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)
Predicting Self-Esteem Instability Using Rejection Sensitivity, Daily Stress Reactivity, Social Feedback Valence, and Emotional Reactivity in LSTM Models
Objective: To evaluate the efficacy of a Long Short-Term Memory ( ) neural network in predicting intra-individual self-esteem instability utilizing continuous ecological momentary assessment data encompassing trait rejection sensitivity, daily stress reactivity, social feedback valence, and momentary emotional reactivity. Methods and Materials: A longitudinal Ecological Momentary Assessment study was conducted over days with a final sample of young adults from Spain. Participants completed the Rejection Sensitivity Questionnaire and responded to multiple daily prompts assessing momentary stress, social feedback valence, emotional reactivity, and state self-esteem. An neural network was designed to capture the non-linear, time-dependent relationships within this multi-dimensional data, and its performance was benchmarked against traditional linear regression and standard Recurrent Neural Network ( ) architectures. Findings: The proposed architecture significantly outperformed baseline models, capturing of the variance in self-esteem instability ( ) with a low prediction error ( ). Permutation feature importance analysis revealed that momentary emotional reactivity and trait rejection sensitivity were the strongest predictors of self-esteem fluctuations. Additionally, an ablation study on look-back windows identified a -hour period ( assessments) as the optimal temporal framework for forecasting instability. Conclusion: Advanced deep learning models, specifically networks, can accurately decode the complex, non-linear temporal dynamics of self-esteem, providing a powerful computational foundation for future personalized, real-time psychological interventions.
Copyright (c) 2026 Takumi Shibata (Author); Álvaro Montoya (Corresponding Author)
Predicting Problematic Internet Use via Impulsivity, Emotion Dysregulation, Reward-System Reactivity, and Loneliness Using SVM Models
Objective: The present study aimed to predict the severity of problematic internet use by examining the complex interplay of impulsivity, emotion dysregulation, reward-system reactivity, and loneliness utilizing advanced Support Vector Machine (SVM) models. Methods and Materials: A cross-sectional predictive design was employed, collecting self-reported data from a sample of Argentine adults using validated psychometric questionnaires to measure problematic internet use, impulsivity, emotion dysregulation, reward-system reactivity, and subjective loneliness. Data analysis involved preliminary bivariate correlations and the application of machine learning classification algorithms, specifically training and evaluating various SVM kernels to distinguish between high and low-to-moderate problematic internet users. Finally, permutation feature importance techniques were utilized to rank the predictive weight of the psychological constructs within the optimal model. Findings: Preliminary bivariate analyses revealed significant positive correlations between problematic internet use and all predictor variables, most notably emotion dysregulation ( ) and impulsivity ( ). The Radial Basis Function (RBF) kernel emerged as the optimal SVM model, demonstrating robust predictive capability with an overall classification accuracy of and an F1-score of . Furthermore, permutation feature importance analysis identified emotion dysregulation as the most critical predictor of pathological digital behavior, followed sequentially by impulsivity, loneliness, and reward-system reactivity. Conclusion: Problematic internet use is profoundly driven by underlying deficits in emotional and behavioral self-regulation, underscoring the necessity for targeted psychological interventions that address these core vulnerabilities rather than merely restricting screen time.
Copyright (c) 2026 José Maldonado (Author); Paula Quiroga (Corresponding Author); Ethan Holloway (Author)
Machine‑Learning Identification of Subtypes of Adolescent Perfectionism Based on Concern Over Mistakes, Parental Expectations, Cognitive Rigidity, and Negative Affect
Objective: The objective of this study was to identify distinct latent subtypes of adolescent perfectionism by applying unsupervised machine-learning algorithms to a comprehensive set of variables, specifically concern over mistakes, parental expectations, cognitive rigidity, and negative affect. Methods and Materials: The research utilized a cross-sectional, school-based design, sampling adolescents from Germany. Data were gathered using standardized self-report instruments to measure the four target variables, with all raw scores converted to standardized -scores. Following data preprocessing, unsupervised machine learning via Gaussian Mixture Modeling (GMM) was employed to uncover latent subpopulations, evaluating models ranging from two to six classes using fit indices such as the Bayesian Information Criterion and the Bootstrapped Likelihood Ratio Test. Findings: The GMM algorithm identified an optimal three-class structural model (Entropy = ). The sample was categorized into Class : Low Perfectionism ( , ); Class : Externally Pressured Perfectionism ( , ), defined predominantly by elevated perceived parental expectations ( ); and Class : Maladaptive Perfectionism ( , ), characterized by pervasive elevations across all domains, notably severe negative affect ( ) and extreme cognitive rigidity ( ). Demographic analyses revealed significant differences, indicating that older adolescents ( , ) and females ( , ), who comprised of Class , were significantly overrepresented in the Maladaptive Perfectionism profile. Conclusion: Adolescent perfectionism manifests in distinct multivariate phenotypes, highlighting the critical necessity of identifying high-risk, maladaptive subtypes to deliver highly targeted clinical interventions focusing on cognitive flexibility and emotion regulation.
