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)
Parental Reflective Functioning and Object Relations in Adolescents: A Path Model of the Direct and Indirect Effects on Reactive Aggression through Mentalization
Objective: The present study aimed to investigate the direct and indirect effects of parental reflective functioning and object relations on reactive aggression among adolescents through the mediating role of mentalization. Methods and Materials: This study employed a descriptive-correlational design based on path analysis. The statistical population included adolescents enrolled in the second cycle of secondary education (grades 10–12) during the 2025–2026 academic year. Participants were selected using a multistage cluster sampling method, and after screening incomplete responses, the final sample consisted of 463 adolescents aged 15–18 years. Data were collected using the Parental Reflective Functioning Questionnaire–Adolescent Version (PRFQ-A), Bell Object Relations and Reality Testing Inventory (BORRTI), Reflective Functioning Questionnaire for Youth (RFQY), and Reactive–Proactive Aggression Questionnaire (RPQ). Data analysis was performed using SPSS version 27 and AMOS version 26. Descriptive statistics, Pearson correlation analysis, and path analysis with bootstrap estimation were used to examine direct and indirect relationships among variables. Findings: The results showed that parental reflective functioning significantly and positively predicted mentalization (β = 0.389, p < .001), and object relations also had a significant positive effect on mentalization (β = 0.208, p < .001). Mentalization significantly and negatively predicted reactive aggression (β = -0.447, p < .001). Furthermore, parental reflective functioning (β = -0.145, p < .001) and object relations (β = -0.183, p < .001) had significant direct negative effects on reactive aggression. Bootstrap analysis confirmed the significant mediating role of mentalization in the relationship between parental reflective functioning and reactive aggression (indirect effect = -0.174, 95% CI [-0.224, -0.129]) and between object relations and reactive aggression (indirect effect = -0.093, 95% CI [-0.140, -0.051]). The final model explained 38.5% of the variance in reactive aggression. Conclusion: The findings highlight the importance of parental reflective functioning and adaptive object relations in reducing adolescents’ reactive aggression by strengthening mentalization capacities.
Copyright (c) 2026 Hanieh Abbasi (Author); Afnan Jafari (Corresponding Author); Mona Khalednejad, Javad Khalatbari, Fakhri Sadat Hosseini (Author)
Dynamic Bayesian Networks of Self-Esteem Fluctuations and Social Feedback Sensitivity
Objective: The present study aimed to model the dynamics of state self-esteem fluctuations and social feedback sensitivity using Dynamic Bayesian Networks. Methods and Materials: A quantitative intensive longitudinal design was implemented with 287 university students from Slovenia who completed a 21-day ecological momentary assessment protocol. Participants responded to five semi-random prompts per day assessing state self-esteem, perceived social feedback valence, and negative affect, yielding 26,874 momentary observations. Baseline measures included trait self-esteem, rejection sensitivity, depressive symptoms, and social anxiety. Data were person-mean centered and preprocessed to ensure stationarity. A multilevel vector autoregressive model was first estimated to identify preliminary lagged associations. Subsequently, Dynamic Bayesian Networks were constructed using a two-time-slice framework with hill-climbing structure learning and Bayesian Information Criterion scoring. Bootstrapped model averaging (1,000 resamples) ensured stability of directed edges. Hierarchical Bayesian extensions examined moderation by trait rejection sensitivity, and predictive accuracy was evaluated via 10-fold cross-validation. Findings: State self-esteem demonstrated significant autoregressive stability (β ≈ 0.50, p < 0.001). Positive social feedback prospectively predicted increases in subsequent state self-esteem (β ≈ 0.24, credible intervals excluding zero), whereas negative affect predicted decreases (β ≈ -0.20, p < 0.001). Reciprocal effects between self-esteem and negative affect formed a significant feedback loop. Trait rejection sensitivity significantly moderated the feedback-to-self-esteem pathway, amplifying evaluative reactivity (posterior mean ≈ 0.15, credible intervals excluding zero). Bootstrapped inclusion probabilities for key edges exceeded 90%, and cross-validated predictive performance demonstrated satisfactory out-of-sample accuracy (RMSE ≈ 0.41). Conclusion: Self-esteem operates as a dynamically regulated state embedded within a probabilistic network of social and affective processes, where social feedback functions as a central driver and individual differences in rejection sensitivity shape the magnitude of evaluative updating across time.
Copyright (c) 2026 Selamawit Bekele (Author); Tanja Kralj (Corresponding Author)
Random Forest Prediction of Adolescent Risk-Taking Behaviors Based on Sensation Seeking, Online Disinhibition, Emotional Impulsivity, Peer Deviance, and Executive Dysfunction
Objective: The present study aimed to predict adolescent risk-taking behaviors using a Random Forest machine learning model based on sensation seeking, online disinhibition, emotional impulsivity, peer deviance, and executive dysfunction among adolescents in Argentina. Methods and Materials: The study employed a cross-sectional predictive-correlational design with a machine learning framework. The statistical population consisted of Argentine adolescents enrolled in secondary schools in Buenos Aires, Córdoba, and Rosario during the 2025–2026 academic year. Using multistage cluster sampling, 1,284 adolescents aged 14 to 18 years were selected. Data were collected using the Adolescent Risk-Taking Questionnaire, Brief Sensation Seeking Scale, Online Disinhibition Scale, UPPS-P Negative Urgency Subscale, Peer Deviance Scale, and the Behavior Rating Inventory of Executive Function. Preliminary statistical analyses were conducted using SPSS-28, while the Random Forest predictive model was implemented in Python using the Scikit-learn library. Data were divided into training and testing subsets using an 80/20 split procedure. Model performance was evaluated through accuracy, precision, recall, F1-score, area under the curve (AUC), cross-validation scores, and SHAP feature-importance analyses. Findings: The findings demonstrated significant positive correlations among all study variables. Peer deviance showed the strongest positive association with adolescent risk-taking behaviors, followed by emotional impulsivity and executive dysfunction. The Random Forest model demonstrated strong predictive performance, with testing accuracy of 0.87 and AUC of 0.90, indicating excellent classification capability. Feature importance analysis revealed that peer deviance was the most influential predictor, followed respectively by emotional impulsivity, executive dysfunction, online disinhibition, and sensation seeking. SHAP analyses further indicated substantial interaction effects among emotional, cognitive, and social variables in predicting adolescent risk-taking behaviors. Conclusion: The findings suggest that adolescent risk-taking behaviors emerge through complex interactions among emotional dysregulation, cognitive-control deficits, peer-related influences, and digital behavioral tendencies. The strong performance of the Random Forest model highlights the usefulness of machine learning approaches for identifying high-risk adolescents and understanding multidimensional psychosocial predictors of maladaptive behaviors. Early prevention efforts targeting emotional impulsivity, executive dysfunction, peer deviance, and online disinhibition may contribute significantly to reducing behavioral risk among adolescents.
