u0026rdquo; versus \u0026ldquo;Resilient\u0026rdquo; trajectory, with an AUC of .79 (95% CI .75-.83). Similar to the previous model, the SHAP values suggest that individual and social environment characteristics are crucial, while PTEs and other adversities play a smaller role in distinguishing the two trajectories. Out of 37 predictors, 36 contributed to the model. The two most important predictors of the \u0026ldquo;Moderate chronic decreasing\u0026rdquo; problems trajectory, noted in descending order of importance, were family conflicts and increased screentime, followed by higher levels of BIS, caregiver mental health problems, school disengagement, and pronounced BAS. Higher levels of prosocial behaviour, having more educated caregivers, higher family income, engaging in physical activity, enhanced parental acceptance and monitoring, and perceiving school as a resource all played a protective role. SHAP values of all predictors are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to investigate the courses of adjustment following PTEs and to identify the most important risk and protective factors contributing to “Resilient” and “Moderate chronic” trajectories, utilizing a representative community sample derived from the ABCD study [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMental Health Trajectories\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConcerning internalizing problems, most youth followed a “Resilient” trajectory, demonstrating healthy functioning across the six assessments. This aligns with a previous study on posttraumatic stress symptoms in children/adolescents who experienced childhood sexual abuse, as well as a meta-analysis including various childhood PTEs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The percentage of trauma-exposed individuals following a chronic trajectory in the meta-analysis aligns with our results. The increasing trend of the “Moderate chronic increasing” trajectory in our study may be due to age effects. The onset of adolescence, which can bring reduced well-being and increased vulnerability to internalizing mental health problems, may contribute to this rise [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This trend could also be a result of ongoing stressors over time, as seen in previous research and exacerbated by the COVID-19 pandemic [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In contrast to the commonly identified trajectories of recovery and delayed onset, the third trajectory of internalizing problems in our study was “Mild stable”. Due to missing information on the timing of PTEs, one possible explanation for this could be that the events occurred long ago, and our study did not capture the early trauma adjustment stages, potentially involving recovery and delayed symptom onset. Furthermore, the differing findings may be due to the fact that we focused on internalizing problems, which may offer a broader perspective on mental health, rather than solely examining trauma adjustment.\u003c/p\u003e\u003cp\u003eIt is noteworthy that all three trajectories of externalizing problems show relatively low average means, which may be the result of a tendency for children/adolescents to underestimate their level of externalizing problems as shown in previous research [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Furthermore, internalizing problems, such as depression or anxiety, have been more frequently associated with trauma exposure than externalizing problems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, our first hypothesis was partially confirmed as the majority of participants followed the “Resilient” trajectory only concerning internalizing problems. The four expected trajectories - chronic, emerging, recovering, and resilient - were not clearly distinguished in the present study. One possible explanation could be that the PTEs occurred a long time ago, preventing the present study from capturing early trauma adjustment stages involving recovery and delayed symptom onset.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictors of the “Resilient” versus “Moderate Chronic” Trajectories\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePsychological characteristics, increased screentime, sociodemographic variables, family, social environment, and physical activity were identified as the most influential predictors of trajectories for both internalizing and externalizing problems. These findings align with a meta-analysis that identified psychological, environmental, individual and social characteristics, as the most frequently identified predictors of outcome trajectories [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Regarding PTE characteristics, the meta-analysis found mixed results, such as high rates of resilience among samples with high trauma exposure, leading to the conclusion that the severity and types of PTEs are not key contributors to the trajectories [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe substantial contribution of BIS is not surprising, given the strong positive associations between BIS sensitivity and various psychological difficulties, including anxiety, depression, stress sensitivity, and emotion dysregulation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the identification of screentime as one of the most important features of “Moderate chronic” trajectories is remarkable, considering the inconsistent findings of meta-analyses and reviews on the relationship between screentime and youth mental health [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. One recent