Copyright (c) 2026 Nathaniel Brooks (Author); Lena Hoffmann (Corresponding Author); Miguel Figueiredo (Author)
Modeling Adolescent Aggressive Behavior Using Trait Anger, Executive Function Inhibition Scores, Family Chaos, and Social Rejection via Gradient Boosted Trees
Objective: The objective of this study was to rigorously predict and model adolescent aggressive behavior through the complex, non-linear interactions of trait anger, executive function inhibition scores, family chaos, and social rejection via a Gradient Boosted Trees approach. Methods and Materials: A cross-sectional predictive study design was utilized, involving a sample of adolescents (aged – years) recruited from central Mexico. Data were collected using the Buss-Perry Aggression Questionnaire, State-Trait Anger Expression Inventory, a computerized Stroop Color and Word Test, the Confusion, Hubbub, and Order Scale, and the Ostracism Experience Scale. Data analysis was conducted using the XGBoost machine learning algorithm, with the dataset split into an training set ( ) and a testing set ( ). Model evaluation and feature importance were calculated using , , , and SHapley Additive exPlanations (SHAP) values. Findings: The XGBoost model exhibited robust predictive performance on the unseen testing set ( , , ). Based on Mean Absolute SHAP values, trait anger was identified as the paramount predictor of aggressive behavior ( relative importance), sequentially followed by social rejection ( ), family chaos ( ), and executive function inhibition ( ). Furthermore, interaction analysis revealed profound synergistic effects, with the strongest non-linear interaction occurring between trait anger and executive function inhibition (SHAP Interaction Value ), alongside a highly significant interaction between social rejection and family chaos (SHAP Interaction Value ). Conclusion: Adolescent aggression is a non-linear, compounded phenomenon that is most accurately predicted by the synergistic interaction of high emotional reactivity, poor cognitive inhibition, and severe environmental stressors.
Copyright (c) 2026 Ingrid Solbakken (Author); Ana Lucía Martínez (Corresponding Author); Liam Desrosiers (Author)
Deep Neural Network Modeling of Adolescent Generalized Anxiety Using Attentional Bias, Physiological Arousal, Worry Severity, and Parent–Child Conflict
Objective: To develop and evaluate a Deep Neural Network model capable of accurately predicting generalized anxiety in adolescents by integrating multimodal cognitive, neurocognitive, physiological, and environmental features. Methods and Materials: This cross-sectional study included adolescents from Chile. Participants completed a comprehensive, multimodal assessment protocol comprising the Generalized Anxiety Disorder Assessment- (GAD- ) to classify anxiety severity, the Penn State Worry Questionnaire for Children to measure conscious worry severity, and the Conflict Behavior Questionnaire to assess parent-child conflict. Implicit neurocognitive attentional bias to threat was quantified using a computerized dot-probe task. Concurrently, continuous electrocardiography and electrodermal activity recordings were utilized to capture objective physiological arousal, specifically heart rate variability and skin conductance responses. A Deep Neural Network architecture was subsequently constructed, trained, and tested to model the complex, non-linear relationships among these disparate variables and predict adolescent generalized anxiety classification. Findings: The trained Deep Neural Network demonstrated exceptional predictive performance, achieving an overall classification accuracy of and an Area Under the Receiver Operating Characteristic Curve of on unseen testing data. Feature importance analysis derived from the network’s weights revealed that physiological arousal constituted the largest broad predictive domain ( total; comprising for heart rate variability and for skin conductance responses). Conscious worry severity emerged as the strongest individual predictor, accounting for of the model’s predictive capacity. Furthermore, environmental and neurocognitive factors contributed significantly to the predictive architecture, with parent-child conflict accounting for and attentional bias to threat accounting for of the model’s weight. Conclusion: Multimodal deep learning architectures provide a highly accurate and comprehensive framework for predicting adolescent generalized anxiety, highlighting the critical, synergistic interplay of somatic hyperarousal, severe cognitive worry, and familial discord.
