Journal of Assessment and Research in Applied Counseling (JARAC)
E-ISSN: 3041-8518 Director-in-Charge: Dr. Mehdi Rostami Editor-in-Chief: Dr. Valiolah Farzad Associate editor: Dr. Mehdi Rostami Owner: KMAN Research Institute Publisher: KMAN Publication Inc. (KMANPUB) Contact email: jarac@kmanpub.com / journalarcp@gmail.com Open access: YES Journal of Assessment and Research in Applied Counseling is a scientific open-access double-blind anonymous peer-review 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 four times per year, in English, by KMANPUB. It covers scientific subjects in the following fields: Assessment, evaluation and measurement tools for counseling and psychotherapy New individual and family psychotherapy and counseling approaches Journal of Assessment and Research in Applied Counseling is indexed and abstracted by the Google Scholar, APA PsycInfo, <a href="https://www.linkedin.com/in/assessment-and-research-in-applied-counseling-%D9%81%D8%B5%D9%84%D9%86%D8%A7%D9%85%D9%87-%D8%B3%D9%86%D8%AC%D8%B4-%D9%88-%D9%BE%DA%98%D9%88%D9%87%D8%B4-%D8%AF%D8%B1-%D9%85%D8%B4%D8%A7%D9%88%D8%B1%D9%87-%DA%A9%D8%A7%D8%B1%D8%A8%D8%B1%D8%AF%DB%8C-b40aa4249/" data-cke-saved-href="https://www.linkedin.com/in/assessment-and-research-in-applied-counseling-%D9%81%D8%B5%D9%84%D9%86%D8%A7%D9%85%D9%87-%D8%B3%D9%86%D8%AC%D8%B4-%D9%88-%D9%BE%DA%98%D9%88%D9%87%D8%B4-%D8%AF%D8%B1-%D9%85%D8%B4%D8%A7%D9%88%D8%B1%D9%87-%DA%A9%D8%A7%D8%B1%D8%A8%D8%B1%D8%AF%DB%8C-b40aa4249/">LinkedIn, ABCD Index, Researchgate, ICMJE, and <a href="https://www.ebsco.com/m/ee/Marketing/titleLists/afh-journals.htm#:~:text=Journal%20of%20Assessment%20&%20Research%20in%20Applied%20Counseling" data-cke-saved-href="https://doaj.org/toc/2821-2738?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%222821-2738%22%5D%7D%7D%5D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%2C%22track_total_hits%22%3Atrue%7D">EBSCO. 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)
CatBoost Prediction of Obsessive-Compulsive Symptoms Using Thought Suppression, Cognitive Fusion, Perfectionism, and Anxiety Sensitivity
Objective: This study aimed to develop and interpret a CatBoost machine learning model for predicting obsessive-compulsive symptom severity based on thought suppression, cognitive fusion, perfectionism, and anxiety sensitivity among adults in Spain. Methods and Materials: This cross-sectional predictive study was conducted on 1,214 adults from Spain. Participants completed standardized self-report measures assessing obsessive-compulsive symptoms, thought suppression, cognitive fusion, perfectionism, and anxiety sensitivity. After data screening, the final dataset was analyzed using descriptive and inferential statistics, Pearson correlation analysis, multicollinearity assessment, and CatBoost regression modeling. The dataset was divided into training and testing subsets, and hyperparameter tuning was performed using five-fold cross-validation. Model performance was evaluated using R², adjusted R², root mean squared error, mean absolute error, mean absolute percentage error, and explained variance. SHAP and permutation importance analyses were used to interpret the relative contribution of each predictor. Findings: Obsessive-compulsive symptoms were significantly and positively correlated with thought suppression (r = .71, p < .001), cognitive fusion (r = .67, p < .001), anxiety sensitivity (r = .64, p < .001), and perfectionism (r = .58, p < .001). The optimized CatBoost model demonstrated strong predictive performance, explaining 89.2% of variance in the training dataset and 85.4% of variance in the testing dataset. The testing model showed low prediction error, with RMSE = 4.28, MAE = 3.24, MAPE = 13.18%, and explained variance = .855. Five-fold cross-validation confirmed stable model performance, with a mean R² of .849. SHAP and permutation importance analyses identified thought suppression as the strongest predictor, followed by cognitive fusion, anxiety sensitivity, and perfectionism. Conclusion: The findings indicate that obsessive-compulsive symptoms can be predicted with high accuracy using a CatBoost model based on key cognitive-affective vulnerability factors. Thought suppression and cognitive fusion showed the strongest predictive influence, suggesting that maladaptive responses to intrusive thoughts are central to obsessive-compulsive symptom severity. The model provides an interpretable framework for identifying psychological mechanisms relevant to assessment, prevention, and intervention.
Copyright (c) 2026 Rosa Alcázar Campos , Margarita Izquierdo Morán (Author); Ferran Fernández-Llario (Corresponding Author); Luís F. Seoane (Author)
Explainable AI Modeling of Special Education Teachers’ Psychological Well-Being During Artificial Intelligence Integration: The Roles of Occupational Stress, Self-Regulation, and Technological Confidence
Objective: This study aimed to develop an explainable artificial intelligence model for predicting psychological well-being among special education teachers during artificial intelligence integration by examining the roles of occupational stress, self-regulation, and technological confidence. Methods and Materials: This quantitative, cross-sectional, predictive-correlational study was conducted among 318 special education teachers working in public and non-public exceptional schools in Tehran during the 2025–2026 academic year. Participants were selected through multistage cluster sampling. Data were collected using a demographic and professional information form, the Ryff Psychological Well-Being Scale, the Teacher Stress Inventory, the Short Self-Regulation Questionnaire, and the Computer Self-Efficacy Scale adapted to educational technology and artificial intelligence contexts. Data analysis included descriptive statistics, Pearson correlation, multiple linear regression, and supervised machine-learning modeling. Five predictive models were compared, including multiple linear regression, support vector regression, random forest regression, gradient boosting regression, and extreme gradient boosting regression. SHAP analysis was used to interpret feature importance and explain the final artificial intelligence model. Findings: Psychological well-being was significantly and negatively correlated with occupational stress and significantly and positively correlated with self-regulation and technological confidence. The multiple regression model was significant, F(6, 311) = 73.71, p < 0.001, and explained 58.7% of the variance in psychological well-being. Self-regulation was the strongest positive predictor, followed by occupational stress as a negative predictor and technological confidence as a positive predictor. Frequency of AI-supported tool use also significantly predicted psychological well-being, whereas teaching experience and educational level were not significant predictors. Among the machine-learning models, extreme gradient boosting showed the best performance, with the highest test R² of 0.73 and the lowest prediction error. SHAP analysis identified occupational stress, self-regulation, and technological confidence as the most influential features in the final model. Conclusion: The findings indicate that special education teachers’ psychological well-being during artificial intelligence integration is primarily shaped by occupational stress, self-regulatory capacity, and technological confidence. Explainable artificial intelligence can provide an interpretable and practical framework for identifying psychological and technological factors that support teacher well-being. Keywords: Explainable artificial intelligence; psychological well-being; special education teachers; occupational stress; self-regulation; technological confidence; artificial intelligence integration.
Copyright (c) 2026 Ali Aghaziarati (Corresponding Author); Hasan Rahimi (Author)
Comparing the Effectiveness of Cognitive Empowerment Based on Cognitive Games and Executive Function–Based Parenting on Working Memory in Children with Executive Function Deficits
Objective: This study aimed to compare the effectiveness of cognitive empowerment based on cognitive games and executive function–based parenting training on improving working memory among children with executive function deficits. Methods and Materials: This research employed a quasi-experimental design with a pretest–posttest control group structure. The statistical population consisted of all 5- and 6-year-old children with executive function deficits in the city of Qaemshahr. A total of 60 participants were selected through purposive sampling based on inclusion and exclusion criteria and were randomly assigned to three groups (n = 20 per group): a cognitive game–based cognitive empowerment group, an executive function–based parenting group, and a control group. The first experimental group participated in 11 sessions of 90-minute cognitive game activities designed to enhance working memory, while the second experimental group received 11 sessions of 90-minute executive function–based parenting training. The control group received no intervention during the study period. Working memory was assessed using a standardized Working Memory Test. Data were analyzed using Multivariate Analysis of Covariance (MANCOVA). Findings: The results of the multivariate analysis of covariance indicated significant differences among the three groups in posttest working memory scores after controlling for pretest performance (p < .05). Both cognitive empowerment based on cognitive games and executive function–based parenting interventions significantly improved working memory in children with executive function deficits compared with the control group (p < .05). Furthermore, post hoc comparisons demonstrated that the cognitive game–based cognitive empowerment intervention produced significantly greater improvements in working memory than the executive function–based parenting program (p < .05), indicating superior effectiveness in enhancing cognitive performance. Conclusion: The findings suggest that both cognitive game–based cognitive empowerment and executive function–based parenting interventions are effective approaches for improving working memory in children with executive function deficits. However, cognitive empowerment through structured cognitive games appears to yield stronger outcomes. These results highlight the value of incorporating cognitive training programs and parent-focused executive function interventions into educational and therapeutic settings to support cognitive development in young children experiencing executive function challenges.