Bayesian Network Modeling of Youth Substance Use Risk Based on Trauma Exposure, Reward Sensitivity, Peer Pressure, Sleep Instability, and Psychological Inflexibility
Objective: The present study aimed to develop and evaluate a Bayesian network model for predicting youth substance use risk based on trauma exposure, reward sensitivity, peer pressure, sleep instability, and psychological inflexibility among adolescents and emerging adults in the United States. Methods and Materials: This study employed a cross-sectional predictive modeling design using Bayesian probabilistic network analysis. The statistical population consisted of adolescents and emerging adults aged 15 to 22 years from educational institutions in the United States, from whom 1,248 participants were selected through multistage stratified sampling. Data were collected using the Substance Use Risk Profile Scale, Childhood Trauma Questionnaire-Short Form, Behavioral Activation System Scale, Peer Pressure Inventory, Sleep Condition Indicator, and Acceptance and Action Questionnaire-II. Data analysis was conducted using SPSS version 29 and Python 3.11. Bayesian network structure learning was implemented using the Hill-Climbing optimization algorithm with Bayesian Information Criterion scoring. Predictive performance was evaluated using classification accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the receiver operating characteristic curve. Findings: The findings demonstrated significant positive relationships among trauma exposure, reward sensitivity, peer pressure, sleep instability, psychological inflexibility, and substance use risk. Psychological inflexibility showed the strongest correlation with substance use risk (r = .65, p < .01), followed by trauma exposure (r = .61, p < .01). The Bayesian network model demonstrated high predictive performance with a training accuracy of 89.41%, testing accuracy of 86.93%, and area under the curve value of 0.91. Sensitivity analysis indicated that psychological inflexibility represented the strongest predictor within the network structure, followed by trauma exposure, peer pressure, reward sensitivity, and sleep instability. The probabilistic network further revealed that trauma exposure functioned as a central upstream variable influencing psychological inflexibility, peer pressure susceptibility, and sleep instability pathways associated with elevated substance use vulnerability. Conclusion: The findings suggest that youth substance use vulnerability is shaped by a multidimensional network of emotional, cognitive, behavioral, and social risk mechanisms. Bayesian network modeling provided a powerful computational framework for identifying conditional dependency pathways among trauma exposure, reward sensitivity, peer pressure, sleep instability, psychological inflexibility, and substance use risk. The results highlight the importance of trauma-informed and multidimensional prevention strategies targeting emotional regulation, sleep health, peer influence processes, and psychological flexibility in adolescents and emerging adults.
A Multimodal Machine Learning Approach to Adolescent Loneliness Using Social Disconnection, Parasocial Relationships, Emotional Inhibition, Family Cohesion, and Digital Isolation
Objective: The present study aimed to develop and evaluate a multimodal machine learning model for predicting adolescent loneliness based on social disconnection, parasocial relationships, emotional inhibition, family cohesion, and digital isolation among adolescents in the United States. Methods and Materials: The study employed a cross-sectional predictive correlational design using multimodal machine learning techniques. The statistical population consisted of adolescents aged 13 to 18 years enrolled in secondary schools across the United States during the 2025–2026 academic year. Using stratified multistage sampling, 1,248 adolescents were recruited, of whom 1,182 participants were included in the final analysis after data screening. Data collection involved standardized psychological instruments assessing loneliness, social disconnection, parasocial relationships, emotional inhibition, and family cohesion, alongside objective digital behavioral indicators including screen time, passive scrolling duration, nocturnal device usage, and solitary online activity. Data were analyzed using Python-based machine learning libraries and included Random Forest, Support Vector Machine, XGBoost, Multilayer Perceptron Neural Network, and a multimodal ensemble learning model. Model evaluation indices included accuracy, precision, recall, F1-score, and AUC-ROC values. SHAP-based explainable artificial intelligence analysis was additionally employed to determine feature importance. Findings: The findings indicated significant positive relationships between adolescent loneliness and social disconnection, emotional inhibition, parasocial relationships, and digital isolation, while family cohesion demonstrated a significant negative association with loneliness. The multimodal ensemble model achieved the highest predictive performance compared to all other machine learning models, with an accuracy of 0.94 and an AUC-ROC value of 0.97. SHAP feature importance analysis revealed that digital isolation was the strongest predictor of adolescent loneliness, followed by social disconnection, family cohesion, emotional inhibition, and parasocial relationships. Hierarchical regression analysis further demonstrated that the predictor variables collectively explained 71% of the variance in loneliness scores. Conclusion: The findings suggest that adolescent loneliness is a multidimensional phenomenon shaped by the interaction of emotional, social, familial, and digital behavioral factors. Multimodal machine learning approaches integrating psychosocial measures with objective digital behavior indicators may provide highly accurate and interpretable predictive models for identifying adolescents at risk for loneliness. Strengthening family cohesion, improving emotional communication, and promoting healthier patterns of digital engagement may contribute significantly to reducing loneliness and improving adolescent mental health outcomes.
