Journal of Adolescent and Youth Psychological Studies (JAYPS)
E-ISSN: 2981-2526 Director-in-Charge: Dr. Nadereh Saadati Editor-in-Chief: Dr. Ahmad Abedi Owner: KMAN Research Institute Publisher: KMAN Publication Inc. (KMANPUB) Contact email: jayps@kmanpub.com / aypsjournal@Gmail.com Open access: YES Journal of Adolescent and Youth Psychological Studies is a scientific open access peer-reviewed journal publishing original articles, reviews, short communications and scientific reports of a high scientific and ethical standard in psychology, counseling and related academic disciplines. This journal is published in the English language by the KMAN Publication Inc.. It covers all the scientific subjects including family, educational, occupational, rehabilitation counseling and psychotherapy and other areas related to youth psychology and counseling. This journal publishes articles in the following fields: Counseling and adolescent psychology Youth psychology and counseling Educational psychology and counseling (educational field) Occupational, educational psychology and counseling of young people Educational psychology and counseling (educational sciences) General and psychological health of teenagers and the young population Topics related to the training of school administrators and teachers Counseling and psychology of marriage and family About the Publisher Publisher: KMAN Publication Inc. Publisher Office: Unit 5‑10825 Yonge St, Richmond Hill, Ontario, Canada, L4C 3E3 https://journals.kmanpub.com Email: kmanpu@kmanpub.com Telegram: +1 (647) 656‑4401 | WhatsApp: +1 (437) 429‑3507 Germany Office: Dratelnstrasse, Wohnung A60, 21119 Hamburg, Germany (+4915207720487)
Deep Learning Classification of Suicidal Ideation from Electronic Health Records
Objective: To develop, validate, and evaluate the performance of multimodal deep learning architectures in the automated classification and early detection of suicidal ideation utilizing comprehensively extracted electronic health record data. Methods and Materials: This retrospective observational cohort study utilized a dataset of unique patient electronic health records from Armenian healthcare facilities spanning to . Data extraction included both structured clinical variables (demographics, diagnosis codes) and unstructured clinical narratives (progress notes). Unstructured text was processed using advanced Natural Language Processing pipelines. Model development involved an / train-test split, employing the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance. We evaluated multiple architectures, including Bidirectional Long Short-Term Memory networks, standalone clinical Transformers, and a multimodal Deep Neural Network integrated with a Transformer via late fusion. Findings: The overall prevalence of suicidal ideation within the cohort was ( out of records), with significant baseline differences ( ) observed in psychiatric history and demographic distributions between the ideation and non-ideation groups. The multimodal DNN+Transformer model demonstrated superior predictive performance, achieving an Area Under the Curve (AUC) of , an overall accuracy of , and an F1-score of . By comparison, the standalone text-based Transformer achieved an AUC of . Model attention mechanisms revealed that textual tokens such as “hopelessness” and structured features including Major Depressive Disorder and prior suicide attempts were the most heavily weighted predictive variables. Conclusion: The integration of structured clinical data and unstructured clinical narratives through multimodal deep learning architectures significantly outperforms single-modality approaches in classifying suicidal ideation. This validates the use of advanced computational modeling within existing electronic health record systems as a proactive, highly accurate tool for early clinical decision support.
Copyright (c) 2026 Gayane Harutyunyan (Author); Arman Sargsyan (Corresponding Author)
Predicting Adolescent Depressive Relapse via LSTM Modeling of Smartphone Telemetry
Objective: To evaluate the predictive efficacy of a Long Short-Term Memory (LSTM) neural network in forecasting the likelihood of an impending major depressive relapse among a remitted adolescent cohort using longitudinal, passively collected smartphone behavioral telemetry. Methods and Materials: This longitudinal observational study tracked a cohort of N=427remitted adolescents in Morocco over an eighteen-month period. Behavioral data was continuously and unobtrusively collected using a custom passive sensing smartphone application that recorded high-resolution telemetry, including geospatial mobility (GPS), accelerometer-based physical activity, total screen time, and keystroke dynamics. Clinical mental health status was evaluated bi-weekly via Ecological Momentary Assessment (EMA). An LSTM recurrent neural network, designed to capture temporal dependencies in time-series data, was trained on these sequential behavioral inputs to predict the future probability of a clinical depressive relapse. Findings: During the observation window, N=142adolescents experienced a formal depressive relapse. The LSTM predictive model achieved strong performance metrics, forecasting an impending relapse with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.88over a 14-day predictive horizon. The model demonstrated a sensitivity of 0.83 and a specificity of 0.81. A subsequent feature ablation analysis revealed that severe reductions in geospatial mobility variance and profound disruptions in circadian sleep patterns—quantified by acute spikes in nocturnal device usage—served as the most significant digital biomarkers driving the algorithm’s predictive accuracy. Conclusion: Passively collected smartphone telemetry contains highly predictive, sequential signals of clinical deterioration that precede self-reported depressive symptoms. Utilizing advanced LSTM architectures to model these digital footprints provides a highly accurate, objective mechanism for forecasting adolescent depressive relapses, offering a critical window for proactive, digitally-augmented preventative psychiatric interventions.
Copyright (c) 2026 Elena Stoyanova (Author); Rachid El Amrani (Corresponding Author); Rezki Hmeyada (Author)
The Impact of Childhood Trauma on Adolescent Resilience: An Explainable Machine Learning Analysis of Protective Factors in Low-Socioeconomic Contexts
Objective: This study aims to utilize an explainable machine learning framework to identify and rank the critical protective factors that buffer against the deleterious effects of specific childhood trauma subtypes on adolescent resilience within low-socioeconomic Iraqi contexts. Methods and Materials: A cross-sectional design was employed with a sample of adolescents (mean age years; female) from low-socioeconomic districts in Iraq. Data were collected using culturally adapted and validated instruments, including the Childhood Trauma Questionnaire-Short Form (CTQ-SF), the Connor-Davidson Resilience Scale (CD-RISC), and the Resilience Scale for Adolescents (READ). To analyze the complex, non-linear relationships between variables, an eXtreme Gradient Boosting (XGBoost) machine learning model was developed. The model’s interpretability was enhanced using SHapley Additive exPlanations (SHAP) to calculate the global predictive importance and interaction effects of specific risk and protective factors. Findings: The XGBoost model demonstrated robust predictive performance, explaining a significant portion of the variance in adolescent resilience ( , ). SHAP value analysis revealed that Family Cohesion emerged as the paramount protective factor (SHAP ), while Emotional Abuse (SHAP ) and Emotional Neglect (SHAP ) were identified as the most detrimental risk factors, surpassing physical and sexual abuse in predictive weight. Furthermore, a critical non-linear threshold effect was discovered: the protective utility of peer support increased significantly only when emotional neglect scores were below a specific clinical threshold (CTQ-SF ). Conclusion: Explainable machine learning provides granular insights into trauma recovery, highlighting that emotional maltreatment profoundly damages adolescent adaptation. Interventions in low-resource settings must prioritize systemic family-based therapies to foster cohesion and adopt a phased clinical approach that addresses foundational emotional neglect before introducing peer-based support systems.