Copyright (c) 2026 Fatemeh Abbaszadeh (Author); Seyedeh Hamideh Salim Bahrami (Corresponding Author); Seyedeh Olia Emadian (Author)
Identifying Factors Associated with the Occurrence of Marital Infidelity: A Systematic Review
Objective: This study aimed to identify and classify the factors associated with the occurrence of marital infidelity based on a systematic review of national and international studies. Methods and Materials: The present study was conducted using a documentary research design and a systematic review approach. Relevant studies were identified through systematic searches of national databases, including IranDoc, the Scientific Information Database (SID), and Civilica, as well as international databases, including Scopus, PsycINFO, and Google Scholar. Keywords such as marital infidelity, extramarital relationships, factors affecting infidelity, and their Persian and English equivalents were used in the search process. After screening titles, abstracts, and full texts according to predefined inclusion and exclusion criteria, 46 eligible studies were selected for final analysis. Data were extracted from the included studies and analyzed through coding and qualitative content analysis to identify recurring themes and categories. Findings: The qualitative synthesis indicated that the factors associated with marital infidelity could be classified into five main components. Individual factors included personality traits and disorders, psychological and emotional conditions, and personal beliefs and attitudes. Relational factors included emotional and communicative quality, sexual and physical functioning, and the structure and history of the marital relationship. Familial factors included developmental background, family patterns, and kinship relationship networks. Socioeconomic factors included occupational and economic contexts, as well as social and cultural pressures. Technological factors included cyberspace, online interactions, and the role of digital technologies in facilitating extramarital relationships. Conclusion: Marital infidelity is a multidimensional phenomenon shaped by the interaction of individual vulnerabilities, relational dynamics, family background, socioeconomic pressures, and technological contexts. Therefore, prevention and intervention programs should adopt an integrative perspective that addresses both personal and contextual determinants of marital relationships. Keywords: Marital infidelity; extramarital relationships; marital satisfaction; psychological factors; systematic review.
Copyright (c) 2026 Solmaz Saeidi Haghi (Author); Yaser Madani (Corresponding Author); Keyvan Salehi, Ahmad Behpajooh (Author)
Artificial Intelligence-Based Assessment and Intervention for Specific Learning Disabilities: A Systematic Review
Objective: This study aimed to systematically review the existing evidence on the application of artificial intelligence-based methods in the assessment, screening, prediction, diagnosis, intervention, and progress monitoring of specific learning disabilities. Methods and Materials: This systematic review was conducted according to PRISMA-based principles. Scientific databases including Scopus, Web of Science, PubMed/MEDLINE, ERIC, PsycINFO, IEEE Xplore, and ScienceDirect were searched for studies published between January 2014 and May 2026. The search combined terms related to artificial intelligence, machine learning, deep learning, expert systems, adaptive learning, intelligent tutoring, dyslexia, dyscalculia, dysgraphia, and specific learning disabilities. From 1,286 initially identified records, 362 duplicates were removed, 924 titles and abstracts were screened, and 153 full-text articles were assessed for eligibility. Finally, 42 studies met the inclusion criteria and were analyzed through descriptive and narrative synthesis. Findings: Inferential synthesis of the included studies indicated that artificial intelligence-based models showed the strongest evidence in assessment-related functions, particularly early screening, diagnostic classification, and academic-risk prediction. Classification and prediction models generally demonstrated acceptable to high performance, especially when multidimensional cognitive, linguistic, academic, behavioral, handwriting, or digital-learning data were used. Evidence was strongest for dyslexia and reading disorder, while dyscalculia and dysgraphia were less frequently investigated but showed promising results, particularly in handwriting analysis and mathematics-risk detection. Intervention-related findings indicated that AI-based adaptive learning systems, intelligent tutoring platforms, mobile applications, serious games, and personalized feedback tools were associated with improvement in short-term academic outcomes, especially reading accuracy, decoding, mathematical performance, writing accuracy, engagement, and progress monitoring. However, evidence for long-term intervention effectiveness, transfer, and sustained educational outcomes remained limited. Conclusion: Artificial intelligence has considerable potential to support the early identification, individualized assessment, adaptive intervention, and continuous monitoring of learners with specific learning disabilities. Nevertheless, current evidence is stronger for screening and classification than for intervention effectiveness. Future studies should prioritize external validation, longitudinal designs, explainability, ethical implementation, cultural adaptation, and integration with professional educational and clinical judgment.
Copyright (c) 2026 Azizreza Ghasemzadeh (Corresponding Author)
A Structural Equation Analysis of Parental Psychological Control, Adolescent Self-Esteem, Emotional Intelligence, and Internalizing Symptoms
Objective: This study aimed to examine the direct and indirect relationships among parental psychological control, adolescent self-esteem, emotional intelligence, and internalizing symptoms using structural equation modeling. Methods and Materials: This cross-sectional correlational study was conducted among 618 adolescents from public secondary schools in Ontario and British Columbia, Canada. Participants completed standardized self-report measures assessing parental psychological control, self-esteem, emotional intelligence, and internalizing symptoms. Parental psychological control was measured using the Psychological Control Scale–Youth Self-Report, self-esteem using the Rosenberg Self-Esteem Scale, emotional intelligence using the Schutte Self-Report Emotional Intelligence Test, and internalizing symptoms using the Revised Child Anxiety and Depression Scale. Data were analyzed using IBM SPSS Statistics version 29 and AMOS version 29. Preliminary analyses included assessment of normality, reliability, convergent validity, and correlations among the study variables. Confirmatory factor analysis was performed to evaluate the measurement model, followed by structural equation modeling to test the hypothesized direct and indirect pathways. Mediation effects were examined using bootstrap resampling with 5,000 samples and bias-corrected 95% confidence intervals. Findings: The measurement model demonstrated good fit to the data, χ²/df = 2.08, CFI = .964, TLI = .960, GFI = .923, SRMR = .041, and RMSEA = .042. All hypothesized structural paths were statistically significant. Parental psychological control negatively predicted self-esteem (β = −.56, p < .001) and emotional intelligence (β = −.27, p < .001), while positively predicting internalizing symptoms (β = .31, p < .001). Self-esteem positively predicted emotional intelligence (β = .53, p < .001) and negatively predicted internalizing symptoms (β = −.46, p < .001). Emotional intelligence also negatively predicted internalizing symptoms (β = −.24, p < .001). Bootstrap analysis confirmed significant indirect effects, indicating that self-esteem and emotional intelligence mediated the association between parental psychological control and internalizing symptoms. Conclusion: The findings suggest that parental psychological control contributes to adolescent internalizing symptoms both directly and indirectly through reduced self-esteem and emotional intelligence. Strengthening adolescents’ self-worth and emotional competencies may reduce the psychological impact of controlling parenting and support healthier emotional adjustment.
Copyright (c) 2026 Marien Bremmer (Corresponding Author); Jim Selli , Annelle Termes-Rife (Author)
Explainable XGBoost Models for Predicting Generalized Anxiety Disorder from Digital Mental Health Indicators
Objective: This study aimed to develop and evaluate an explainable XGBoost model for predicting generalized anxiety disorder from psychological, behavioral, and digital mental health indicators among Canadian adults. Methods and Materials: This cross-sectional predictive study was conducted with 2,412 adults from Canada. Data were collected using standardized measures of generalized anxiety, psychological distress, depressive symptoms, sleep quality, smartphone addiction, and social media addiction, along with a structured digital lifestyle questionnaire assessing daily screen time, sleep duration, physical activity, technology-related stress, digital notification frequency, remote work hours, online gaming, and demographic characteristics. Generalized anxiety disorder risk was classified using the GAD-7 clinical cut-off. After preprocessing, missing-data imputation, feature encoding, and feature engineering, the dataset was divided into stratified training and testing subsets. An XGBoost classification model was optimized through cross-validation, and model explainability was examined using SHapley Additive exPlanations. Findings: Generalized anxiety symptoms were strongly correlated with psychological distress (r = .79, p < .01) and depressive symptoms (r = .74, p < .01), and moderately to strongly correlated with poor sleep quality (r = .61, p < .01), smartphone addiction (r = .54, p < .01), and social media addiction (r = .46, p < .01). The optimized XGBoost model showed excellent predictive performance on the independent test set, with accuracy of 91.8%, precision of 89.6%, recall of 88.2%, specificity of 93.5%, F1-score of 88.9%, ROC-AUC of .957, PR-AUC of .944, balanced accuracy of 90.9%, Matthews correlation coefficient of .819, and Brier score of .081. SHAP analysis identified psychological distress, depression, sleep quality, smartphone addiction, previous mental health diagnosis, screen time, social media addiction, and technology-related stress as the most influential predictors. Conclusion: Explainable XGBoost modeling demonstrated high accuracy and interpretability for predicting generalized anxiety disorder from digital mental health indicators, supporting its potential use in early screening, risk stratification, and personalized digital mental health assessment.
