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)
A Comparison of the Effectiveness of Solution-Focused Therapy and Compassion-Focused Therapy on Rumination and Intolerance of Uncertainty among Adolescents with Inadequate Parental Care
Objective: The present study aimed to compare the effectiveness of Solution-Focused Therapy (SFT) and Compassion-Focused Therapy (CFT) on reducing rumination and intolerance of uncertainty among adolescents with inadequate parental care. Methods and Materials: This applied, quasi-experimental study employed a pretest–posttest control group design with a 9-month follow-up period. The study population consisted of adolescents with inadequate parental care who were under the supervision of welfare centers in Mashhad, Iran, during 2025. A total of 45 participants were selected through convenience sampling and randomly assigned to three groups: Solution-Focused Therapy (n = 15), Compassion-Focused Therapy (n = 15), and a control group (n = 15). Data were collected using the Ruminative Responses Scale (RRS) developed by Treynor et al. (2003) and the Intolerance of Uncertainty Scale (IUS) developed by Carleton et al. (2007). Participants in the intervention groups received the respective therapeutic protocols, while the control group received no intervention during the study period. Data were analyzed using repeated-measures analysis of variance in SPSS-26. Findings: The results revealed significant differences among the study groups over time. Compassion-Focused Therapy demonstrated superior effectiveness in reducing rumination compared with both Solution-Focused Therapy and the control group. Furthermore, Compassion-Focused Therapy produced a statistically significant reduction in intolerance of uncertainty compared with the control group. However, no significant difference was observed between Compassion-Focused Therapy and Solution-Focused Therapy regarding intolerance of uncertainty. In contrast, Solution-Focused Therapy did not produce statistically significant improvements in either rumination or intolerance of uncertainty when compared with the control group. The therapeutic gains associated with Compassion-Focused Therapy remained stable throughout the 9-month follow-up period. Conclusion: The findings suggest that Compassion-Focused Therapy is a more effective intervention than Solution-Focused Therapy for addressing maladaptive cognitive-emotional processes, particularly rumination, among adolescents with inadequate parental care.
Adolescents with Inadequate Parental Care.
Copyright (c) 2026 Ehsan Masoumzadeh Bejestani, Ahmad Mansouri, Hamid Nejat, Mustafa Bolghan Abadi (Author)
Explaining FOMO Symptoms Based on Maladaptive Perfectionism and Emotional Loneliness with Emphasis on the Mediating Role of Artificial Intelligence Dependency among Female High School Adolescents
Objective: The present study aimed to explain FOMO (Fear of Missing Out) symptoms based on maladaptive perfectionism and emotional loneliness, with a particular focus on the mediating role of artificial intelligence dependency among female high school adolescents. Methods and Materials: This correlational study employed a structural equation modeling approach to investigate the relationships among maladaptive perfectionism, emotional loneliness, artificial intelligence dependency, and FOMO symptoms. The statistical population consisted of all female students enrolled in secondary high schools in Dezful, Iran, during the 2025–2026 academic year. A total of 250 students were selected through convenience sampling. Data were collected using the Fear of Missing Out Scale developed by Przybylski et al. (2013), the Multidimensional Perfectionism Questionnaire by Hewitt and Flett (2004), the Social and Emotional Loneliness Scale by DiTommaso et al. (2004), and the Artificial Intelligence Dependency Questionnaire developed by Huang et al. (2024). Data analysis was conducted using descriptive and inferential statistics in SPSS and AMOS software packages. Path analysis and structural equation modeling were utilized to evaluate both direct and indirect relationships among the study variables and to assess the overall model fit. Findings: The results demonstrated that maladaptive perfectionism and emotional loneliness significantly predicted FOMO symptoms both directly and indirectly through artificial intelligence dependency. Maladaptive perfectionism showed a significant positive effect on artificial intelligence dependency (β = 0.411, p < .01), while emotional loneliness also exhibited a significant positive effect on artificial intelligence dependency (β = 0.506, p < .01). Furthermore, maladaptive perfectionism had a significant direct effect on FOMO symptoms (β = 0.267, p < .01) and a significant indirect effect through artificial intelligence dependency (β = 0.124, p < .01). Similarly, emotional loneliness exerted a significant direct effect on FOMO symptoms (β = 0.259, p < .01) and a significant indirect effect mediated by artificial intelligence dependency (β = 0.152, p < .01). Artificial intelligence dependency itself significantly and positively predicted FOMO symptoms (β = 0.301, p < .01). Model fit indices indicated a satisfactory fit of the proposed structural model to the observed data. Conclusion: The findings suggest that adolescents with higher levels of maladaptive perfectionism and emotional loneliness are more likely to develop dependency on artificial intelligence technologies, which in turn intensifies FOMO symptoms. Artificial intelligence dependency functions as a significant psychological mechanism linking individual vulnerabilities and social-emotional difficulties to heightened fear of missing out. These results highlight the importance of addressing maladaptive perfectionistic standards, strengthening real-world emotional relationships, and promoting healthy patterns of AI use in interventions designed to reduce FOMO among adolescents.
Copyright (c) 2026 Zohre Rajabzade Dezfuli (Author); Saeed Moshtaghi (Corresponding Author); Shahram Noori Samarein (Author)
Designing a professional learning community model for primary school teachers
Background and Aim: Professional learning community is an extensive or moderate learning process to strengthen cooperation between job-related tasks in the work environment and has a significant effect on improving the performance of human resources. This research was conducted with the aim of designing a professional learning community model for primary school teachers. Methods: A phenomenological qualitative method based on the Claizian stage analysis approach was used. The participants of the research are 26 people, including professors and faculty members of universities in the field of educational management, principals, assistant professors and successful teachers in elementary schools, and doctoral students or graduates of the field of educational management working in education, who through sampling A targeted opinion was selected. In order to collect data, in-depth interviews with research participants were used. In order to evaluate the validity, the methods of review by members, triangulation (three-way perspective) of data sources, review by colleagues and review by supervisors are used to increase the validity of the research. Also, to check the reliability, the strategies of reliability, assurance and trustworthiness, confirmation are used. or verifiability and transferability were exploited. Results: After analyzing the data, the final model of the research includes 2 major dimensions of the antecedents of teachers' professional learning community, including 4 components of educational leadership, organizational culture, collaborative learning, and organizational structure, and finally, the consequences of teachers' professional learning community, including 3 major dimensions of quality development performance, belonging to work and school, and job and professional satisfaction were depicted. Conclusion: In order to realize the professional learning community of teachers, it is necessary and necessary to pay attention to cultural elements and dimensions. The task of teachers' professional learning community is to stimulate things such as mission, vision, collective commitment and common goals, and value creativity, risk-taking, initiative and innovation, and the existence of an atmosphere based on mutual respect as an important part of teachers' professional learning community.
Copyright (c) 2023 Raheleh Amoozadeh (Author); Hamid Shafizadeh (Corresponding Author); Fakhredin Ahmadi (Author)
Comparing the Effectiveness of Metacognitive Therapy and Compassion-Focused Therapy on Repetitive Thinking in Female Students with Non-Clinical Depression
Objective: The present study aimed to compare the effectiveness of Metacognitive Therapy (MCT) and Compassion-Focused Therapy (CFT) in reducing repetitive thinking among female university students with non-clinical depression and to identify the more effective intervention. Methods and Materials: This quasi-experimental study employed a pretest–posttest control group design with a three-month follow-up assessment. The statistical population consisted of female students with non-clinical depression at Islamic Azad University, Sari Branch, during the 2025 academic year. Forty-five participants were selected through purposive sampling and assigned to three groups: Metacognitive Therapy (n=15), Compassion-Focused Therapy (n=15), and a control group (n=15). Participants in the intervention groups received eight 90-minute sessions based on Wells’ Metacognitive Therapy protocol and Gilbert’s Compassion-Focused Therapy protocol, respectively. Data were collected using the Repetitive Negative Thinking Scale and analyzed through repeated-measures ANOVA, Bonferroni post hoc tests, and Tukey comparisons. Findings: Repeated-measures ANOVA demonstrated significant main effects for group (F=25.519, p<0.01, η²=0.549) and time (F=50.583, p<0.01, η²=0.546), as well as a significant group-by-time interaction effect (F=14.483, p<0.01, η²=0.408). Bonferroni analyses revealed significant reductions in repetitive thinking from pretest to posttest and follow-up (p<0.001), whereas the difference between posttest and follow-up was not significant, indicating maintenance of treatment gains. Tukey comparisons further showed that Metacognitive Therapy produced significantly greater reductions in repetitive thinking than Compassion-Focused Therapy (p<0.01). Conclusion: Both therapeutic approaches effectively reduced repetitive thinking among female students with non-clinical depression; however, Metacognitive Therapy demonstrated superior effectiveness. The findings suggest that directly modifying maladaptive metacognitive beliefs and cognitive processing styles may be more beneficial than compassion-based interventions for reducing repetitive thinking and preventing progression toward clinical depression.