Copyright (c) 2026 Ahmed Abdulkadhim Johni (Author); Zainab Al-Obaidi (Corresponding Author)
Algorithmic Identification of Academic Burnout: Integrating Wearable Sensor Data and School Performance Metrics via Gradient Boosted Decision Trees
Objective: To develop and validate a multimodal machine learning pipeline utilizing Gradient Boosted Decision Trees to accurately identify academic burnout by integrating continuous physiological data from wearable sensors with behavioral school performance metrics. Methods and Materials: A prospective longitudinal study was conducted with a cohort of undergraduate students enrolled in high-intensity programs in Brazil. Multimodal data collection encompassed continuous physiological monitoring via wrist-worn sensors to capture heart rate variability, electrodermal activity, and sleep architecture, alongside institutional academic registry data tracking lecture attendance, assignment submission latency, and examination scores. Ground-truth burnout classification was established using the Maslach Burnout Inventory-Student Survey. A Gradient Boosted Decision Trees (GBDT) algorithm was trained and optimized to classify students into burnout and non-burnout categories, evaluating performance through accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). Findings: The demographic analysis revealed a burnout prevalence of ( ) within the sample. The optimized GBDT model demonstrated superior predictive capability, achieving an overall classification accuracy of , an F1 score of , and an AUC-ROC of . Feature importance analysis indicated that physiological indicators of allostatic load, primarily rolling heart rate variability ( relative importance), and behavioral withdrawal metrics, specifically assignment submission latency ( relative importance), were the most significant predictors of the syndrome. Furthermore, the burnout cohort exhibited marked autonomic dysregulation, characterized by severely reduced deep sleep and elevated electrodermal activity peaks, directly mirroring their concurrent academic decline. Conclusion: The integration of continuous biometric sensor data with institutional academic metrics via gradient boosting algorithms provides a highly accurate, objective framework for the early identification of academic burnout.
Copyright (c) 2026 Jacinto dos Santos Lino (Author); Thiago Moreira (Corresponding Author); Justas Rimkus (Author)
Clustering Adolescent Emotion Regulation Profiles via Gaussian Mixture Models
Objective: The objective of this study was to identify and characterize the latent profiles of cognitive and habitual emotion regulation strategies among a normative adolescent sample utilizing a person-centered analytical framework. Methods and Materials: This cross-sectional study involved a sample of Greek adolescents (aged to years) recruited via cluster sampling. Participants completed the validated Greek translations of the Cognitive Emotion Regulation Questionnaire (CERQ) and the Emotion Regulation Questionnaire (ERQ). Data were analyzed using Gaussian Mixture Modeling (GMM) to identify unobserved, multivariate configurations of emotion regulation strategies. Model fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy, were utilized to determine the optimal number of latent profiles (denoted as ). Findings: The GMM analysis revealed a profile solution as optimal. The largest group, labeled Adaptive Regulators ( , ), exhibited high use of constructive strategies (e.g., cognitive reappraisal) and low use of detrimental strategies. Maladaptive Regulators ( , ) were characterized by high levels of catastrophizing, rumination, and expressive suppression, alongside deficits in adaptive coping. The remaining Average Regulators ( , ) demonstrated mean-level scores across all assessed dimensions. Demographic analyses indicated a significant association between biological sex and profile membership ( , ), with females significantly overrepresented in the Maladaptive Regulators profile. No significant differences were observed across early versus late age groups ( , ). Conclusion: Adolescents do not utilize emotion regulation strategies in isolation but rather deploy complex, distinct multivariate configurations. Identifying these latent profiles highlights the critical vulnerability of the maladaptive subgroup and underscores the necessity for targeted, personalized mental health interventions, such as tailored cognitive-behavioral or socio-emotional therapies, to mitigate psychopathology risk and promote normative developmental trajectories.
Copyright (c) 2026 Nikos Papadakis (Corresponding Author)
Identifying Novel Endophenotypes of Adolescent Insomnia and their Association with Emotional Dysregulation
Objective: This study aimed to identify unobservable endophenotypes of adolescent insomnia utilizing latent variable modeling and to evaluate their differential associations with specific facets of emotional dysregulation. Methods and Materials: A cross-sectional design was employed, encompassing a community sample of adolescents in Egypt. Sleep parameters were rigorously assessed using continuous objective actigraphy worn in the home environment, paired with the subjective self-report Insomnia Severity Index. Emotional dysregulation was quantified using the multidimensional Difficulties in Emotion Regulation Scale. To uncover hidden sub-populations, Latent Profile Analysis was conducted in R using the combined objective and subjective sleep metrics. Subsequently, a Multivariate Analysis of Covariance was utilized to determine how the derived sleep profiles differentially predicted impairments across the various emotional dysregulation subscales, controlling for demographic covariates. Findings: The Latent Profile Analysis successfully identified distinct insomnia endophenotypes within the adolescent sample: Normative Sleepers, Sleep Onset Delay, and High Fragmentation. The multivariate analyses revealed highly specific associations between these profiles and emotional functioning. Adolescents classified within the Sleep Onset Delay profile demonstrated specialized emotional deficits, specifically characterized by a lack of emotional clarity and severe difficulties engaging in goal-directed behaviors ( ). Conversely, the High Fragmentation profile—marked by extensive wake after sleep onset and reduced total sleep time—was associated with the most severe global impairments in emotion regulation, manifesting primarily as profound deficits in impulse control and a high nonacceptance of emotional responses ( ). Conclusion: Adolescent insomnia is not a monolithic disorder but a heterogeneous construct comprising distinct endophenotypes linked to specific emotional vulnerabilities. Precision behavioral medicine that tailors interventions to exact sleep architectural disruptions may significantly optimize clinical outcomes and mitigate psychopathological risks.