Explainable Deep Learning Models of Adolescent Eating Disorders Based on Appearance Comparison, Influencer Exposure, Emotion Dysregulation, Self-Criticism, and Weight Stigma
Objective: The present study aimed to develop and interpret explainable deep learning models for predicting adolescent eating disorder symptoms based on appearance comparison, influencer exposure, emotion dysregulation, self-criticism, and weight stigma. Methods and Materials: This study employed a cross-sectional predictive design using explainable artificial intelligence approaches among 1,219 Canadian adolescents aged 14 to 18 years recruited from secondary schools in Ontario, British Columbia, and Quebec. Participants completed standardized measures assessing eating disorder symptoms, appearance comparison, influencer exposure, emotion dysregulation, self-criticism, and weight stigma through an online survey platform. Data were analyzed using TensorFlow, Keras, and Scikit-learn libraries. The dataset was divided into training, validation, and testing subsets, and a multilayer explainable deep neural network was developed. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to enhance model interpretability and identify the relative contribution of predictors. Model performance was compared with logistic regression, support vector machine, and random forest algorithms. Findings: Significant positive associations were observed among all study variables and eating disorder symptoms. Self-criticism demonstrated the strongest association with eating pathology, followed by emotion dysregulation and weight stigma. The explainable deep learning model achieved the highest predictive performance compared to alternative machine learning models, with superior accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve values. SHAP analyses identified self-criticism as the strongest predictor, followed by emotion dysregulation, weight stigma, appearance comparison, and influencer exposure. Interaction analyses further demonstrated that the predictive effects of appearance comparison and weight stigma intensified when combined with elevated self-criticism and emotional dysregulation. Conclusion: The findings suggest that adolescent eating disorders are shaped by complex interactions among emotional vulnerabilities, maladaptive self-evaluative processes, and digitally reinforced appearance pressures. Explainable deep learning models provide a powerful and interpretable framework for identifying multidimensional psychological risk patterns associated with eating pathology and may support more personalized prevention and intervention strategies for adolescents.
Predicting Romantic Relationship Instability in Emerging Adults Through LightGBM Models of Attachment Anxiety, Jealousy Cognitions, Social Media Surveillance, Emotional Dependency, and Impulsivity
Objective: The present study aimed to predict romantic relationship instability in emerging adults through Light Gradient Boosting Machine (LightGBM) models based on attachment anxiety, jealousy cognitions, social media surveillance, emotional dependency, and impulsivity. Methods and Materials: The study employed a cross-sectional predictive correlational design using machine learning methodology. The statistical population consisted of emerging adults aged 18 to 29 years residing in major urban regions of Canada during 2025–2026. A total of 1,137 participants were selected through online and university-based recruitment procedures. Data were collected using the Relationship Stability Scale, Experiences in Close Relationships-Revised questionnaire, Multidimensional Jealousy Scale, Social Media Surveillance Scale, Emotional Dependency Questionnaire, and Barratt Impulsiveness Scale-Version 11. Data analysis was conducted using Python and machine learning libraries including Scikit-learn and LightGBM. The dataset was divided into training and testing subsets using an 80/20 split procedure. Model performance was evaluated using accuracy, precision, recall, F1-score, area under the curve (AUC), and log-loss indices. SHAP analysis was additionally used to examine predictor importance and model interpretability. Findings: The findings demonstrated significant positive correlations among all study variables and romantic relationship instability. Emotional dependency showed the strongest correlation with relationship instability, followed by attachment anxiety, jealousy cognitions, social media surveillance, and impulsivity (p < 0.01). The LightGBM model demonstrated high predictive performance with a testing accuracy of 0.88 and an AUC value of 0.91, indicating excellent discriminative ability. SHAP analysis revealed that emotional dependency and attachment anxiety were the most influential predictors of romantic instability. Hierarchical regression analysis further indicated that the predictor variables collectively explained 67% of the variance in relationship instability (R² = 0.67, p < 0.001). Conclusion: The findings suggest that romantic relationship instability among emerging adults is strongly associated with emotional insecurity, maladaptive jealousy cognitions, impulsive tendencies, and digitally mediated surveillance behaviors. The study further demonstrates that machine learning approaches such as LightGBM provide powerful tools for identifying complex nonlinear interaction patterns underlying romantic dysfunction. These findings highlight the importance of addressing attachment insecurity, emotional dependency, and unhealthy digital relational behaviors in prevention and intervention programs targeting young adults’ romantic well-being.