Copyright (c) 2026 Nika Kovač (Author); Valentina Rojas (Corresponding Author); Siobhán O’Donnell (Author)
Predicting Cyberbullying Perpetration from Moral Disengagement, Online Disinhibition, Trait Aggression, and Social Network Density Using Random Forests
Objective: The aim of this study was to predict cyberbullying perpetration from moral disengagement, online disinhibition, trait aggression, and social network density using a Random Forest machine learning algorithm among a sample of South African adolescents and young adults. Methods and Materials: The study employed a quantitative, cross-sectional predictive research design with a sample of participants from three South African provinces. Data were collected using the Cyberbullying Offending Scale, Cyberbullying Moral Disengagement Scale, Online Disinhibition Scale, Buss-Perry Aggression Questionnaire, and a structural proxy questionnaire for social network density. A Random Forest model was trained on of the data and tested on the remaining , utilizing -fold cross-validation for hyperparameter tuning. Findings: The Random Forest model demonstrated high predictive performance on the testing set ( ), achieving an Accuracy of , Precision of , Recall of , F1-score of , and . Variable importance metrics revealed that trait aggression was the strongest predictor of cyberbullying perpetration (Mean Decrease Gini ), followed by moral disengagement (Mean Decrease Gini ) and online disinhibition. Social network density was the weakest predictor in the model (Mean Decrease Gini ). Conclusion: Individual psychological factors, specifically trait aggression and moral disengagement, overwhelmingly drive cyberbullying perpetration compared to digital structural environments, emphasizing the need for interventions focused on anger management and cognitive restructuring.
Copyright (c) 2026 Marcus Ouellet (Author); Nomvula Dlamini (Corresponding Author)
Machine‑Learning Prediction of Non‑Suicidal Self‑Injury Based on Emotion Dysregulation Facets, Alexithymia, Impulsivity, and Online Social Interaction Patterns
Objective: This study aimed to develop and validate a high-accuracy machine-learning model to predict non-suicidal self-injury (NSSI) by integrating psychological variables (emotion dysregulation, alexithymia, impulsivity) and digital behavior patterns. Methods and Materials: A cross-sectional study was conducted with one thousand two hundred forty-eight individuals ( ) aged fifteen to twenty-nine. Participants completed validated self-report measures assessing NSSI (Inventory of Statements About Self-Injury), emotion dysregulation (DERS), alexithymia (TAS-20), impulsivity (UPPS-P), and online social interaction patterns. The dataset was partitioned into training ( ) and testing ( ) sets. Three machine-learning models (Support Vector Machines, Random Forest, and XGBoost) were trained and evaluated, with feature importance analyzed using SHapley Additive exPlanations (SHAP). Findings: The prevalence of lifetime NSSI was ( ). The eXtreme Gradient Boosting (XGBoost) model demonstrated superior predictive performance on the testing set, achieving an accuracy of and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of . SHAP analysis identified the most influential predictors of NSSI as the negative emotional valence of online interactions ( ), impulse control difficulties ( ), and negative urgency ( ). Conclusion: Machine-learning models can accurately predict non-suicidal self-injury, highlighting the critical role of digital emotional distress and impulsivity as primary risk factors.