Copyright (c) 2026 Marziyeh Zare Zari Mahaleh (Author); Yar Ali Dousti (Corresponding Author); Saeid Ahmad Khosravi (Author)
Investigating the Structural Relationships Between Self-Regulated Learning and Academic Motivation with the Mediating Role of Self-Esteem Among Upper Secondary School Students in Babolsar
Objective: The present study aimed to examine the structural relationships between self-regulated learning and academic motivation and to determine the mediating role of self-esteem among upper secondary school students in Babolsar. Methods and Materials: This applied study employed a correlational design based on structural equation modeling (SEM). The statistical population consisted of all tenth-, eleventh-, and twelfth-grade students in Babolsar during the 2025–2026 academic year. Using multistage cluster random sampling, 300 students were selected and included in the final analysis. Data were collected using the Self-Regulated Learning Questionnaire developed by Bouffard et al. (1995), the Academic Motivation Scale developed by Vallerand (1992), and the Rosenberg Self-Esteem Scale (1965). Descriptive statistics and Pearson correlation coefficients were calculated using SPSS, while the hypothesized structural model was tested using AMOS through maximum likelihood estimation. The adequacy of the model was evaluated using standard fit indices, including χ²/df, RMSEA, CFI, IFI, GFI, PNFI, and PCFI. Findings: The results indicated that all correlations among the principal study variables were positive and statistically significant (p < .001). Self-regulated learning demonstrated a significant positive direct effect on academic motivation (β = .545, p < .001) and self-esteem (β = .504, p < .001). Self-esteem also had a significant positive direct effect on academic motivation (β = .264, p < .001). Furthermore, the indirect effect of self-regulated learning on academic motivation through self-esteem was significant (β = .133, p < .001), confirming the mediating role of self-esteem. The structural model demonstrated satisfactory fit with the observed data (χ²/df = 1.47, RMSEA = .040, CFI = .995, IFI = .995, GFI = .986, PNFI = .563, PCFI = .569), supporting the proposed theoretical framework. Conclusion: The findings suggest that self-regulated learning is a significant predictor of academic motivation both directly and indirectly through self-esteem. Students with stronger self-regulatory skills tend to possess higher self-esteem, which in turn enhances their academic motivation.
Copyright (c) 2026 Mohammad Zavvari Amiri (Author); Ghasem Yaghoubi (Corresponding Author); Seyedeh Zahra Sadati, Jamal Sadeghi (Author)
Health on the Way to School: A Comparison of High-Risk Behaviors Among Students Across Different Modes of School Transportation
Objective: The manner in which students commute to school and its psychological implications have received limited attention from parents and psychologists. Although observational learning (modeling) during students’ daily journeys to and from school plays a significant role in shaping their behaviors and everyday learning experiences, relatively little research has been conducted in this area. The present study aimed to examine the tendency toward high-risk behaviors among secondary school students across different modes of school transportation. Methods and Materials: The present research employed an ex post facto (causal-comparative) design. The sample consisted of 360 students, including 180 female and 180 male students. Participants were selected using a combination of cluster and stratified sampling methods. Data were collected using the Iranian Adolescents Risk-Taking Scale (IARS) developed by Zadeh Mohammadi et al. (2011). Findings: The results of multivariate analysis of variance (MANOVA) indicated a statistically significant difference between school transportation methods and students’ tendencies toward high-risk behaviors. Except for two cases in which female students demonstrated higher mean scores than males on tendencies toward high-risk behaviors, male students generally exhibited higher mean scores across different school transportation methods. Female students reported higher mean scores for sexual relationships and sexual behaviors in the active transportation group compared to male students. Furthermore, female students demonstrated higher mean scores for relationships with the opposite sex in the family transportation group than their male counterparts. In addition, the mean tendency toward substance use was higher among students who used public transportation. Conclusion: Although students’ commuting patterns constitute an important component of their daily activities, peer and community modeling—whether positive or negative—also represents an inseparable aspect of every culture and remains a recurring concern for parents. Therefore, greater attention should be paid to this issue and its psychological effects on both female and male students.
Copyright (c) 2026 Vahid Firuz Jahantigh (Author); Gholamreza Sanagouye Moharer (Corresponding Author); Mohammadali Fardin (Author)
Predicting Cyberbullying Victimization Experiences Based on Family Communication Patterns and Inner Self-Talk in Adolescents
Objective: The present study aimed to predict adolescents’ experiences of cyberbullying victimization based on family communication patterns and dimensions of inner self-talk. Methods and Materials: This applied correlational study was conducted among high school students in Shiraz, Iran, during the 2026 academic year. The statistical population consisted of adolescents enrolled in secondary schools, from which 200 participants were selected through convenience sampling. Data were collected using the Cyber Bully/Victim Experience Questionnaire, the Family Communication Patterns Questionnaire, and the Inner Self-Talk Questionnaire. After data collection, the responses were analyzed using SPSS version 26. Pearson correlation coefficients were employed to examine relationships among variables, and multiple regression analysis was conducted to determine the predictive power of family communication patterns and inner self-talk dimensions in explaining cyberbullying victimization experiences. Findings: The results of the multiple regression analysis indicated that the combined dimensions of inner self-talk and family communication patterns explained 31.5% of the variance in cyberbullying victimization experiences. Social assessment emerged as a significant negative predictor of cyberbullying victimization (β = −0.256, p = 0.005), indicating that higher levels of constructive social evaluation were associated with lower victimization experiences. Self-regulation was also a significant negative predictor (β = −0.311, p < 0.001), suggesting that adolescents with stronger self-regulatory inner dialogue reported fewer cyberbullying victimization experiences. Regarding family communication patterns, conversation orientation significantly and negatively predicted cyberbullying victimization (β = −0.298, p < 0.001), whereas conformity orientation significantly and positively predicted victimization experiences (β = 0.303, p < 0.001). These findings demonstrate that both personal cognitive processes and family communication dynamics contribute significantly to adolescents’ vulnerability to cyberbullying victimization. Conclusion: The findings indicate that inner self-talk and family communication patterns play substantial roles in understanding and predicting cyberbullying victimization among adolescents. Specifically, higher levels of self-regulation, constructive social assessment, and open family conversation are associated with reduced vulnerability to cyberbullying, whereas greater conformity-oriented family communication is linked to increased victimization risk.
Copyright (c) 2026 Seyedeh Maryam Mousavi, Shoukofeh Naderlou (Author); Farnaz Nasiri (Corresponding Author)
The Relationship Between Early Maladaptive Schemas and Adolescents’ Social Anxiety: The Mediating Role of Locus of Control
Objective: The present study aimed to investigate the relationship between early maladaptive schemas and adolescents’ social anxiety and to examine the mediating role of locus of control in this relationship. Methods and Materials: This descriptive-correlational study was conducted among male students enrolled in vocational high schools in District 8 of Tehran, Iran. Based on Morgan’s sample size table, 210 students were selected using convenience sampling. Data were collected using the Young Schema Questionnaire–Short Form (YSQ-SF), the Social Phobia Inventory (SPIN), and Rotter’s Locus of Control Scale. The study focused on five schema domains that demonstrated the strongest relationships with social anxiety, including Social Isolation/Alienation, Defectiveness/Shame, Emotional Inhibition, Negativity/Pessimism, and Failure. Descriptive statistics and Pearson correlation coefficients were computed to examine relationships among variables. Multiple regression analysis was used to identify the strongest predictors of social anxiety, and structural equation modeling was employed to test the mediating role of locus of control. Statistical analyses were performed using SPSS and AMOS software at a significance level of 0.05. Findings: The results revealed significant positive correlations between all five schema domains and social anxiety (p < .01). Social Isolation/Alienation demonstrated the strongest association with social anxiety (r = .61), followed by Defectiveness/Shame (r = .58), Negativity/Pessimism (r = .57), Emotional Inhibition (r = .56), and Failure (r = .54). All schema domains were also significantly associated with external locus of control (p < .01). Multiple regression analysis indicated that the five schema domains collectively explained 55% of the variance in social anxiety scores (R² = .55, p < .001), with Social Isolation/Alienation emerging as the strongest predictor. Structural equation modeling demonstrated that locus of control significantly mediated the relationship between maladaptive schemas and social anxiety. The indirect effect through locus of control was statistically significant (β = .16, p < .001), while the direct effect remained significant (β = .51, p < .001), indicating partial mediation. Conclusion: The findings suggest that maladaptive schemas related to social disconnection, shame, emotional suppression, pessimistic thinking, and perceived failure are important cognitive vulnerabilities associated with adolescent social anxiety.