Copyright (c) 2026 Eleni Kouris (Author); Yara Mahfouz (Corresponding Author); Andrei Dumitrescu (Author)
Identifying Non-Linear Predictors of Suicidal Ideation in Hospitalized Youth using SHAP-explained Extreme Gradient Boosting
Objective: The objective of this study is to identify and interpret the non-linear clinical and demographic predictors of suicidal ideation among hospitalized youth using a SHAP-explained Extreme Gradient Boosting predictive model. Methods and Materials: A retrospective cross-sectional study was conducted using the electronic medical records of hospitalized youths aged to years in Nigeria. Clinical and demographic data, including evaluations from the Columbia-Suicide Severity Rating Scale, Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder scale (GAD-7), and Adverse Childhood Experiences (ACE) count, were extracted. An Extreme Gradient Boosting (XGBoost) machine learning algorithm was trained to predict the presence of suicidal ideation. To resolve the “black-box” nature of the algorithm, the SHapley Additive exPlanations (SHAP) framework was utilized to interpret model outputs, rank global feature importance, and map complex, non-linear risk thresholds via dependence plots. Findings: The overall prevalence of suicidal ideation in the clinical cohort was ( ). The XGBoost predictive model demonstrated excellent diagnostic performance on the independent test set, achieving an accuracy of and an Area Under the Curve (AUC) of . SHAP analysis identified depressive severity as the most dominant global predictor, revealing a distinct non-linear threshold where predictive risk exponentially spiked once a PHQ-9 score reached exactly . The cumulative burden of trauma emerged as the second strongest predictor, with or more adverse childhood experiences significantly amplifying risk, specifically interacting with older adolescence (ages to ). Furthermore, anxiety demonstrated a bimodal, non-monotonic risk curve, while a history of non-suicidal self-injury within the past months formed a steep logarithmic risk trajectory. Conclusion: Traditional linear clinical assessments fail to capture the complex, exponential tipping points of youth suicide risk. Integrating SHAP-explained XGBoost algorithms into psychiatric practice can uncover critical non-linear thresholds.
Copyright (c) 2026 Petrus Amutenya (Corresponding Author); Sofía Alvarado, Manuel Vaultier (Author)
Predictive Modeling of Cognitive Flexibility and Creative Problem-Solving in Adolescents
Objective: The present study aimed to develop and test a predictive model examining the extent to which cognitive flexibility explains individual differences in creative problem-solving among adolescents beyond executive functioning and demographic variables. Methods and Materials: This quantitative cross-sectional study was conducted with a sample of 438 adolescents aged 13–17 years recruited from secondary schools in Spain using multistage cluster random sampling. Cognitive flexibility was assessed using the Spanish adaptation of the Cognitive Flexibility Inventory, and creative problem-solving was measured through a combined performance-based task battery and self-report scale evaluating fluency, flexibility, originality, and elaboration. Executive functioning variables, including working memory and inhibitory control, were measured using standardized teacher-report instruments, and academic achievement data were obtained from school records. Data analysis included descriptive statistics, Pearson correlations, hierarchical multiple regression, and structural equation modeling. In addition, a machine learning random forest regression model with 10-fold cross-validation was applied to compare predictive accuracy with traditional regression approaches. Findings: Cognitive flexibility demonstrated a strong positive association with creative problem-solving (p < 0.001). Hierarchical regression analyses revealed that cognitive flexibility significantly increased the explained variance in creative problem-solving beyond demographic and executive control variables, with the final model accounting for 47 percent of the variance (ΔR² = 0.26, p < 0.001). Both flexibility dimensions emerged as significant predictors (p < 0.001). Structural equation modeling confirmed a strong latent path from cognitive flexibility to creative problem-solving (β = 0.68, p < 0.001), with the overall model demonstrating excellent fit indices and explaining 52 percent of the variance. Machine learning analyses corroborated the dominant predictive contribution of cognitive flexibility. Conclusion: The findings indicate that cognitive flexibility functions as a central and robust predictor of creative problem-solving in adolescence, integrating executive control processes and contributing substantial incremental validity.
Explainable Machine Learning Prediction of Dropout Risk Using Psychosocial and Cognitive Variables
Objective: The present study aimed to develop and validate an explainable machine learning model capable of accurately predicting school dropout risk by integrating psychosocial and cognitive variables. Methods and Materials: A cross-sectional predictive design was employed with a sample of 1.172 secondary school students from three federal states in Germany. Standardized instruments were used to assess psychosocial variables including depressive symptoms, academic self-efficacy, school belonging, teacher and peer support, self-regulation, and academic motivation. Cognitive performance was measured through computerized tasks assessing working memory, processing speed, and fluid reasoning. Socioeconomic status and migration background were included as contextual covariates. Data preprocessing involved multiple imputation, normalization, and class imbalance correction using SMOTE. The dataset was partitioned into training, validation, and independent test subsets. Multiple supervised learning algorithms—logistic regression with elastic net regularization, support vector machines, random forest, and gradient boosting—were trained and compared using cross-validated hyperparameter optimization. Model performance was evaluated using AUC, balanced accuracy, F1-score, and calibration indices. Explainability was ensured through SHAP-based global and local feature attribution analyses. Findings: Gradient boosting achieved the highest predictive performance (AUC = 0.92; balanced accuracy = 0.86), significantly outperforming linear models. Psychosocial variables demonstrated stronger predictive power than cognitive variables alone, yet the integration of both domains significantly improved overall model accuracy. Depressive symptoms, academic self-efficacy, and school belonging emerged as the most influential predictors, while processing speed and working memory provided incremental predictive validity. Nonlinear threshold effects were observed, indicating that elevated emotional distress and reduced cognitive efficiency substantially increased dropout probability. Conclusion: The findings demonstrate that explainable machine learning models integrating psychosocial and cognitive indicators can reliably predict dropout risk while preserving interpretability.
Explainable Boosting Models of Attachment Insecurity and Digital Dependency in Adolescents
Objective: The present study aimed to develop and interpret explainable machine learning models examining the nonlinear and interactive associations between attachment insecurity dimensions and digital dependency among adolescents. Methods and Materials: This cross-sectional quantitative study was conducted among 742 secondary school adolescents aged 13–18 years recruited through multistage cluster sampling from urban regions. Participants completed standardized self-report measures assessing attachment anxiety and attachment avoidance, digital dependency, and demographic variables including daily screen time. Data were analyzed using Explainable Boosting Machines (EBMs), a transparent generalized additive modeling approach enhanced with gradient boosting. The dataset was divided into training and testing subsets, and model performance was evaluated using R², RMSE, and MAE indices. Five-fold cross-validation was implemented to optimize hyperparameters. Global feature importance scores, partial dependence functions, and interaction analyses were generated to examine nonlinear thresholds and synergistic risk patterns. Findings: The EBM model explained a substantial proportion of variance in digital dependency scores (R² = 0.47) with satisfactory predictive accuracy. Attachment anxiety emerged as a strong psychological predictor, demonstrating a nonlinear threshold effect in which digital dependency increased sharply at moderate levels of anxiety and plateaued at very high levels. Attachment avoidance showed a weaker but significant positive association with digital dependency, characterized by a gradual linear trend. Daily screen time was the most influential predictor overall, and a significant interaction effect was identified between attachment anxiety and screen time, indicating amplified dependency risk among adolescents high on both variables. Conclusion: The findings underscore the central role of attachment insecurity—particularly attachment anxiety—in shaping adolescents’ vulnerability to digital dependency and demonstrate the utility of explainable machine learning approaches for identifying nonlinear developmental risk patterns.