Copyright (c) 2026 Ivana Radonjić (Author); Arman Hovhannisyan (Corresponding Author); Kevin Zelaya (Author)
A Gradient‑Boosting Model of Adolescent Social Anxiety Incorporating Rejection Sensitivity, Intolerance of Uncertainty, Emotional Reactivity, and Peer Network Centrality
Objective: This study aimed to develop a highly predictive machine learning model to delineate the complex, non-linear, and synergistic contributions of rejection sensitivity, intolerance of uncertainty, emotional reactivity, and peer network centrality to adolescent social anxiety. Methods and Materials: A cross-sectional design was employed with a sample of adolescents (ages ) recruited from schools in Armenia via multistage cluster sampling. Participants completed validated Armenian translations of the Social Anxiety Scale for Adolescents (SASA), Rejection Sensitivity Questionnaire (RSQ), Intolerance of Uncertainty Scale (IUS-12), and the Emotion Reactivity Scale (ERS). Peer network centrality was quantified using eigenvector centrality scores derived from a sociometric nomination procedure. An Extreme Gradient Boosting (XGBoost) algorithm was trained and optimized using randomized search cross-validation, with model interpretability established through SHapley Additive exPlanations (SHAP). Findings: The final XGBoost model demonstrated robust predictive power on the unseen test set, explaining of the variance in social anxiety scores ( , RMSE , MAE ). Global feature importance analysis, based on mean absolute SHAP values, identified emotional reactivity as the most dominant predictor (Mean SHAP value ), followed by rejection sensitivity (Mean SHAP value ), intolerance of uncertainty (Mean SHAP value ), and peer network centrality (Mean SHAP value ). SHAP analysis further revealed significant synergistic effects, particularly between high emotional reactivity and high rejection sensitivity, which exponentially increased predicted social anxiety. Conclusion: Emotional reactivity operates as the primary driver of adolescent social anxiety, with its impact significantly amplified by concurrent cognitive and social vulnerabilities, highlighting the necessity of integrated, skills-based clinical interventions.
Copyright (c) 2026 Siti Hajar Ismail (Author); Lilit Martirosyan (Corresponding Author); Iryna Kazak (Author)
Predicting Adolescent Depressive Symptom Severity from Rumination, Sleep Variability, and Heart-Rate Variability Using Multimodal Deep Learning
Objective: The objective of this study was to develop and evaluate a multimodal deep learning architecture capable of predicting adolescent depressive symptom severity by dynamically integrating static cognitive rumination scores with continuous, time-series sequences of sleep variability and heart-rate variability. Methods and Materials: A two-week prospective observational study was conducted involving adolescents (ages ) from urban and semi-urban districts of Lagos, Nigeria. Baseline cognitive vulnerability and depressive symptoms were assessed via self-report using the Ruminative Responses Scale (RRS) and the Patient Health Questionnaire for Adolescents (PHQ-A). Continuous physiological data, specifically nocturnal sleep variability (standard deviation of total sleep time) and heart-rate variability (HRV; specifically RMSSD), were collected continuously using wrist-worn actigraphy and photoplethysmography (PPG) devices. Data analysis was executed using a hybrid multimodal deep learning architecture featuring late fusion, which utilized Long Short-Term Memory (LSTM) networks to process the physiological time-series data and a Multilayer Perceptron (MLP) for the static cognitive data. The model was trained using the Adam optimizer to minimize Mean Squared Error and evaluated utilizing -fold cross-validation. Findings: The final sample had a mean age of years ( ), with baseline PHQ-A scores of ( ) and baseline RRS scores of . Bivariate analyses indicated that follow-up depression severity was significantly predicted by rumination ( ), sleep variability ( ), and nocturnal RMSSD ( ). The multimodal late-fusion network demonstrated exceptional predictive accuracy ( , ), substantially outperforming all isolated unimodal models (Static MLP ; Sleep LSTM ; HRV LSTM ). Ablation studies confirmed the necessity of all modalities and temporal dynamics; excluding the rumination feature caused a significant performance drop ( ), and replacing the time-series physiological sequences with aggregated -day averages resulted in an identical loss of predictive power ( ). Conclusion: Fusing objective, time-series physiological biomarkers with cognitive vulnerability profiles via multimodal deep learning provides a highly accurate and transformative computational framework for the proactive risk stratification and early clinical intervention of adolescent depression.