Copyright (c) 2026 Zahra Asnafy (Author); Rajabali Mohammadzadeh (Corresponding Author); Mehdi Arastehfard (Author)
The Effectiveness of Emotional Schema Therapy on Executive Functions in Adolescents with Emotional Dysregulation in Isfahan
Objective: The present study aimed to investigate the effectiveness of Emotional Schema Therapy on the executive functions of adolescents with emotional dysregulation. Materials and Methods: This study employed a quasi-experimental design with a pretest–posttest control group. The statistical population consisted of middle and high school students in Isfahan during 2025 who had been referred to educational counseling centers. The sample included 30 adolescents selected through purposive sampling from individuals with low emotion regulation scores and randomly assigned to an experimental group and a control group (15 participants in each group). The research instruments included the Executive Functions Questionnaire and the Difficulties in Emotion Regulation Scale. The experimental group received Emotional Schema Therapy in twelve 60-minute sessions. Data were analyzed using Multivariate Analysis of Covariance (MANCOVA). Findings: The results indicated that after the intervention, there was a significant difference between the experimental and control groups regarding the dimensions of executive functions (p < .01). Specifically, executive functions improved in the experimental group. Conclusion: The findings suggest that Emotional Schema Therapy is effective in improving the cognitive-behavioral components of adolescents with emotional dysregulation. This therapeutic approach may serve as an effective intervention in counseling and treatment programs for adolescents.
Copyright (c) 2026 Azadeh Malekian (Corresponding Author); Leila Prande (Author)
The Relationship Between Impulsivity, Cognitive Consequences, and the Severity of Sports Injuries Among Iraqi Swimmers
Objective: This study aimed to investigate the relationship between impulsivity, Cognitive consequences of sports injuries, and the severity of sports injuries among Iraqi swimmers. Methods and Materials: This applied, quantitative, descriptive-survey study was conducted among competitive swimmers in Iraq. The statistical population consisted of Iraqi swimmers, from whom 129 participants were selected using purposive and convenience sampling methods based on the sample size estimated through G*Power software. Data were collected using the Cognitive consequences of Sports Injuries Questionnaire developed by Rokan Hassan et al. (2022) and the Barratt Impulsiveness Scale (1994). Descriptive statistics were used to summarize participant characteristics and study variables. Inferential analyses were performed using Pearson correlation coefficients and linear regression analyses in SPSS version 23. All statistical tests were conducted at a significance level of 0.05. Findings: The results demonstrated significant negative relationships between all dimensions of Cognitive consequences of sports injuries and swimmers’ impulsivity levels (p < .001). Greater awareness and understanding of the Cognitive consequences associated with sports injuries were linked to lower levels of impulsive behavior among athletes. Regarding the relationship between impulsivity dimensions and sports injuries, cognitive impulsivity was not significantly associated with injury occurrence or severity (p = .677). However, a significant positive relationship was found between non-planning impulsivity and sports injuries (p = .004), indicating that swimmers who exhibited poorer planning tendencies experienced higher injury rates. Furthermore, motor impulsivity showed a significant positive association with sports injuries (p = .050), suggesting that athletes with greater tendencies toward acting without sufficient control were more likely to sustain injuries. Overall, the findings highlight the predictive role of specific impulsivity dimensions in sports injury occurrence among swimmers. Conclusion: The findings suggest that cognitive awareness regarding the consequences of sports injuries and lower levels of impulsive behavior are associated with a reduced likelihood of injury among swimmers. In particular, non-planning and motor impulsivity appear to be important risk factors for sports injuries, whereas cognitive impulsivity does not significantly contribute to injury occurrence.
Copyright (c) 2026 Omar Rokan Hassan Moniralajili (Author); Mohammadtaghi Aghdasi (Corresponding Author); Seyed Hojat Zamani Sani (Author)
Parental Reflective Functioning and Object Relations in Adolescents: A Path Model of the Direct and Indirect Effects on Reactive Aggression through Mentalization
Objective: The present study aimed to investigate the direct and indirect effects of parental reflective functioning and object relations on reactive aggression among adolescents through the mediating role of mentalization. Methods and Materials: This study employed a descriptive-correlational design based on path analysis. The statistical population included adolescents enrolled in the second cycle of secondary education (grades 10–12) during the 2025–2026 academic year. Participants were selected using a multistage cluster sampling method, and after screening incomplete responses, the final sample consisted of 463 adolescents aged 15–18 years. Data were collected using the Parental Reflective Functioning Questionnaire–Adolescent Version (PRFQ-A), Bell Object Relations and Reality Testing Inventory (BORRTI), Reflective Functioning Questionnaire for Youth (RFQY), and Reactive–Proactive Aggression Questionnaire (RPQ). Data analysis was performed using SPSS version 27 and AMOS version 26. Descriptive statistics, Pearson correlation analysis, and path analysis with bootstrap estimation were used to examine direct and indirect relationships among variables. Findings: The results showed that parental reflective functioning significantly and positively predicted mentalization (β = 0.389, p < .001), and object relations also had a significant positive effect on mentalization (β = 0.208, p < .001). Mentalization significantly and negatively predicted reactive aggression (β = -0.447, p < .001). Furthermore, parental reflective functioning (β = -0.145, p < .001) and object relations (β = -0.183, p < .001) had significant direct negative effects on reactive aggression. Bootstrap analysis confirmed the significant mediating role of mentalization in the relationship between parental reflective functioning and reactive aggression (indirect effect = -0.174, 95% CI [-0.224, -0.129]) and between object relations and reactive aggression (indirect effect = -0.093, 95% CI [-0.140, -0.051]). The final model explained 38.5% of the variance in reactive aggression. Conclusion: The findings highlight the importance of parental reflective functioning and adaptive object relations in reducing adolescents’ reactive aggression by strengthening mentalization capacities.
Copyright (c) 2026 Hanieh Abbasi (Author); Afnan Jafari (Corresponding Author); Mona Khalednejad, Javad Khalatbari, Fakhri Sadat Hosseini (Author)
Dynamic Bayesian Networks of Self-Esteem Fluctuations and Social Feedback Sensitivity
Objective: The present study aimed to model the dynamics of state self-esteem fluctuations and social feedback sensitivity using Dynamic Bayesian Networks. Methods and Materials: A quantitative intensive longitudinal design was implemented with 287 university students from Slovenia who completed a 21-day ecological momentary assessment protocol. Participants responded to five semi-random prompts per day assessing state self-esteem, perceived social feedback valence, and negative affect, yielding 26,874 momentary observations. Baseline measures included trait self-esteem, rejection sensitivity, depressive symptoms, and social anxiety. Data were person-mean centered and preprocessed to ensure stationarity. A multilevel vector autoregressive model was first estimated to identify preliminary lagged associations. Subsequently, Dynamic Bayesian Networks were constructed using a two-time-slice framework with hill-climbing structure learning and Bayesian Information Criterion scoring. Bootstrapped model averaging (1,000 resamples) ensured stability of directed edges. Hierarchical Bayesian extensions examined moderation by trait rejection sensitivity, and predictive accuracy was evaluated via 10-fold cross-validation. Findings: State self-esteem demonstrated significant autoregressive stability (β ≈ 0.50, p < 0.001). Positive social feedback prospectively predicted increases in subsequent state self-esteem (β ≈ 0.24, credible intervals excluding zero), whereas negative affect predicted decreases (β ≈ -0.20, p < 0.001). Reciprocal effects between self-esteem and negative affect formed a significant feedback loop. Trait rejection sensitivity significantly moderated the feedback-to-self-esteem pathway, amplifying evaluative reactivity (posterior mean ≈ 0.15, credible intervals excluding zero). Bootstrapped inclusion probabilities for key edges exceeded 90%, and cross-validated predictive performance demonstrated satisfactory out-of-sample accuracy (RMSE ≈ 0.41). Conclusion: Self-esteem operates as a dynamically regulated state embedded within a probabilistic network of social and affective processes, where social feedback functions as a central driver and individual differences in rejection sensitivity shape the magnitude of evaluative updating across time.