Machine Learning Detection of Online Social Exclusion and Its Association with Adolescent Loneliness
Objective: The present study aimed to examine whether machine learning–detected online social exclusion predicts adolescent loneliness and whether computationally derived exclusion indicators provide incremental explanatory power beyond self-reported perceived exclusion. Methods and Materials: A cross-sectional correlational design was employed with a sample of 718 adolescents aged 13–17 years recruited from secondary schools in central Mexico. Participants completed the Spanish-adapted UCLA Loneliness Scale and a validated self-report measure of online social exclusion. In addition, anonymized digital interaction data from the previous 30 days were collected and processed using natural language processing techniques. A supervised transformer-based machine learning model was trained to classify exclusionary linguistic and interactional patterns within 96,438 message entries. Individual-level online exclusion probability scores were computed based on linguistic markers, response latency asymmetry, and unanswered message ratios. Hierarchical regression analyses and structural equation modeling were conducted to test direct and indirect associations between machine learning–detected exclusion, perceived exclusion, and loneliness. Findings: Machine learning–detected online social exclusion was positively and significantly associated with adolescent loneliness (p < 0.001). Hierarchical regression analysis demonstrated that computationally detected exclusion predicted loneliness above and beyond demographic variables and self-reported exclusion (ΔR² = 0.06, p < 0.001). Structural equation modeling indicated acceptable model fit and revealed that perceived exclusion partially mediated the relationship between machine learning–detected exclusion and loneliness (indirect effect p < 0.001), while the direct path remained significant (p < 0.001). Conclusion: The findings indicate that algorithmically detected online exclusionary patterns constitute a significant and independent predictor of adolescent loneliness. Integrating machine learning–based behavioral analytics with psychosocial assessment enhances the precision of loneliness risk identification and offers promising avenues for early detection and prevention strategies in digitally mediated youth environments.
Predicting Cyberbullying Perpetration via Random Forest Modeling of Moral Disengagement and Empathy Deficits
Objective: The present study aimed to predict cyberbullying perpetration among adolescents using Random Forest modeling of moral disengagement mechanisms and empathy deficits. Methods and Materials: This cross-sectional quantitative study was conducted among 742 secondary school students (ages 13–18 years) from three provinces in South Africa using multi-stage cluster sampling. Participants completed validated self-report instruments measuring cyberbullying perpetration, moral disengagement, and empathy deficits, alongside demographic indicators and daily internet usage. Data were screened, cleaned, and randomly divided into training (70%) and testing (30%) datasets. A Random Forest classifier with 500 trees was trained to distinguish high versus low cyberbullying perpetration. Hyperparameters were optimized using cross-validation. Model performance was evaluated through accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). A logistic regression model was estimated as a baseline comparator. Variable importance indices and partial dependence plots were generated to examine predictor contributions and non-linear interaction patterns. Findings: Inferential analyses indicated that moral disengagement and empathy deficits were significant predictors of cyberbullying perpetration (p < .001). Male students reported higher levels of cyberbullying and moral disengagement (p < .001). The Random Forest model outperformed logistic regression, achieving superior classification accuracy (0.86 vs. 0.74) and AUC-ROC (0.91 vs. 0.78). Variable importance metrics identified overall moral disengagement, dehumanization, and attribution of blame as the strongest predictors, followed by empathy deficits. Partial dependence analysis revealed non-linear threshold effects, with sharp increases in predicted cyberbullying probability at higher levels of moral disengagement, particularly when combined with elevated empathy deficits. Conclusion: The findings demonstrate that cyberbullying perpetration is most strongly predicted by moral disengagement mechanisms and empathy deficits.
Psychometric Network Analysis of Anhedonia and Reward Responsivity in Adolescents with Subclinical Depression
Objective: The present study aimed to model the symptom-level network structure of anhedonia and reward responsivity in Moroccan adolescents with subclinical depression. Methods and Materials: A cross-sectional quantitative design was employed with a community sample of 468 adolescents aged 14–18 years recruited from public high schools in Rabat, Casablanca, and Fez, Morocco. Participants met criteria for subclinical depressive symptoms based on standardized screening thresholds while not fulfilling diagnostic criteria for major depressive disorder. Measures included the Snaith–Hamilton Pleasure Scale (SHAPS) to assess anhedonia, the Temporal Experience of Pleasure Scale (TEPS) to capture anticipatory and consummatory pleasure, and the Behavioral Activation System (BAS) Reward Responsiveness subscale. Data were analyzed using Gaussian Graphical Modeling with graphical LASSO regularization and EBIC model selection. Centrality indices (strength, closeness, betweenness) and bridge centrality metrics were computed. Network accuracy and stability were evaluated using nonparametric bootstrapping procedures and correlation stability coefficients. Findings: Network estimation revealed a stable and densely interconnected structure among reward-related symptoms. Anticipatory pleasure deficits demonstrated the highest strength centrality, indicating their structural prominence within the network. Significant negative partial correlations were observed between anhedonia and both anticipatory and consummatory pleasure, as well as reward responsiveness. Bridge centrality analyses identified reduced anticipatory excitement and diminished emotional reactivity to positive stimuli as key connectors linking anhedonia and reward responsivity clusters. Bootstrapped confidence intervals supported the robustness of edge weights, and the correlation stability coefficient for strength centrality exceeded recommended thresholds, confirming network reliability. Conclusion: The findings suggest that anticipatory motivational deficits occupy a central and bridging position within the symptom architecture of subclinical adolescent depression.
Structural Equation Modeling of Intolerance of Uncertainty and Anxiety Sensitivity in Early Adolescence
Objective: The present study aimed to examine, through structural equation modeling, the direct predictive effect of intolerance of uncertainty on anxiety sensitivity among early adolescents. Methods and Materials: This quantitative cross-sectional study was conducted with a sample of 428 early adolescents aged 11 to 14 years enrolled in public middle schools in Turkey. Participants were selected using multistage cluster sampling from urban districts representing middle socioeconomic status. Data were collected using standardized self-report measures assessing intolerance of uncertainty and anxiety sensitivity, both of which demonstrated satisfactory internal consistency in the present sample. After preliminary screening for missing data, normality, and outliers, confirmatory factor analyses were conducted to validate the measurement models. Structural equation modeling with maximum likelihood estimation was then employed to test the hypothesized direct path from intolerance of uncertainty to anxiety sensitivity. Model fit was evaluated using multiple goodness-of-fit indices, including chi-square to degrees of freedom ratio, Comparative Fit Index, Tucker–Lewis Index, Root Mean Square Error of Approximation, and Standardized Root Mean Square Residual. Findings: The measurement models demonstrated acceptable to good fit indices, confirming the adequacy of the latent constructs. The structural model also showed satisfactory fit to the data. Intolerance of uncertainty significantly and positively predicted anxiety sensitivity (β = .63, p < .001). The model explained 40 percent of the variance in anxiety sensitivity, indicating a substantial effect size. All standardized factor loadings were statistically significant and within acceptable ranges, supporting the reliability of the latent structure. Conclusion: The findings indicate that intolerance of uncertainty functions as a strong cognitive vulnerability factor predicting anxiety sensitivity in early adolescence. These results support transdiagnostic models of anxiety and highlight the importance of targeting intolerance of uncertainty in early preventive and intervention efforts aimed at reducing anxiety-related sensitivity and emotional distress during adolescence.