Copyright (c) 2026 Emily Cartwright (Author); Chinedu Okonkwo (Corresponding Author); Salma Al-Hinai (Author)
Effectiveness of an Academic Motivation Training Model on Cognitive Flexibility, Academic Resilience, and Academic Motivation among Female Lower Secondary School Students in Tehran
Objective: The present study aimed to examine the effectiveness of an academic motivation training model on cognitive flexibility, academic resilience, and academic motivation among female students in lower secondary schools in Tehran. Methods and Materials: This applied study employed a quasi-experimental design with a pretest–posttest format and a control group. The statistical population consisted of all female lower secondary school students in Tehran during the 2025–2026 academic year. Based on sample size estimation using G*Power software and Cohen’s criteria, 42 participants were selected through convenience sampling and randomly assigned to an experimental group (n = 21) and a control group (n = 21). The experimental group received training based on the academic motivation model over eight sessions, while the control group received no intervention. Research instruments included the Cognitive Flexibility Inventory developed by Dennis and Vander Wal (2010), the Academic Motivation Scale by Vallerand et al. (1992), and the Academic Resilience Scale by Cassidy (2016). Data were analyzed using analysis of covariance (ANCOVA) in SPSS at a significance level of 0.05. Findings: The results indicated that, after controlling for pretest effects, the academic motivation training model had a positive and statistically significant effect on cognitive flexibility, academic resilience, and academic motivation (p < 0.001). Specifically, significant differences were observed between the two groups in posttest scores for cognitive flexibility (F = 24.82, η² = 0.39), academic resilience (F = 32.27, η² = 0.46), and academic motivation (F = 43.80, η² = 0.53). Conclusion: Based on the findings, the academic motivation training model can be considered an effective educational intervention that enhances academic motivation, increases cognitive flexibility, and strengthens academic resilience among students. Therefore, implementing this model in educational settings and school counseling programs may improve learning quality, increase academic engagement, and facilitate better student adaptation to educational challenges.
Copyright (c) 2026 Faezeh Shokri Javan (Author); Faezeh Jahan (Corresponding Author); Noor Mohammad Rezae , Dariush Mehrafzoon (Author)
Designing a Model of Fear of Missing Out (FoMO) Based on Parenting Styles with the Mediating Role of Brain Behavioral Systems in Female University Students Using Virtual Space
Objective: The aim of this study was to design a model of Fear of Missing Out (FoMO) based on parenting styles with the mediating role of brain behavioral systems among female university students who are users of virtual space. Methods and Materials: The research method was descriptive–correlational and was conducted within the framework of structural equation modeling (SEM). The statistical population consisted of undergraduate female students at the Islamic Azad University, Arak branch, from which 224 participants were selected using convenience sampling. Data were collected using the Baumrind Parenting Styles Questionnaire, the Behavioral Inhibition System and Behavioral Activation System Scales, and the FoMO Scale. Data analysis was performed using SPSS version 27 and AMOS version 24. Findings: Model fit indices indicated an acceptable fit to the data (χ²/df > 3, RMSEA = 0.08, AGFI < 0.90, CFI < 0.90). Direct path coefficients showed that the authoritative parenting style had significant relationships with behavioral inhibition, behavioral activation, and FoMO (p ≤ 0.01). Direct effects between the authoritarian parenting style and behavioral inhibition, behavioral activation, and FoMO were also significant (p ≤ 0.01). Additionally, direct relationships between the permissive parenting style and both behavioral inhibition and behavioral activation were significant (p ≤ 0.01), but its relationship with FoMO was not significant (p ≥ 0.05). Indirect effects were significant in all cases. Conclusion: The findings indicate a significant mediating role of brain behavioral systems in the relationship between parenting styles and FoMO. Overall, the results highlight the importance of parenting styles and brain behavioral systems in explaining the phenomenon of FoMO.