The Relationship Between Cyberbullying and Suicidal Ideation in Adolescent Girls The Mediating Role of Perceived Maternal Acceptance and Perceived Burdensomeness
Objective: The present study aimed to investigate the relationship between cyberbullying and suicidal ideation among adolescent girls and to examine the mediating roles of perceived maternal acceptance and perceived burdensomeness in this relationship. Methods and Materials: This study employed a descriptive-correlational design using structural equation modeling (SEM). The statistical population consisted of female high school students in Qazvin, Iran, during the 2025–2026 academic year. Using a multistage cluster sampling method, 520 students were recruited, of whom 498 valid questionnaires were retained for analysis. Data were collected using the Cyberbullying Scale (CBS), the Beck Scale for Suicide Ideation (BSSI), the Perceived Burdensomeness subscale of the Interpersonal Needs Questionnaire (INQ), and the Perceived Maternal Acceptance Scale (PMAS). Descriptive statistics, Pearson correlation coefficients, and structural equation modeling were performed using SPSS version 28 and AMOS version 24. The significance of indirect effects was assessed through bootstrap analysis with 5,000 resamples and 95% confidence intervals. Findings: The results revealed significant positive correlations between cyberbullying and suicidal ideation (r = .48, p < .01) and between cyberbullying and perceived burdensomeness (r = .42, p < .01). Cyberbullying was negatively associated with perceived maternal acceptance (r = -.39, p < .01). Structural equation modeling demonstrated satisfactory model fit (χ²/df = 2.29, CFI = .95, TLI = .94, RMSEA = .051). Cyberbullying had significant direct effects on perceived burdensomeness (β = .45, p < .001), perceived maternal acceptance (β = -.41, p < .001), and suicidal ideation (β = .19, p < .001). Perceived burdensomeness positively predicted suicidal ideation (β = .47, p < .001), whereas perceived maternal acceptance negatively predicted suicidal ideation (β = -.34, p < .001). Bootstrap analyses confirmed significant indirect effects through perceived burdensomeness (Effect = .21, 95% CI [.14, .29]) and perceived maternal acceptance (Effect = .14, 95% CI [.08, .21]). Conclusion: The findings indicate that cyberbullying is a significant predictor of suicidal ideation among adolescent girls. Perceived burdensomeness and perceived maternal acceptance partially mediate this relationship, suggesting that both interpersonal cognitive vulnerabilities and family-based protective factors play crucial roles in understanding adolescent suicide risk. Interventions aimed at reducing cyberbullying, strengthening maternal acceptance, and addressing maladaptive perceptions of burdensomeness may contribute to the prevention of suicidal ideation in adolescents.
Copyright (c) 2026 Kiana Naghash Souratgar (Corresponding Author); Hadis Aziziani Khoshnami, Mehran Haidari Nia (Author)
Modeling the Psychological Determinants of Digital Addiction: The Mediating Roles of Loneliness, Self-Control, and Emotional Dysregulation
Objective: This study aimed to model the psychological determinants of digital addiction among Canadian adults by examining the direct and indirect effects of loneliness, self-control, and emotional dysregulation within an integrated structural equation model. Methods and Materials: This quantitative cross-sectional correlational study was conducted among 642 adults residing in Canada who reported regular daily use of digital technologies. Participants completed standardized self-report measures assessing digital addiction, loneliness, self-control, and emotional dysregulation. Data were analyzed using IBM SPSS Statistics version 29 and AMOS version 29. Preliminary analyses included assessment of normality, reliability, convergent validity, and correlations among study variables. The hypothesized relationships among the constructs were tested using structural equation modeling with maximum likelihood estimation. Model fit was evaluated using χ²/df, CFI, TLI, GFI, AGFI, RMSEA, and SRMR. Indirect effects were examined through bootstrap resampling with 5,000 samples and bias-corrected 95% confidence intervals. Findings: The measurement model demonstrated satisfactory fit to the data, χ²/df = 2.11, CFI = .970, TLI = .966, GFI = .941, AGFI = .929, RMSEA = .042, and SRMR = .039. Digital addiction was positively associated with loneliness and emotional dysregulation and negatively associated with self-control. In the structural model, emotional dysregulation significantly predicted digital addiction (β = .42, p < .001), self-control significantly and negatively predicted digital addiction (β = −.39, p < .001), and loneliness significantly predicted digital addiction (β = .23, p < .001). Loneliness significantly predicted emotional dysregulation (β = .48, p < .001) and negatively predicted self-control (β = −.37, p < .001), while emotional dysregulation negatively predicted self-control (β = −.46, p < .001). Bootstrap results confirmed significant indirect effects of loneliness on digital addiction through emotional dysregulation and self-control. The final model explained 68% of the variance in digital addiction. Conclusion: The findings indicate that digital addiction is shaped by interconnected interpersonal, emotional, and self-regulatory mechanisms. Loneliness contributes to problematic digital use both directly and indirectly by increasing emotional dysregulation and weakening self-control. These results highlight the importance of addressing emotional regulation, self-control, and social connectedness in prevention and intervention programs for digital addiction.
Copyright (c) 2026 Danielle Laprade , Babak Nazarpour (Author); Frederick Stanley (Corresponding Author)
Explainable XGBoost Models for Predicting Burnout among Mental Health Professionals Using Compassion Fatigue, Psychological Capital, Self-Care, and Occupational Stress
Objective: This study aimed to develop and interpret an explainable XGBoost model for predicting burnout among mental health professionals in Malaysia using compassion fatigue, psychological capital, self-care, and occupational stress as primary predictors. Methods and Materials: This cross-sectional predictive study was conducted among 742 mental health professionals in Malaysia, including counselors, clinical psychologists, psychiatric nurses, psychiatrists, social workers, psychotherapists, and other mental health specialists. Burnout was assessed using the Maslach Burnout Inventory–Human Services Survey, compassion fatigue using the Professional Quality of Life Scale, psychological capital using the Psychological Capital Questionnaire, self-care using the Self-Care Assessment Worksheet, and occupational stress using the Health and Safety Executive Management Standards Indicator Tool. Data were analyzed using Python-based machine learning procedures. After preprocessing, the dataset was divided into training and testing sets using stratified sampling. An XGBoost classifier was trained and optimized through five-fold cross-validation and hyperparameter tuning. Model performance was evaluated using accuracy, precision, recall, specificity, F1-score, Matthews correlation coefficient, AUC-ROC, and Brier score. SHAP analysis was applied to interpret global and individual predictor contributions. Findings: Compassion fatigue showed a strong positive association with burnout, while occupational stress also demonstrated a strong positive relationship with burnout. Psychological capital and self-care were negatively associated with burnout, indicating their protective role. The optimized XGBoost model demonstrated excellent predictive performance, with test accuracy of 91.9%, precision of 91.3%, recall of 90.7%, specificity of 92.6%, F1-score of 91.0%, Matthews correlation coefficient of 0.864, AUC-ROC of 0.962, and Brier score of 0.071. SHAP analysis identified compassion fatigue as the most influential predictor, followed by occupational stress, psychological capital, and self-care. Conclusion: Explainable XGBoost modeling provided an accurate and interpretable approach for predicting burnout among Malaysian mental health professionals. The findings emphasize that burnout is primarily shaped by modifiable psychological and occupational factors, particularly compassion fatigue, occupational stress, psychological capital, and self-care.