Latent Growth Curve Modeling of Rumination Subtypes and Suicidal Ideation in High-Risk Adolescents
Objective: The present study aimed to examine longitudinal trajectories of brooding and reflective rumination and to determine their parallel predictive effects on changes in suicidal ideation among high-risk adolescents using a latent growth curve modeling framework. Methods and Materials: A prospective four-wave longitudinal design was employed with a sample of 362 high-risk adolescents aged 13–17 years recruited from secondary schools and youth mental health services. Participants completed validated self-report measures of brooding and reflective rumination, suicidal ideation, and depressive symptoms at three-month intervals over a 12-month period. Measurement invariance across time was established prior to growth analyses. Unconditional latent growth curve models were first estimated separately for brooding, reflection, and suicidal ideation to assess intercepts and linear slopes. A parallel process latent growth model was subsequently specified to examine whether baseline levels and rates of change in rumination subtypes predicted trajectories of suicidal ideation while controlling for depressive symptoms and gender. Model fit was evaluated using CFI, TLI, RMSEA, and SRMR indices, and missing data were handled using full information maximum likelihood estimation. Findings: Results indicated significant positive linear growth in brooding and suicidal ideation over time, with substantial inter-individual variability in slopes. Reflective rumination demonstrated a stable mean trajectory but significant slope variance. In the parallel process model, the brooding intercept significantly predicted the suicidal ideation intercept (β = .41, p < .001), and the brooding slope significantly predicted growth in suicidal ideation (β = .38, p < .001). Reflective rumination did not significantly predict changes in suicidal ideation trajectories. Depressive symptoms remained a significant covariate of baseline suicidal ideation. Overall model fit indices indicated good structural fit. Conclusion: Findings identify brooding, but not reflective rumination, as a dynamic longitudinal risk mechanism contributing to escalating suicidal ideation among high-risk adolescents, underscoring the importance of targeting maladaptive rumination processes in prevention and intervention efforts.
Explainable AI Analysis of Cognitive Distortions and Their Predictive Role in Adolescent Major Depressive Episodes
Objective: The present study aimed to investigate the predictive role of cognitive distortions in adolescent major depressive episodes using explainable artificial intelligence techniques to enhance both classification accuracy and interpretability of cognitive risk factors. Methods and Materials: A cross-sectional predictive-correlational design was employed with a sample of 612 adolescents aged 13 to 18 years recruited from secondary schools in Georgia through multistage cluster sampling. Cognitive distortions were assessed using a validated self-report inventory measuring catastrophizing, overgeneralization, personalization, mind reading, and dichotomous thinking. Major depressive episodes were identified using a structured screening protocol based on DSM-5 criteria supplemented by the PHQ-9 adolescent version. Data analysis integrated traditional statistical methods and supervised machine learning algorithms. The dataset was divided into training and testing subsets using stratified sampling. Logistic regression, support vector machine, random forest, multilayer perceptron, and gradient boosting (XGBoost) models were implemented with cross-validation and hyperparameter tuning. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Explainability was achieved using SHAP (Shapley Additive Explanations) to determine feature importance and nonlinear effects. Findings: Cognitive distortions were significantly and positively associated with depressive symptoms (p < 0.01). Machine learning models demonstrated high predictive accuracy, with the XGBoost model achieving the strongest performance (AUC = 0.95). SHAP analysis revealed that catastrophizing, overgeneralization, and mind reading contributed the highest predictive weight to classification outcomes. Nonlinear threshold effects indicated substantially increased depression probability beyond upper-quartile distortion scores. Conclusion: Cognitive distortions represent powerful and interpretable predictors of adolescent major depressive episodes, and the integration of explainable
Explainable AI Analysis of Grit, Academic Hope, and Persistence in Iranian EFL Students
Objective: The objective of this study was to examine the predictive roles and relative importance of grit and academic hope in explaining academic persistence among Iranian EFL students using explainable artificial intelligence techniques. Methods and Materials: This study employed a cross-sectional correlational design with a sample of Iranian EFL learners enrolled in language institutes and university language centers in Tehran. Participants completed standardized self-report measures assessing grit (perseverance of effort and consistency of interests), academic hope (agency and pathways), and academic persistence. Data were analyzed using supervised machine learning models suitable for psychological tabular data, with academic persistence specified as the outcome variable and grit and academic hope components as predictors. Model performance was evaluated using explained variance and error-based indices. To ensure interpretability, explainable AI methods, specifically SHapley Additive exPlanations (SHAP), were applied to identify global and local feature contributions and to clarify the direction and magnitude of predictor effects. Findings: The machine learning model explained a substantial proportion of variance in academic persistence, indicating strong predictive performance. Explainable AI analyses revealed that academic hope–agency was the most influential predictor of persistence, followed by perseverance of effort, academic hope–pathways, and consistency of interests. Higher levels of agency beliefs and perseverance of effort were associated with higher predicted persistence scores, while consistency of interests showed a comparatively weaker contribution. The results demonstrated non-linear and differential effects of motivational components on persistence, highlighting individual variability in predictor influence. Conclusion: The findings indicate that academic persistence among Iranian EFL students is primarily driven by agency-based hope and sustained effort, and that explainable AI provides a powerful framework for uncovering nuanced motivational mechanisms beyond traditional linear models.
Explainable artificial intelligence; grit; academic hope; academic persistence; EFL learners; motivational psychology
Copyright (c) 2025 Keivan Seyyedi (Author)
Comparative Analysis of the Role of Premarital Education and Counseling in Appropriate Mate Selection and Sustainable Marriage from the Perspectives of University Students and Family Counselors
Objective: The objective of this study was to compare the perspectives of university students and family counselors regarding the effectiveness of premarital education and counseling in appropriate mate selection and the formation of sustainable marriages. Methods and Materials: This study employed a descriptive–survey design with a cross-sectional approach. The population consisted of students from Islamic Azad University, Sari Branch, and professional family counselors in Mazandaran Province. A total of 450 students and 58 family counselors were selected using multi-stage cluster sampling and purposive sampling, respectively. Data were collected using the Mate Selection Criteria Questionnaire and a researcher-developed instrument. Psychometric properties were verified through confirmatory factor analysis, Cronbach’s alpha, KMO, and Bartlett’s test. Data analysis was performed using SPSS version 24, employing factor analysis, Friedman test, Mann–Whitney U test, and Kolmogorov–Smirnov test at a significance level of .05. Findings: Inferential analyses revealed that premarital counseling, educational programs, educational content, and educational delivery significantly influenced appropriate mate selection and sustainable marriage (p < .05). Friedman test results indicated significant differences in the prioritization of these components within both participant groups (p < .001), with premarital counseling ranked as the most influential factor by both students and family counselors. Mann–Whitney U test results showed no statistically significant differences between the perspectives of students and counselors regarding the evaluated components (p > .05). Conclusion: Premarital education and counseling play a decisive role in enhancing conscious partner selection and promoting long-term marital stability, with consistent recognition of their importance among both emerging adults and professional practitioners.