Copyright (c) 2026 Pegah Taghvaei (Author); Hossein Mohagheghi (Corresponding Author); Abolghasem Yaghobi, Shahriar Yarmohammadi Vasel (Author)
The Mediating Role of School Belonging and Parental Educational Aspirations in the Relationship Between Goal Congruence and Academic Persistence
Objective: The present study aimed to examine the mediating role of school belonging and parental educational aspirations in the relationship between goal congruence and academic persistence among high school students. Methods and Materials: This study employed a quantitative, cross-sectional correlational design using structural equation modeling. The statistical population consisted of high school students in Tehran during the 2025 academic year, from which a sample of 412 students (209 females and 203 males) was selected through multistage cluster sampling. Data were collected using standardized instruments, including the Goal Congruence Scale, the Psychological Sense of School Membership Scale, the Parental Educational Aspirations Scale, and the Academic Persistence Scale. The validity and reliability of all instruments had been confirmed in previous studies. Data analysis was conducted using SPSS-27 for preliminary analyses and AMOS for structural equation modeling. Model fit was evaluated using indices such as χ²/df, CFI, TLI, NFI, and RMSEA, and mediation effects were tested using bootstrapping procedures. Findings: The results indicated that goal congruence had a significant direct effect on academic persistence (β = 0.29, p < 0.001). Additionally, goal congruence significantly predicted school belonging (β = 0.51, p < 0.001) and parental educational aspirations (β = 0.43, p < 0.001), both of which had significant positive effects on academic persistence (β = 0.34 and β = 0.21, respectively; p < 0.001). Bootstrapping analysis confirmed that both school belonging (β = 0.17, 95% CI [0.11, 0.24]) and parental educational aspirations (β = 0.09, 95% CI [0.05, 0.14]) significantly mediated the relationship between goal congruence and academic persistence, with school belonging demonstrating a stronger mediating effect. The overall model demonstrated good fit to the data (χ²/df = 2.41, CFI = 0.93, TLI = 0.92, NFI = 0.91, RMSEA = 0.058). Conclusion: The findings highlight the critical role of both individual motivational alignment and relational-contextual factors in shaping academic persistence.
Copyright (c) 2026 Samira Ataei Dizaji (Corresponding Author); Shokoufeh Moradi, Azad Khodadadi, Sara Memari (Author)
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)
Comparison of the Effectiveness of Cognitive Behavioral Therapy and Schema Therapy on Perseverative Thinking in Young Adults with Depressive Symptoms
Objective: The present study aimed to compare the effectiveness of Cognitive Behavioral Therapy and Schema Therapy on reducing perseverative thinking in young adults with depressive symptoms. Methods and Materials: This applied study employed a quasi-experimental design with a pretest–posttest control group and a two-month follow-up. The statistical population consisted of young adults with depressive symptoms who referred to counseling and psychological clinics in District 1 of Tehran between December 2024 and June 2025. From this population, 45 participants who met inclusion criteria were selected using purposive sampling and randomly assigned to three groups: Cognitive Behavioral Therapy (n = 15), Schema Therapy (n = 15), and control (n = 15). The experimental groups received eight structured group sessions based on established CBT and Schema Therapy protocols, while the control group received no intervention during the study period. Perseverative thinking was assessed at pretest, posttest, and follow-up using the Perseverative Thinking Questionnaire. Data were analyzed using descriptive statistics, mixed-design repeated measures ANOVA, multivariate analysis of variance, and Bonferroni post hoc tests in SPSS software. Findings: Repeated measures analysis indicated no significant main effect of group; however, the main effect of time was statistically significant, demonstrating overall changes in perseverative thinking across measurement points. Importantly, the interaction effect between time and group was statistically significant, indicating differential patterns of change among groups. Bonferroni post hoc comparisons revealed that Schema Therapy produced a significant reduction in perseverative thinking compared to the control group at posttest, whereas Cognitive Behavioral Therapy did not significantly differ from the control group. Within-group analyses showed that Schema Therapy led to significant reductions from pretest to posttest and from pretest to follow-up, with maintained effects over time, while no significant changes were observed in the CBT and control groups. Conclusion: The findings suggest that Schema Therapy is more effective than Cognitive Behavioral Therapy in reducing perseverative thinking among young adults with depressive symptoms, with effects sustained over a two-month follow-up period.
Copyright (c) 2026 Shily Rahemi Nooran (Author); Taher Tizdast (Corresponding Author); Bita Nasrolahi (Author)