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)
Interpretable Deep Learning Analysis of Online Social Anxiety in Adolescents Using Feature Saliency Mapping
Objective: The objective of this study was to develop and validate an interpretable deep learning model for predicting adolescents’ online social anxiety and to identify the most influential psychological and digital behavioral features contributing to its development. Methods and Materials: A cross-sectional predictive modeling design was employed with a sample of 523 adolescents aged 13–18 years recruited from secondary schools in Austria. Participants completed standardized measures assessing online social anxiety, general social anxiety, self-esteem, perceived social support, emotion regulation, and digital behavior patterns. Data were analyzed using a deep neural network with multiple hidden layers and regularization techniques. Model performance was evaluated using RMSE, MAE, R², and Pearson correlation. Interpretability was achieved through advanced feature saliency mapping, including SHAP values and gradient-based attribution methods, enabling identification of both global and individual-level predictors of online social anxiety. Findings: The deep learning model demonstrated strong predictive performance, explaining 81% of the variance in online social anxiety (R² = .81) with high correspondence between predicted and observed scores (r = .90). Feature saliency analysis revealed that online social comparison exerted the strongest positive influence on anxiety predictions, followed by low self-esteem, emotion dysregulation, general social anxiety, and daily social media use. Perceived social support displayed a robust negative contribution, functioning as a protective factor. Subgroup analysis indicated that adolescents with the highest anxiety levels exhibited intensified contributions from social comparison, emotional instability, excessive media engagement, and negative feedback sensitivity. Conclusion: Interpretable deep learning provides a powerful and transparent framework for understanding the complex psychological mechanisms underlying adolescents’ online social anxiety, offering critical insights for early identification and targeted intervention.
Explainable AI Modeling of Hope in Youth Using Feature Attribution of Optimism, Goal Orientation, and Family Support
Objective: The objective of this study was to develop and interpret an explainable artificial intelligence model of youth hope by quantifying the individual and interactive contributions of optimism, goal orientation, and family support. Methods and Materials: This cross-sectional study was conducted with 653 adolescents and emerging adults aged 15–24 from educational and community institutions in Georgia, United States. Participants completed standardized measures of hope, optimism, goal orientation, and perceived family support. Machine learning models including Random Forest, Gradient Boosting, and XGBoost were trained to predict hope, with model performance evaluated using cross-validation and error metrics. Explainability was achieved through SHAP and permutation-based feature attribution methods, enabling identification of global and individual predictor effects and nonlinear interactions. Findings: The XGBoost model demonstrated the strongest predictive performance, explaining 84% of the variance in youth hope (R² = .84, RMSE = 2.19, MAE = 1.68). Feature attribution analyses indicated that family support contributed the largest proportion of predictive influence (38.6%), followed by optimism (32.1%) and goal orientation (25.3%). Significant interaction effects were observed between optimism and family support, as well as between goal orientation and family support, amplifying their combined impact on hope. Conclusion: Youth hope is best explained as a multilevel construct arising from the integrated influence of motivational beliefs and family relational processes. Explainable AI offers a powerful methodological framework for advancing theoretical understanding and guiding personalized intervention strategies.
Feature Sensitivity Mapping of Adolescents’ Identity Development: An Interpretable Machine Learning Study in Brazil
Objective: The objective of this study was to identify and quantify the relative and interactive contributions of psychosocial, relational, cultural, and individual predictors of adolescents’ identity development using feature sensitivity mapping within an interpretable machine learning framework. Methods and Materials: This cross-sectional study was conducted among 1,032 adolescents aged 14–18 years recruited from public and private secondary schools in three major metropolitan regions of Brazil using multi-stage cluster sampling. Participants completed validated measures assessing identity development dimensions, parental autonomy support, peer attachment quality, emotional self-regulation, future orientation, academic self-efficacy, social competence, and demographic characteristics. Data were analyzed using an ensemble of gradient boosting and random forest models with ten-fold cross-validation and Bayesian hyperparameter optimization. Model interpretability was achieved through SHAP-based feature sensitivity mapping, partial dependence plots, and accumulated local effects analysis to examine nonlinear and interactive predictor effects. Findings: The machine learning models explained substantial variance in identity outcomes, with the highest performance observed for identity commitment (R² = .61), followed by exploration in breadth (R² = .57), exploration in depth (R² = .55), and ruminative exploration (R² = .49). Parental autonomy support, peer attachment quality, emotional self-regulation, future orientation, academic self-efficacy, and social competence emerged as the most influential predictors across identity dimensions. Emotional self-regulation demonstrated the strongest protective effect against ruminative exploration, while parental autonomy support and peer attachment quality exhibited the highest positive contributions to identity commitment. Conclusion: Adolescents’ identity development operates as a nonlinear, interactive system in which relational security, self-regulation, and motivational resources play central roles, and interpretable machine learning provides a powerful framework for advancing developmental theory and intervention design.
Interpretable Gradient Boosting Analysis of Academic Cheating in Adolescents Using Moral Disengagement and Achievement Goals
Objective: This study aimed to develop an interpretable machine learning model to examine the predictive roles of moral disengagement and achievement goal orientations in academic cheating among high school adolescents. Methods and Materials: The study employed a cross-sectional correlational design with a predictive analytics framework and was conducted among 681 adolescents aged 14–18 years enrolled in public high schools in California. Participants completed standardized self-report measures assessing academic cheating behavior, moral disengagement, and achievement goal orientations, along with demographic information. Data were analyzed using an Extreme Gradient Boosting (XGBoost) regression model with five-fold cross-validation and Bayesian hyperparameter optimization. Model performance was evaluated using root mean squared error, mean absolute error, and explained variance. To ensure interpretability, Shapley Additive Explanations were applied to quantify the relative and local contributions of predictors, and partial dependence analyses were conducted to examine nonlinear and interactive effects. Findings: The gradient boosting model demonstrated strong predictive performance, accounting for 56% of the variance in academic cheating. Moral disengagement emerged as the most influential predictor, followed by performance-avoidance and performance-approach goals. Mastery-approach goals exhibited a consistent negative association with cheating. The model identified nonlinear threshold effects for moral disengagement and significant interaction patterns between motivational orientations and moral cognition, indicating that performance-based goals amplified the impact of moral disengagement on cheating behavior. Conclusion: The findings indicate that academic cheating in adolescence is primarily driven by cognitive moral mechanisms operating in conjunction with achievement-related motivational pressures. Interpretable machine learning offers a powerful framework for uncovering these complex psychological dynamics and provides actionable insights for the design of targeted educational interventions aimed at promoting academic integrity.