Identifying Profiles of Adolescent Flourishing Through Unsupervised Machine Learning and Variables Including Self-Compassion, Meaning in Life, Digital Wellbeing, Gratitude, and Peer Support
Objective: The present study aimed to identify distinct profiles of adolescent flourishing using unsupervised machine learning techniques based on self-compassion, meaning in life, digital wellbeing, gratitude, and peer support among Canadian adolescents. Methods and Materials: This study employed a cross-sectional correlational design with an unsupervised machine learning approach. The statistical population consisted of Canadian high school students enrolled in public secondary schools during the 2025–2026 academic year. Using multistage cluster sampling, 742 adolescents were recruited, and after data screening, 711 participants were retained for final analysis. Data were collected using standardized instruments including the Flourishing Scale, Self-Compassion Scale–Short Form, Meaning in Life Questionnaire, Digital Wellbeing Scale for Adolescents, Gratitude Questionnaire–Six Item Form, and the peer support subscale of the Multidimensional Scale of Perceived Social Support. Data analysis was conducted using Python and SPSS-28. Hierarchical clustering and K-means clustering were applied to identify latent flourishing profiles. Cluster validity was evaluated using silhouette coefficients, Davies–Bouldin index, and Calinski–Harabasz index. Multivariate analysis of variance and Bonferroni post hoc tests were used to compare identified profiles across study variables. Findings: The findings indicated that the three-cluster solution demonstrated the strongest statistical performance and conceptual interpretability compared to alternative clustering solutions. Three distinct profiles were identified, including the High Flourishing Adaptive Profile, Moderate Flourishing Vulnerable Profile, and Low Flourishing At-Risk Profile. Significant multivariate differences were observed among the profiles across flourishing, self-compassion, meaning in life, digital wellbeing, gratitude, and peer support (p < .001). Adolescents within the High Flourishing Adaptive Profile reported significantly higher scores across all psychological and relational variables compared to the other profiles, whereas the Low Flourishing At-Risk Profile demonstrated consistently lower functioning across all domains. Meaning in life and flourishing produced the largest effect sizes in distinguishing profile membership. Visualization analyses using t-distributed stochastic neighbor embedding additionally confirmed clear separation and structural distinctiveness among the identified flourishing profiles. Conclusion: The findings suggest that adolescent flourishing is a multidimensional and heterogeneous construct shaped by emotional, existential, relational, and digital wellbeing factors. Self-compassion, meaning in life, gratitude, peer support, and healthy digital engagement appear to function as major protective resources associated with adaptive flourishing. The application of unsupervised machine learning techniques provided a nuanced person-centered understanding of adolescent wellbeing and highlighted the existence of distinct flourishing trajectories among youth populations. These findings support the development of strengths-based and personalized interventions aimed at enhancing flourishing and psychological resilience during adolescence.
Ensemble Learning Approaches to Predicting Youth Suicidal Ideation Using Emotional Numbing, Cyberbullying Exposure, AI Chatbot Attachment, Hopelessness, and Social Withdrawal
Objective: The present study aimed to investigate the predictive role of emotional numbing, cyberbullying exposure, AI chatbot attachment, hopelessness, and social withdrawal in youth suicidal ideation using advanced ensemble learning approaches among Canadian adolescents and emerging adults. Methods and Materials: This cross-sectional predictive study was conducted among 742 adolescents and emerging adults aged 16 to 24 years recruited from educational institutions and youth communities across Canada. Data were collected using standardized instruments assessing suicidal ideation, emotional numbing, cyberbullying exposure, hopelessness, social withdrawal, and attachment to AI chatbot systems. After preprocessing procedures including missing data imputation and feature normalization, several ensemble machine learning algorithms including Random Forest, Gradient Boosting Machine, AdaBoost, XGBoost, LightGBM, and a stacked ensemble classifier were implemented using Python-based analytical frameworks. Model performance was evaluated through accuracy, precision, recall, F1-score, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Feature importance and SHAP analyses were additionally performed to examine the relative contribution of predictors within the final classification model. Findings: The findings demonstrated significant positive relationships between suicidal ideation and all predictor variables, including emotional numbing, cyberbullying exposure, AI chatbot attachment, hopelessness, and social withdrawal (p < .01). Hopelessness emerged as the strongest predictor within the final ensemble model, followed by emotional numbing and social withdrawal. The stacked ensemble classifier demonstrated the highest predictive performance with an accuracy of 94%, precision of 92%, recall of 91%, F1-score of 91%, specificity of 95%, and an AUC-ROC value of 0.97. XGBoost and LightGBM also demonstrated strong classification capability. SHAP analysis confirmed that higher levels of hopelessness, emotional numbing, cyberbullying exposure, social withdrawal, and AI chatbot attachment significantly increased the probability of high-risk suicidal ideation classification. Conclusion: The findings suggest that youth suicidal ideation is shaped by multidimensional interactions among emotional dysregulation, interpersonal isolation, cybervictimization, hopelessness, and emerging forms of technological attachment. Ensemble learning approaches demonstrated exceptional effectiveness in identifying adolescents at elevated suicide risk and may provide valuable computational tools for early psychological screening and suicide prevention programs. The study further highlights the growing psychological significance of AI chatbot attachment within contemporary youth mental health contexts.
