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)
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)
Predicting Risky Behaviors Based on Emotional Schemas and Rumination in Adolescents
Objective: This study aimed to predict risky behaviors in adolescents based on emotional schemas and rumination. Methods and Materials: This applied, descriptive–correlational study was conducted among 150 male high school students in District 1 of Baharestan during the 2024–2025 academic year. Participants were selected through purposive sampling based on the population structure of the region. Data were collected using three validated instruments: the Risky Behaviors Questionnaire (Zadehmohammadi & Ahmadabadi, 2008), the Emotional Schema Scale—Second Version (Leahy, 2012), and the Rumination Response Scale (Nolen-Hoeksema & Morrow, 1991). After verifying statistical assumptions, inferential analyses were performed using Pearson correlation and simultaneous multiple regression in SPSS-27 to examine the predictive power of emotional schemas and rumination for risky behaviors. Findings: Pearson correlation results indicated significant positive relationships between risky behaviors and rumination, emotional rumination, guilt and shame, and blame, while comprehensibility showed a weak negative correlation. The simultaneous regression model was significant, F(6,143) = 27.99, p = 0.004, demonstrating that emotional schemas and rumination collectively explained 38.8% of the variance in risky behaviors (R = 0.623, R² = 0.388). Among predictors, rumination (β = 0.14, p = 0.01), guilt and shame (β = 0.08, p = 0.04), emotional rumination (β = 0.09, p = 0.05), and blame (β = 0.12, p = 0.01) had significant positive effects, while comprehensibility did not significantly predict risky behaviors (p = 0.11). Conclusion: The findings highlight the substantial role of emotional schemas and rumination in shaping adolescents’ risky behavioral tendencies, demonstrating that maladaptive emotional beliefs and repetitive negative thinking significantly contribute to engagement in harmful behaviors. Interventions targeting emotional schemas and rumination may effectively reduce risk-taking among adolescents.
Copyright (c) 2025 Majid Zolfi Oshtolagh (Author); Alireza Pirkhaefi (Corresponding Author)
A Comparative Study Between the Paradigmatic Models of Girls and Boys in the Formation Process of Marriage Trauma
Objective: This study aimed to compare the paradigmatic models of girls and boys in the formation process of marriage trauma using a grounded theory framework. Methods and Materials: This qualitative study employed the systematic grounded theory approach of Strauss and Corbin. Semi-structured, in-depth, face-to-face interviews were conducted with 21 unmarried girls and 17 unmarried boys residing in Shahreza who did not have psychological disorders. Participants were recruited through an Instagram call posted by a psychological clinic and selected via snowball sampling. Interviews lasted 60–80 minutes and explored causal conditions, contextual conditions, intervening conditions, strategies, and consequences related to fear of marriage. Data were analyzed through open, axial, and selective coding to identify core categories and reconstruct gender-specific paradigmatic models explaining marriage trauma. Findings: The analysis revealed distinct gendered pathways in the formation of marriage trauma. For girls, the core category was “the ambiguity of the value of marriage and perfectionism of self and parents,” arising from emotional failures of others, familial expectations, religious–moral incongruence, and economic insecurity. For boys, the core category was “the meaninglessness of marital authenticity,” driven by fear of unsuccessful marriage, economic instability, perceived legal vulnerability, past relational hurt, and sociocultural constraints. Across both groups, intervening conditions such as parental conflict, emotional dependency, and intergenerational narratives intensified trauma trajectories. Strategies included seeking emotional reassurance, emphasizing compatibility, and attempting to learn communication skills; however, consequences such as increased dependency, avoidance, and loss of perceived marriage opportunities were evident. Conclusion: Marriage trauma among youth arises from intertwined emotional, familial, sociocultural, and structural factors, with clear gender-specific mechanisms shaping fear and avoidance of marriage; understanding these paradigmatic models can inform culturally responsive interventions and support systems.