study in U.S. adolescents found an association between elevated smartphone use and escalating symptom trajectories, which aligns with our findings [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Exposure to PTEs, especially cumulative stressors, can facilitate problematic social media use as a coping mechanism and, in turn, foster mental health problems [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Research on adolescents exposed to PTEs has shown that they are more likely to engage in high-risk internet and social media activities, e.g., heightened usage, encountering sexual content and being victims of cyberbullying, which can significantly impact their well-being [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Further clarification is needed on the intricate relationship between PTEs and screentime, and how they affect youth´s mental health. It is crucial to take into account factors such as excessive and problematic screen media usage, use of various types of media, specific online behaviours, motivations for use, household rules regarding screen time, the screen habits of other family members, and experiences of online victimization.\u003c/p\u003e\u003cp\u003eThe predominance of girls in the “Moderate chronic increasing” internalizing problems trajectory is consistent with previous research that has found females, particularly during adolescence, to be more vulnerable to developing trauma-related symptoms and internalizing problems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConsistent with existing literature, various family factors such as family conflicts, parental mental health, acceptance and monitoring appear to play a crucial role in trauma adjustment. A child´s exposure to PTEs may trigger trauma-related symptoms in their caregivers, impairing their ability to effectively address the child´s needs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These symptoms can negatively impact parenting strategies (e.g., becoming overprotective), contribute to the development of unhelpful trauma-related beliefs (e.g., blaming the child for the PTE), and result in maladaptive coping mechanisms (e.g., avoidance) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Research on parent-child relationships in the face of adversity has shown that children’s symptoms can influence parental symptoms and vice versa. The bidirectional influences, shared genetic and environmental factors, parental role as a caregiver and role model, as well as parental history of PTEs and their involvement in the current PTE need to be considered when addressing childhood trauma [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePTEs and other adversities, although playing a smaller role compared to individual and environmental factors in differentiating between the two trajectories, primarily served as risk factors, aligning with expectations based on the current literature [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTaken together, our hypothesis that predictors indicating more severe PTEs and heightened psychosocial problems would increase the likelihood of “Moderate chronic” trajectories has been confirmed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Information on PTEs was collected through caregiver reports, which raises the possibility that caregivers may not have disclosed all PTEs the child experienced or that their own history of PTEs may have influenced their reporting. The history of PTEs was assessed using a basic checklist, therefore specific details such as timing, description, impact, or severity of the events were not available, even though they are crucial to consider. Additionally, there was no assessment with the BPM-Y before the PTEs occurred, preventing us from comparing problem levels before and after the events.\u003c/p\u003e"},{"header":"Conclusions and Clinical Implications","content":"\u003cp\u003eGiven the high frequency of PTEs and their potential long-term consequences, it is crucial to implement regular trauma screening into clinical practice when working with children/adolescents. When a history of trauma is identified, it is vital to consider both individual (e.g., psychological characteristics and screentime) and social environmental factors (e.g., family conflicts) to predict the course of psychological adjustment. Our findings emphasize the significance of monitoring screen media usage in individual risk estimation and considering it in treatment. Finally, addressing parental symptoms following a child´s PTE is crucial to empower parents to actively participate in their child´s trauma recovery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.T. and A.H. designed the present study. D.T. conducted data analyses and prepared figures with support from A.H. and T.N. D.T. wrote the main manuscript text. A.H., T.N., and J.F. helped in drafting the manuscript. A.H. provided supervision. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eOne goal of the ABCD Study is to create a unique data resource for the entire scientific community by embracing an open science model. The ABCD Study releases curated, anonymized data annually and makes it available to the research community. Information on how to access ABCD data is available at: https://abcdstudy.org/scientists/data-sharing/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th edn. Washington DC\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandolt MA, Schnyder U, Maier T et al (2013) Trauma exposure and posttraumatic stress disorder in adolescents: A national survey in Switzerland. 