Feature Contribution Analysis of Youth Life Satisfaction Using Psychological Capital and Social Connectedness
Objective: The objective of this study was to develop an explainable predictive model of youth life satisfaction by quantifying the individual and combined contributions of psychological capital components and social connectedness. Methods and Materials: A cross-sectional correlational design was employed with a sample of 684 Moroccan adolescents and emerging adults recruited from secondary schools, vocational institutes, and universities. Participants completed standardized measures of life satisfaction, psychological capital, and social connectedness. Advanced machine learning models including Elastic Net Regression, Random Forest, and Gradient Boosting Machine were trained using ten-fold cross-validation. Explainable artificial intelligence techniques based on SHAP values and permutation feature importance were applied to interpret model predictions, identify dominant predictors, and examine nonlinear interactions among psychological and social variables. Findings: The Gradient Boosting Machine demonstrated superior predictive performance (R² = .76, RMSE = 2.78, MAE = 2.19). Feature contribution analysis revealed that hope was the strongest predictor of life satisfaction, followed by social connectedness and self-efficacy. Optimism and resilience showed moderate but substantial contributions, whereas demographic variables such as age and gender exerted comparatively minor influence. Interaction effects indicated that high social connectedness amplified the positive effects of psychological capital components on predicted life satisfaction. Conclusion: Youth life satisfaction is primarily driven by dynamic psychological and social resources rather than static demographic characteristics. Explainable machine learning provides a powerful framework for uncovering complex predictive structures and offers actionable insight for designing personalized youth well-being interventions focused on strengthening psychological capital and social integration.
Explainable AI Modeling of Adolescents’ Psychological Adjustment Using Coping Styles, Social Support Quality, and Life Stress Exposure
Objective: The objective of this study was to develop and interpret an explainable artificial intelligence model to predict adolescents’ psychological adjustment from coping styles, social support quality, and life stress exposure in a representative sample of Iraqi secondary school students. Methods and Materials: This cross-sectional study was conducted among 612 adolescents aged 14–18 years recruited from public secondary schools in Baghdad, Najaf, and Basra using multistage cluster sampling. Participants completed validated measures assessing psychological adjustment, coping styles, perceived social support quality, and life stress exposure. Data were preprocessed through normalization, missing-value imputation, and outlier screening. Machine learning models including random forest, extreme gradient boosting, and multilayer perceptron neural networks were trained using five-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations, permutation feature importance, and partial dependence analyses. Findings: The XGBoost model demonstrated the highest predictive performance (R² = 0.76, RMSE = 0.29, MAE = 0.22). Social support quality emerged as the strongest positive predictor of psychological adjustment, followed by problem-focused coping. Life stress exposure exerted a substantial negative effect. Avoidance coping significantly predicted poorer adjustment, whereas emotion-focused coping displayed nonlinear effects depending on stress levels. Interaction analysis revealed that high social support significantly buffered the adverse effects of life stress on psychological adjustment. Conclusion: The findings demonstrate that adolescents’ psychological adjustment is governed by complex nonlinear interactions among coping strategies, social support, and stress exposure, and that explainable artificial intelligence offers a powerful framework for modeling these processes with high predictive accuracy and theoretical transparency.
Neural Network Analysis of Adolescent Depression: The Interactive Roles of Loneliness, Family Communication Quality, and Digital Media Dependency
Objective: This study aimed to examine the nonlinear and interactive effects of loneliness, family communication quality, and digital media dependency on depressive symptoms among Indonesian adolescents using neural network modeling. Methods and Materials: A cross-sectional design was implemented with 684 secondary school students aged 14–18 years selected via multistage cluster sampling from urban regions of Indonesia. Participants completed validated measures assessing depressive symptoms, loneliness, family communication quality, and digital media dependency. Data were analyzed using a multilayer perceptron neural network constructed in Python with TensorFlow. The dataset was partitioned into training, validation, and test sets. Model performance was evaluated using root mean square error, mean absolute error, and coefficient of determination. Predictor contributions and interaction effects were interpreted using SHAP values and partial dependence analyses. Findings: The neural network demonstrated high predictive accuracy (R² = .79 on the test set). Loneliness emerged as the strongest predictor of depressive symptoms, followed by digital media dependency and family communication quality. Significant nonlinear interaction effects were observed, indicating that the combination of high loneliness, poor family communication, and elevated digital media dependency produced the highest levels of depressive symptoms. Family communication quality exerted a strong buffering effect that attenuated the impact of loneliness and digital dependency on depression. Conclusion: Adolescent depression is shaped by complex, interactive psychosocial and digital factors that are effectively captured through neural network modeling. Strengthening family communication and promoting healthy digital engagement may substantially reduce depressive risk among adolescents.
Explainable AI Modeling of Academic Burnout in High School Students Using Cognitive Flexibility, School Climate, and Online Learning Engagement
Objective: The objective of this study was to develop and validate an explainable artificial intelligence model for predicting academic burnout among Chinese high school students based on cognitive flexibility, perceived school climate, and online learning engagement. Methods and Materials: This quantitative cross-sectional study was conducted with 1,042 high school students from public schools in three major urban regions of eastern China using multi-stage cluster sampling. Participants completed standardized measures of academic burnout, cognitive flexibility, school climate, and online learning engagement. Data were analyzed using an ensemble machine learning framework combining Random Forest, Gradient Boosting, and XGBoost algorithms. Model performance was evaluated via nested cross-validation. Explainable AI techniques including SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations were applied to ensure transparency and interpretability of predictions. Findings: The ensemble model demonstrated strong predictive performance (RMSE = 0.32, MAE = 0.24) and explained 81% of the variance in academic burnout. Cognitive flexibility emerged as the most influential predictor (38.7% relative importance), followed by school climate (31.2%) and online learning engagement (22.5%). The model exhibited high stability across gender and grade-level subgroups, with explained variance ranging from 78% to 83%. Conclusion: Academic burnout among Chinese high school students is best explained through a dynamic interaction of cognitive, environmental, and behavioral factors, and the proposed explainable AI framework provides a powerful and transparent tool for early identification and targeted prevention of burnout risk in educational settings.