Copyright (c) 2025 Samaneh Shadmand (Author); Seyed Ali Hashemianfar (Corresponding Author); Saeid Aghasi (Author)
Development of a Cognitive–Physical Dual-Task Intervention Package and Its Effectiveness on Evidence Rate (Evidence Accumulation Speed) in Adolescent Girls
Objective: The present study was conducted with the aim of developing a cognitive–physical dual-task intervention package and examining its effectiveness on the evidence rate (evidence accumulation speed) in adolescent girls. Methods and Materials: This research employed a quasi-experimental design with pretest–posttest–follow-up and a control group. The statistical population consisted of low-active adolescent girls in Tehran in the 2024–25 academic year. Using convenience sampling, 50 participants were selected and randomly assigned to experimental and control groups (25 in each group). The experimental group received training based on the designed cognitive–physical dual-task package over eight weeks in twenty-four 20-minute sessions. The control group performed moderate-intensity aerobic exercises, starting from below-threshold intensity and progressing to moderately high intensity. To assess cognitive changes, the Wisconsin Card Sorting Test (WCST) was used, with “perseverative errors” and “total errors” serving as indices of cognitive flexibility and evidence accumulation speed. The process of developing the intervention package included designing training lesson plans, a teenager’s workbook, and evaluating content validity using CVR and CVI indices, which ranged from 0.8 to 1 and 0.9 to 1, respectively, confirming the satisfactory validity of the package. Data were analyzed using repeated measures ANOVA. Findings: The results showed that the mean perseverative errors and total errors in the experimental group decreased significantly compared to the control group at posttest and follow-up (p < 0.01). Effect size and eta squared also indicated a considerable impact of the cognitive–physical exercises on increasing the evidence accumulation rate. Conclusion: The designed package significantly improved cognitive flexibility and decision-making processing speed in adolescent girls. These findings support the effectiveness of integrative mind–body approaches in enhancing fine-grained cognitive indices during adolescence.
Copyright (c) 2025 Fereshteh Arsalandeh (Author); Maryam Moghadasin (Corresponding Author); Jafar Hasani, Hamid Rajabi (Author)
The Effectiveness of Life Skills Training on Self-Handicapping among Students
Objective: This study aimed to examine the effectiveness of life skills training in reducing self-handicapping behaviors and their components among master’s students. Methods and Materials: This applied, quantitative study used a semi-experimental pretest–posttest design with a control group and a two-month follow-up. The statistical population comprised all master’s students in Economic Management at the Islamic Azad University, Shiraz Branch, during the 2024–2025 academic year. Using convenience sampling, 78 students were selected and randomly assigned to an experimental group (n = 39) and a control group (n = 39). The experimental group participated in ten 45-minute sessions of life skills training based on Klinke’s (1994) protocol, covering self-awareness, empathy, communication, anger management, problem-solving, stress management, decision-making, creative and critical thinking, and emotional regulation. Data were collected using the Self-Handicapping Questionnaire developed by Jones and Rhodewalt (1978) and analyzed through analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) using SPSS 26. Findings: The results revealed significant reductions in total self-handicapping and its components among the experimental group compared to the control group at the posttest stage (p < .001). ANCOVA results indicated strong intervention effects on total self-handicapping (F = 92.34, η² = .591), effort (F = 32.25, η² = .342), and negative mood (F = 21.65, η² = .259), with a moderate effect on excuse-making (F = 8.90, p = .004, η² = .126). The follow-up analysis confirmed the persistence of effects after two months, except for the excuse-making component (p > .05). Conclusion: Life skills training effectively reduces self-handicapping and its cognitive-emotional components among university students and maintains its impact over time, highlighting its importance as a preventive and developmental intervention in higher education.
Copyright (c) 2025 Zahra Taghipour (Author); Ali Naseri (Corresponding Author)
Comparison of the Effectiveness of Psychological Debriefing–Based Intervention and Schema Therapy on Post-Traumatic Stress Arousal Symptoms and Coping Styles in Adolescent Girls Affected by Sexual Abuse
Objective: This study aimed to compare the effectiveness of psychological debriefing and schema therapy on post-traumatic stress arousal symptoms and coping styles among adolescent girls affected by sexual abuse. Methods and Materials: This quasi-experimental study employed a pretest–posttest control group design with three groups: psychological debriefing, schema therapy, and control. The sample included 45 adolescent girls aged 12–18 years who were referred to the Social Emergency Center in Varamin, Iran, in 2024, after experiencing sexual abuse. Participants were selected through purposive sampling based on inclusion criteria and randomly assigned to one of the three groups (15 per group). The interventions consisted of a 12-session schema therapy program based on Young’s model and a single-session (two-hour) psychological debriefing protocol based on Mitchell and Everly’s model. Research instruments included the Endler and Parker Coping Inventory for Stressful Situations (CISS) and the PTSD Symptom Scale developed by Weathers et al. Data were analyzed using mixed-design analysis of variance (ANOVA) with a significance level of p < .05. Findings: The results showed that both interventions led to a significant increase in problem-focused coping and a significant reduction in emotion-focused and avoidance coping styles, as well as decreased emotional arousal symptoms among adolescent girls in the experimental groups compared to the control group (p < .05). The within-group and interaction effects of time and group were statistically significant, while no significant differences were observed between the two intervention groups, indicating comparable efficacy of psychological debriefing and schema therapy in reducing post-traumatic symptoms. Conclusion: Both schema therapy and psychological debriefing are effective trauma-focused interventions for adolescent girls affected by sexual abuse, improving coping strategies and reducing hyperarousal symptoms. Integrating these methods into a staged treatment protocol may optimize recovery outcomes for this vulnerable population.