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Nat Rev Psychol 3:407\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s44159-024-00307-y\u003c/span\u003e\u003cspan address=\"10.1038/s44159-024-00307-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaag A-C, Nick EA, Chen MS et al (2025) Investigating risk profiles of smartphone activities and psychosocial factors in adolescents during the COVID-19 pandemic. J Res Adolesc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jora.13045\u003c/span\u003e\u003cspan address=\"10.1111/jora.13045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePazdur M, Tutus D, Haag A-C (2025) Risk factors for problematic social media use in youth: A systematic review of longitudinal studies. Adolesc Res Rev 10:237\u0026ndash;253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40894-025-00264-4\u003c/span\u003e\u003cspan address=\"10.1007/s40894-025-00264-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7048562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7048562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to investigate the trajectories of internalizing and externalizing problems following childhood potentially traumatic events (PTEs) and analyse a comprehensive set of baseline variables (PTEs, individual, environmental) to elucidate their predictive role as contributors to different mental health trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThe sample consisted of 4141 participants (\u003cem\u003eM \u003c/em\u003e= 9.48, \u003cem\u003eSD \u003c/em\u003e= 0.51 years at baseline; 48.7% girls; 72.1% White) from the \u003cem\u003eAdolescent Brain Cognitive Development\u003c/em\u003e study who had experienced at least one PTE. Participants’ mental health problems were assessed using the \u003cem\u003eBrief Problem Monitor\u003c/em\u003e self-report form. \u003cem\u003eLatent Growth Mixture Modelling\u003c/em\u003e was used to identify trajectories of youth´s internalizing and externalizing problems across the six assessments. \u003cem\u003eExtreme Gradient Boosting, \u003c/em\u003ea machine learning approach, was utilized to investigate 37 predictors of different trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThree distinct trajectories were identified: “Resilient”, “Mild stable” and “Moderate chronic increasing”, for internalizing and “Resilient”, “Mild increasing” and “Moderate chronic decreasing” for externalizing problems. Predictors of the “Moderate chronic” versus “Resilient” trajectories were identified using machine learning. The three most important predictors of the internalizing problems trajectory were: behavioural inhibition, female gender, and less parental monitoring, whereas predictors of the externalizing problems trajectory were family conflicts, screentime and behavioural inhibition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe findings can help characterize individual variation in mental health trajectories following childhood PTEs and provide potential targets for intervention to foster mental health.\u003c/p\u003e","manuscriptTitle":"Predicting Mental Health Trajectories After Potentially Traumatic Events: A Machine Learning Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:28:45","doi":"10.21203/rs.3.rs-7048562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T05:02:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T16:47:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T02:22:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T02:19:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"663408536975769740815755474726875829","date":"2025-08-11T12:26:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T14:42:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302548648057456862584170800202280425023","date":"2025-08-04T12:07:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326739730858424544112626999047453044285","date":"2025-08-04T00:00:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252258012511322733320955494658685472260","date":"2025-07-24T16:10:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87363616833675640482483396832362614178","date":"2025-07-21T12:40:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T13:27:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T14:54:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T14:53:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Child \u0026 Adolescent Psychiatry","date":"2025-07-04T16:33:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-child-and-adolescent-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ecap","sideBox":"Learn more about [European Child \u0026 Adolescent Psychiatry](http://link.springer.com/journal/787)","snPcode":"787","submissionUrl":"https://submission.nature.com/new-submission/787/3","title":"European Child \u0026 Adolescent Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"24c744d5-11e0-47e5-b155-f946d6b7f29d","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:03:11+00:00","versionOfRecord":{"articleIdentity":"rs-7048562","link":"https://doi.org/10.1007/s00787-026-03022-6","journal":{"identity":"european-child-and-adolescent-psychiatry","isVorOnly":false,"title":"European Child \u0026 Adolescent Psychiatry"},"publishedOn":"2026-04-01 15:58:39","publishedOnDateReadable":"April 1st, 2026"},"versionCreatedAt":"2025-07-18 13:28:45","video":"","vorDoi":"10.1007/s00787-026-03022-6","vorDoiUrl":"https://doi.org/10.1007/s00787-026-03022-6","workflowStages":[]},"version":"v1","identity":"rs-7048562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7048562","identity":"rs-7048562","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}