Support Vector Machine Prediction of Adolescent Anxiety Based on Nomophobia, Fear of Missing Out (FoMO), Perfectionism, Cognitive Fusion, and Parent–Child Communication
Objective: The present study aimed to predict adolescent anxiety using a Support Vector Machine (SVM) model based on nomophobia, Fear of Missing Out (FoMO), perfectionism, cognitive fusion, and parent–child communication among Tunisian adolescents. Methods and Materials: The study employed a cross-sectional correlational design with a machine learning approach. The statistical population consisted of secondary school adolescents in Tunisia during the 2025–2026 academic year. Using multistage cluster random sampling, 742 students were selected, of whom 718 valid questionnaires were retained for final analysis after data screening. Data were collected using the Beck Anxiety Inventory, Nomophobia Questionnaire, Fear of Missing Out Scale, Multidimensional Perfectionism Scale, Cognitive Fusion Questionnaire, and Parent–Adolescent Communication Scale. Descriptive statistics and Pearson correlation analyses were conducted using SPSS-27, while machine learning analyses were performed using Python and Scikit-learn libraries. The dataset was divided into training and testing subsets using an 80/20 ratio, and a radial basis function kernel Support Vector Machine model with five-fold cross-validation was implemented to predict adolescent anxiety. Findings: The findings indicated significant positive correlations between adolescent anxiety and nomophobia, FoMO, perfectionism, and cognitive fusion, while parent–child communication demonstrated a significant negative relationship with anxiety symptoms. Cognitive fusion emerged as the strongest predictor variable, followed by nomophobia, parent–child communication, FoMO, and perfectionism. The Support Vector Machine model demonstrated high predictive performance, achieving 88.76% classification accuracy in the testing dataset, with an F1-score of 87.16% and an area under the curve (AUC) value of 0.91. The confusion matrix additionally revealed high sensitivity and specificity in distinguishing adolescents with elevated anxiety symptoms from those with lower anxiety levels. Conclusion: The results demonstrated that adolescent anxiety can be effectively predicted through the combined influence of cognitive, technological, emotional, and family-related factors using machine learning techniques. Cognitive fusion and nomophobia represented the strongest psychological risk factors, while healthy parent–child communication functioned as a protective factor against anxiety symptoms. The findings support the usefulness of Support Vector Machine models in adolescent mental health screening and highlight the importance of addressing maladaptive cognitive processes, digital dependency, and family communication patterns in preventive and intervention programs targeting adolescent anxiety.
A Deep Neural Network Analysis of Youth Psychological Distress Based on Cybervictimization, Loneliness, Academic Pressure, Screen-Time Fragmentation, and Family Emotional Climate
Objective: The present study aimed to investigate youth psychological distress through a deep neural network framework based on cybervictimization, loneliness, academic pressure, screen-time fragmentation, and family emotional climate among adolescents and emerging adults in Georgia. Methods and Materials: This study employed a quantitative cross-sectional predictive design using deep learning methodologies. The statistical population consisted of adolescents and emerging adults aged 16 to 24 years enrolled in secondary schools and universities in Georgia during the 2025–2026 academic year. Using multistage cluster sampling, 842 participants were initially recruited, and after data screening, 814 cases were retained for final analysis. Data were collected using standardized instruments including the Kessler Psychological Distress Scale, Cyberbullying Victimization Scale, UCLA Loneliness Scale, Educational Stress Scale for Adolescents, a screen-time fragmentation measure, and the Family Environment Scale. Data analysis involved descriptive statistics, Pearson correlation coefficients, and deep neural network modeling using Python, TensorFlow, and Keras libraries. Model performance was evaluated using mean squared error, root mean squared error, mean absolute error, coefficient of determination, and cross-validation procedures. SHAP analysis was additionally employed to examine relative predictor importance. Findings: The findings demonstrated significant positive relationships between psychological distress and cybervictimization, loneliness, academic pressure, and screen-time fragmentation, while family emotional climate showed a significant negative relationship with psychological distress. Loneliness emerged as the strongest predictor within the deep neural network model, followed by family emotional climate and cybervictimization. Conclusion: Comparative analyses indicated that the deep neural network outperformed traditional machine learning approaches including random forest, support vector machine, and multiple linear regression models in predicting psychological distress outcomes.
Comparison of the Effectiveness of Mentalization-Based Therapy and Emotion Efficacy-Based Therapy on Cognitive Insight and Distress Tolerance in Adolescents with Generalized Anxiety Symptoms
Objective: The present study aimed to compare the effectiveness of Mentalization-Based Therapy and Emotion Efficacy-Based Therapy on cognitive insight and distress tolerance among adolescents with generalized anxiety symptoms. Methods and Materials: This study was applied in terms of purpose and quasi-experimental in terms of methodology, utilizing a pretest–posttest design with two experimental groups and one control group. The statistical population consisted of male and female adolescents with generalized anxiety symptoms in Shiraz during 2025. The sample included 45 adolescents selected through convenience sampling and randomly assigned into three groups of 15 participants each. The first experimental group received Mentalization-Based Therapy, the second experimental group received Emotion Efficacy-Based Therapy, and the control group received no intervention. Data collection instruments included the Beck Cognitive Insight Scale, the Distress Tolerance Scale, and the Generalized Anxiety Disorder Questionnaire (GAD-7). The interventions were implemented over eight weekly 90-minute sessions. Data were analyzed using descriptive statistics and multivariate analysis of covariance (MANCOVA) in IBM SPSS Statistics. Findings: The results indicated significant differences between the experimental and control groups in cognitive insight and distress tolerance (Wilks’ Lambda = 0.266, F₄,₇₈ = 18.293, p < .01). Univariate covariance analysis demonstrated that both Mentalization-Based Therapy and Emotion Efficacy-Based Therapy significantly improved cognitive insight (F₂,₄₀ = 20.593, p < .001, η² = 0.507) and distress tolerance (F₂,₄₀ = 17.987, p < .001, η² = 0.474) compared to the control group. Bonferroni post hoc comparisons further revealed that Mentalization-Based Therapy was significantly more effective than Emotion Efficacy-Based Therapy in enhancing cognitive insight (p < .01), whereas Emotion Efficacy-Based Therapy demonstrated greater effectiveness in improving distress tolerance (p < .05). Conclusion: These results emphasize the importance of selecting therapeutic interventions based on the primary psychological needs and treatment goals of adolescents with generalized anxiety symptoms.