Copyright (c) 2026 Abdul-Wahab Inusah , Ting Yang (Author); Mohammad Rahman Washim (Corresponding Author); Mohamad Mustapa (Author)
Random Forest Classification of Suicide Risk Using Hopelessness, Defeat, Entrapment, and Psychological Pain
Objective: This study aimed to develop and evaluate a Random Forest classification model for predicting suicide risk among Moroccan adults using hopelessness, defeat, entrapment, and psychological pain as psychological predictors. Methods and Materials: This cross-sectional predictive study was conducted among 1,284 adults from Morocco. Participants completed standardized self-report measures assessing suicide risk, hopelessness, defeat, entrapment, and psychological pain. Suicide risk was classified as a binary outcome variable, distinguishing low-risk and high-risk participants according to standardized suicide risk scores. Data were screened for missing values, distributional properties, and outliers before analysis. The dataset was divided into training and testing subsets using an 80:20 stratified split to preserve the proportion of suicide risk categories. A Random Forest classifier was trained on the training dataset, and hyperparameter tuning was performed using grid search with five-fold stratified cross-validation. Model performance was evaluated on the independent test dataset using accuracy, sensitivity, specificity, precision, F1-score, balanced accuracy, ROC-AUC, Cohen’s Kappa, Matthews Correlation Coefficient, and confusion matrix indices. Feature importance was examined using Gini importance, permutation importance, and SHAP-based interpretability analysis. Findings: Hopelessness, defeat, entrapment, and psychological pain were all positively associated with suicide risk, with psychological pain showing the strongest relationship. The optimized Random Forest model demonstrated excellent classification performance, with accuracy of 92.61%, sensitivity of 88.96%, specificity of 93.87%, precision of 90.74%, F1-score of 89.84%, balanced accuracy of 91.42%, and ROC-AUC of 0.964. Cohen’s Kappa was 0.819 and the Matthews Correlation Coefficient was 0.824, indicating strong agreement beyond chance. Feature-importance results showed that psychological pain was the strongest predictor, followed by entrapment, hopelessness, and defeat. SHAP analysis confirmed that higher levels of all four predictors increased the probability of classification into the high-suicide-risk group. Conclusion: The findings indicate that Random Forest classification can accurately distinguish suicide risk levels using theoretically grounded psychological variables. Psychological pain and entrapment were the most influential predictors, suggesting that unbearable emotional suffering and perceived inability to escape distress are central markers of elevated suicide risk. The model may provide a useful decision-support approach for suicide risk screening, although it should complement rather than replace professional clinical judgment.
Copyright (c) 2026 Nabil Mouhajir (Corresponding Author); Mohammed Abubaker, Thiago Saab (Author)
Predicting Major Depressive Disorder Using Random Forest Models Based on Psychological, Behavioral, and Lifestyle Indicators
Objective: This study aimed to develop and evaluate a Random Forest classification model for predicting Major Depressive Disorder among Canadian adults using integrated psychological, behavioral, and lifestyle indicators. Methods and Materials: This cross-sectional predictive study was conducted among 1,742 adults residing in Canada. Participants completed standardized self-report instruments assessing depressive symptoms, anxiety, stress, emotion regulation difficulties, perceived stress, sleep quality, physical activity, and lifestyle behaviors. Major Depressive Disorder status was determined using the Patient Health Questionnaire-9 cut-off score for clinically significant depressive symptoms. Psychological indicators included DASS depression, anxiety, and stress scores, perceived stress, and emotion regulation difficulties. Behavioral and lifestyle variables included sleep quality, sleep duration, physical activity, screen time, body mass index, alcohol consumption, and demographic characteristics. Data preprocessing included missing-value management, categorical encoding, and feature preparation. The dataset was divided into training and testing subsets using stratified sampling. A Random Forest classification algorithm was trained and optimized through five-fold cross-validation and grid-search hyperparameter tuning. Model performance was evaluated using accuracy, sensitivity, specificity, precision, F1-score, balanced accuracy, ROC-AUC, Cohen’s Kappa, Matthews Correlation Coefficient, and feature-importance analysis. Findings: The optimized Random Forest model demonstrated strong predictive performance on the independent test dataset, with accuracy of 91.38%, sensitivity of 89.12%, specificity of 92.31%, precision of 87.64%, F1-score of 88.37%, balanced accuracy of 90.72%, ROC-AUC of 0.957, Cohen’s Kappa of 0.804, and Matthews Correlation Coefficient of 0.806. Five-fold cross-validation confirmed model stability, with mean accuracy of 91.38%, mean precision of 87.89%, mean recall of 89.14%, mean F1-score of 88.51%, and mean ROC-AUC of 0.957. Feature-importance analysis identified DASS depression, perceived stress, emotion regulation difficulties, sleep quality, anxiety, stress, screen time, and physical activity as the strongest predictors. Conclusion: The findings indicate that Random Forest modeling can accurately predict probable Major Depressive Disorder using psychological, behavioral, and lifestyle indicators. The model showed high discrimination, stable validation performance, and clinically interpretable predictor patterns, supporting its potential value as a scalable screening approach for identifying adults at elevated risk of depression.
Copyright (c) 2026 Liam Wooderson (Author); Milena Fini (Corresponding Author); Mariusz Szypa (Author)
Modeling Emotional Eating through Self-Compassion, Body Dissatisfaction, Emotion Regulation, and Psychological Distress
Objective: This study aimed to model emotional eating through self-compassion, body dissatisfaction, emotion regulation difficulties, and psychological distress among Canadian adults. Methods and Materials: This cross-sectional correlational study was conducted on 742 adults residing in Canada. Participants were recruited through online advertisements, university mailing lists, community organizations, and social media platforms. Data were collected using the Emotional Eating Scale, Self-Compassion Scale, Body Shape Questionnaire-34, Difficulties in Emotion Regulation Scale, and Depression Anxiety Stress Scales-21. After screening the data for missing values, normality, outliers, and multicollinearity, descriptive statistics, reliability analysis, Pearson correlations, confirmatory factor analysis, and structural equation modeling were performed. Model fit was evaluated using χ²/df, CFI, TLI, IFI, GFI, AGFI, RMSEA, and SRMR. Indirect effects were examined through bias-corrected bootstrapping with 5,000 resamples. Findings: The measurement model demonstrated excellent fit to the data, χ²(392) = 823.47, χ²/df = 2.10, CFI = .965, TLI = .961, IFI = .965, GFI = .929, AGFI = .914, RMSEA = .039, and SRMR = .041. In the structural model, self-compassion negatively predicted emotional eating, emotion regulation difficulties, and psychological distress. Body dissatisfaction positively predicted emotional eating and psychological distress. Emotion regulation difficulties positively predicted both emotional eating and psychological distress, while psychological distress positively predicted emotional eating. Bootstrap analysis confirmed significant indirect effects of self-compassion on emotional eating through emotion regulation difficulties and psychological distress, body dissatisfaction on emotional eating through psychological distress, and emotion regulation difficulties on emotional eating through psychological distress. The model explained 69% of the variance in emotional eating. Conclusion: The findings indicate that emotional eating is shaped by a network of protective and risk-related psychological factors. Higher self-compassion appears to reduce emotional eating directly and indirectly by lowering emotional dysregulation and psychological distress, whereas body dissatisfaction and emotion regulation difficulties increase emotional eating through distress-related pathways. These results highlight the importance of integrated interventions targeting self-compassion, body image, emotion regulation, and psychological distress.
A Cross-Cultural Structural Equation Model of Social Anxiety: The Roles of Intolerance of Uncertainty, Fear of Negative Evaluation, and Self-Compassion
Objective: This study aimed to test a cross-cultural structural equation model of social anxiety by examining the direct and indirect roles of intolerance of uncertainty, fear of negative evaluation, and self-compassion among adults residing in Canada. Methods and Materials: This cross-sectional correlational study was conducted among 701 adults from culturally diverse backgrounds in Canada. Participants completed standardized self-report measures assessing social anxiety, intolerance of uncertainty, fear of negative evaluation, and self-compassion. Data were analyzed using IBM SPSS Statistics and AMOS. Preliminary analyses examined reliability, normality, and correlations among the main variables. Confirmatory factor analysis was used to evaluate the measurement model, and structural equation modeling was applied to test the hypothesized direct and indirect pathways. Bootstrap analysis with 5,000 resamples was used to examine mediation effects. Findings: The measurement model demonstrated excellent fit to the data, χ²/df = 2.23, CFI = .965, TLI = .961, IFI = .965, GFI = .929, RMSEA = .042, and SRMR = .039. In the structural model, intolerance of uncertainty significantly predicted fear of negative evaluation (β = .61, p < .001), self-compassion (β = −.36, p < .001), and social anxiety (β = .19, p < .001). Fear of negative evaluation significantly predicted social anxiety (β = .57, p < .001), while self-compassion negatively predicted social anxiety (β = −.29, p < .001). Bootstrap results confirmed significant indirect effects of intolerance of uncertainty on social anxiety through fear of negative evaluation (β = .35, p < .001) and self-compassion (β = .10, p < .001). The total indirect effect was significant (β = .45, p < .001), and the total effect reached β = .64 (p < .001). Conclusion: The findings support an integrated cross-cultural model in which intolerance of uncertainty contributes to social anxiety both directly and indirectly through increased fear of negative evaluation and reduced self-compassion.