Copyright (c) 2026 Selamawit Bekele (Author); Tanja Kralj (Corresponding Author)
Random Forest Prediction of Adolescent Risk-Taking Behaviors Based on Sensation Seeking, Online Disinhibition, Emotional Impulsivity, Peer Deviance, and Executive Dysfunction
Objective: The present study aimed to predict adolescent risk-taking behaviors using a Random Forest machine learning model based on sensation seeking, online disinhibition, emotional impulsivity, peer deviance, and executive dysfunction among adolescents in Argentina. Methods and Materials: The study employed a cross-sectional predictive-correlational design with a machine learning framework. The statistical population consisted of Argentine adolescents enrolled in secondary schools in Buenos Aires, Córdoba, and Rosario during the 2025–2026 academic year. Using multistage cluster sampling, 1,284 adolescents aged 14 to 18 years were selected. Data were collected using the Adolescent Risk-Taking Questionnaire, Brief Sensation Seeking Scale, Online Disinhibition Scale, UPPS-P Negative Urgency Subscale, Peer Deviance Scale, and the Behavior Rating Inventory of Executive Function. Preliminary statistical analyses were conducted using SPSS-28, while the Random Forest predictive model was implemented in Python using the Scikit-learn library. Data were divided into training and testing subsets using an 80/20 split procedure. Model performance was evaluated through accuracy, precision, recall, F1-score, area under the curve (AUC), cross-validation scores, and SHAP feature-importance analyses. Findings: The findings demonstrated significant positive correlations among all study variables. Peer deviance showed the strongest positive association with adolescent risk-taking behaviors, followed by emotional impulsivity and executive dysfunction. The Random Forest model demonstrated strong predictive performance, with testing accuracy of 0.87 and AUC of 0.90, indicating excellent classification capability. Feature importance analysis revealed that peer deviance was the most influential predictor, followed respectively by emotional impulsivity, executive dysfunction, online disinhibition, and sensation seeking. SHAP analyses further indicated substantial interaction effects among emotional, cognitive, and social variables in predicting adolescent risk-taking behaviors. Conclusion: The findings suggest that adolescent risk-taking behaviors emerge through complex interactions among emotional dysregulation, cognitive-control deficits, peer-related influences, and digital behavioral tendencies. The strong performance of the Random Forest model highlights the usefulness of machine learning approaches for identifying high-risk adolescents and understanding multidimensional psychosocial predictors of maladaptive behaviors. Early prevention efforts targeting emotional impulsivity, executive dysfunction, peer deviance, and online disinhibition may contribute significantly to reducing behavioral risk among adolescents.
Bayesian Network Modeling of Youth Substance Use Risk Based on Trauma Exposure, Reward Sensitivity, Peer Pressure, Sleep Instability, and Psychological Inflexibility
Objective: The present study aimed to develop and evaluate a Bayesian network model for predicting youth substance use risk based on trauma exposure, reward sensitivity, peer pressure, sleep instability, and psychological inflexibility among adolescents and emerging adults in the United States. Methods and Materials: This study employed a cross-sectional predictive modeling design using Bayesian probabilistic network analysis. The statistical population consisted of adolescents and emerging adults aged 15 to 22 years from educational institutions in the United States, from whom 1,248 participants were selected through multistage stratified sampling. Data were collected using the Substance Use Risk Profile Scale, Childhood Trauma Questionnaire-Short Form, Behavioral Activation System Scale, Peer Pressure Inventory, Sleep Condition Indicator, and Acceptance and Action Questionnaire-II. Data analysis was conducted using SPSS version 29 and Python 3.11. Bayesian network structure learning was implemented using the Hill-Climbing optimization algorithm with Bayesian Information Criterion scoring. Predictive performance was evaluated using classification accuracy, precision, recall, F1-score, sensitivity, specificity, and area under the receiver operating characteristic curve. Findings: The findings demonstrated significant positive relationships among trauma exposure, reward sensitivity, peer pressure, sleep instability, psychological inflexibility, and substance use risk. Psychological inflexibility showed the strongest correlation with substance use risk (r = .65, p < .01), followed by trauma exposure (r = .61, p < .01). The Bayesian network model demonstrated high predictive performance with a training accuracy of 89.41%, testing accuracy of 86.93%, and area under the curve value of 0.91. Sensitivity analysis indicated that psychological inflexibility represented the strongest predictor within the network structure, followed by trauma exposure, peer pressure, reward sensitivity, and sleep instability. The probabilistic network further revealed that trauma exposure functioned as a central upstream variable influencing psychological inflexibility, peer pressure susceptibility, and sleep instability pathways associated with elevated substance use vulnerability. Conclusion: The findings suggest that youth substance use vulnerability is shaped by a multidimensional network of emotional, cognitive, behavioral, and social risk mechanisms. Bayesian network modeling provided a powerful computational framework for identifying conditional dependency pathways among trauma exposure, reward sensitivity, peer pressure, sleep instability, psychological inflexibility, and substance use risk. The results highlight the importance of trauma-informed and multidimensional prevention strategies targeting emotional regulation, sleep health, peer influence processes, and psychological flexibility in adolescents and emerging adults.
A Multimodal Machine Learning Approach to Adolescent Loneliness Using Social Disconnection, Parasocial Relationships, Emotional Inhibition, Family Cohesion, and Digital Isolation
Objective: The present study aimed to develop and evaluate a multimodal machine learning model for predicting adolescent loneliness based on social disconnection, parasocial relationships, emotional inhibition, family cohesion, and digital isolation among adolescents in the United States. Methods and Materials: The study employed a cross-sectional predictive correlational design using multimodal machine learning techniques. The statistical population consisted of adolescents aged 13 to 18 years enrolled in secondary schools across the United States during the 2025–2026 academic year. Using stratified multistage sampling, 1,248 adolescents were recruited, of whom 1,182 participants were included in the final analysis after data screening. Data collection involved standardized psychological instruments assessing loneliness, social disconnection, parasocial relationships, emotional inhibition, and family cohesion, alongside objective digital behavioral indicators including screen time, passive scrolling duration, nocturnal device usage, and solitary online activity. Data were analyzed using Python-based machine learning libraries and included Random Forest, Support Vector Machine, XGBoost, Multilayer Perceptron Neural Network, and a multimodal ensemble learning model. Model evaluation indices included accuracy, precision, recall, F1-score, and AUC-ROC values. SHAP-based explainable artificial intelligence analysis was additionally employed to determine feature importance. Findings: The findings indicated significant positive relationships between adolescent loneliness and social disconnection, emotional inhibition, parasocial relationships, and digital isolation, while family cohesion demonstrated a significant negative association with loneliness. The multimodal ensemble model achieved the highest predictive performance compared to all other machine learning models, with an accuracy of 0.94 and an AUC-ROC value of 0.97. SHAP feature importance analysis revealed that digital isolation was the strongest predictor of adolescent loneliness, followed by social disconnection, family cohesion, emotional inhibition, and parasocial relationships. Hierarchical regression analysis further demonstrated that the predictor variables collectively explained 71% of the variance in loneliness scores. Conclusion: The findings suggest that adolescent loneliness is a multidimensional phenomenon shaped by the interaction of emotional, social, familial, and digital behavioral factors. Multimodal machine learning approaches integrating psychosocial measures with objective digital behavior indicators may provide highly accurate and interpretable predictive models for identifying adolescents at risk for loneliness. Strengthening family cohesion, improving emotional communication, and promoting healthier patterns of digital engagement may contribute significantly to reducing loneliness and improving adolescent mental health outcomes.