Copyright (c) 2026 Fariba Haghshenas (Corresponding Author); Bahareh Rava, Razieh Hemmati, Gholamreza Sanagouye Mohrar, Mohammadali Fardin (Author)
Effectiveness of Dialectical Behavior Therapy on Distress Tolerance and Difficulties in Emotion Regulation among Adolescents with Impulsive Behaviors
Objective: The present study was conducted to determine the effectiveness of dialectical behavior therapy on distress tolerance and difficulties in emotion regulation among adolescents with impulsive behaviors. Materials and Methods: This was a quasi-experimental study with a pretest–posttest design and a two-month follow-up period including a control group. The statistical population consisted of adolescents aged 13 to 18 years with impulsive behaviors in Tehran in 2026. A total of 30 eligible adolescents were selected through purposive sampling and were randomly assigned to either the experimental group or the control group, with 15 participants in each group. The experimental group received dialectical behavior therapy skills training based on Linehan’s treatment package during eight 90-minute sessions, whereas the control group received no intervention during this period. Data were collected using the Distress Tolerance Scale developed by Simons and Gaher and the Difficulties in Emotion Regulation Scale developed by Gratz and Roemer. Data were analyzed using the independent samples t-test, Fisher’s exact test, and two-way repeated measures analysis of variance in SPSS version 26. Findings: The results indicated that there were no significant differences between the experimental and control groups in terms of demographic characteristics. Furthermore, the results of the two-way repeated measures analysis of variance demonstrated that the interaction effect of group and time on distress tolerance and difficulties in emotion regulation was significant. Mean comparisons showed that distress tolerance scores in the experimental group increased from the pretest to the posttest stage, whereas difficulties in emotion regulation scores decreased. These changes remained largely stable at the two-month follow-up stage, while no considerable changes were observed in the control group. Conclusion: Based on the findings of the study, dialectical behavior therapy can increase distress tolerance and reduce difficulties in emotion regulation among adolescents with impulsive behaviors. Therefore, the application of this approach in schools, counseling centers, and adolescent clinics may be beneficial for improving emotional skills, enhancing the ability to tolerate difficult situations, and reducing impulsive reactions.
Copyright (c) 2026 Seyed Emad Hosseini , Motahareh Ahangar (Author); Marzieh Sadigh (Corresponding Author)
The Effectiveness of Schema Mode Therapy on Resilience and Self-Control in Adolescent Girls with a History of Non-Suicidal Self-Injury
Objective: The present study aimed to investigate the effectiveness of schema mode therapy on resilience and self-control in adolescent girls with a history of non-suicidal self-injury. Methods and Materials: The present study employed a quasi-experimental design with a pretest–posttest format and a control group. The statistical population consisted of adolescent girls aged 12 to 18 years with a history of non-suicidal self-injury in Isfahan during 2024. A total of 30 participants were selected using purposive sampling and randomly assigned to experimental and control groups, with 15 participants in each group. Data collection instruments included the Connor and Davidson Resilience Scale (CD-RISC) and the Tangney Self-Control Questionnaire (SCQ). The experimental group participated in 10 weekly 90-minute sessions of schema mode therapy, while the control group received no intervention. Data were analyzed using multivariate analysis of covariance (MANCOVA) in SPSS-26 software. Findings: The findings demonstrated that schema mode therapy had a statistically significant effect on both resilience and self-control in adolescent girls with a history of non-suicidal self-injury. The results of multivariate covariance analysis indicated a significant difference between the experimental and control groups in at least one dependent variable at the posttest stage (p < 0.05). Univariate covariance analysis further revealed that resilience significantly increased in the experimental group compared to the control group (F = 21.749, p = 0.001, η² = 0.465). Additionally, self-control scores significantly improved following the intervention (F = 13.103, p = 0.001, η² = 0.344). Conclusion: The findings of the present study indicate that schema mode therapy is an effective intervention for improving resilience and self-control among adolescent girls with a history of non-suicidal self-injury. By targeting maladaptive schemas, dysfunctional coping modes, and emotional dysregulation, schema mode therapy can enhance adaptive psychological functioning and reduce vulnerability to self-injurious behaviors in adolescents.
Copyright (c) 2026 Zahra Adelnasab (Author); Hajar Torkan (Corresponding Author)
Designing a Positive Psychological Self-Care Educational Package for Male Adolescents with High-Risk Behaviors
Objective: The present study aimed to design and validate a positive psychological self-care educational package for male adolescents with high-risk behaviors. Methods and Materials: This study was conducted using a qualitative applied research design based on grounded theory and thematic analysis. The study population included psychologists with direct professional experience working with adolescents with high-risk behaviors, theoretical and empirical literature related to positive psychology and psychological self-care, and university experts in psychology. Twenty-two psychologists working in counseling centers affiliated with the Department of Education in Tehran were selected through purposive sampling and participated in semi-structured interviews until theoretical saturation was achieved. In addition, domestic and international scientific sources related to positive psychology, self-care, emotion regulation, coping self-efficacy, and adolescent high-risk behaviors were reviewed and analyzed. Data collection tools included semi-structured interviews, a researcher-made content validity form, and an inter-rater agreement form. Data were analyzed through open, axial, and selective coding. After the educational package was developed, six faculty experts evaluated its content validity and overall quality. Findings: The qualitative analysis resulted in the extraction of 60 initial codes, which were organized into 12 main categories, including communication establishment, emotion recognition, recognition of strengths, optimism, stress management, anger management, social skills, empathy, gratitude, meaning in life, spirituality, and resilience. These categories formed the conceptual and structural foundation of the final 12-session positive psychological self-care educational package. The package integrated emotional, cognitive, interpersonal, and resilience-oriented components designed to improve adolescents’ psychological self-care capacities and coping resources. Content validity analysis showed that all extracted components achieved acceptable content validity ratios and were confirmed as essential by the expert panel. The inter-rater agreement coefficient also demonstrated a high level of expert consensus regarding the appropriateness, coherence, and applicability of the package. Conclusion: The findings suggest that positive psychological self-care for male adolescents with high-risk behaviors should be conceptualized as a multidimensional process integrating emotional regulation, adaptive coping, interpersonal competence, optimism, meaning, spirituality, and resilience.