Deep Learning Prediction of Emotional Dysregulation in Adolescents Based on Social Media Use Patterns, Sleep Quality, and Parental Attachment Styles
Objective: The objective of this study was to develop and validate a deep learning model for predicting emotional dysregulation in Colombian adolescents using social media use patterns, sleep quality, and parental attachment styles. Methods and Materials: This quantitative, cross-sectional predictive study was conducted among 842 adolescents aged 13–18 years from secondary schools in Bogotá, Medellín, and Cali, Colombia. Participants completed standardized measures of emotional dysregulation, sleep quality, and parental attachment, and objective social media usage metadata were collected via smartphone monitoring over a 14-day period. Data were preprocessed and analyzed using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture. Model performance was evaluated using mean absolute error, root mean square error, coefficient of determination, area under the receiver operating characteristic curve, and classification accuracy. Explainable artificial intelligence techniques were applied to determine the relative importance of predictors. Findings: The deep learning model demonstrated high predictive performance (R² = .79; RMSE = 5.37; AUC = .91; classification accuracy = 87.6%). Emotional dysregulation was significantly associated with social media use (r = .58, p < .001), sleep quality (r = .62, p < .001), and parental attachment (r = −.55, p < .001). Feature importance analysis identified sleep quality (32.4%), nocturnal screen exposure (21.8%), emotional dependency on social media (18.6%), parental attachment security (15.3%), and daily social media duration (11.9%) as the most influential predictors. Conclusion: The findings indicate that emotional dysregulation in adolescence is strongly shaped by the interaction of digital behavior, sleep processes, and attachment relationships, and that deep learning models offer a powerful tool for early identification of adolescents at elevated emotional risk.
Explainable AI Forecast of Psychological Distress in Adolescents Based on Family Conflict, School Pressure, and Emotion Regulation Capacity
Objective: The objective of this study was to develop and interpret an explainable artificial intelligence model for forecasting psychological distress in adolescents by quantifying the joint and individual contributions of family conflict, school pressure, and emotion regulation capacity. Methods and Materials: This cross-sectional study was conducted among 1,142 secondary school students aged 13–18 years in Germany using multi-stage cluster sampling. Participants completed validated self-report measures of psychological distress, family conflict, school pressure, and emotion regulation capacity. Data were analyzed using an explainable gradient boosting machine learning framework with five-fold cross-validation. Model performance was evaluated using root mean square error, mean absolute error, and coefficient of determination. Feature contributions and interaction effects were examined using Shapley Additive Explanations and partial dependence analyses to ensure full interpretability of predictions. Findings: The explainable model demonstrated strong predictive accuracy, accounting for 69% of the variance in adolescent psychological distress on the test dataset (R² = 0.69, RMSE = 3.58, MAE = 2.71). Feature attribution analysis revealed that school pressure was the most influential predictor (36.2% relative contribution), followed by emotion regulation capacity (31.1%) and family conflict (24.7%), while demographic variables showed minimal impact. Interaction analyses indicated that high emotion regulation capacity substantially attenuated the negative effects of elevated school pressure and family conflict on psychological distress. Conclusion: Adolescent psychological distress is primarily shaped by the combined influence of academic stress, family dynamics, and emotional self-regulation. Explainable artificial intelligence provides a powerful and transparent framework for identifying individualized risk profiles and informing targeted mental health interventions in educational and clinical settings.
Structural Equation Modeling of Addiction Proneness in Male High School Students Based on Parental Emotional Tone and Cognitive Bias with the Mediating Role of Coherent Self-Knowledge
Objective: The objective of this study was to examine the structural relationships between parental emotional tone and cognitive bias with addiction proneness in male high school students, considering the mediating role of integrative self-knowledge. Methods and Materials: This correlational study employed a structural equation modeling design. The statistical population included male high school students in Tehran during the 2024–2025 academic year, from whom 590 participants were selected through convenience sampling. Data were collected using the Addiction Proneness Questionnaire, Family Emotional Expressiveness Questionnaire, Cognitive Bias Questionnaire, and Integrative Self-Knowledge Questionnaire. Data analysis was conducted using SPSS 26 and AMOS 26. Model fit was evaluated using χ²/df, RMSEA, CFI, GFI, AGFI, NFI, IFI, and SRMR indices. Indirect effects were tested using the bootstrap method. Findings: Positive parental emotional expressiveness had a significant positive effect on integrative self-knowledge (β = 0.38, p < .001) and a significant negative direct effect on addiction proneness (β = −0.18, p < .001). Negative parental emotional expressiveness (β = 0.24, p < .001) and cognitive bias (β = 0.21, p < .001) showed significant positive direct effects on addiction proneness, while both negatively predicted integrative self-knowledge (β = −0.31 and β = −0.29, respectively, p < .001). Integrative self-knowledge significantly reduced addiction proneness (β = −0.36, p < .001) and mediated the relationships between parental emotional tone, cognitive bias, and addiction proneness. The model explained 52% of the variance in addiction proneness and 41% of the variance in integrative self-knowledge, with satisfactory goodness-of-fit indices (χ²/df = 2.18, RMSEA = 0.052, CFI = 0.94). Conclusion: The findings highlight integrative self-knowledge as a central protective mechanism through which emotional family environments and cognitive processing patterns shape adolescents’ vulnerability to addiction.
Copyright (c) 2025 Sabina Nobari, Mansour Ali Mahdi (Author); Mahtab Moraveji (Corresponding Author); Mehrdad Sabet, Amin Rafiepoor (Author)
Development of a Structured Intervention and Its Effectiveness on High-Risk Behaviors in Substance-Using Adolescents
Objective: The objective of this study was to develop a structured, developmentally informed intervention and to examine its effectiveness in reducing high-risk behaviors among substance-using adolescents. Methods and Materials: This study employed a mixed-methods sequential exploratory design. In the qualitative phase, semi-structured interviews were conducted with substance-using adolescents, clinicians, and experts to identify core psychological, familial, and social mechanisms underlying high-risk behaviors, and the findings were used to develop a structured intervention protocol. In the quantitative phase, a quasi-experimental pretest–posttest design with an intervention and a control group was implemented. Participants were adolescents with a history of substance use recruited from counseling and support centers in Shahroud. The intervention group received a structured, multi-session program focusing on emotion regulation, cognitive restructuring, problem-solving, interpersonal skills, and future orientation, while the control group received routine services. Standardized self-report measures of high-risk behaviors were administered before and after the intervention. Findings: Inferential analyses revealed a statistically significant reduction in high-risk behaviors in the intervention group compared to the control group. Repeated-measures analysis showed a significant time × group interaction, indicating that changes over time differed significantly between groups. The intervention produced large effect sizes for overall high-risk behaviors as well as for emotional, behavioral, and social risk components, demonstrating the strong impact of the structured program beyond natural change or routine care. Conclusion: The findings indicate that a structured intervention developed through qualitative exploration and evaluated using quantitative methods can effectively reduce high-risk behaviors among substance-using adolescents. Integrating emotional, cognitive, and interpersonal components within a coherent framework appears to be a promising approach for intervention programs targeting adolescent substance use.