LightGBM-Based Prediction of Academic Burnout among High School Students from Perfectionism, Test Anxiety, Academic Self-Efficacy, and School Climate Indicators
Objective: To develop and evaluate a machine learning model using LightGBM to predict academic burnout among high school students based on psychological and school environment factors. Methods and Materials: A total of 1,248 high school students from Denmark participated in this cross-sectional study. Data were collected on perfectionism, test anxiety, academic self-efficacy, and perceptions of school climate using validated self-report instruments. Academic burnout was measured with the Maslach Burnout Inventory–Student Survey. Demographic data including age, gender, grade level, and school type were also obtained. Data were cleaned, standardized, and analyzed using Python and LightGBM algorithms. The dataset was divided into training (80%) and testing (20%) sets, and five-fold cross-validation was employed for model optimization. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and feature importance via SHAP values. Findings: The LightGBM model accurately classified students into low- and high-risk burnout categories with 89.1% accuracy on the testing dataset. Test anxiety emerged as the strongest predictor, followed by academic self-efficacy, school climate, and perfectionism. Higher test anxiety and perfectionism were associated with increased burnout risk, whereas higher self-efficacy and more positive school climate perceptions were protective. The SHAP summary plot revealed the individual contributions of each predictor and indicated heterogeneity in their effects across students. Demographic variables contributed minimally to predictive performance. Conclusion: This study demonstrates the effectiveness of a LightGBM-based model for predicting academic burnout using psychological and environmental factors. Findings emphasize the importance of addressing test anxiety, promoting academic self-efficacy, and fostering supportive school climates to reduce burnout risk among adolescents. Machine learning approaches can inform targeted interventions and early identification of at-risk students.
Copyright (c) 2026 Ricardo Fúnez (Author); Søren Mikkelsen (Corresponding Author); Jihyun Park (Author)
Explainable Deep Learning Models of Adolescent Eating Disorders Based on Appearance Comparison, Influencer Exposure, Emotion Dysregulation, Self-Criticism, and Weight Stigma
Objective: The present study aimed to develop and interpret explainable deep learning models for predicting adolescent eating disorder symptoms based on appearance comparison, influencer exposure, emotion dysregulation, self-criticism, and weight stigma. Methods and Materials: This study employed a cross-sectional predictive design using explainable artificial intelligence approaches among 1,219 Canadian adolescents aged 14 to 18 years recruited from secondary schools in Ontario, British Columbia, and Quebec. Participants completed standardized measures assessing eating disorder symptoms, appearance comparison, influencer exposure, emotion dysregulation, self-criticism, and weight stigma through an online survey platform. Data were analyzed using TensorFlow, Keras, and Scikit-learn libraries. The dataset was divided into training, validation, and testing subsets, and a multilayer explainable deep neural network was developed. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to enhance model interpretability and identify the relative contribution of predictors. Model performance was compared with logistic regression, support vector machine, and random forest algorithms. Findings: Significant positive associations were observed among all study variables and eating disorder symptoms. Self-criticism demonstrated the strongest association with eating pathology, followed by emotion dysregulation and weight stigma. The explainable deep learning model achieved the highest predictive performance compared to alternative machine learning models, with superior accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve values. SHAP analyses identified self-criticism as the strongest predictor, followed by emotion dysregulation, weight stigma, appearance comparison, and influencer exposure. Interaction analyses further demonstrated that the predictive effects of appearance comparison and weight stigma intensified when combined with elevated self-criticism and emotional dysregulation. Conclusion: The findings suggest that adolescent eating disorders are shaped by complex interactions among emotional vulnerabilities, maladaptive self-evaluative processes, and digitally reinforced appearance pressures. Explainable deep learning models provide a powerful and interpretable framework for identifying multidimensional psychological risk patterns associated with eating pathology and may support more personalized prevention and intervention strategies for adolescents.
Random Forest Classification of Non-Suicidal Self-Injury Risk among Adolescents Using Trauma History, Shame, Impulsivity, and Peer Victimization Variables
Objective: The objective of this study was to develop and evaluate a Random Forest classification model to identify adolescents at elevated risk of non-suicidal self-injury (NSSI) using trauma history, shame, impulsivity, and peer victimization as predictors. Methods and Materials: A cross-sectional study was conducted in Chile with 1,248 adolescents aged 13–18 years (mean = 15.67, SD = 1.42) recruited from public and private schools. Participants completed validated self-report measures assessing trauma history, shame, impulsivity, peer victimization, and NSSI behaviors. The dataset was split into training (80%) and testing (20%) subsets. A Random Forest classification algorithm was applied to predict NSSI risk, and hyperparameters were optimized using grid search with five-fold cross-validation. Performance metrics included accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Feature importance was calculated to determine the relative contribution of each predictor variable. Findings: Inferential analyses revealed that adolescents classified as high-risk for NSSI reported significantly higher levels of trauma history, shame, impulsivity, and peer victimization compared with low-risk peers. The Random Forest model achieved an overall accuracy of 89.2%, precision of 86.1%, recall of 84.5%, F1-score of 85.3%, and AUC of 0.938. Feature importance rankings indicated that shame was the strongest predictor, followed by trauma history, peer victimization, and impulsivity. These results suggest that emotional, developmental, interpersonal, and behavioral factors jointly contribute to adolescent self-injury risk. Conclusion: The findings demonstrate that Random Forest algorithms can accurately classify adolescents at elevated risk for NSSI, highlighting the importance of integrating trauma history, shame, impulsivity, and peer victimization into early identification and prevention efforts. Interventions should target emotional regulation, trauma recovery, and social support to reduce the likelihood of self-injurious behaviors.
Copyright (c) 2026 Rúnar Guðmundsson (Author); Valentina Paredes (Corresponding Author); Ilze Kalniņa (Author)
LightGBM Classification of Academic Procrastination Risk Among Adolescents Based on Executive Function, Motivation, Smartphone Use, and Learning Strategy Variables
Objective: The objective of this study was to develop and evaluate a LightGBM machine learning model for predicting academic procrastination risk among adolescents using cognitive, motivational, behavioral, and learning strategy variables. Methods and Materials: A total of 1,248 adolescents aged 13–18 years from public secondary schools in Germany participated in this cross-sectional study. Participants completed standardized self-report instruments assessing executive function (Behavior Rating Inventory of Executive Function–Self-Report), academic motivation (Academic Motivation Scale), smartphone use patterns (Smartphone Addiction Scale–Short Version), and learning strategies (Motivated Strategies for Learning Questionnaire). Demographic information, including age, gender, grade, parental education, and academic achievement, was also collected. The dataset was preprocessed to handle missing data, outliers, and standardization, and academic procrastination scores were categorized into low, moderate, and high-risk groups. A LightGBM classification model was trained on 80% of the data using stratified sampling and five-fold cross-validation, with hyperparameter tuning performed to optimize model performance. Model evaluation was conducted on the remaining 20% of the data using accuracy, precision, recall, F1-score, AUC-ROC, and interpretability analysis via SHAP values. Findings: Executive function difficulties, low academic motivation, problematic smartphone use, and ineffective learning strategies were significant predictors of academic procrastination. Metacognitive self-regulation, working memory difficulties, intrinsic motivation, smartphone dependence, and time management emerged as the most influential features. The LightGBM model achieved an overall accuracy of 88.7%, macro-averaged F1-score of 0.877, and an AUC-ROC of 0.931. Misclassifications primarily occurred between adjacent risk categories, indicating the model’s robustness in distinguishing different levels of procrastination risk. SHAP analysis confirmed the relative contributions of cognitive, motivational, behavioral, and learning strategy factors in shaping predicted risk levels. Conclusion: The study demonstrates that an interpretable LightGBM model can accurately classify adolescents according to academic procrastination risk based on executive function, motivation, smartphone use, and learning strategy variables, highlighting the critical roles of cognitive self-regulation, motivation, and digital behavior in academic outcomes.
Copyright (c) 2026 Jean-Paul Nkurunziza (Author); Lars Becker (Corresponding Author)
Predicting Romantic Relationship Instability in Emerging Adults Through LightGBM Models of Attachment Anxiety, Jealousy Cognitions, Social Media Surveillance, Emotional Dependency, and Impulsivity
Objective: The present study aimed to predict romantic relationship instability in emerging adults through Light Gradient Boosting Machine (LightGBM) models based on attachment anxiety, jealousy cognitions, social media surveillance, emotional dependency, and impulsivity. Methods and Materials: The study employed a cross-sectional predictive correlational design using machine learning methodology. The statistical population consisted of emerging adults aged 18 to 29 years residing in major urban regions of Canada during 2025–2026. A total of 1,137 participants were selected through online and university-based recruitment procedures. Data were collected using the Relationship Stability Scale, Experiences in Close Relationships-Revised questionnaire, Multidimensional Jealousy Scale, Social Media Surveillance Scale, Emotional Dependency Questionnaire, and Barratt Impulsiveness Scale-Version 11. Data analysis was conducted using Python and machine learning libraries including Scikit-learn and LightGBM. The dataset was divided into training and testing subsets using an 80/20 split procedure. Model performance was evaluated using accuracy, precision, recall, F1-score, area under the curve (AUC), and log-loss indices. SHAP analysis was additionally used to examine predictor importance and model interpretability. Findings: The findings demonstrated significant positive correlations among all study variables and romantic relationship instability. Emotional dependency showed the strongest correlation with relationship instability, followed by attachment anxiety, jealousy cognitions, social media surveillance, and impulsivity (p < 0.01). The LightGBM model demonstrated high predictive performance with a testing accuracy of 0.88 and an AUC value of 0.91, indicating excellent discriminative ability. SHAP analysis revealed that emotional dependency and attachment anxiety were the most influential predictors of romantic instability. Hierarchical regression analysis further indicated that the predictor variables collectively explained 67% of the variance in relationship instability (R² = 0.67, p < 0.001). Conclusion: The findings suggest that romantic relationship instability among emerging adults is strongly associated with emotional insecurity, maladaptive jealousy cognitions, impulsive tendencies, and digitally mediated surveillance behaviors. The study further demonstrates that machine learning approaches such as LightGBM provide powerful tools for identifying complex nonlinear interaction patterns underlying romantic dysfunction. These findings highlight the importance of addressing attachment insecurity, emotional dependency, and unhealthy digital relational behaviors in prevention and intervention programs targeting young adults’ romantic well-being.