Copyright (c) 2026 Merve Okan (Corresponding Author); Cheng-Xiong Lu (Author)
Stacked Generalization Model for Predicting Happiness: Positive Affect, Gratitude, Social Connectedness, and Psychological Flexibility
Objective: The present study aimed to develop and evaluate a stacked generalization machine learning model to predict happiness based on positive affect, gratitude, social connectedness, and psychological flexibility. Methods and Materials: This cross-sectional predictive study was conducted on a sample of 742 adults recruited from Canada using stratified online sampling to ensure demographic diversity. Participants completed validated self-report instruments including the Subjective Happiness Scale (SHS), the Positive Affect subscale of the Positive and Negative Affect Schedule (PANAS), the Gratitude Questionnaire-6 (GQ-6), the Social Connectedness Scale-Revised (SCS-R), and the Acceptance and Action Questionnaire-II (AAQ-II). Data preprocessing involved handling missing values via multiple imputation, outlier adjustment using robust scaling, and feature standardization. The dataset was split into training (80%) and testing (20%) sets, and model training utilized 10-fold cross-validation. A stacked generalization model was constructed using base learners including linear regression, support vector regression, random forest, and gradient boosting, with a Ridge regression meta-learner. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²), and interpretability was enhanced באמצעות SHapley Additive exPlanations (SHAP) analysis. Findings: The stacked generalization model demonstrated superior predictive performance compared to all base models, achieving the highest explained variance (R² = 0.74) and the lowest prediction error (RMSE = 0.52). Among the predictors, positive affect exhibited the strongest contribution to happiness, followed by social connectedness, gratitude, and psychological flexibility. SHAP analysis revealed significant nonlinear and interaction effects among predictors, indicating that the combined influence of these variables enhances predictive accuracy beyond additive models. Conclusion: The findings highlight the effectiveness of stacked generalization in modeling complex psychological constructs and underscore the central role of positive affect, gratitude, social connectedness, and psychological flexibility in predicting happiness, supporting the integration of machine learning approaches in psychological research and intervention design.
Copyright (c) 2026 Merve Okan (Author); Cheng-Xiong Lu , Laura Ros-García (Corresponding Author); Wilmer Antonio Hernandez (Author)
Explainable Boosting Machine (EBM) for Decision-Making Competence: Cognitive Reflection, Risk Perception, Emotional Bias, and Uncertainty Tolerance
Objective: The present study aimed to model and explain decision-making competence using an Explainable Boosting Machine (EBM) by examining the individual and interactive contributions of cognitive reflection, risk perception, emotional bias, and uncertainty tolerance. Methods and Materials: This study employed a cross-sectional predictive design with a sample of 428 adults recruited from Canada through online research platforms. Participants completed a battery of validated instruments, including the Adult Decision-Making Competence scale (A-DMC), Cognitive Reflection Test (CRT), Domain-Specific Risk-Taking (DOSPERT) scale (risk perception subscale), Emotional Decision-Making Questionnaire (EDMQ), and the Intolerance of Uncertainty Scale (IUS-12). Data were collected via a secure online system and screened for missing values and outliers. The primary analytical approach involved the use of an Explainable Boosting Machine (EBM), implemented in Python, to model both linear and non-linear relationships among variables while preserving interpretability. Model performance was evaluated using R², RMSE, and MAE indices, and k-fold cross-validation (k = 10) was applied to ensure robustness. Feature importance metrics, interaction effects, and partial dependence functions were extracted to interpret the contribution of each predictor. Findings: The EBM model demonstrated strong predictive performance, explaining 48% of the variance in decision-making competence (R² = 0.48). Cognitive reflection emerged as the strongest positive predictor, followed by uncertainty tolerance and risk perception, while emotional bias showed a significant negative effect. All predictors contributed significantly to the model, with non-linear patterns indicating diminishing returns at higher levels of cognitive reflection. Interaction analyses revealed that cognitive reflection significantly attenuated the negative impact of emotional bias, while uncertainty tolerance enhanced the positive effects of accurate risk perception. These results indicate that both cognitive and affective variables, as well as their interactions, play a critical role in shaping decision-making competence. Conclusion: The findings highlight the multifactorial and interactive nature of decision-making competence, demonstrating that higher cognitive reflection and uncertainty tolerance enhance decision quality, whereas emotional bias impairs it, with explainable machine learning offering a powerful and interpretable framework for capturing these complex relationships.
Copyright (c) 2026 Juan José Ripoll Escartí (Corresponding Author); Puffy Soundy , Oronde Campbell (Author); Guangteng Liu (Corresponding Author); Taraneh Bahri (Author)
Bayesian Network Modeling of Marital Conflict: Attachment Styles, Communication Patterns, Emotional Reactivity, and Cognitive Distortions
Objective: The present study aimed to develop and validate a Bayesian network model to examine the probabilistic relationships among attachment styles, communication patterns, emotional reactivity, cognitive distortions, and marital conflict in married adults. Methods and Materials: This cross-sectional correlational study was conducted on 486 married individuals (243 couples) residing in Canada, selected through community-based and online recruitment strategies. Participants met inclusion criteria including a minimum of two years of marital duration and absence of severe psychiatric conditions. Data were collected using validated instruments, including the Revised Conflict Tactics Scales (CTS2) for marital conflict, the Experiences in Close Relationships-Revised (ECR-R) for attachment styles, the Communication Patterns Questionnaire (CPQ), the Emotional Reactivity Scale (ERS), and the Cognitive Distortions Scale (CDS). Data preprocessing included missing value imputation and normalization procedures. Bayesian network modeling was conducted using a hybrid structure-learning approach combining constraint-based and score-based algorithms. Parameter estimation was performed using maximum likelihood and Bayesian methods. Model validation included 10-fold cross-validation, bootstrapping, and evaluation of predictive accuracy indices. Findings: The Bayesian network revealed that demand-withdraw communication (β = 0.73), emotional reactivity (β = 0.71), and cognitive distortions (β = 0.69) were the strongest direct predictors of marital conflict, while constructive communication showed a significant negative effect (β = -0.66). Attachment anxiety exerted indirect effects through emotional reactivity (β = 0.64), whereas attachment avoidance influenced conflict via demand-withdraw patterns (β = 0.58). Emotional reactivity also significantly predicted cognitive distortions (β = 0.62), indicating a mediating pathway. The model demonstrated high predictive performance (AUC = 0.87, accuracy = 0.81), confirming strong discriminative capacity and structural stability. Conclusion: The findings highlight marital conflict as a multifactorial phenomenon emerging from dynamic interactions among emotional, cognitive, and communication processes, with attachment styles functioning as distal vulnerabilities. The Bayesian network approach provides a robust framework for capturing these complex interdependencies and offers valuable implications for targeted and personalized interventions in couple therapy.
Copyright (c) 2026 Kieran L. O'Meara , Stuart Giles-Haigh , Antonella Panebianco (Author); Richard Marques (Corresponding Author)
Predicting Emotional Dysregulation Using Machine Learning: The Role of Digital Rumination, Sleep Variability, Cognitive Load Reactivity, and Social Micro-Withdrawal
Objective: The present study aimed to develop and validate a machine learning model to predict emotional dysregulation based on digital rumination, sleep variability, cognitive load reactivity, and social micro-withdrawal among young adults. Methods and Materials: This quantitative predictive study was conducted on a sample of 428 young adults in Japan using a multimodal data collection approach integrating self-report measures, ecological momentary assessment, and passive digital tracking over a six-week period. Emotional dysregulation was assessed using a validated scale, while predictor variables included digital rumination, sleep variability derived from wearable actigraphy, cognitive load reactivity measured through task-based and self-report indices, and social micro-withdrawal operationalized via behavioral smartphone data. Data preprocessing involved normalization, missing data imputation, and feature engineering. Multiple machine learning algorithms, including random forest, support vector machine, gradient boosting, and XGBoost, were trained and evaluated using a 70-15-15 data split. Model performance was assessed using RMSE, MAE, and R², and interpretability was enhanced through SHAP analysis to determine feature importance and interaction effects. Findings: The results indicated that the XGBoost model achieved the highest predictive accuracy (R² = 0.76), outperforming other algorithms. Digital rumination emerged as the strongest predictor, followed by social micro-withdrawal, cognitive load reactivity, and sleep variability. Significant interaction effects were observed, particularly between digital rumination and social micro-withdrawal, as well as between digital rumination and sleep variability, indicating synergistic influences on emotional dysregulation. SHAP analysis revealed nonlinear relationships and threshold effects, demonstrating that higher levels of digital rumination and behavioral disengagement substantially increased the likelihood of elevated emotional dysregulation. Conclusion: The findings highlight the effectiveness of machine learning approaches in capturing the complex and interactive determinants of emotional dysregulation, emphasizing the central role of digitally mediated cognitive and behavioral processes alongside physiological variability. Integrating digital rumination, sleep patterns, cognitive load sensitivity, and micro-level social behaviors provides a comprehensive framework for understanding emotional dysregulation in contemporary contexts and offers valuable implications for targeted, data-driven mental health interventions.