Copyright (c) 2025 Farshad Ebrahimi Ebrahimi (Corresponding Author)
Comparison of the Effectiveness of Emotion Regulation Training and Self-Differentiation Training on Behavioral Problems of Tenth-Grade Female Students in Baghbahadoran
Objective: The objective of this study was to compare the effectiveness of emotion regulation training and self-differentiation training on reducing internalizing and externalizing behavioral problems among tenth-grade female students. Methods and Materials: This study adopted a quantitative, applied, quasi-experimental design with pretest, posttest, and a three-month follow-up, including two experimental groups and one control group. The statistical population consisted of tenth-grade female students in Baghbahadoran during the 2023–2024 academic year, from which 81 students were initially selected through cluster random sampling and convenience sampling. Participants were randomly assigned to an emotion regulation training group, a self-differentiation training group, or a control group. The emotion regulation intervention was implemented using the ten-session “Think Cool, Act Cool” program, while self-differentiation training was delivered based on Bowen’s theory in ten structured sessions. Behavioral problems were assessed at three time points using the Achenbach Youth Self-Report questionnaire. Data were analyzed using repeated measures analysis of variance and mixed analysis of variance in SPSS version 27. Findings: The results indicated a significant main effect of time for both internalizing and externalizing behavioral problems, demonstrating substantial reductions from pretest to posttest that were maintained at follow-up. Significant group differences were observed, with both intervention groups showing significantly lower levels of internalizing and externalizing problems compared to the control group. No statistically significant differences were found between the emotion regulation and self-differentiation training groups. Effect sizes were large for both outcome variables, indicating strong intervention effects. Conclusion: Both emotion regulation training and self-differentiation training were equally effective in reducing internalizing and externalizing behavioral problems in adolescent girls, and the beneficial effects remained stable over time. These findings support the use of either intervention as an effective school-based approach for improving adolescents’ emotional and behavioral adjustment.
Copyright (c) 2025 Monir Khorsandi (Author); Ahmad Ghazanfari (Corresponding Author); Mohammad Ghasemi Pirbalouti (Author)
Comparison of the Effectiveness of Short-Term Psychodynamic Therapy and Mentalization-Based Therapy on Self-Injurious Behaviors, Rejection Sensitivity, and Self-Control in Adolescents with Borderline Personality Organization
Objective: The aim of the present study was to compare the effectiveness of short-term psychodynamic therapy and mentalization-based therapy on self-injurious behaviors, rejection sensitivity, and self-control in adolescents with borderline personality organization. Method: The present research was a quasi-experimental study using a pretest–posttest design with a control group. The statistical population consisted of all adolescents with borderline personality organization who referred to clinics in western Tehran during the second half of the year 2025 (September 2025 to March 2026). From this population, 60 participants were selected through non-random purposive sampling and were then randomly assigned to three groups: short-term psychodynamic therapy, mentalization-based therapy, and a control group. Data were collected using the Personality Organization Questionnaire by Kernberg (2002), the Self-Injury Questionnaire by Sansone, Wiederman, and Sansone (1998), the Rejection Sensitivity Questionnaire by Downey and Feldman (1996), and the Self-Control Scale (SCS). Subsequently, therapeutic interventions were implemented for the experimental groups based on the short-term psychodynamic therapy protocol and the mentalization-based therapy protocol developed by Bateman and Fonagy (2016) across nine 90-minute sessions, while the control group was placed on a waiting list. After the completion of treatment, posttests were administered to both groups. Data were analyzed using multivariate analysis of variance (MANOVA) and repeated measures analysis of variance. Bonferroni post hoc tests were used to test the research hypotheses. Results: The results of the analysis indicated that short-term psychodynamic therapy was not effective in reducing self-injurious behaviors in adolescents with borderline personality organization, whereas mentalization-based therapy led to a significant reduction in self-injurious behaviors in this population. Furthermore, the findings showed that both treatments had equal effects on rejection sensitivity and self-control in adolescents with borderline personality organization. Conclusion: Based on the findings, the use of short-term psychodynamic therapy and mentalization-based therapy is recommended for improving self-injurious behaviors, rejection sensitivity, and self-control in adolescents with borderline personality organization.
Copyright (c) 2025 Reyhane Chenari, Masoumeh SaedKazemi, Farzaneh Khosravi (Author); Souzan Mardanirad (Corresponding Author); Giti Shahbazi, Kamran Pourmohammad Ghouchani (Author)
Interplay of Cognitive Flexibility and Adaptive Emotion Regulation as Predictors of Academic Success in AI-Enhanced Learning Environments
Objective: The objective of this study was to examine the interactive predictive roles of cognitive flexibility and adaptive emotion regulation on academic success among university students learning in artificial intelligence–enhanced educational environments. Methods and Materials: This quantitative cross-sectional correlational study was conducted among 317 undergraduate students from major public universities in Tehran who were enrolled in courses supported by AI-based learning platforms. Participants completed validated questionnaires measuring cognitive flexibility, adaptive emotion regulation, engagement with AI-enhanced learning systems, and academic success. Data were analyzed using descriptive statistics, Pearson correlations, hierarchical multiple regression, and structural equation modeling with SPSS 26 and AMOS 24. Model fit was evaluated using standard goodness-of-fit indices including CFI, TLI, RMSEA, and SRMR. Findings: Hierarchical regression revealed that after controlling for demographic variables and AI-learning engagement, cognitive flexibility (β = .31, p < .001) and adaptive emotion regulation (β = .36, p < .001) significantly predicted academic success, together explaining 56% of the total variance. Structural equation modeling demonstrated strong direct effects of cognitive flexibility (β = .34, p < .001) and adaptive emotion regulation (β = .39, p < .001) on academic success, as well as significant indirect effects mediated through AI-learning engagement (β = .41, p < .001). The overall model exhibited satisfactory fit to the data (CFI = .95, TLI = .94, RMSEA = .061, SRMR = .047). Conclusion: The findings indicate that cognitive flexibility and adaptive emotion regulation are critical psychological determinants of academic success in AI-enhanced learning environments and operate both directly and through strengthening students’ engagement with intelligent educational systems.