Copyright (c) 2025 Marzieh Azizi (Author); Alinaghi Aghdasi (Corresponding Author); Alireza Agdamibaher, Javad Mesrabadi (Author)
Identifying the Subjective Experience of Learning Under High Performance Pressure
Objective: This study aimed to identify and explore the subjective emotional, cognitive, and social experiences of learners operating under high performance pressure within South African educational contexts. Methods and Materials: Using a qualitative design, this study adopted an interpretivist approach to understand the lived experiences of individuals facing intense performance expectations. Data were collected through semi-structured interviews with 24 participants drawn from universities and professional training programs across South Africa. Participants were selected purposively to ensure diversity in academic and occupational backgrounds, and interviews continued until theoretical saturation was reached. All interviews were audio-recorded, transcribed verbatim, and analyzed thematically using NVivo 14 software. Thematic analysis followed Braun and Clarke’s six-step method, and credibility was enhanced through member checking, peer debriefing, and reflexive documentation. Findings: Three overarching themes emerged from the data. First, emotional and cognitive responses to pressure revealed that anxiety, cognitive overload, and fluctuating motivation coexisted with resilience and growth mindset. Second, social and environmental influences demonstrated that institutional grading systems, peer comparison, family expectations, and digital monitoring amplified stress, while supportive instructor feedback and empathetic climates enhanced engagement. Third, coping, adaptation, and meaning-making highlighted that learners adopted both adaptive (mindfulness, reframing, time management) and maladaptive (avoidance, overstudying) strategies, with resilience and self-reflection transforming pressure into an opportunity for personal and academic development. Conclusion: Learning under high performance pressure simultaneously challenges and strengthens learners, shaping emotional resilience, motivation, and self-concept. The findings underscore the need for educational systems to balance high expectations with psychological safety, supportive pedagogy, and reflective learning practices to transform pressure into growth.
Identifying the Role of Digital Self-Presentation in Adolescent Identity Formation
Objective: This study aimed to identify and interpret the role of digital self-presentation in shaping adolescent identity formation among Chilean youth within the broader context of social media engagement and self-concept development. Methods and Materials: A qualitative research design was employed using semi-structured, in-depth interviews with 33 adolescents aged 14–19 years from Santiago, Valparaíso, and Concepción, Chile. Participants were selected through purposive sampling to ensure diversity in gender, socioeconomic background, and social media usage. Data collection continued until theoretical saturation was reached. All interviews were conducted in Spanish, transcribed verbatim, and analyzed using thematic analysis with NVivo 14 software. The analysis involved open, axial, and selective coding to generate core themes reflecting participants’ lived experiences and meaning-making processes in digital environments. To enhance the study’s credibility, member checking and peer debriefing were performed. Findings: Three overarching themes emerged: (1) Digital Platforms as Identity Arenas, illustrating how adolescents strategically curate and manage their online personas; (2) Negotiating Authenticity and Social Expectation, revealing tensions between genuine self-expression and conformity to peer norms; and (3) Identity Development through Digital Interaction, emphasizing how feedback, role experimentation, and online reflection foster evolving self-awareness. The findings demonstrated that adolescents actively use social media to construct, test, and regulate their sense of self, while feedback mechanisms and social comparison dynamics significantly influence emotional well-being and self-esteem. Participants with stronger self-regulation and supportive family relationships displayed greater digital resilience and identity coherence. Conclusion: Digital self-presentation functions as both a developmental opportunity and a psychosocial challenge. It enables adolescents to explore diverse self-aspects and foster identity growth while simultaneously exposing them to social pressures and validation dependence. Conscious and reflective engagement in digital spaces can enhance identity integration and emotional balance in adolescence.