Copyright (c) 2025 Askari Asghariganji (Corresponding Author); Mohsen Nouraei (Author)
The Effectiveness of Social Problem-Solving Skills Training on Peer Conflict and Social Adjustment in Adolescents
Objective: The present study aimed to examine the effectiveness of social problem-solving skills training on reducing peer conflict and improving social adjustment among adolescents. Methods and Materials: This study employed a randomized controlled trial design with an intervention group and a control group. The participants consisted of 30 adolescents from Tehran who were randomly assigned to the intervention (n = 15) and control (n = 15) groups. The intervention group received a structured social problem-solving skills training program delivered in group format over eight weekly sessions, while the control group received no psychological intervention during the study period. Data were collected at three time points: pretest, posttest, and a five-month follow-up. Peer conflict and social adjustment were assessed using standardized self-report instruments with established psychometric properties. Data analysis was conducted using repeated-measures analysis of variance and Bonferroni post-hoc tests with IBM SPSS Statistics version 27. Findings: The results of repeated-measures ANOVA revealed a significant main effect of time and a significant time × group interaction for both peer conflict and social adjustment (p < .001). Adolescents in the intervention group demonstrated a significant reduction in peer conflict scores and a significant increase in social adjustment scores from pretest to posttest, and these improvements were maintained at the five-month follow-up. In contrast, no significant changes were observed in the control group across the three measurement points. Bonferroni post-hoc comparisons confirmed that the differences between pretest and posttest, as well as between pretest and follow-up, were statistically significant for both outcome variables. Conclusion: The findings indicate that social problem-solving skills training is an effective intervention for reducing peer conflict and enhancing social adjustment among adolescents, with effects that remain stable over time, supporting its use in school-based and preventive mental health programs.
Copyright (c) 2025 Maryam Hosseinzadeh (Corresponding Author)
The Structural Model of Identity Instability Based on Object Relations and Childhood Trauma with the Mediating Role of Mental Pain
Objective: This study aimed to examine a structural model in which adverse childhood experiences and relational disturbances predict self-injurious behaviors through the mediating roles of identity diffusion and psychological pain in adolescents. Methods and Materials: The study employed a descriptive–correlational design using structural equation modeling. Participants were adolescents who completed standardized self-report measures assessing adverse childhood experiences, relational disturbances, identity diffusion, psychological pain, and self-injurious behaviors. Data were analyzed using confirmatory factor analysis to evaluate the measurement model, followed by structural equation modeling to test direct and indirect pathways among variables. Model fit was assessed using multiple goodness-of-fit indices, and indirect effects were examined to determine the mediating roles of identity diffusion and psychological pain. Findings: The results indicated that adverse childhood experiences and relational disturbances had significant positive effects on identity diffusion and psychological pain. Identity diffusion was a significant predictor of psychological pain, and psychological pain showed a strong direct effect on self-injurious behaviors. Indirect pathway analyses revealed that the effects of early relational adversity on self-injury were partially mediated by identity diffusion and psychological pain. The overall structural model demonstrated acceptable fit indices and substantial explanatory power for self-injurious behaviors. Conclusion: The findings support a developmental–relational model of adolescent self-injury in which early adversity and relational disruptions contribute to identity fragmentation and psychological pain, thereby increasing vulnerability to self-injurious behaviors, highlighting the importance of addressing internal suffering and identity processes in prevention and intervention efforts.
Copyright (c) 2025 Fatemeh Zhinous Malekzadeh (Author); Mohammad Mohammadipour (Corresponding Author); Mahmoud Jajarmi (Author)
Effectiveness of a Creative Problem-Solving Skills Training Package on Self-Efficacy and Personal Growth Initiative in Students
Objective: The present study was conducted with the aim of examining the effectiveness of a creative problem-solving skills training package on self-efficacy and personal growth initiative among first-cycle secondary school students in the city of Shiraz. Methods and Materials: This study employed a quasi-experimental design with pre-test, post-test, and one-month follow-up phases, including an experimental group and a control group. The statistical population consisted of female students enrolled in public schools in Shiraz during the 2024–2025 academic year. Using multi-stage cluster sampling, 44 students were randomly assigned to two groups: an experimental group and a control group (22 participants in each group). The research instruments included the Self-Efficacy Questionnaire (Morris, 2001) and the Personal Growth Initiative Scale (Robitschek, 1998). The experimental group received the creative problem-solving skills training package over eight 45-minute sessions, whereas the control group received no intervention. Data were analyzed at the descriptive level (mean and standard deviation) and at the inferential level (mixed repeated-measures analysis of variance) using SPSS version 26. Findings: The results indicated that the creative problem-solving skills training package had a statistically significant effect on increasing self-efficacy and personal growth initiative (p < .05). The one-month follow-up demonstrated that the improvements in self-efficacy and personal growth initiative remained stable in the experimental group, whereas in the control group, no statistically significant differences were observed between the pre-test and follow-up stages for either variable. Conclusion: The findings of this study suggest that the creative problem-solving skills training package can effectively enhance self-efficacy and personal growth initiative among first-cycle secondary school students. These results have practical implications for educational and counseling programs aimed at promoting students’ cognitive, social, and emotional competencies.
Creative problem-solving skills training
Copyright (c) 2025 Fatemeh Dadvand, Reza Manzaritavakoli (Author); Farhad Khormaei (Corresponding Author)
The Effectiveness of an Integrated Educational Package of Growth Mindset and Time Perspective on Personal Growth Initiative and Social–Emotional Competence of Students
Objective: The present study aimed to examine the effectiveness of an integrated educational package based on growth mindset and time perspective on personal growth initiative and social–emotional competence among upper secondary school students. Methods and Materials: This study was conducted using a randomized controlled trial with a pretest–posttest–follow-up design and a control group. The participants consisted of 30 upper secondary school students from Shiraz, Iran, who were randomly assigned to an experimental group (n = 15) and a control group (n = 15). The experimental group received an integrated educational intervention combining growth mindset and time perspective principles over structured group sessions, while the control group received no intervention during the study period. Data were collected at three time points: pretest, posttest, and a five-month follow-up. Personal Growth Initiative and Social–Emotional Competence were assessed using standardized self-report questionnaires. Data analysis was performed using repeated-measures analysis of variance in SPSS version 27. Findings: The results of repeated-measures ANOVA indicated significant main effects of time and group, as well as significant time × group interaction effects for both personal growth initiative and social–emotional competence (p < .001). Bonferroni post-hoc comparisons showed that the experimental group demonstrated significant improvements from pretest to posttest in both variables, and these gains were largely maintained at the five-month follow-up, whereas no significant changes were observed in the control group. Conclusion: The findings suggest that an integrated educational package combining growth mindset and time perspective is effective in enhancing personal growth initiative and social–emotional competence among upper secondary school students, with effects that remain relatively stable over time.