Copyright (c) 2025 Malihe Ghafourimanesh (Author); Katauon Haddadi (Corresponding Author); Fatemeh Mirchenari, Fateme Haj Manouchehri (Author)
Predicting Adolescent Psychological Well-Being Using Gradient Boosting Models and Multidimensional Life Satisfaction Indicators
Objective: The objective of this study was to examine the extent to which adolescent psychological well-being can be accurately predicted using gradient boosting machine learning models integrating multidimensional life satisfaction indicators. Methods and Materials: This cross-sectional study was conducted among secondary school adolescents in Taiwan using a school-based sampling framework. Psychological well-being was assessed as a continuous outcome variable, while multidimensional life satisfaction domains—including emotional health, family life, peer relationships, school experience, academic self-satisfaction, physical health, neighborhood context, and perceived economic status—were used as predictive features alongside key demographic and behavioral covariates. Advanced gradient boosting algorithms were trained and validated using a hold-out testing approach with cross-validated hyperparameter optimization. Model performance was evaluated using inferential predictive metrics, and explainable machine learning techniques were applied to quantify feature contributions and non-linear effects. Findings: Inferential results demonstrated that gradient boosting models explained a substantial proportion of variance in adolescent psychological well-being, with ensemble models achieving high predictive accuracy and low estimation error. Emotional health satisfaction emerged as the strongest predictor, followed by family life satisfaction and school life satisfaction, indicating statistically meaningful and non-linear contributions to well-being. Peer life satisfaction and academic self-satisfaction showed moderate but significant predictive influence, while health-related behaviors such as sleep duration exhibited curvilinear effects. Explainability analyses revealed marked inter-individual heterogeneity in predictor importance, supporting the presence of multiple predictive pathways to psychological well-being rather than a single dominant profile. Conclusion: The findings indicate that adolescent psychological well-being can be robustly predicted using gradient boosting models that integrate multidimensional life satisfaction indicators.
Identifying High-Risk Profiles for Substance Use in Youth Through Explainable Machine Learning Models
Objective: The objective of this study was to identify and interpret high-risk substance use profiles among youth by applying explainable machine learning models that integrate psychological, familial, peer, and sociodemographic factors. Methods and Materials: A cross-sectional study design was employed with a large, community-based sample of adolescents and young adults recruited from educational institutions and youth organizations in Ireland. Participants completed standardized self-report measures assessing substance use behaviors, psychological characteristics, family and peer contexts, and demographic factors. Supervised machine learning models, including regularized logistic regression and ensemble-based algorithms, were trained to classify high-risk substance use status. Model performance was evaluated using cross-validated inferential metrics, including area under the receiver operating characteristic curve, sensitivity, specificity, and balanced accuracy. Explainable artificial intelligence techniques based on SHapley Additive exPlanations were used to interpret both global predictor importance and individual-level risk patterns. Findings: Inferential analyses demonstrated that ensemble machine learning models significantly outperformed linear models in classifying high-risk substance use, with the highest-performing model achieving excellent discrimination and sensitivity. Explainability analyses revealed that peer substance use norms, impulsivity, parental monitoring, sensation seeking, and emotional dysregulation exerted statistically meaningful and nonlinear effects on risk classification. Distinct high-risk profiles were identified, including socially driven risk, emotionally vulnerable risk, sensation-seeking–dominant risk, and structurally disadvantaged risk, each characterized by unique constellations of predictors with differential contributions to model output. Conclusion: The findings indicate that explainable machine learning models can accurately and transparently identify heterogeneous high-risk substance use profiles among youth, offering a robust and interpretable framework for advancing early detection, targeted prevention, and data-informed public health decision-making.
Machine Learning–Based Prediction of Emotional Eating Patterns in Adolescents Using Psychological and Lifestyle Variables
Objective: This study aimed to develop and evaluate explainable machine learning models to predict emotional eating patterns among adolescents by integrating psychological distress indicators and lifestyle-related variables. Methods and Materials: A cross-sectional, school-based study was conducted among adolescents aged 13–18 years in Poland. Participants completed validated self-report measures assessing emotional eating, perceived stress, depressive and anxiety symptoms, emotion regulation difficulties, impulsivity, self-esteem, sleep quality and duration, physical activity, screen time, and dietary habits, alongside sociodemographic information. Data were preprocessed using standardization, imputation, and encoding procedures. Multiple supervised machine learning algorithms, including regularized logistic regression, random forest, gradient boosting, and extreme gradient boosting, were trained and evaluated using nested cross-validation. Model performance was assessed using area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1-score. Explainable artificial intelligence techniques based on SHAP values were applied to interpret predictor contributions. Findings: Ensemble-based machine learning models significantly outperformed linear models in predicting emotional eating, with extreme gradient boosting demonstrating the highest discriminative performance. Psychological variables, particularly perceived stress, emotion regulation difficulties, and depressive symptoms, showed the strongest positive associations with emotional eating risk, while poor sleep quality and higher impulsivity further increased predicted vulnerability. Protective effects were observed for higher self-esteem and greater physical activity. Explainability analyses revealed consistent directional effects across predictors and identified nonlinear interactions between psychological distress and lifestyle factors. Subgroup analyses indicated higher predictive accuracy among female adolescents compared to males. Conclusion: Explainable machine learning models provide robust and interpretable tools for identifying adolescents at risk of emotional eating.
A Deep Neural Network Model for Predicting Stress Sensitivity in Adolescents Using Multidimensional Psychological Data
Objective: The objective of this study was to develop and evaluate a deep neural network model capable of accurately predicting stress sensitivity in adolescents based on a comprehensive set of psychological variables. Methods and Materials: This cross-sectional study was conducted among secondary school adolescents in Malaysia using a school-based sampling design. Participants completed a battery of standardized self-report instruments assessing stress sensitivity, perceived stress, anxiety and depressive symptoms, emotion regulation strategies, psychological resilience, self-esteem, and social support, along with demographic information. After data preprocessing, including normalization and handling of missing values, a deep neural network with multiple hidden layers was trained to predict continuous stress sensitivity scores. The dataset was partitioned into training, validation, and test subsets, and model optimization was performed using adaptive gradient-based learning with regularization and early stopping to ensure generalizability. Model performance was evaluated using error-based and variance-based metrics, and comparative analyses were conducted against traditional statistical and machine learning models. Findings: Inferential results indicated that the deep neural network achieved high predictive accuracy, explaining a substantial proportion of variance in stress sensitivity scores. The model significantly outperformed linear regression, support vector regression, and random forest models across all evaluation metrics. Permutation-based analyses revealed that perceived stress and anxiety symptoms were the strongest predictors, followed by emotion regulation through suppression, psychological resilience, depressive symptoms, and social support, while demographic variables contributed minimally. Cross-validation analyses demonstrated stable performance, supporting the robustness of the predictive framework. Conclusion: The findings demonstrate that deep neural network models can effectively capture the complex, non-linear psychological processes underlying adolescent stress sensitivity and offer a promising data-driven approach for early identification of youth at heightened stress-related risk.