Identifying Perceived Barriers to Seeking Psychological Help Among Rural Youth
Objective: This study aimed to identify and explore the perceived sociocultural, structural, and psychological barriers that prevent rural youth in Greece from seeking professional psychological help. Methods and Materials: This qualitative study employed a phenomenological design to capture the lived experiences of rural youth regarding help-seeking for mental health concerns. A purposive sample of 22 participants aged 18–25 from rural areas across Greece was selected. Data were collected through in-depth semi-structured interviews conducted face-to-face and via online platforms until theoretical saturation was reached. Each interview lasted between 45 and 70 minutes and was audio-recorded with participants’ consent. Transcripts were analyzed using Braun and Clarke’s thematic analysis approach, supported by NVivo 14 software to code, categorize, and identify recurring patterns. Ethical standards, including confidentiality, informed consent, and participant anonymity, were strictly maintained throughout the research process. Findings: Analysis revealed four major themes explaining barriers to professional help-seeking: (1) cultural and social stigma, including fear of labeling, gender norms, and family expectations; (2) structural and logistical barriers such as distance, financial hardship, and lack of local services; (3) psychological and personal factors, including low mental health literacy, self-stigma, and fear of self-disclosure; and (4) interpersonal and environmental influences involving peers, family, schools, and community norms. These themes reflect the intertwined effects of cultural identity, limited infrastructure, and individual beliefs on help-seeking behaviors. The results indicate that stigma, limited perceived behavioral control, and restrictive social norms jointly contribute to the low utilization of psychological services among rural youth. Conclusion: The study underscores that addressing help-seeking barriers requires multifaceted interventions integrating cultural sensitivity, mental health literacy, and accessible rural services. Promoting community-based psychoeducation and destigmatization campaigns may enhance openness toward professional psychological support among rural Greek youth.
Identifying Generational Gaps in Emotional Expression among Youth and Parents
Objective: This study aimed to identify and interpret generational differences in emotional expression between youth and parents, focusing on how social, cognitive, and cultural factors influence emotional communication patterns across generations. Methods and Materials: This qualitative research employed an exploratory design grounded in the interpretivist paradigm to capture the lived experiences of emotional communication within families. The study involved 20 participants from Canada, comprising 10 youth (aged 18–25) and 10 parents (aged 40–60), selected through purposive sampling to ensure diversity in gender, culture, and socioeconomic background. Semi-structured interviews were conducted in person and online, each lasting 45–70 minutes, until theoretical saturation was reached. Data were transcribed verbatim and analyzed thematically using NVivo 14 software. Thematic analysis followed Braun and Clarke’s six-step framework, with codes and categories refined through constant comparison and reflexive journaling to ensure credibility and consistency. Findings: Five major themes emerged: (1) Communication Styles Across Generations, reflecting youth’s verbal openness versus parental restraint; (2) Technological Mediation of Emotion, highlighting digital communication as both a bridge and barrier; (3) Emotional Norms and Cultural Expectations, emphasizing the impact of cultural scripts and gendered expression; (4) Perceptions of Empathy and Understanding, revealing emotional misattunement and validation gaps; and (5) Adaptation and Emotional Bridging Mechanisms, showing efforts toward intergenerational empathy and emotional literacy. The findings indicated that differences in emotional vocabulary, digital fluency, and cultural norms contribute to misinterpretations and emotional distance, while reflective dialogue and hybrid communication foster improved understanding. Conclusion: Generational gaps in emotional expression arise from interwoven social, cultural, and cognitive mechanisms that shape emotion socialization within families. Promoting emotional literacy, reciprocal empathy, and adaptive communication practices can enhance intergenerational cohesion and emotional well-being.