Copyright (c) 2026 Mohsen Khakrand (Author); Afsaneh Ghanbari Panah (Corresponding Author); Zohreh Sadeghi Afjeh, Mandana Niknam (Author)
The Effectiveness of Compassion-Focused Therapy on Internalized Shame and Self-Criticism in Adolescent Girls with Body Image Concerns
Objective: The present study aimed to determine the effectiveness of Compassion-Focused Therapy (CFT) on internalized shame and self-criticism in adolescent girls with body image concerns. Materials and Methods: This was a quasi-experimental study with a pretest–posttest design and a two-month follow-up with a control group. The statistical population consisted of 13- to 18-year-old adolescent girls with body image concerns in Tehran in 2026. After administering the Body Image Concern Inventory developed by Littleton, Axsom, and Pury, adolescents who scored above 52 were identified as individuals with body image concerns. Subsequently, 30 eligible participants were selected through purposive sampling and randomly assigned to an experimental group and a control group, with 15 participants in each group. The experimental group received Compassion-Focused Therapy based on Gilbert’s protocol (Gilbert, 2009) in eight 60-minute sessions, whereas the control group received no intervention during this period. Data were collected using the Internalized Shame Scale developed by Cook (1993) and the Self-Criticism Scale developed by Gilbert et al. (2004). Data were analyzed using the independent samples t-test, Fisher’s exact test, and two-way repeated measures analysis of variance in IBM SPSS Statistics. Findings: The results indicated that the experimental and control groups did not differ significantly in terms of demographic characteristics. Furthermore, the results of the two-way repeated measures analysis of variance showed that the interaction effect of group and time on internalized shame and self-criticism was statistically significant. Based on mean comparisons, the scores of internalized shame and self-criticism in the experimental group decreased from the pretest to the posttest stage, and this reduction largely remained stable at the two-month follow-up stage, whereas no substantial changes were observed in the control group. Conclusion: Based on the findings of the study, Compassion-Focused Therapy can reduce internalized shame and self-criticism in adolescent girls with body image concerns.
Copyright (c) 2026 Seyedeh Negar Memari (Author); Negar Zolfaghari (Corresponding Author); Masoumeh Hassani (Author)
Predicting Cyberbullying–Cybervictimization Experience Based on Parent–Child Relationship, Psychological Well-Being, and Internet Addiction in Adolescents in Isfahan City
Objective: The present study aimed to predict cyberbullying–cybervictimization experience based on parent–child relationship, psychological well-being, and internet addiction among adolescents in Isfahan City. Methods and Materials: The present study employed a descriptive-correlational design. The statistical population consisted of all male and female high school students enrolled during the 2024–2025 academic year in Isfahan City. Based on Morgan’s table, 382 students were selected using cluster random sampling. Data were collected using Young’s Internet Addiction Test (1998), the Cyber Bullying–Victimization Experience Questionnaire developed by Antoniadou et al. (2016), Ryff’s Short Form Psychological Well-Being Scale (1989), and Pianta’s Parent–Child Relationship Scale (2011). Data analysis was conducted using Pearson correlation coefficients and multiple linear regression analysis through SPSS version 27. Findings: The findings demonstrated that internet addiction had a significant positive relationship with cyberbullying–cybervictimization experience (β = 0.886, p < .001), whereas psychological well-being had a significant negative relationship with cyberbullying–cybervictimization experience (β = -0.154, p < .001). However, the overall parent–child relationship did not significantly predict cyberbullying–cybervictimization experience (β = -0.002, p = .959). The regression model was statistically significant (F = 208.95, p < .001) and explained 62.5% of the variance in cyberbullying–cybervictimization experience. In addition, the closeness component of the parent–child relationship showed a significant negative correlation with both cybervictimization and cyberbullying, whereas the conflict component demonstrated a significant positive correlation with these variables. No significant relationship was found between the dependency component and cyberbullying–cybervictimization. Conclusion: The findings indicate that internet addiction and psychological well-being are important predictors of cyberbullying–cybervictimization among adolescents. Excessive internet use may increase adolescents’ exposure to harmful online interactions, whereas higher psychological well-being may function as a protective factor against involvement in cyberbullying behaviors.
Copyright (c) 2026 Zahra Gharagozloo, Tayebeh Taheri Jebeli, Solmaz Dabiri (Author); Somayeh Ashrafifard (Corresponding Author)
The Effectiveness of Anger Management Skills Training and Assertive Behavior Training on Interpersonal Relationships and Tendency Toward Drug Use Among Female High School Students
Objective: The study aimed to investigate the effectiveness of anger management skills training and assertive behavior training on interpersonal relationships and tendency toward drug use among female high school students in Qaem Shahr. Methods and Materials: This study employed an applied quasi-experimental design with a pretest–posttest format and a control group. The statistical population consisted of all female high school students in Qaem Shahr during the 2024–2025 academic year (N = 15,435). Using convenience and voluntary sampling, 45 eligible students were selected and randomly assigned into three groups: anger management training (n = 15), assertive behavior training (n = 15), and control (n = 15). The anger management intervention was conducted in 12 weekly 90-minute sessions, whereas the assertive behavior intervention was implemented in 10 weekly 60-minute sessions. The control group received no intervention. Data were collected using the FIRO Interpersonal Relationship Scale and the Mirhashemi Tendency Toward Drug Use Questionnaire. Data analysis was performed using SPSS through descriptive statistics, repeated-measures analysis of variance, and Bonferroni post-hoc tests. Findings: The repeated-measures analysis of variance revealed a significant effect of time on interpersonal relationships, F(1, 42) = 29.81, p = .001, and a significant time × group interaction, F(2, 42) = 7.33, p = .002. Significant between-group differences were also observed, F(2, 42) = 6.06, p = .005. In addition, significant effects were found for tendency toward drug use, including the effect of time, F(1, 42) = 33.21, p = .001, and the time × group interaction, F(2, 42) = 9.73, p = .001, with significant between-group differences, F(2, 42) = 5.44, p = .008. Bonferroni post-hoc comparisons indicated that both intervention groups differed significantly from the control group in interpersonal relationships and tendency toward drug use; however, no statistically significant difference was found between anger management training and assertive behavior training. Conclusion: The findings demonstrated that both anger management skills training and assertive behavior training were effective in improving interpersonal relationships and reducing tendency toward drug use among female adolescents.