Copyright (c) 2026 Hayato Ichikawa (Author); Shimoguri Hasegawa (Corresponding Author); Oleksandr Panchenko, Vitalii Stankevych (Author)
Predicting Therapist Effectiveness by Empathy Accuracy, Session Synchrony, Linguistic Alignment, and Reflective Depth: A Machine Learning Analysis
Objective: The present study aimed to develop and validate a machine learning–based predictive model of therapist effectiveness by integrating empathy accuracy, session synchrony, linguistic alignment, and reflective depth within a multimodal analytical framework. Methods and Materials: This quantitative predictive study was conducted with 214 licensed psychotherapists and 642 clients in Canada, forming 642 therapist–client dyads. Data were collected using a multimethod approach, including behavioral observation, physiological synchrony measurement, computational linguistic analysis, and standardized assessments of therapist effectiveness derived from client-reported outcomes, alliance measures, and expert ratings. Empathy accuracy was assessed through moment-to-moment emotional inference tasks, session synchrony was measured via behavioral and physiological coordination indicators, linguistic alignment was quantified using natural language processing techniques applied to session transcripts, and reflective depth was coded based on therapist verbal interventions. Data were preprocessed and analyzed using multiple supervised machine learning models, including random forest, gradient boosting, support vector machines, and deep neural networks, with model performance evaluated using cross-validation and predictive accuracy indices. Findings: The deep neural network model demonstrated the highest predictive performance, explaining a substantial proportion of variance in therapist effectiveness. Reflective depth and empathy accuracy emerged as the strongest predictors, with significant positive contributions, while session synchrony and linguistic alignment also showed meaningful predictive effects. Interaction analyses revealed significant nonlinear relationships, particularly between empathy accuracy and reflective depth, as well as between synchrony and linguistic alignment, indicating synergistic effects among predictors in enhancing model performance. Conclusion: The findings indicate that therapist effectiveness can be accurately predicted using machine learning models that integrate cognitive, emotional, and interactional dimensions of therapeutic processes, highlighting the importance of reflective depth and empathy accuracy as core mechanisms while underscoring the complementary roles of synchrony and linguistic alignment in shaping effective psychotherapy outcomes.
Copyright (c) 2026 Anastasios Kyrillidis (Corresponding Author); Jacqueline Woolley , Julie Larson (Author)
LSTM-Based Longitudinal Prediction of Psychological Resilience: The Role of Self-Compassion, Meaning in Life, Cognitive Reappraisal, and Social Support
Objective: The present study aimed to investigate the longitudinal prediction of psychological resilience using an LSTM-based deep learning model by examining the dynamic contributions of self-compassion, meaning in life, cognitive reappraisal, and social support over time. Methods and Materials: This longitudinal study was conducted on 428 participants recruited from Canada, with 392 retained for final analysis across four time points over a 12-month period. Data were collected using validated self-report instruments, including the Connor–Davidson Resilience Scale, Self-Compassion Scale, Meaning in Life Questionnaire, Emotion Regulation Questionnaire (cognitive reappraisal subscale), and the Multidimensional Scale of Perceived Social Support. Data preprocessing included multiple imputation and sequence structuring for time-series analysis. The primary analytical approach involved the application of Long Short-Term Memory (LSTM) neural networks implemented in Python using TensorFlow and Keras. The dataset was divided into training, validation, and test sets (70/15/15), and model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². SHAP analysis was conducted to determine the temporal importance of predictors. Findings: The LSTM model demonstrated strong predictive performance (R² = 0.66, RMSE = 2.52), indicating substantial explained variance in psychological resilience. Self-compassion emerged as the most significant predictor (β ≈ .54, p < .001), followed by social support (β ≈ .51, p < .001), meaning in life (β ≈ .49, p < .001), and cognitive reappraisal (β ≈ .43, p < .001). Longitudinal analyses revealed significant increases in resilience and all predictor variables across time (p < .01). SHAP results indicated that self-compassion and meaning in life showed increasing contributions over time, whereas social support demonstrated stronger early influence and cognitive reappraisal maintained a stable effect across all time points. Conclusion: The findings highlight the dynamic and multifactorial nature of psychological resilience, emphasizing the central role of self-compassion and the evolving contributions of internal and external resources over time. The integration of LSTM modeling with explainable AI provides a robust framework for capturing temporal patterns and enhancing predictive accuracy in psychological research.
Copyright (c) 2026 Kamal Darabi (Corresponding Author); Mark L. Wightman , Pieter Muysken , Nikolaos G. Sarafianos (Author)
Artificial Intelligence Modeling of Risk-Taking Behavior: Contributions of Sensation Seeking, Delay Discounting, Emotional Dysregulation, and Peer Influence
Objective: The present study aimed to develop and evaluate an artificial intelligence-based model for predicting risk-taking behavior based on sensation seeking, delay discounting, emotional dysregulation, and peer influence. Methods and Materials: This cross-sectional predictive study was conducted on 512 young adults aged 18 to 30 years in Canada, selected through stratified convenience sampling. Data were collected using validated psychometric instruments, including the Domain-Specific Risk-Taking Scale (DOSPERT), Brief Sensation Seeking Scale (BSSS), Delay Discounting Task, Difficulties in Emotion Regulation Scale (DERS), and Resistance to Peer Influence Scale (RPI). After preprocessing procedures such as normalization and missing data imputation, both traditional statistical analysis and machine learning approaches were applied. Multiple regression analysis was used to examine linear relationships, while machine learning models including Random Forest, Support Vector Machine, and XGBoost were implemented using 10-fold cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC, and SHAP analysis was employed to interpret feature importance. Findings (inferentials only): The regression model was statistically significant (F(4, 507) = 128.64, p < 0.001), explaining 50.38% of the variance in risk-taking behavior. Sensation seeking (β = 0.41, p < 0.001), peer influence (β = 0.34, p < 0.001), emotional dysregulation (β = 0.27, p < 0.001), and delay discounting (β = 0.22, p < 0.001) were all significant predictors. Among machine learning models, XGBoost demonstrated the highest performance (accuracy = 0.87, AUC-ROC = 0.92), followed by Random Forest and Support Vector Machine. SHAP analysis confirmed sensation seeking as the most influential predictor, followed by peer influence, emotional dysregulation, and delay discounting. Conclusion: The findings indicate that risk-taking behavior can be effectively predicted using an integrative artificial intelligence framework that captures the combined effects of dispositional, cognitive, emotional, and social factors, with sensation seeking and peer influence were the most influential determinants.
Copyright (c) 2026 Ralph McCallum (Corresponding Author); Camélia Pelletier Paquette , Jonathan E. Rosenberg , Gustavo Blais-Rochette (Author)
AI-Based Prediction of Social Anxiety: Interactions Among Intolerance of Uncertainty, Self-Focused Attention, Safety Behaviors, and Fear of Evaluation
Objective: The present study aimed to develop and evaluate an artificial intelligence-based predictive model of social anxiety severity by examining the individual and interactive effects of intolerance of uncertainty, self-focused attention, safety behaviors, and fear of evaluation. Methods and Materials: This cross-sectional predictive study was conducted on a sample of 412 young adults recruited from universities and community settings in Canada. Participants completed standardized self-report measures assessing social anxiety, intolerance of uncertainty, self-focused attention, safety behaviors, and fear of evaluation. Data were collected through an online survey platform and subjected to preprocessing procedures including missing data imputation, outlier management, and feature standardization. The dataset was divided into training and testing subsets, and multiple machine learning algorithms, including Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost), were implemented to predict social anxiety severity. Model performance was evaluated using R², root mean square error (RMSE), and mean absolute error (MAE), while SHAP (Shapley Additive Explanations) analysis was employed to interpret feature importance and interaction effects among predictors. Findings: The results indicated that all predictors were significantly associated with social anxiety, with fear of evaluation emerging as the strongest predictor, followed by safety behaviors, intolerance of uncertainty, and self-focused attention. Among the tested models, XGBoost demonstrated the highest predictive accuracy (R² = 0.74), outperforming Random Forest and Support Vector Machine. Interaction analyses revealed that fear of evaluation combined with safety behaviors, as well as intolerance of uncertainty combined with self-focused attention, exerted the strongest synergistic effects on social anxiety severity, indicating the presence of nonlinear and interaction-based relationships among variables. Conclusion: The findings underscore the importance of integrating cognitive and behavioral mechanisms in understanding social anxiety and highlight the utility of machine learning approaches in capturing complex interactions among psychological predictors. The study provides evidence for the central role of fear of evaluation and safety behaviors, particularly in interaction with intolerance of uncertainty and self-focused attention, and suggests that AI-based models can enhance predictive accuracy and inform personalized intervention strategies for social anxiety.