Ranking Adolescent Stressors Linked to Urban Lifestyle and Overexposure
Objective: This study aimed to identify and rank key stressors affecting adolescents in urban environments, focusing on how digital exposure, academic competition, environmental overload, and social comparison contribute to mental and emotional distress. Methods and Materials: The research employed a sequential exploratory mixed-methods design. In the qualitative phase, an extensive literature review was conducted using NVivo 14 software to identify major categories of urban stressors until theoretical saturation was achieved. Eight primary themes emerged—academic pressure, digital overexposure, social comparison, lifestyle imbalance, sensory overload, media-driven anxiety, urban isolation, and spatial constraints. In the quantitative phase, a structured questionnaire derived from these themes was distributed to 200 adolescents (aged 13–18) from urban regions of Hungary. Data were analyzed using SPSS version 26, with descriptive statistics, the Friedman test, and reliability analysis (Cronbach’s α = 0.89) applied to determine the relative significance of stressors. Findings: The Friedman test results indicated statistically significant differences in the mean ranks of stressors (p < 0.001). Academic and performance pressure ranked highest (mean rank = 4.67), followed by digital overexposure (4.51), social comparison (4.38), and lifestyle imbalance (4.21). Environmental and media-related stressors—such as sensory overload and media-driven anxiety—were moderately ranked, while urban isolation and physical constraints had the lowest rankings. These findings suggest that cognitive and social pressures related to performance and technology use are stronger predictors of adolescent distress than physical or environmental factors. Conclusion: The study demonstrates that the urban adolescent experience is characterized by overlapping academic and digital stressors that operate within a high-demand environment. Understanding the hierarchical structure of these stressors can inform interventions promoting balance, digital mindfulness, and academic reform to mitigate chronic urban stress among youth.
Ranking Internalized Beliefs Contributing to Body Image Dissatisfaction
Objective: This study aimed to identify and rank the internalized belief systems that contribute to body image dissatisfaction among adults through an integrated qualitative–quantitative framework. Methods and Materials: A sequential exploratory mixed-method design was employed. In the qualitative phase, a systematic literature review of 126 scholarly sources published between 2010 and 2025 was conducted to identify belief domains related to body image dissatisfaction. Data were analyzed using NVivo 14 through open, axial, and selective coding until theoretical saturation was achieved, resulting in seven overarching belief themes. In the quantitative phase, a structured survey derived from these qualitative results was distributed to 215 participants in Malaysia, aged 18–35 years. Responses were analyzed in SPSS 26 using the Friedman ranking test to determine the relative importance of each belief domain. Reliability was confirmed with Cronbach’s alpha above 0.70, and demographic variations were explored via independent t-tests and ANOVA. Findings: The Friedman test revealed significant differences across belief domains (χ² = 198.37, p < 0.001). The highest mean ranks were recorded for socio-cultural appearance ideals (6.45) and self-worth contingent on appearance (6.10), indicating that external cultural pressures and appearance-based self-evaluation are dominant predictors of body dissatisfaction. Perfectionistic body standards (5.72) and cognitive distortions and negative self-talk (5.21) were moderately influential, while gender and body role expectations (4.63), emotional regulation via body control (4.15), and consumerism and commercial influence (3.74) ranked lower. These results highlight a cognitive–sociocultural hierarchy of internalized beliefs shaping dissatisfaction. Conclusion: The findings suggest that body image dissatisfaction is primarily sustained by sociocultural idealization and self-worth dependency, with perfectionistic and cognitive distortions acting as reinforcing mechanisms. Addressing these internalized beliefs through integrative psychological, educational, and media-based interventions may reduce vulnerability to body dissatisfaction.