Identifying Psychological Predictors of Cyberbullying Involvement in Adolescents Using Machine Learning
Objective: The objective of this study was to identify and model the key psychological predictors of cyberbullying involvement among adolescents using supervised and explainable machine learning approaches. Methods and Materials: This cross-sectional study was conducted among secondary school adolescents in Bulgaria using a multi-stage cluster sampling strategy. Participants completed a comprehensive battery of validated self-report instruments assessing cyberbullying involvement, moral disengagement, aggression, impulsivity, emotion regulation difficulties, empathy, loneliness, perceived peer support, problematic internet use, and online disinhibition. Data were analyzed using multiple supervised machine learning algorithms, including regularized logistic regression, support vector machines, random forest, gradient boosting, and extreme gradient boosting. Model performance was evaluated using stratified cross-validation and inferential performance metrics, and explainability analyses were conducted using SHAP values to determine the relative contribution of psychological predictors. Findings: Inferential machine learning results demonstrated that ensemble-based models significantly outperformed linear models in predicting cyberbullying involvement, indicating the presence of nonlinear and interactive psychological effects. Moral disengagement, online disinhibition, trait aggression, and emotion regulation difficulties emerged as the most influential predictors, showing strong positive associations with cyberbullying involvement. Problematic internet use and impulsivity also contributed significantly to model predictions, while empathy and perceived peer support showed weaker but statistically meaningful protective effects. Subgroup analyses revealed distinct psychological profiles across victims, perpetrators, and perpetrator–victims, with the latter group exhibiting the highest cumulative risk across multiple psychological dimensions. Model robustness analyses indicated stable predictive performance across gender and age groups. Conclusion: The findings demonstrate that cyberbullying involvement among adolescents can be effectively predicted using machine learning models integrating multidimensional psychological data.
Identifying Latent Psychological Subtypes of Adolescents With Internet Addiction Using Unsupervised Machine Learning Techniques
Objective: The objective of this study was to identify empirically derived latent psychological subtypes of adolescents with internet addiction by applying unsupervised machine learning techniques to multidimensional emotional, behavioral, and contextual data. Methods and Materials: This cross-sectional study was conducted among secondary school adolescents in Tunisia using a multi-stage cluster sampling strategy. Participants completed standardized self-report measures assessing internet addiction severity, depressive and anxiety symptoms, perceived stress, emotion regulation difficulties, impulsivity, loneliness, self-esteem, family support, academic engagement, and sleep quality. After data preprocessing, including standardization and handling of missing values, unsupervised machine learning techniques were applied. Dimensionality reduction was used to address multicollinearity, followed by multiple clustering algorithms, including k-means, hierarchical clustering, and Gaussian mixture modeling. Internal validation indices and stability analyses were employed to determine the optimal clustering solution, and external variables were used to profile and validate the resulting subtypes. Findings: Unsupervised analyses identified four distinct latent psychological subtypes. Inferential comparisons demonstrated significant between-cluster differences in internet addiction severity, emotional distress, impulsivity, loneliness, self-esteem, sleep quality, family support, and academic engagement. The Emotionally Distressed subtype exhibited significantly higher depression, anxiety, stress, and internet addiction severity. The Impulsive–Sensation Seeking subtype showed significantly elevated impulsivity and night-time internet use. The Socially Withdrawn subtype was characterized by significantly higher loneliness and poorer peer functioning. The Relatively Adaptive subtype demonstrated significantly lower psychological symptoms and internet addiction severity alongside higher protective factors. Conclusion: The findings demonstrate that adolescents with internet addiction are psychologically heterogeneous and can be meaningfully classified into distinct latent subtypes with differing risk and protective profiles.
Psychological Resilience in Youth: A Machine Learning Analysis of Protective Factors and Stress Exposure
Objective: This study aimed to identify and model the relative and interactive contributions of protective factors and cumulative stress exposure in predicting psychological resilience among youth using machine learning techniques. Methods and Materials: A cross-sectional study was conducted with a diverse sample of adolescents and emerging adults from urban and peri-urban regions of South Africa. Participants completed validated self-report measures assessing psychological resilience, cumulative stress exposure, and a range of individual and contextual protective factors, including emotion regulation, self-efficacy, optimism, family support, peer support, and school belonging. Data were analyzed using supervised machine learning algorithms, including regularized linear models, support vector machines, random forest, and gradient boosting machines. Model performance was evaluated using repeated cross-validation procedures, and feature importance and interaction effects were examined through permutation-based methods and partial dependence analyses. Findings: Inferential analyses indicated that ensemble machine learning models significantly outperformed linear approaches in predicting psychological resilience. The gradient boosting model explained a substantial proportion of variance in resilience scores and demonstrated high classification accuracy for distinguishing low, moderate, and high resilience profiles. Family support, emotion regulation, and self-efficacy emerged as the strongest predictors of resilience. Interaction analyses revealed that the protective effects of key resources intensified under higher levels of cumulative stress exposure, indicating robust stress-buffering effects. Conclusion: The findings demonstrate that youth psychological resilience is best understood as a multidimensional and non-linear outcome shaped by dynamic interactions between stress exposure and protective factors. Machine learning approaches provide valuable tools for advancing resilience research by capturing complex patterns and identifying high-impact protective resources. These results underscore the importance of strengthening relational and emotional capacities to promote resilience among youth facing adversity.
A Hybrid Machine Learning Framework for Predicting Emotional Reactivity in Adolescents Using Neurocognitive and Environmental Factors
Objective: The objective of this study was to develop and validate a hybrid machine learning framework capable of predicting individual differences in adolescent emotional reactivity by integrating neurocognitive indicators and environmental factors within a community-based adolescent sample. Methods and Materials: This cross-sectional study was conducted among adolescents aged 12–18 years recruited from secondary schools in Germany using a multistage cluster sampling approach. Participants completed a comprehensive assessment battery including standardized neurocognitive tasks measuring inhibitory control, working memory, cognitive flexibility, and attentional regulation, alongside validated self-report instruments assessing emotional reactivity and key environmental variables such as family emotional climate, parenting consistency, peer stress exposure, school climate, and perceived social support. Data preprocessing included missing-value imputation, feature scaling, and feature engineering. Multiple machine learning algorithms, including regularized regression, tree-based ensemble models, and kernel-based methods, were trained and integrated into a hybrid ensemble using a meta-learning strategy. Model performance was evaluated using nested cross-validation procedures. Findings: Inferential analyses demonstrated that the hybrid ensemble model significantly outperformed individual models, achieving lower prediction error and higher explained variance in emotional reactivity outcomes. Environmental factors, particularly peer stress exposure and family emotional climate, accounted for the largest proportion of predictive importance, followed by neurocognitive variables, with socio-demographic characteristics contributing comparatively less. Deficits in inhibitory control and cognitive flexibility were positively associated with emotional reactivity, whereas supportive family environments and higher perceived social support were associated with reduced emotional reactivity. Conclusion: The findings indicate that adolescent emotional reactivity is best understood as an emergent outcome of interacting neurocognitive and environmental systems and that hybrid.