Copyright (c) 2026 Sahar Jalilzadeh (Author); Arsalan Khanmohammadi Otaghsara (Corresponding Author)
Copyright (c) 2026 Ingrid Løvik (Author); Tanja Kralj (Corresponding Author); Joseph Zammit (Author)
The Relationship Between Maladaptive Perfectionism and Academic Burnout with Students’ Mental Health: The Mediating Role of Spiritual Intelligence
Objective: The present study aimed to investigate the relationship between maladaptive perfectionism and academic burnout with students’ mental health through the mediating role of spiritual intelligence. Methods and Materials: This study was conducted using a descriptive-correlational design based on structural equation modeling. The statistical population included all high school students in Tehran during the 2025–2026 academic year. A total of 412 students were selected through multistage cluster random sampling. Data were collected using the Frost Multidimensional Perfectionism Scale, Maslach Burnout Inventory–Student Survey, General Health Questionnaire-28, and Spiritual Intelligence Self-Report Inventory. Descriptive statistics and Pearson correlation coefficients were calculated using SPSS-27 software, and structural equation modeling was performed using AMOS-24. The significance of indirect effects was examined using the bootstrap method. Findings: The findings demonstrated that maladaptive perfectionism had a significant positive relationship with academic burnout (r = 0.51, p < 0.01) and mental health problems (r = 0.58, p < 0.01). Academic burnout was also positively associated with mental health problems (r = 0.61, p < 0.01). In contrast, spiritual intelligence showed significant negative relationships with maladaptive perfectionism (r = -0.46, p < 0.01), academic burnout (r = -0.49, p < 0.01), and mental health problems (r = -0.55, p < 0.01). Structural equation modeling indicated that maladaptive perfectionism directly predicted mental health problems (β = 0.37, p < 0.001), while academic burnout also had a significant direct effect on mental health problems (β = 0.41, p < 0.001). Spiritual intelligence significantly mediated the relationships between maladaptive perfectionism, academic burnout, and mental health. The proposed model demonstrated acceptable fit indices (χ²/df = 2.31, CFI = 0.94, TLI = 0.93, RMSEA = 0.057). Conclusion: The findings suggest that maladaptive perfectionism and academic burnout are important psychological risk factors negatively influencing students’ mental health, whereas spiritual intelligence serves as a protective psychological resource that reduces emotional distress and improves psychological well-being.
Copyright (c) 2026 Moslem Bamari , Zeynab Rashidizadeh (Author); Mohammad Elahi (Corresponding Author)
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)
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)
The Relationship Between Schema Modes, Psychological Flexibility, and Alexithymia with Subjective Well-Being Among Students in Qazvin
Objective: The present study aimed to investigate the relationship between schema modes, psychological flexibility, and alexithymia with subjective well-being among university students in Qazvin. Methods and Materials: This study was a fundamental, descriptive-correlational research conducted among students of Imam Khomeini International University during the 2022–2023 academic year. A total of 305 participants were selected using multistage cluster random sampling. Data were collected using the Satisfaction with Life Scale (SWLS), the Positive and Negative Affect Schedule (PANAS), the Schema Mode Questionnaire, the Psychological Flexibility Questionnaire, and the Toronto Alexithymia Scale (TAS-20). After obtaining informed consent, questionnaires were administered, and data were analyzed using descriptive statistics, Pearson correlation coefficients, and multiple regression analysis via SPSS. Findings: The results indicated that maladaptive schema modes (child modes, maladaptive parent modes, and maladaptive coping modes) were significantly negatively correlated with life satisfaction and positive affect and positively correlated with negative affect (p < .01). In contrast, adaptive schema modes showed significant positive correlations with life satisfaction and positive affect and negative correlations with negative affect (p < .01). Psychological flexibility was positively associated with life satisfaction (β = 0.19, p < .001) and positive affect (β = 0.42, p < .001), and negatively associated with negative affect (β = -0.23, p < .001). Alexithymia demonstrated significant negative relationships with life satisfaction (β = -0.35, p < .001) and positive affect (β = -0.38, p < .001), and a positive relationship with negative affect (β = 0.39, p < .001). Regression analyses further revealed that schema modes accounted for the largest proportion of variance in subjective well-being components, followed by alexithymia and psychological flexibility. Conclusion: The findings suggest that maladaptive schema modes and alexithymia are significant risk factors for reduced subjective well-being, whereas adaptive schema modes and psychological flexibility function as protective factors.
Copyright (c) 2026 Kiana Naghash Souratgar (Author); Majid Mahmoud Alilou (Corresponding Author); Touraj Hashemi Nosratabad (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)