Copyright (c) 2025 Zeinab Azadikhah (Author); Fatemeh Dadvand (Corresponding Author)
The Effectiveness of Schema Therapy on Distress Tolerance and Subjective Well-Being in Adolescents with Parental Bereavement Experience
Objective: The objective of this study was to examine the effectiveness of schema therapy in improving distress tolerance and subjective well-being among adolescents who have experienced parental bereavement. Methods and Materials: This study employed a quasi-experimental design with a pretest–posttest control group. The statistical population consisted of adolescents with parental bereavement experience who referred to counseling centers in Tehran in 2024. Following initial screening using the Grief Experience Questionnaire, 30 adolescents with a moderate level of grief were selected through purposive and convenience sampling and randomly assigned to an experimental group (n = 15) and a control group (n = 15). Data were collected using standardized measures of distress tolerance and subjective well-being. The experimental group received schema therapy in 10 group-based sessions of 90 minutes, while the control group received no intervention. Pretest and posttest assessments were conducted for both groups. Findings: Inferential statistical analysis using multivariate analysis of covariance indicated a significant effect of group membership on both outcome variables after controlling for pretest scores. Schema therapy had a statistically significant effect on distress tolerance, with a large effect size, and a statistically significant effect on subjective well-being, also with a large effect size. The statistical power for both effects was high, indicating strong confidence in the detected differences between the experimental and control groups at the posttest stage. Conclusion: The findings demonstrate that schema therapy is an effective intervention for enhancing distress tolerance and subjective well-being in adolescents experiencing parental bereavement, suggesting its utility as a comprehensive therapeutic approach for addressing emotional vulnerability and promoting psychological adjustment following loss.
adolescents with parental bereavement experience
Copyright (c) 2025 Maryam Akbari (Corresponding Author); Mahsa Fazeli , Alireza Islami , Zeinab Gholami Ghadi, Zeinab Sadat Esmaeili Taba (Author)
Interpretable Deep Learning Analysis of Online Social Anxiety in Adolescents Using Feature Saliency Mapping
Objective: The objective of this study was to develop and validate an interpretable deep learning model for predicting adolescents’ online social anxiety and to identify the most influential psychological and digital behavioral features contributing to its development. Methods and Materials: A cross-sectional predictive modeling design was employed with a sample of 523 adolescents aged 13–18 years recruited from secondary schools in Austria. Participants completed standardized measures assessing online social anxiety, general social anxiety, self-esteem, perceived social support, emotion regulation, and digital behavior patterns. Data were analyzed using a deep neural network with multiple hidden layers and regularization techniques. Model performance was evaluated using RMSE, MAE, R², and Pearson correlation. Interpretability was achieved through advanced feature saliency mapping, including SHAP values and gradient-based attribution methods, enabling identification of both global and individual-level predictors of online social anxiety. Findings: The deep learning model demonstrated strong predictive performance, explaining 81% of the variance in online social anxiety (R² = .81) with high correspondence between predicted and observed scores (r = .90). Feature saliency analysis revealed that online social comparison exerted the strongest positive influence on anxiety predictions, followed by low self-esteem, emotion dysregulation, general social anxiety, and daily social media use. Perceived social support displayed a robust negative contribution, functioning as a protective factor. Subgroup analysis indicated that adolescents with the highest anxiety levels exhibited intensified contributions from social comparison, emotional instability, excessive media engagement, and negative feedback sensitivity. Conclusion: Interpretable deep learning provides a powerful and transparent framework for understanding the complex psychological mechanisms underlying adolescents’ online social anxiety, offering critical insights for early identification and targeted intervention.
Explainable AI Modeling of Hope in Youth Using Feature Attribution of Optimism, Goal Orientation, and Family Support
Objective: The objective of this study was to develop and interpret an explainable artificial intelligence model of youth hope by quantifying the individual and interactive contributions of optimism, goal orientation, and family support. Methods and Materials: This cross-sectional study was conducted with 653 adolescents and emerging adults aged 15–24 from educational and community institutions in Georgia, United States. Participants completed standardized measures of hope, optimism, goal orientation, and perceived family support. Machine learning models including Random Forest, Gradient Boosting, and XGBoost were trained to predict hope, with model performance evaluated using cross-validation and error metrics. Explainability was achieved through SHAP and permutation-based feature attribution methods, enabling identification of global and individual predictor effects and nonlinear interactions. Findings: The XGBoost model demonstrated the strongest predictive performance, explaining 84% of the variance in youth hope (R² = .84, RMSE = 2.19, MAE = 1.68). Feature attribution analyses indicated that family support contributed the largest proportion of predictive influence (38.6%), followed by optimism (32.1%) and goal orientation (25.3%). Significant interaction effects were observed between optimism and family support, as well as between goal orientation and family support, amplifying their combined impact on hope. Conclusion: Youth hope is best explained as a multilevel construct arising from the integrated influence of motivational beliefs and family relational processes. Explainable AI offers a powerful methodological framework for advancing theoretical understanding and guiding personalized intervention strategies.
Feature Sensitivity Mapping of Adolescents’ Identity Development: An Interpretable Machine Learning Study in Brazil
Objective: The objective of this study was to identify and quantify the relative and interactive contributions of psychosocial, relational, cultural, and individual predictors of adolescents’ identity development using feature sensitivity mapping within an interpretable machine learning framework. Methods and Materials: This cross-sectional study was conducted among 1,032 adolescents aged 14–18 years recruited from public and private secondary schools in three major metropolitan regions of Brazil using multi-stage cluster sampling. Participants completed validated measures assessing identity development dimensions, parental autonomy support, peer attachment quality, emotional self-regulation, future orientation, academic self-efficacy, social competence, and demographic characteristics. Data were analyzed using an ensemble of gradient boosting and random forest models with ten-fold cross-validation and Bayesian hyperparameter optimization. Model interpretability was achieved through SHAP-based feature sensitivity mapping, partial dependence plots, and accumulated local effects analysis to examine nonlinear and interactive predictor effects. Findings: The machine learning models explained substantial variance in identity outcomes, with the highest performance observed for identity commitment (R² = .61), followed by exploration in breadth (R² = .57), exploration in depth (R² = .55), and ruminative exploration (R² = .49). Parental autonomy support, peer attachment quality, emotional self-regulation, future orientation, academic self-efficacy, and social competence emerged as the most influential predictors across identity dimensions. Emotional self-regulation demonstrated the strongest protective effect against ruminative exploration, while parental autonomy support and peer attachment quality exhibited the highest positive contributions to identity commitment. Conclusion: Adolescents’ identity development operates as a nonlinear, interactive system in which relational security, self-regulation, and motivational resources play central roles, and interpretable machine learning provides a powerful framework for advancing developmental theory and intervention design.