Explainable Artificial Intelligence for Identifying Psychological Risk Profiles of Youth Suicidal Ideation: A SHAP-Based Machine Learning Analysis
Objective: The objective of this study was to develop and interpret an explainable machine learning model capable of accurately identifying psychological risk profiles associated with suicidal ideation among youth. Methods and Materials: This study employed a cross-sectional design involving adolescents and young adults recruited from educational settings. Participants completed standardized self-report measures assessing suicidal ideation, depressive and anxiety symptoms, emotional dysregulation, hopelessness, perceived family and peer support, academic stress, bullying exposure, and problematic digital use. Multiple supervised machine learning algorithms were trained to predict suicidal ideation, with model performance evaluated using area under the receiver operating characteristic curve, accuracy, precision, recall, and F1-score. The best-performing ensemble model was selected and interpreted using Shapley Additive Explanations to generate both global feature importance and individual-level explanatory profiles. Findings: Ensemble-based machine learning models significantly outperformed traditional classifiers, achieving excellent discriminative performance in identifying suicidal ideation. Depressive symptoms, hopelessness, and emotional dysregulation emerged as the strongest positive predictors, while perceived family support demonstrated a robust protective effect. SHAP-based analyses revealed substantial heterogeneity in risk patterns, identifying multiple psychological profiles characterized by internalizing distress, affective instability, interpersonal disconnection, and contextual stress. These profiles explained individual predictions with high transparency, demonstrating that similar levels of suicidal ideation risk can arise from distinct configurations of psychological and social factors. Conclusion: The findings indicate that explainable artificial intelligence can simultaneously achieve high predictive accuracy and meaningful psychological interpretability in youth suicide risk assessment. SHAP-based machine learning offers a powerful framework for identifying individualized risk profiles.
Predicting Adolescent Emotional Dysregulation Using Ensemble Machine Learning Models Integrating Family, School, and Digital Behavior Indicators
Objective: The objective of this study was to develop and evaluate an ensemble machine learning framework for predicting adolescent emotional dysregulation by integrating family, school, and digital behavior indicators. Methods and Materials: This study adopted a cross-sectional predictive design and was conducted among secondary school adolescents in Mexico. Data were collected using validated self-report instruments assessing emotional dysregulation, family functioning, school climate, and digital behavior patterns, alongside demographic variables. After data preprocessing, including normalization, imputation of missing values, and feature selection, multiple supervised machine learning models were developed. These included linear regression, support vector regression, random forest, gradient boosting, and a stacked ensemble model combining heterogeneous base learners. Model training and evaluation were performed using repeated k-fold cross-validation to ensure robustness and to minimize overfitting. Predictive performance was assessed using root mean square error, mean absolute error, and explained variance. Findings: Inferential analyses demonstrated that ensemble-based machine learning models significantly outperformed traditional linear and single-algorithm approaches in predicting emotional dysregulation. The stacked ensemble model achieved the highest explained variance and the lowest prediction error. Digital behavior indicators accounted for the largest proportion of predictive importance, followed closely by family-related factors and school-related variables. At the individual predictor level, problematic digital use, family conflict, parental warmth, nighttime device use, and teacher support emerged as the most influential features. Risk-related predictors showed positive associations with emotional dysregulation, whereas relational and contextual support variables showed negative associations.
Examining the Mediating Role of Mindfulness in the Relationship Between Components of Resilience and Self-Efficacy with Psychological Well-Being and Emotion Regulation
Objective: The present study aimed to examine a structural model explaining psychological well-being and emotion regulation based on resilience and self-efficacy, with the mediating role of mindfulness. Methods and Materials: This applied study employed a descriptive–correlational design using structural equation modeling. The statistical population consisted of individuals diagnosed with multiple sclerosis (MS) who referred to medical centers in Tehran in 2021. Using purposive convenience sampling and considering model requirements, 170 participants were initially recruited, of whom 152 provided complete and analyzable questionnaires. Data were collected using Ryff’s Psychological Well-Being Questionnaire (long form), the Cognitive Emotion Regulation Questionnaire, the Connor–Davidson Resilience Scale, the General Self-Efficacy Scale, and the Mindful Attention Awareness Scale (MAAS). Data analysis was conducted using AMOS software, and model fit was evaluated through multiple goodness-of-fit indices, including CMIN/DF, GFI, AGFI, CFI, IFI, TLI, and RMSEA. Findings: Pearson correlation analyses indicated significant positive associations among mindfulness, resilience, self-efficacy, psychological well-being, and emotion regulation (p < .01). Structural equation modeling revealed that resilience and self-efficacy had significant direct effects on psychological well-being and emotion regulation. Mindfulness also showed significant direct effects on both outcome variables. Moreover, indirect path analyses and Sobel test results confirmed that mindfulness significantly mediated the relationships between resilience and psychological well-being, resilience and emotion regulation, self-efficacy and psychological well-being, and self-efficacy and emotion regulation. Conclusion: The findings support the central role of mindfulness as a key psychological mechanism through which resilience and self-efficacy contribute to enhanced psychological well-being and adaptive emotion regulation, highlighting the importance of integrative models in mental health research and intervention design.
Copyright (c) 2025 Raheleh Joharian (Author); Masoumeh Behboodi (Corresponding Author); Simindokht Rezakhani (Author)
Modeling Academic Well-Being Based on Self-Regulated Learning and Critical Thinking with the Mediating Role of Academic Motivation in Female Upper Secondary Students with Maladaptive Mobile Phone Dependence
Objective: The objective of this study was to model academic well-being based on self-regulated learning and critical thinking, with the mediating role of academic motivation, among female upper secondary school students with maladaptive mobile phone dependence. Methods and Materials: This applied study employed a correlational design using structural equation modeling. The statistical population consisted of all female students enrolled in the second cycle of upper secondary education in Tabriz during the 2023–2024 academic year. Using Cochran’s formula and an accessible sampling method, 376 students identified as maladaptive mobile phone users through a screening questionnaire were selected. Data were collected using standardized instruments measuring maladaptive mobile phone dependence, academic well-being, academic motivation, self-regulated learning, and critical thinking. Descriptive statistics were used to summarize the data, and Pearson correlation coefficients were calculated to examine initial relationships among variables. Structural equation modeling was conducted using AMOS software to test the proposed model and to estimate direct and indirect effects. Findings: Structural equation modeling indicated an acceptable fit of the proposed model to the data (χ²/df = 1.68, CFI = 0.95, TLI = 0.94, RMSEA = 0.042). Self-regulated learning had significant direct effects on academic motivation (β = 0.44, p < .001) and academic well-being (β = 0.26, p < .001). Critical thinking also showed significant direct effects on academic motivation (β = 0.37, p < .001) and academic well-being (β = 0.20, p = .002). Academic motivation exerted a strong direct effect on academic well-being (β = 0.49, p < .001). In addition, academic motivation partially mediated the relationships between self-regulated learning and academic well-being, as well as between critical thinking and academic well-being. Conclusion: The findings highlight the central role of academic motivation in linking self-regulated learning and critical thinking to academic well-being, suggesting that strengthening cognitive regulation and higher-order thinking skills can enhance students’ academic well-being, particularly among those with maladaptive mobile phone dependence.