Copyright (c) 2026 Kathleen M Shanahan (Corresponding Author); Pablo Luciano Cutuli , Laura Stephens (Author)
Modeling Internet Addiction Severity Using Deep Learning: Effects of Impulsivity, Emotional Dysregulation, Reward Sensitivity, and Fear of Missing Out
Objective: The present study aimed to model internet addiction severity using deep learning techniques by examining the combined effects of impulsivity, emotional dysregulation, reward sensitivity, and fear of missing out. Methods and Materials: This quantitative cross-sectional study was conducted on 412 university students from Mexico selected through stratified convenience sampling. Participants completed standardized self-report measures including the Internet Addiction Test (IAT), Barratt Impulsiveness Scale (BIS-11), Difficulties in Emotion Regulation Scale (DERS), Behavioral Activation System (BAS) scale, and Fear of Missing Out (FoMO) scale. Data were collected via an online survey platform. After preprocessing, including handling missing values and standardization, a deep neural network model was developed to predict internet addiction severity. The dataset was divided into training (70%), validation (15%), and test (15%) sets. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²), and hyperparameters were optimized using grid search techniques. Findings: The deep learning model demonstrated strong predictive performance, with R² values of 0.72, 0.69, and 0.68 in the training, validation, and test sets, respectively, indicating high explanatory power and generalizability. All predictor variables showed significant positive associations with internet addiction severity, with fear of missing out emerging as the strongest predictor, followed by emotional dysregulation, impulsivity, and reward sensitivity. Shapley value analysis confirmed the relative importance of these variables, highlighting a multidimensional pattern of influence. The model exhibited low error rates across datasets, supporting its robustness and stability in predicting internet addiction severity. Conclusion: The findings indicate that internet addiction severity is best understood as a multidimensional construct influenced by interrelated psychological factors, with fear of missing out and emotional dysregulation playing central roles. The application of deep learning provides a powerful and accurate approach for modeling complex behavioral phenomena, offering both theoretical insights and practical implications for prevention and intervention strategies.
Copyright (c) 2026 Mario Martinez-Reviriego (Corresponding Author); Sara Sandín Vázquez , Goral Gondnale (Author)
Predicting Digital Burnout Using Machine Learning: The Role of Cognitive Flexibility, Emotional Regulation, Social Comparison, and Online Disinhibition
Objective: The present study aimed to predict digital burnout using machine learning techniques by examining the roles of cognitive flexibility, emotional regulation, social comparison, and online disinhibition. Methods and Materials: This study employed a cross-sectional predictive design with a sample of 512 adult participants from the United States who were active daily users of digital technologies. Data were collected through standardized self-report instruments measuring digital burnout, cognitive flexibility, emotional regulation (reappraisal and suppression), social comparison, and online disinhibition. Data preprocessing included normalization, multiple imputation for missing values, and encoding of variables. Machine learning models, including Random Forest, Gradient Boosting, and Support Vector Machine, were implemented to predict digital burnout. Model performance was evaluated using 10-fold cross-validation with metrics such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Feature importance was assessed using SHAP (Shapley Additive Explanations) values to determine the relative contribution of predictors. Findings (inferentials only): The Gradient Boosting model demonstrated the highest predictive performance (R² = 0.68, RMSE = 0.38), outperforming Random Forest (R² = 0.62) and Support Vector Machine (R² = 0.55). Social comparison emerged as the strongest positive predictor of digital burnout (importance = 0.31), followed by online disinhibition (0.26). Cognitive flexibility (β = negative contribution; importance = 0.18) and emotional regulation via reappraisal (importance = 0.14) were significant protective factors, whereas emotional suppression showed a smaller positive effect (0.11). All predictors were statistically significant (p < .01), and the model demonstrated high stability across cross-validation folds (R² range = 0.66–0.71). Conclusion: Digital burnout is a multifactorial phenomenon best predicted through integrative machine learning models, with social comparison and online disinhibition as primary risk factors and cognitive flexibility and adaptive emotional regulation as key protective mechanisms.
Copyright (c) 2026 Marianne Rathje Lennert (Corresponding Author); Guangjie Zheng , Riley Tedrow (Author)
Identifying Predictors of Therapy Responsiveness from Meta-Emotion Beliefs and Cognitive Flexibility Using ML
Objective: This study aimed to identify transdiagnostic cognitive and affective predictors of therapy responsiveness by applying machine learning algorithms to evaluate meta-emotion beliefs and cognitive flexibility in an outpatient clinical sample. Methods and Materials: A prospective, longitudinal predictive design was employed with a sample of N=514 adult participants from Malaysia. Baseline data were collected utilizing the Meta-Emotion Scale and the Cognitive Flexibility Inventory, while treatment outcomes were measured using the Outcome Questionnaire-45.2. Data analysis was conducted in Python using Scikit-Learn, which involved handling missing values, Z-score standardization, and an 80/20train-test split. Machine learning models underwent hyperparameter optimization via 10-fold cross-validation and were thoroughly evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics, with SHAP (Shapley Additive Explanations) values utilized to determine explicit feature interpretability. Findings: Results indicated that 62.4%of the participants (N=514) demonstrated clinically significant improvement following therapeutic intervention. Therapy responsiveness exhibited significant positive correlations with meta-emotion facets (acceptability: r=〖0.41〗^(**); controllability: r=〖0.53〗^(**)) and cognitive flexibility domains (alternatives: r=〖0.61〗^(**); control: r=〖0.48〗^(**)). Among the evaluated machine learning classifiers, the XGBoost model achieved the highest predictive performance on the test set (n=103), yielding an overall accuracy of 85.4%and an ROC-AUC of 0.91. Furthermore, SHAP value analysis explicitly identified the Alternatives facet of Cognitive Flexibility (Mean Abs SHAP: 1.24) and the Controllability facet of Meta-Emotion (Mean Abs SHAP: 0.98) as the most highly significant positive predictors of successful therapeutic outcomes. Conclusion: Assessing cognitive flexibility and meta-emotion utilizing advanced algorithmic modeling provides a highly accurate framework for predicting therapy responsiveness, thereby directly facilitating the crucial transition toward proactive, personalized mental health care.
Copyright (c) 2026 Musli Salleh Sahimi , Noorhayati Saharuddin (Author); Ahmad Ibrahim Ahmad (Corresponding Author); Nora Syed Alwi (Author)
Detecting Post-Traumatic Stress Risk via ML Analysis of Dissociative Tendencies and Arousal Dysregulation
Objective: To evaluate the predictive utility of machine learning models in detecting post-traumatic stress risk by analyzing the complex interplay between dissociative tendencies and physiological arousal markers. Methods and Materials: A cross-sectional observational study was conducted with a sample of N=1,245 adult participants from the USA with documented trauma exposure. Data collection utilized the Posttraumatic Stress Disorder Checklist (PCL), the Dissociative Experiences Scale (DES), subjective hyperarousal scales, and wearable biometric sensors capturing electrodermal activity and heart rate variability during a standardized stress-reactivity paradigm. Extreme Gradient Boosting, Random Forest, and Support Vector Machine algorithms were trained and evaluated using stratified ten-fold cross-validation. Findings: The Extreme Gradient Boosting model demonstrated superior predictive performance, achieving an AUC=0.91, which significantly outperformed the Random Forest (AUC=0.88) and Support Vector Machine (AUC=0.82) models. Feature importance analysis revealed that derealization (22.4%), depersonalization (18.1%), and low heart rate variability during recovery (15.3%) were the most critical predictors of post-traumatic stress risk. Furthermore, a significant non-linear interaction demonstrated that objective physiological arousal strongly predicted risk primarily in highly dissociative individuals who underreported subjective distress. Conclusion: Machine learning models integrating objective physiological data with subjective dissociative measures offer a powerful, highly sensitive approach for detecting hidden post-traumatic stress risk.