XGBoost Prediction of Eating Disorder Symptom Severity among Adolescent Girls Using Body Dissatisfaction, Social Comparison, Self-Esteem, and Instagram Use Patterns
Objective: The present study aimed to examine the relative contributions of body dissatisfaction, social comparison, self-esteem, and Instagram use patterns in predicting eating disorder symptom severity among adolescent girls using the Extreme Gradient Boosting (XGBoost) machine learning algorithm. Methods and Materials: This cross-sectional predictive study was conducted among 1,284 adolescent girls aged 13–18 years recruited from secondary schools across Canada. Participants completed a battery of validated self-report measures assessing eating disorder symptom severity, body dissatisfaction, social comparison orientation, self-esteem, and Instagram-related behaviors. Eating disorder symptoms were measured using the Eating Disorder Examination Questionnaire, body dissatisfaction was assessed using the Body Shape Questionnaire, social comparison was evaluated through the Iowa-Netherlands Comparison Orientation Measure, and self-esteem was measured using the Rosenberg Self-Esteem Scale. Instagram use patterns included daily usage duration, appearance-focused content viewing, influencer engagement, photo-editing behaviors, and emotional sensitivity to social feedback. Data preprocessing procedures included missing-value treatment, feature normalization, and quality screening. The dataset was divided into training and testing subsets using an 80:20 ratio. An optimized XGBoost regression model was developed using five-fold cross-validation and hyperparameter tuning. Model performance was evaluated using the coefficient of determination (R²), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). SHapley Additive exPlanations (SHAP) analyses were employed to interpret feature importance and predictor contributions. Findings: The XGBoost model demonstrated strong predictive performance, explaining 81.6% of the variance in eating disorder symptom severity within the testing dataset (R² = 0.816). Body dissatisfaction emerged as the strongest predictor, accounting for the largest proportion of predictive gain, followed by appearance-focused Instagram viewing, self-esteem, and social comparison. SHAP analyses indicated that higher levels of body dissatisfaction, greater engagement with appearance-oriented Instagram content, stronger social comparison tendencies, more frequent photo-editing behaviors, longer Instagram use duration, greater influencer engagement, and heightened emotional sensitivity to likes and comments were associated with increased predicted eating disorder symptom severity. In contrast, higher self-esteem exerted a substantial protective effect and was associated with lower predicted symptom severity. The consistency between training and testing performance metrics indicated minimal overfitting and strong model generalizability. Conclusion: The findings demonstrate that eating disorder symptom severity among adolescent girls can be predicted with high accuracy through the combined assessment of psychological vulnerabilities and Instagram-related behavioral patterns. Body dissatisfaction appears to represent the central risk factor, while appearance-focused social media engagement, social comparison processes, and diminished self-esteem further amplify vulnerability. These results support integrated theoretical models of eating disorder development and highlight the value of machine learning approaches in identifying high-risk individuals. Prevention and intervention programs should simultaneously target body image concerns, self-esteem enhancement, social comparison reduction, and healthy social media engagement to reduce eating disorder risk among adolescent girls.
Copyright (c) 2026 Hana Tesfaye (Author); Marcus Ouellet (Corresponding Author); Francesca Lombardi (Author)
Identifying Profiles of Adolescent Flourishing Through Unsupervised Machine Learning and Variables Including Self-Compassion, Meaning in Life, Digital Wellbeing, Gratitude, and Peer Support
Objective: The present study aimed to identify distinct profiles of adolescent flourishing using unsupervised machine learning techniques based on self-compassion, meaning in life, digital wellbeing, gratitude, and peer support among Canadian adolescents. Methods and Materials: This study employed a cross-sectional correlational design with an unsupervised machine learning approach. The statistical population consisted of Canadian high school students enrolled in public secondary schools during the 2025–2026 academic year. Using multistage cluster sampling, 742 adolescents were recruited, and after data screening, 711 participants were retained for final analysis. Data were collected using standardized instruments including the Flourishing Scale, Self-Compassion Scale–Short Form, Meaning in Life Questionnaire, Digital Wellbeing Scale for Adolescents, Gratitude Questionnaire–Six Item Form, and the peer support subscale of the Multidimensional Scale of Perceived Social Support. Data analysis was conducted using Python and SPSS-28. Hierarchical clustering and K-means clustering were applied to identify latent flourishing profiles. Cluster validity was evaluated using silhouette coefficients, Davies–Bouldin index, and Calinski–Harabasz index. Multivariate analysis of variance and Bonferroni post hoc tests were used to compare identified profiles across study variables. Findings: The findings indicated that the three-cluster solution demonstrated the strongest statistical performance and conceptual interpretability compared to alternative clustering solutions. Three distinct profiles were identified, including the High Flourishing Adaptive Profile, Moderate Flourishing Vulnerable Profile, and Low Flourishing At-Risk Profile. Significant multivariate differences were observed among the profiles across flourishing, self-compassion, meaning in life, digital wellbeing, gratitude, and peer support (p < .001). Adolescents within the High Flourishing Adaptive Profile reported significantly higher scores across all psychological and relational variables compared to the other profiles, whereas the Low Flourishing At-Risk Profile demonstrated consistently lower functioning across all domains. Meaning in life and flourishing produced the largest effect sizes in distinguishing profile membership. Visualization analyses using t-distributed stochastic neighbor embedding additionally confirmed clear separation and structural distinctiveness among the identified flourishing profiles. Conclusion: The findings suggest that adolescent flourishing is a multidimensional and heterogeneous construct shaped by emotional, existential, relational, and digital wellbeing factors. Self-compassion, meaning in life, gratitude, peer support, and healthy digital engagement appear to function as major protective resources associated with adaptive flourishing. The application of unsupervised machine learning techniques provided a nuanced person-centered understanding of adolescent wellbeing and highlighted the existence of distinct flourishing trajectories among youth populations. These findings support the development of strengths-based and personalized interventions aimed at enhancing flourishing and psychological resilience during adolescence.
K-Nearest Neighbors Classification of High-Risk Internet Addiction Profiles among Adolescents Based on Impulsivity, Loneliness, Sleep Quality, and Parental Monitoring
Objective: This study aimed to develop and evaluate a K-Nearest Neighbors classification model for identifying high-risk internet addiction profiles among Canadian adolescents based on impulsivity, loneliness, sleep quality, and parental monitoring. Methods and Materials: This cross-sectional predictive study was conducted among 1,284 adolescents aged 13 to 18 years from secondary schools in Canada. Participants completed standardized self-report measures, including the Internet Addiction Test, Barratt Impulsiveness Scale–11, UCLA Loneliness Scale Version 3, Pittsburgh Sleep Quality Index, and Parental Monitoring Scale. Internet addiction risk status was defined using established Internet Addiction Test cutoff scores, and adolescents were classified into high-risk and low-risk groups. Data preprocessing included missing-value treatment, outlier screening, and standardization of continuous predictors. The dataset was divided into training and testing subsets using stratified sampling. A K-Nearest Neighbors algorithm was trained to classify internet addiction risk, and hyperparameter optimization was performed using grid search with five-fold cross-validation. Findings: The optimized KNN model demonstrated strong inferential and predictive performance in classifying adolescent internet addiction risk. The final model achieved an accuracy of 88.72%, precision of 85.91%, recall of 83.47%, specificity of 90.84%, F1-score of 84.67%, AUC-ROC of 0.924, and Cohen’s kappa of 0.742. The confusion matrix showed that the model correctly classified 171 low-risk adolescents and 57 high-risk adolescents, with 17 false positives and 12 false negatives. Hyperparameter optimization indicated that the strongest model performance was obtained with k = 11 using the Euclidean distance metric. Permutation importance analysis identified impulsivity as the strongest predictor, followed by parental monitoring, loneliness, and sleep quality. Conclusion: The findings indicate that K-Nearest Neighbors classification can accurately identify high-risk internet addiction profiles among adolescents using a concise set of psychological, sleep-related, and family-based predictors.