Copyright (c) 2025 Nazila Nourizadeh leilabadi (Author); Alinaghi Aghdasi (Corresponding Author); Gholamreza Chalabianloo , Marziyeh Alivandi Vafa (Author)
Comparison of the Effectiveness of Self-Efficacy Training and Mindfulness Training on Academic Motivation and Anxiety of Students
Objective: The present study was conducted with the aim of comparing the effectiveness of self-efficacy training and mindfulness training on students’ academic motivation and anxiety. Methods and Materials: The research method was applied in nature and employed a quasi-experimental design with a pretest–posttest format including one control group and two experimental groups. The statistical population consisted of female students in the second level of secondary education in District 13 of Tehran during the 2024–2025 academic year. The sample included 60 students who were selected through convenience sampling and randomly assigned to three groups of 20 participants (two experimental groups and one control group). For data collection, the Harter Academic Motivation Questionnaire (AMQ, 2002) and the Depression, Anxiety, and Stress Scale—21 items (DASS-21; Lovibond & Lovibond, 1995) were used. The intervention programs included self-efficacy training based on Bandura’s theory (Bandura, 1997) and adolescent-centered mindfulness training developed by Bordiak (2014). The research data were analyzed using multivariate analysis of covariance (MANCOVA). Findings: The findings indicated that both self-efficacy and mindfulness interventions had a positive and statistically significant effect on increasing academic motivation (particularly intrinsic motivation) and reducing anxiety in the experimental groups (p < .001). Bonferroni post hoc test results revealed that at the posttest stage, for the academic motivation variable, the control group scores differed significantly from both the self-efficacy and mindfulness groups (p < .001), whereas no significant difference was observed between the two experimental groups (p > .05). Similarly, for the anxiety variable, the control group scores showed a significant difference from those of both the self-efficacy and mindfulness groups (p < .001), while no statistically significant difference was found between the two experimental groups (p > .05). Conclusion: It can therefore be concluded that both approaches, by enhancing cognitive and psychological skills, help students improve their academic performance and mental health through better concentration, non-judgmental acceptance of thoughts, and increased self-confidence.
Copyright (c) 2025 Zahra Azimi (Author); Mohsen Ahmadi Tahour Soltani (Corresponding Author); Fateme Khoeini (Author)
The Effectiveness of Adolescent-Centered Mindfulness Training on Self-Compassion in Adolescent Boys with Psychological Insecurity
Objective: The present study aimed to examine the effectiveness of adolescent-centered mindfulness training on self-compassion among adolescent boys experiencing psychological insecurity. Methods and Materials: This study employed a quasi-experimental design with pretest, posttest, and follow-up stages, including an experimental group and a control group. The statistical population consisted of adolescent boys with psychological insecurity in the city of Isfahan during the 2022–2023 academic year. Using convenience sampling, 60 eligible participants aged 14–18 years were selected and randomly assigned to either the experimental group or the control (waitlist) group. The experimental group received eight 90-minute sessions of adolescent-centered mindfulness training delivered in a group format, while the control group received no intervention during the study period. Data were collected at three time points using standardized self-report measures of psychological insecurity and self-compassion. The intervention emphasized age-appropriate mindfulness practices, emotional awareness, acceptance, and compassion-based exercises. Data analysis was conducted using descriptive statistics and repeated measures analysis of variance to examine within-group and between-group differences over time. Findings: Repeated measures analysis of variance revealed a significant main effect of time on self-compassion, indicating meaningful changes across assessment points. A significant time × group interaction effect was observed, demonstrating that changes in self-compassion were significantly greater in the adolescent-centered mindfulness group compared with the control group. In addition, a significant between-groups effect indicated that the experimental group reported higher self-compassion scores overall than the control group. Effect size indices suggested moderate to large intervention effects, and statistical power values indicated robust reliability of the findings. Conclusion: The findings indicate that adolescent-centered mindfulness training is an effective intervention for enhancing self-compassion in adolescent boys with psychological insecurity and may serve as a valuable preventive and therapeutic approach for promoting adolescent psychological well-being.
Copyright (c) 2025 Alireza Fattahi Dolatabadi (Corresponding Author)
Developing a Causal Model of Academic Buoyancy in Gifted Students in Tehran Based on Perceived Parental Relationships and Perceived Teacher–Student Relationships: The Mediating Role of Academic Pressure and Socio-Emotional Competence
Objective: This study aimed to develop and test a causal model of academic buoyancy in gifted secondary school students based on perceived parent–child relationships and teacher–student relationship quality, with the mediating roles of socio-emotional competence and perceived academic pressure. Methods and Materials: The study adopted an applied, descriptive–correlational design using structural equation modeling (SEM). The population consisted of gifted students enrolled in the first and second levels of secondary education in Tehran during the 2023–2024 academic year. A total of 392 students were selected through simple random sampling. Data were collected using standardized questionnaires assessing academic buoyancy, parent–child relationships, teacher–student relationship quality, socio-emotional competence, and perceived academic pressure. Data analysis was conducted using SPSS and AMOS software. Pearson correlation coefficients were calculated, and SEM with maximum likelihood estimation was employed. Indirect effects were tested using bootstrapping with 2,000 resamples to evaluate mediating pathways. Findings: Structural equation modeling indicated that mother–child relationships (β = 0.37, p < .05) and teacher–student relationship quality (β = 0.42, p < .05) had significant direct positive effects on academic buoyancy, whereas the direct effect of father–child relationships was not significant. Socio-emotional competence showed a strong positive direct effect on academic buoyancy (β = 0.45, p < .01), while perceived academic pressure had a significant negative direct effect (β = −0.39, p < .01). Bootstrapping results revealed significant indirect effects of parent–child relationships and teacher–student relationship quality on academic buoyancy through socio-emotional competence (positive) and perceived academic pressure (negative). The overall model demonstrated acceptable goodness-of-fit indices. Conclusion: The findings highlight academic buoyancy as a relationally embedded and psychologically mediated construct, indicating that supportive parental and teacher relationships enhance gifted students’ vitality primarily by strengthening socio-emotional competence and reducing perceived academic pressure.
Quality of Student-Teacher Relationships
Adolescents’ Perceived Parental Academic Pressure
Copyright (c) 2025 Soudabeh Ershadi Manesh (Author)