Copyright (c) 2026 Hadassah N. Griffin (Corresponding Author); Spyridon Derdemezis , Guillermo Umpiérrez-Failache , Charlotte Wray (Author)
Machine‑Learning Prediction of Academic Stress from Working Memory Load and Neuroticism Facets
Objective: To employ advanced machine learning algorithms to predict the severity of academic stress among university students by synthesizing metrics of working memory load and specific facets of the neuroticism personality trait. Methods and Materials: This predictive cross-sectional study evaluated a sample of N=452 Brazilian university students. Data were collected utilizing the Perception of Academic Stress Scale, a dual N-back task to measure working memory capacity, and the NEO Personality Inventory-Revised to assess neuroticism facets. The predictive modeling utilized several machine learning algorithms, with a primary focus on eXtreme Gradient Boosting (XGBoost). The dataset was split into an 80%training set with 10-fold cross-validation and a 20%hold-out test set, and feature importance was interpreted using SHapley Additive exPlanations (SHAP) values. Findings: The XGBoost algorithm yielded the most robust predictive performance, explaining a significant portion of the variance in the test set (R^2=0.58, MAE=4.12, RMSE=5.36). SHAP analysis revealed that the Vulnerability to Stress facet was the strongest positive predictor of academic stress (mean absolute SHAP = 2.84), whereas Working Memory Capacity emerged as the strongest negative, protective predictor (mean absolute SHAP = 1.95), specifically when surpassing a standardized threshold of Z=0.50. Conclusion: Machine learning architectures successfully demonstrate that academic stress is deeply driven by non-linear interactions between a student’s intrinsic emotional vulnerability and their momentary cognitive working memory capacity.
Copyright (c) 2026 Belinda Maree Brucki (Corresponding Author); Letícia Lima de Souza (Author)
Modeling Generalized Anxiety Disorder from Intolerance of Uncertainty and Attentional Control Using AI‑Driven Feature Selection
Objective: The primary objective of this study was to identify the most robust, item-level cognitive predictors of generalized anxiety disorder severity utilizing an artificial intelligence-driven hybrid feature selection pipeline applied to the constructs of intolerance of uncertainty and attentional control. Methods and Materials: A cross-sectional predictive study design was utilized with a sample of 486 Canadian adults. Participants completed standardized self-report measures, including the 7-item Generalized Anxiety Disorder Assessment (GAD-7), the 12-item Intolerance of Uncertainty Scale (IUS-12), and the 20-item Attentional Control Scale (ACS). Data preprocessing involved k-nearest neighbors imputation for missing values and standardization of continuous variables (z=(x-μ)/σ). A hybrid machine learning approach combining Least Absolute Shrinkage and Selection Operator (LASSO) regression and Extreme Gradient Boosting Recursive Feature Elimination (XGBoost-RFE) was employed to extract key item-level predictors. The dataset was split into an 80%training set and a 20%testing set, with model hyperparameter tuning validated via 10-fold cross-validation. Findings: The sample (N=486; 61.3%female; age M=34.2, SD=10.5) exhibited significant baseline correlations between GAD-7 and both IUS-12 (r=.64,p<.001) and ACS (r=-.52,p<.001). From an initial pool of 32cognitive items, the hybrid AI pipeline (using an optimal LASSO penalty of α=0.034) identified a critical subset of just 7key features (4 capturing distress/paralysis from uncertainty and 3capturing difficulties in attentional shifting/focusing). The optimized XGBoost model utilizing these 7selected features achieved superior predictive performance on the test set (R^2=0.73, RMSE=2.40) compared to the baseline model utilizing all 32features (R^2=0.61, RMSE=2.88). Conclusion: Granular, item-level cognitive vulnerabilities—specifically uncertainty-induced paralysis and severe deficits in attentional shifting—are the primary drivers of anxiety severity, demonstrating the transformative potential of AI in precise psychiatric modeling.
Copyright (c) 2026 Merik Voswinkel (Corresponding Author); Sahar Khodaei , Kari Heffner-Solimeo (Author)
A Predictive ML Model of Self‑Compassion, Shame‑Proneness, and Emotion Regulation Strategies in Counseling Outcomes
Objective: The primary objective of this study was to construct and validate a predictive machine learning model to determine the relative mathematical feature importance and prognostic capacity of baseline self-compassion, shame-proneness, and emotion regulation strategies on outpatient clinical counseling outcomes. Methods and Materials: A prospective observational cohort design was utilized with n=452 Canadian adult outpatients attending a minimum of 6 psychotherapy sessions. Baseline psychological variables were assessed using the 26-item Self-Compassion Scale (SCS), the Test of Self-Conscious Affect (TOSCA) shame-proneness subscale, and the 10-item Emotion Regulation Questionnaire (ERQ). Ultimate treatment outcomes were quantified at termination using the 34-item Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM). Data preprocessing incorporated K-Nearest Neighbors imputation for missing values and Z-score normalization (z=(x-μ)/σ) for all psychometric features. Following dimensionality reduction, a Random Forest regressor was trained on an 80%training split (n=361) employing 10-fold cross-validation for hyperparameter tuning, and subsequently evaluated on a 20%testing split (n=91). Findings: The optimized Random Forest model (n_estimators=200, max_depth=10) demonstrated robust predictive generalization on the independent testing set, accounting for 61%of the variance in counseling outcomes (R^2=.61) with a Root Mean Square Error (RMSE) of 3.45and a Mean Absolute Percentage Error (MAPE) of 16.5%. The algorithmic extraction of Gini importance scores revealed a definitive predictive hierarchy: baseline self-compassion emerged as the paramount prognostic indicator (score =.385), followed sequentially by shame-proneness (.312), cognitive reappraisal (.195), and expressive suppression (.108). Conclusion: Baseline self-compassion and shame-proneness serve as the most critical transdiagnostic determinants of psychotherapeutic success, demonstrating that the application of machine learning algorithms to intake assessments can revolutionize personalized prognostic modeling and clinical treatment planning.
Copyright (c) 2026 Lucia Galisova , Lilit Hayrapetyan (Author); Nathan C. Mathewson (Corresponding Author); Andrea Campostrini (Author)
Predicting Obsessive–Compulsive Symptom Severity Using Models of Cognitive Inhibition and Perfectionism Dimensions
Objective: The objective of the present study was to determine the unique and incremental value of cognitive inhibition models and perfectionism dimensions in predicting obsessive-compulsive symptom severity. Methods and Materials: This study utilized a cross-sectional, correlational design with a community sample of 435 adults from Canada (mean age = 28.4years; 64%female). Participants completed a battery of measures including the Obsessive-Compulsive Inventory-Revised (OCI-R) to assess symptom severity, and the Hewitt and Flett Multidimensional Perfectionism Scale to evaluate self-oriented, other-oriented, and socially prescribed perfectionism. Cognitive inhibition was assessed using both subjective and objective measures: the White Bear Suppression Inventory (WBSI) for thought suppression, and a computerized Stop-Signal Task (SST) yielding a Stop-Signal Reaction Time (SSRT) for behavioral response inhibition. Data were analyzed using a three-step hierarchical multiple regression. Findings: The final hierarchical regression model accounted for 48.5%of the total variance in obsessive-compulsive symptom severity. Demographic variables (Step 1) explained 4.2%of the variance. The addition of cognitive inhibition measures (Step 2) significantly explained an additional 35.8%of the variance (ΔR^2=0.358). Finally, perfectionism dimensions (Step 3) contributed an incremental 8.5%(ΔR^2=0.085). In the final comprehensive model, significant positive predictors of obsessive-compulsive severity included subjective thought suppression (β=0.32, p<0.001), socially prescribed perfectionism (β=0.24, p<0.001), objective SSRT (β=0.15, p<0.01), and self-oriented perfectionism (β=0.14, p<0.01). Age was a significant negative predictor (β=-0.09, p<0.05), while gender and other-oriented perfectionism were not significant in the final model. Conclusion: Obsessive-compulsive symptom severity is robustly predicted by a pathological synergy between extreme perfectionistic standards and fundamental neurocognitive deficits in behavioral and cognitive inhibitory control.
Copyright (c) 2026 Rachel Odunlami , Matthew Crawford-Flett , George Greenfield (Author); Lucia Galisova (Corresponding Author)