Copyright (c) 2026 Dilshod Karimov (Author); Emily Cartwright (Corresponding Author); Aigerim Sadykova (Author)
Ensemble Learning Approaches to Predicting Youth Suicidal Ideation Using Emotional Numbing, Cyberbullying Exposure, AI Chatbot Attachment, Hopelessness, and Social Withdrawal
Objective: The present study aimed to investigate the predictive role of emotional numbing, cyberbullying exposure, AI chatbot attachment, hopelessness, and social withdrawal in youth suicidal ideation using advanced ensemble learning approaches among Canadian adolescents and emerging adults. Methods and Materials: This cross-sectional predictive study was conducted among 742 adolescents and emerging adults aged 16 to 24 years recruited from educational institutions and youth communities across Canada. Data were collected using standardized instruments assessing suicidal ideation, emotional numbing, cyberbullying exposure, hopelessness, social withdrawal, and attachment to AI chatbot systems. After preprocessing procedures including missing data imputation and feature normalization, several ensemble machine learning algorithms including Random Forest, Gradient Boosting Machine, AdaBoost, XGBoost, LightGBM, and a stacked ensemble classifier were implemented using Python-based analytical frameworks. Model performance was evaluated through accuracy, precision, recall, F1-score, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Feature importance and SHAP analyses were additionally performed to examine the relative contribution of predictors within the final classification model. Findings: The findings demonstrated significant positive relationships between suicidal ideation and all predictor variables, including emotional numbing, cyberbullying exposure, AI chatbot attachment, hopelessness, and social withdrawal (p < .01). Hopelessness emerged as the strongest predictor within the final ensemble model, followed by emotional numbing and social withdrawal. The stacked ensemble classifier demonstrated the highest predictive performance with an accuracy of 94%, precision of 92%, recall of 91%, F1-score of 91%, specificity of 95%, and an AUC-ROC value of 0.97. XGBoost and LightGBM also demonstrated strong classification capability. SHAP analysis confirmed that higher levels of hopelessness, emotional numbing, cyberbullying exposure, social withdrawal, and AI chatbot attachment significantly increased the probability of high-risk suicidal ideation classification. Conclusion: The findings suggest that youth suicidal ideation is shaped by multidimensional interactions among emotional dysregulation, interpersonal isolation, cybervictimization, hopelessness, and emerging forms of technological attachment. Ensemble learning approaches demonstrated exceptional effectiveness in identifying adolescents at elevated suicide risk and may provide valuable computational tools for early psychological screening and suicide prevention programs. The study further highlights the growing psychological significance of AI chatbot attachment within contemporary youth mental health contexts.
A Multigroup Structural Equation Model of Family Functioning, Identity Development, and Risk-Taking Behaviors among Adolescents
The present study aimed to examine the structural relationships among family functioning, identity development, and risk-taking behaviors among Canadian adolescents and to determine whether these relationships differ between male and female adolescents using a multigroup structural equation modeling approach. Methods and Materials: This cross-sectional correlational study was conducted among 842 adolescents aged 14–18 years recruited from secondary schools in Ontario, British Columbia, and Alberta, Canada. The sample consisted of 421 males and 421 females selected through multistage cluster sampling. Data were collected using the Family Assessment Device (FAD), the Dimensions of Identity Development Scale (DIDS), and the Adolescent Risk-Taking Questionnaire (ARQ). Descriptive statistics, reliability analyses, confirmatory factor analysis, and structural equation modeling were performed using SPSS 29 and AMOS 29. Multigroup structural equation modeling was employed to examine the invariance of the proposed model across gender groups. Model fit was evaluated using χ²/df, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Goodness-of-Fit Index (GFI), Incremental Fit Index (IFI), and Root Mean Square Error of Approximation (RMSEA). Direct, indirect, and total effects were estimated, and mediation effects were tested using bias-corrected bootstrap procedures with 5,000 resamples. Findings: The measurement model demonstrated excellent fit to the data (χ²/df = 2.10, CFI = .961, TLI = .957, RMSEA = .036). Structural equation modeling revealed that family functioning positively predicted identity development (β = .62, p < .001), while identity development negatively predicted risk-taking behaviors (β = -.56, p < .001). Family functioning also exerted a significant direct negative effect on risk-taking behaviors (β = -.31, p < .001). Bootstrap analyses confirmed a significant indirect effect of family functioning on risk-taking behaviors through identity development (β = -.35, p < .001), indicating partial mediation. The model explained 38% of the variance in identity development and 54% of the variance in risk-taking behaviors. Multigroup analyses supported configural, metric, scalar, and structural invariance across male and female adolescents, indicating that the proposed relationships operated similarly across gender groups. Conclusion: The findings demonstrate that family functioning is a significant protective factor against adolescent risk-taking behaviors both directly and indirectly through its positive influence on identity development. Adolescents who experience supportive, cohesive, and effectively functioning family environments are more likely to develop stable and adaptive identities, which in turn reduces their likelihood of engaging in maladaptive risk behaviors. The invariance of the model across genders suggests that these developmental processes are broadly consistent for male and female adolescents. These findings highlight the importance of family-centered and identity-focused interventions in promoting healthy adolescent development and preventing behavioral risk outcomes.
Copyright (c) 2026 Verónica Escalante (Author); Liam Desrosiers (Corresponding Author)
Testing a Structural Model of Social Media Addiction, Sleep Quality, Academic Burnout, and Psychological Well-Being among High School Students
Objective: This study aimed to test a structural model explaining the direct and indirect relationships among social media addiction, sleep quality, academic burnout, and psychological well-being among high school students in Canada. Methods and Materials: This cross-sectional correlational study was conducted among 718 high school students from Ontario, Canada, during the 2025–2026 academic year. Participants were selected through multistage cluster sampling from public high schools and completed standardized self-report questionnaires. Social media addiction was measured using the Bergen Social Media Addiction Scale, sleep quality was assessed using the Pittsburgh Sleep Quality Index, academic burnout was measured using the Maslach Burnout Inventory–Student Survey, and psychological well-being was assessed using Ryff’s Psychological Well-Being Scale. Data were analyzed using SPSS version 29 and AMOS version 29. Descriptive statistics, Pearson correlation coefficients, confirmatory factor analysis, and structural equation modeling were used. Model fit was evaluated using χ²/df, CFI, TLI, GFI, RMSEA, and SRMR. Indirect effects were examined through bootstrapping with 5,000 resamples. Findings: The structural model showed acceptable fit to the data (χ²/df = 2.12, CFI = .957, TLI = .951, GFI = .924, RMSEA = .039, SRMR = .041). Social media addiction significantly predicted poorer sleep quality (β = .52, p < .001), higher academic burnout (β = .39, p < .001), and lower psychological well-being (β = -.24, p < .001). Poor sleep quality significantly predicted academic burnout (β = .31, p < .001) and psychological well-being (β = -.18, p < .001). Academic burnout was the strongest direct predictor of psychological well-being (β = -.49, p < .001). Bootstrapping confirmed significant indirect effects through sleep quality, academic burnout, and the serial pathway from sleep quality to academic burnout. Conclusion: The findings indicate that social media addiction undermines psychological well-being among high school students both directly and indirectly through impaired sleep quality and increased academic burnout. School-based interventions should integrate digital behavior regulation, sleep hygiene education, and burnout prevention strategies.