Interpretable Gradient Boosting Analysis of Academic Cheating in Adolescents Using Moral Disengagement and Achievement Goals
Objective: This study aimed to develop an interpretable machine learning model to examine the predictive roles of moral disengagement and achievement goal orientations in academic cheating among high school adolescents. Methods and Materials: The study employed a cross-sectional correlational design with a predictive analytics framework and was conducted among 681 adolescents aged 14–18 years enrolled in public high schools in California. Participants completed standardized self-report measures assessing academic cheating behavior, moral disengagement, and achievement goal orientations, along with demographic information. Data were analyzed using an Extreme Gradient Boosting (XGBoost) regression model with five-fold cross-validation and Bayesian hyperparameter optimization. Model performance was evaluated using root mean squared error, mean absolute error, and explained variance. To ensure interpretability, Shapley Additive Explanations were applied to quantify the relative and local contributions of predictors, and partial dependence analyses were conducted to examine nonlinear and interactive effects. Findings: The gradient boosting model demonstrated strong predictive performance, accounting for 56% of the variance in academic cheating. Moral disengagement emerged as the most influential predictor, followed by performance-avoidance and performance-approach goals. Mastery-approach goals exhibited a consistent negative association with cheating. The model identified nonlinear threshold effects for moral disengagement and significant interaction patterns between motivational orientations and moral cognition, indicating that performance-based goals amplified the impact of moral disengagement on cheating behavior. Conclusion: The findings indicate that academic cheating in adolescence is primarily driven by cognitive moral mechanisms operating in conjunction with achievement-related motivational pressures. Interpretable machine learning offers a powerful framework for uncovering these complex psychological dynamics and provides actionable insights for the design of targeted educational interventions aimed at promoting academic integrity.
Feature Contribution Analysis of Youth Life Satisfaction Using Psychological Capital and Social Connectedness
Objective: The objective of this study was to develop an explainable predictive model of youth life satisfaction by quantifying the individual and combined contributions of psychological capital components and social connectedness. Methods and Materials: A cross-sectional correlational design was employed with a sample of 684 Moroccan adolescents and emerging adults recruited from secondary schools, vocational institutes, and universities. Participants completed standardized measures of life satisfaction, psychological capital, and social connectedness. Advanced machine learning models including Elastic Net Regression, Random Forest, and Gradient Boosting Machine were trained using ten-fold cross-validation. Explainable artificial intelligence techniques based on SHAP values and permutation feature importance were applied to interpret model predictions, identify dominant predictors, and examine nonlinear interactions among psychological and social variables. Findings: The Gradient Boosting Machine demonstrated superior predictive performance (R² = .76, RMSE = 2.78, MAE = 2.19). Feature contribution analysis revealed that hope was the strongest predictor of life satisfaction, followed by social connectedness and self-efficacy. Optimism and resilience showed moderate but substantial contributions, whereas demographic variables such as age and gender exerted comparatively minor influence. Interaction effects indicated that high social connectedness amplified the positive effects of psychological capital components on predicted life satisfaction. Conclusion: Youth life satisfaction is primarily driven by dynamic psychological and social resources rather than static demographic characteristics. Explainable machine learning provides a powerful framework for uncovering complex predictive structures and offers actionable insight for designing personalized youth well-being interventions focused on strengthening psychological capital and social integration.
Explainable AI Modeling of Adolescents’ Psychological Adjustment Using Coping Styles, Social Support Quality, and Life Stress Exposure
Objective: The objective of this study was to develop and interpret an explainable artificial intelligence model to predict adolescents’ psychological adjustment from coping styles, social support quality, and life stress exposure in a representative sample of Iraqi secondary school students. Methods and Materials: This cross-sectional study was conducted among 612 adolescents aged 14–18 years recruited from public secondary schools in Baghdad, Najaf, and Basra using multistage cluster sampling. Participants completed validated measures assessing psychological adjustment, coping styles, perceived social support quality, and life stress exposure. Data were preprocessed through normalization, missing-value imputation, and outlier screening. Machine learning models including random forest, extreme gradient boosting, and multilayer perceptron neural networks were trained using five-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations, permutation feature importance, and partial dependence analyses. Findings: The XGBoost model demonstrated the highest predictive performance (R² = 0.76, RMSE = 0.29, MAE = 0.22). Social support quality emerged as the strongest positive predictor of psychological adjustment, followed by problem-focused coping. Life stress exposure exerted a substantial negative effect. Avoidance coping significantly predicted poorer adjustment, whereas emotion-focused coping displayed nonlinear effects depending on stress levels. Interaction analysis revealed that high social support significantly buffered the adverse effects of life stress on psychological adjustment. Conclusion: The findings demonstrate that adolescents’ psychological adjustment is governed by complex nonlinear interactions among coping strategies, social support, and stress exposure, and that explainable artificial intelligence offers a powerful framework for modeling these processes with high predictive accuracy and theoretical transparency.
Neural Network Analysis of Adolescent Depression: The Interactive Roles of Loneliness, Family Communication Quality, and Digital Media Dependency
Objective: This study aimed to examine the nonlinear and interactive effects of loneliness, family communication quality, and digital media dependency on depressive symptoms among Indonesian adolescents using neural network modeling. Methods and Materials: A cross-sectional design was implemented with 684 secondary school students aged 14–18 years selected via multistage cluster sampling from urban regions of Indonesia. Participants completed validated measures assessing depressive symptoms, loneliness, family communication quality, and digital media dependency. Data were analyzed using a multilayer perceptron neural network constructed in Python with TensorFlow. The dataset was partitioned into training, validation, and test sets. Model performance was evaluated using root mean square error, mean absolute error, and coefficient of determination. Predictor contributions and interaction effects were interpreted using SHAP values and partial dependence analyses. Findings: The neural network demonstrated high predictive accuracy (R² = .79 on the test set). Loneliness emerged as the strongest predictor of depressive symptoms, followed by digital media dependency and family communication quality. Significant nonlinear interaction effects were observed, indicating that the combination of high loneliness, poor family communication, and elevated digital media dependency produced the highest levels of depressive symptoms. Family communication quality exerted a strong buffering effect that attenuated the impact of loneliness and digital dependency on depression. Conclusion: Adolescent depression is shaped by complex, interactive psychosocial and digital factors that are effectively captured through neural network modeling. Strengthening family communication and promoting healthy digital engagement may substantially reduce depressive risk among adolescents.