Prioritizing Psychological Consequences of Online Peer Comparison: A Mixed-Methods Analysis Among Indonesian Youth
Objective: This study aimed to identify and prioritize the psychological consequences of online peer comparison among adolescents and young adults using a sequential mixed-methods design. Methods and Materials: The research adopted a two-phase sequential exploratory design. The qualitative phase involved a systematic literature review of studies related to online peer comparison, social media use, and mental health outcomes. Data were analyzed using NVivo 14 through open, axial, and selective coding until theoretical saturation was achieved, resulting in nine major psychological themes. The subsequent quantitative phase utilized a cross-sectional survey of 220 Indonesian participants aged 16–30 years, all active social media users. Participants rated the severity and frequency of each identified psychological outcome using a five-point Likert scale. Data were analyzed using SPSS version 26, employing the Friedman test to establish ranking orders and post-hoc Wilcoxon tests for inferential comparisons. Findings: The Friedman test revealed statistically significant differences among the perceived psychological consequences (χ² = 121.37, p < .001). Self-esteem depletion ranked highest (Mean Rank = 8.62), followed by anxiety and distress (Mean Rank = 8.11) and depression and emotional exhaustion (Mean Rank = 7.95). Mid-ranked outcomes included body image dissatisfaction (Mean Rank = 7.78) and envy and resentment (Mean Rank = 7.45). Cognitive distortion, social isolation, identity confusion, and motivation and productivity decline were comparatively lower in impact. The results suggest that emotional and self-evaluative processes constitute the core mechanisms through which online peer comparison affects psychological well-being. Conclusion: Online peer comparison primarily undermines self-esteem and emotional stability, producing multidimensional effects across emotional, cognitive, and behavioral domains. The findings emphasize the need for targeted interventions promoting self-compassion, emotional regulation, and digital literacy to mitigate comparison-induced distress in youth populations.
Prioritizing Pathways Linking Educational Stress to Mental Health Outcomes: A Mixed-Methods Analysis Among Moroccan Students
Objective: This study aimed to identify and prioritize the key pathways through which educational stress influences mental health outcomes among students in Morocco using an integrated qualitative–quantitative framework. Methods and Materials: A sequential exploratory mixed-method design was employed. In the first phase, a qualitative thematic analysis was conducted through a systematic review of global and regional studies on educational stress and psychological well-being until theoretical saturation was achieved. The data were analyzed using NVivo 14, generating seven major themes: academic pressure, parental expectations, cognitive coping, peer climate, institutional factors, emotional outcomes, and resilience. In the second phase, a quantitative survey was administered to 160 Moroccan participants, including students and educators, who ranked the relative importance of the identified pathways using a five-point Likert scale. Data analysis was performed in SPSS 26, employing descriptive statistics, mean ranking, and Kendall’s coefficient of concordance (W = 0.81, p < 0.001) to assess inter-rater agreement. Findings: Results indicated strong consensus among participants regarding the hierarchical influence of stress pathways on mental health. Academic pressure and performance anxiety emerged as the most influential pathway (M = 4.62, SD = 0.48), followed by parental and societal expectations (M = 4.40, SD = 0.55) and cognitive appraisal and coping styles (M = 4.25, SD = 0.59). Peer relations, institutional environment, and emotional outcomes ranked moderately, while resilience and protective mechanisms (M = 3.54, SD = 0.72) were perceived as reactive but essential buffers against stress-induced distress. Conclusion: Educational stress is a multidimensional construct shaped by academic, familial, cognitive, and institutional dynamics. Addressing these interconnected pathways through targeted interventions—such as resilience training, institutional reform, and family-based awareness—can significantly enhance students’ mental health outcomes and academic sustainability in the Moroccan context.
The Effectiveness of Emotion Regulation Training on Frustration Tolerance and Executive Functions in Adolescents of Tehran City
Objective: The present study aimed to investigate the effectiveness of emotion regulation training on frustration tolerance and executive functions among adolescents in Tehran City. Methods and Materials: The research method was quasi-experimental with a pretest–posttest design including a control group. The statistical population comprised all secondary school adolescents in Tehran during 2025, from which 30 participants were selected through convenience sampling and randomly assigned to experimental and control groups. The experimental group participated in ten 90-minute sessions of emotion regulation training, whereas the control group received no intervention. To assess the study variables, the Harrington Frustration Tolerance Questionnaire (2005) and the Barkley Deficits in Executive Functioning Scale (2011) were used. Findings: Findings indicated that emotion regulation training led to a significant increase in frustration tolerance and improvement in executive functions—including inhibitory control, cognitive flexibility, and self-regulation—in the experimental group (p < .05). Conclusion: These results suggest that emotion regulation training can effectively enhance cognitive and emotional resilience in adolescents by improving self-awareness, emotional management, and flexible thinking skills. Therefore, implementing such training programs in educational and counseling settings is recommended to promote mental health and executive functioning among adolescents. Keywords: Emotion Regulation Training, Frustration Tolerance, Executive Functions, Adolescents
Copyright (c) 2025 Zahra Etaati, Nowzar Ghorbani, Shahin Alipanah (Author); Nazanin Haghighat Bayan (Corresponding Author); Sara Rezaei (Author)