Copyright (c) 2026 Natalia Caballero (Author); Ethan Holloway (Corresponding Author); Gabriela Uzcátegui (Author)
Support Vector Machine Prediction of Adolescent Anxiety Based on Nomophobia, Fear of Missing Out (FoMO), Perfectionism, Cognitive Fusion, and Parent–Child Communication
Objective: The present study aimed to predict adolescent anxiety using a Support Vector Machine (SVM) model based on nomophobia, Fear of Missing Out (FoMO), perfectionism, cognitive fusion, and parent–child communication among Tunisian adolescents. Methods and Materials: The study employed a cross-sectional correlational design with a machine learning approach. The statistical population consisted of secondary school adolescents in Tunisia during the 2025–2026 academic year. Using multistage cluster random sampling, 742 students were selected, of whom 718 valid questionnaires were retained for final analysis after data screening. Data were collected using the Beck Anxiety Inventory, Nomophobia Questionnaire, Fear of Missing Out Scale, Multidimensional Perfectionism Scale, Cognitive Fusion Questionnaire, and Parent–Adolescent Communication Scale. Descriptive statistics and Pearson correlation analyses were conducted using SPSS-27, while machine learning analyses were performed using Python and Scikit-learn libraries. The dataset was divided into training and testing subsets using an 80/20 ratio, and a radial basis function kernel Support Vector Machine model with five-fold cross-validation was implemented to predict adolescent anxiety. Findings: The findings indicated significant positive correlations between adolescent anxiety and nomophobia, FoMO, perfectionism, and cognitive fusion, while parent–child communication demonstrated a significant negative relationship with anxiety symptoms. Cognitive fusion emerged as the strongest predictor variable, followed by nomophobia, parent–child communication, FoMO, and perfectionism. The Support Vector Machine model demonstrated high predictive performance, achieving 88.76% classification accuracy in the testing dataset, with an F1-score of 87.16% and an area under the curve (AUC) value of 0.91. The confusion matrix additionally revealed high sensitivity and specificity in distinguishing adolescents with elevated anxiety symptoms from those with lower anxiety levels. Conclusion: The results demonstrated that adolescent anxiety can be effectively predicted through the combined influence of cognitive, technological, emotional, and family-related factors using machine learning techniques. Cognitive fusion and nomophobia represented the strongest psychological risk factors, while healthy parent–child communication functioned as a protective factor against anxiety symptoms. The findings support the usefulness of Support Vector Machine models in adolescent mental health screening and highlight the importance of addressing maladaptive cognitive processes, digital dependency, and family communication patterns in preventive and intervention programs targeting adolescent anxiety.
A Structural Equation Modeling Study of Parental Psychological Control, Emotion Regulation Difficulties, and Depressive Symptoms among Adolescents: The Mediating Role of Rejection Sensitivity
Objective: This study aimed to examine the direct and indirect relationships among parental psychological control, emotion regulation difficulties, rejection sensitivity, and depressive symptoms in adolescents, with a particular focus on the mediating role of rejection sensitivity. Methods and Materials: A cross-sectional design was employed with 718 adolescents aged 13–18 years recruited from secondary schools in Canada. Parental psychological control was measured using the Psychological Control Scale–Youth Self-Report, emotion regulation difficulties using the Difficulties in Emotion Regulation Scale (DERS), rejection sensitivity using the Children’s Rejection Sensitivity Questionnaire, and depressive symptoms using the CES-DC. Structural Equation Modeling (SEM) was used to test direct and indirect pathways, with model fit evaluated using χ², CFI, TLI, IFI, RMSEA, and SRMR indices. Bootstrap resampling with 5,000 iterations was employed to examine the significance of indirect effects. Findings: SEM analyses revealed that parental psychological control was positively associated with emotion regulation difficulties (β = .58, p < .001) and rejection sensitivity (β = .42, p < .001). Emotion regulation difficulties were positively related to both rejection sensitivity (β = .39, p < .001) and depressive symptoms (β = .49, p < .001), while rejection sensitivity also predicted depressive symptoms (β = .31, p < .001). The direct effect of parental psychological control on depressive symptoms was significant but reduced (β = .11, p = .006), indicating partial mediation. Bootstrap analyses confirmed significant indirect effects through emotion regulation difficulties (β = .284, 95% CI [.221, .352]), rejection sensitivity (β = .130, 95% CI [.089, .182]), and the sequential pathway via both mediators (β = .070, 95% CI [.044, .103]). The model explained 58% of the variance in depressive symptoms. Conclusion: The findings indicate that parental psychological control contributes to adolescent depressive symptoms primarily through its impact on emotion regulation difficulties and rejection sensitivity.
Copyright (c) 2026 Erion Kelmendi (Author); Gabrielle Pelletier (Corresponding Author); Enkhzul Batbayar (Author)
A Deep Neural Network Analysis of Youth Psychological Distress Based on Cybervictimization, Loneliness, Academic Pressure, Screen-Time Fragmentation, and Family Emotional Climate
Objective: The present study aimed to investigate youth psychological distress through a deep neural network framework based on cybervictimization, loneliness, academic pressure, screen-time fragmentation, and family emotional climate among adolescents and emerging adults in Georgia. Methods and Materials: This study employed a quantitative cross-sectional predictive design using deep learning methodologies. The statistical population consisted of adolescents and emerging adults aged 16 to 24 years enrolled in secondary schools and universities in Georgia during the 2025–2026 academic year. Using multistage cluster sampling, 842 participants were initially recruited, and after data screening, 814 cases were retained for final analysis. Data were collected using standardized instruments including the Kessler Psychological Distress Scale, Cyberbullying Victimization Scale, UCLA Loneliness Scale, Educational Stress Scale for Adolescents, a screen-time fragmentation measure, and the Family Environment Scale. Data analysis involved descriptive statistics, Pearson correlation coefficients, and deep neural network modeling using Python, TensorFlow, and Keras libraries. Model performance was evaluated using mean squared error, root mean squared error, mean absolute error, coefficient of determination, and cross-validation procedures. SHAP analysis was additionally employed to examine relative predictor importance. Findings: The findings demonstrated significant positive relationships between psychological distress and cybervictimization, loneliness, academic pressure, and screen-time fragmentation, while family emotional climate showed a significant negative relationship with psychological distress. Loneliness emerged as the strongest predictor within the deep neural network model, followed by family emotional climate and cybervictimization. Conclusion: Comparative analyses indicated that the deep neural network outperformed traditional machine learning approaches including random forest, support vector machine, and multiple linear regression models in predicting psychological distress outcomes.
Comparison of the Effectiveness of Mentalization-Based Therapy and Emotion Efficacy-Based Therapy on Cognitive Insight and Distress Tolerance in Adolescents with Generalized Anxiety Symptoms
Objective: The present study aimed to compare the effectiveness of Mentalization-Based Therapy and Emotion Efficacy-Based Therapy on cognitive insight and distress tolerance among adolescents with generalized anxiety symptoms. Methods and Materials: This study was applied in terms of purpose and quasi-experimental in terms of methodology, utilizing a pretest–posttest design with two experimental groups and one control group. The statistical population consisted of male and female adolescents with generalized anxiety symptoms in Shiraz during 2025. The sample included 45 adolescents selected through convenience sampling and randomly assigned into three groups of 15 participants each. The first experimental group received Mentalization-Based Therapy, the second experimental group received Emotion Efficacy-Based Therapy, and the control group received no intervention. Data collection instruments included the Beck Cognitive Insight Scale, the Distress Tolerance Scale, and the Generalized Anxiety Disorder Questionnaire (GAD-7). The interventions were implemented over eight weekly 90-minute sessions. Data were analyzed using descriptive statistics and multivariate analysis of covariance (MANCOVA) in IBM SPSS Statistics. Findings: The results indicated significant differences between the experimental and control groups in cognitive insight and distress tolerance (Wilks’ Lambda = 0.266, F₄,₇₈ = 18.293, p < .01). Univariate covariance analysis demonstrated that both Mentalization-Based Therapy and Emotion Efficacy-Based Therapy significantly improved cognitive insight (F₂,₄₀ = 20.593, p < .001, η² = 0.507) and distress tolerance (F₂,₄₀ = 17.987, p < .001, η² = 0.474) compared to the control group. Bonferroni post hoc comparisons further revealed that Mentalization-Based Therapy was significantly more effective than Emotion Efficacy-Based Therapy in enhancing cognitive insight (p < .01), whereas Emotion Efficacy-Based Therapy demonstrated greater effectiveness in improving distress tolerance (p < .05). Conclusion: These results emphasize the importance of selecting therapeutic interventions based on the primary psychological needs and treatment goals of adolescents with generalized anxiety symptoms.
Copyright (c) 2026 Fariba Haghshenas (Corresponding Author); Bahareh Rava, Razieh Hemmati, Gholamreza Sanagouye Mohrar, Mohammadali Fardin (Author)