Journal of Assessment and Research in Applied Counseling (JARAC)
type: channel
description: 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…
publisher: KMAN Publication Inc. (KMANPUB)
language: en
publication name: Journal of Assessment and Research in Applied Counseling (JARAC)
issn: 3041-8518
items: CatBoost Prediction of Obsessive-Compulsive Symptoms Using Thought Suppression, Cognitive Fusion, Perfectionism, and Anxiety Sensitivity
items: https://journals.kmanpub.com/index.php/jarac/article/view/5716
items: Comparing the Effectiveness of Cognitive Empowerment Based on Cognitive Games and Executive Function–Based Parenting on Working Memory in Children with Executive Function Deficits
items: Identifying Factors Associated with the Occurrence of Marital Infidelity: A Systematic Review
items: Artificial Intelligence-Based Assessment and Intervention for Specific Learning Disabilities: A Systematic Review
items: A Structural Equation Analysis of Parental Psychological Control, Adolescent Self-Esteem, Emotional Intelligence, and Internalizing Symptoms
items: Explainable XGBoost Models for Predicting Generalized Anxiety Disorder from Digital Mental Health Indicators
items: The Relationship Between Cyberbullying and Suicidal Ideation in Adolescent Girls The Mediating Role of Perceived Maternal Acceptance and Perceived Burdensomeness
items: Modeling the Psychological Determinants of Digital Addiction: The Mediating Roles of Loneliness, Self-Control, and Emotional Dysregulation
items: Explainable XGBoost Models for Predicting Burnout among Mental Health Professionals Using Compassion Fatigue, Psychological Capital, Self-Care, and Occupational Stress
items: Random Forest Classification of Suicide Risk Using Hopelessness, Defeat, Entrapment, and Psychological Pain
items: Predicting Major Depressive Disorder Using Random Forest Models Based on Psychological, Behavioral, and Lifestyle Indicators
items: Modeling Emotional Eating through Self-Compassion, Body Dissatisfaction, Emotion Regulation, and Psychological Distress
items: A Cross-Cultural Structural Equation Model of Social Anxiety: The Roles of Intolerance of Uncertainty, Fear of Negative Evaluation, and Self-Compassion
items: Stacked Generalization Model for Predicting Happiness: Positive Affect, Gratitude, Social Connectedness, and Psychological Flexibility
items: Explainable Boosting Machine (EBM) for Decision-Making Competence: Cognitive Reflection, Risk Perception, Emotional Bias, and Uncertainty Tolerance
items: Bayesian Network Modeling of Marital Conflict: Attachment Styles, Communication Patterns, Emotional Reactivity, and Cognitive Distortions
items: Predicting Emotional Dysregulation Using Machine Learning: The Role of Digital Rumination, Sleep Variability, Cognitive Load Reactivity, and Social Micro-Withdrawal
items: Predicting Therapist Effectiveness by Empathy Accuracy, Session Synchrony, Linguistic Alignment, and Reflective Depth: A Machine Learning Analysis
items: LSTM-Based Longitudinal Prediction of Psychological Resilience: The Role of Self-Compassion, Meaning in Life, Cognitive Reappraisal, and Social Support
items: Artificial Intelligence Modeling of Risk-Taking Behavior: Contributions of Sensation Seeking, Delay Discounting, Emotional Dysregulation, and Peer Influence
items: AI-Based Prediction of Social Anxiety: Interactions Among Intolerance of Uncertainty, Self-Focused Attention, Safety Behaviors, and Fear of Evaluation
items: Modeling Internet Addiction Severity Using Deep Learning: Effects of Impulsivity, Emotional Dysregulation, Reward Sensitivity, and Fear of Missing Out
items: Predicting Digital Burnout Using Machine Learning: The Role of Cognitive Flexibility, Emotional Regulation, Social Comparison, and Online Disinhibition
items: Identifying Predictors of Therapy Responsiveness from Meta-Emotion Beliefs and Cognitive Flexibility Using ML
items: Detecting Post-Traumatic Stress Risk via ML Analysis of Dissociative Tendencies and Arousal Dysregulation
items: Machine‑Learning Prediction of Academic Stress from Working Memory Load and Neuroticism Facets
items: Modeling Generalized Anxiety Disorder from Intolerance of Uncertainty and Attentional Control Using AI‑Driven Feature Selection
items: A Predictive ML Model of Self‑Compassion, Shame‑Proneness, and Emotion Regulation Strategies in Counseling Outcomes
items: Predicting Obsessive–Compulsive Symptom Severity Using Models of Cognitive Inhibition and Perfectionism Dimensions

CatBoost Prediction of Obsessive-Compulsive Symptoms Using Thought Suppression, Cognitive Fusion, Perfectionism, and Anxiety Sensitivity
type: item
description: 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 p…
subject: Articles
  topic: section
subject: Obsessive-compulsive symptoms
  topic: keywords
subject: CatBoost
  topic: keywords
subject: thought suppression
  topic: keywords
subject: cognitive fusion
  topic: keywords
subject: perfectionism
  topic: keywords
subject: anxiety sensitivity
  topic: keywords
subject: machine learning
  topic: keywords
creator: Rosa Alcázar Campos
creator: Margarita Izquierdo Morán
creator: Ferran Fernández-Llario
creator: Luís F. Seoane
rights: Copyright (c) 2026 Rosa Alcázar Campos , Margarita Izquierdo Morán (Author); Ferran Fernández-Llario (Corresponding Author); Luís F. Seoane (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 14

https://journals.kmanpub.com/index.php/jarac/article/view/5716
type: item
title: Explainable AI Modeling of Special Education Teachers’ Psychological Well-Being During Artificial Intelligence Integration: The Roles of Occupational Stress, Self-Regulation, and Technological Confidence
description: 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 signi…
subject: Articles
  topic: section
subject: Explainable artificial intelligence
  topic: keywords
subject: psychological well-being
  topic: keywords
subject: special education teachers
  topic: keywords
subject: occupational stress
  topic: keywords
subject: self-regulation
  topic: keywords
subject: technological confidence
  topic: keywords
subject: artificial intelligence integration
  topic: keywords
creator: Ali Aghaziarati
creator: Hasan Rahimi
rights: Copyright (c) 2026 Ali Aghaziarati (Corresponding Author); Hasan Rahimi (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 16

Comparing the Effectiveness of Cognitive Empowerment Based on Cognitive Games and Executive Function–Based Parenting on Working Memory in Children with Executive Function Deficits
type: item
description: 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 find…
subject: Articles
  topic: section
subject: Cognitive Empowerment
  topic: keywords
subject: Cognitive Games
  topic: keywords
subject: Executive Function–Based Parenting
  topic: keywords
subject: Working Memory
  topic: keywords
subject: Executive Function Deficits
  topic: keywords
subject: Cognitive Development
  topic: keywords
creator: Fatemeh Abbaszadeh
creator: Seyedeh Hamideh Salim Bahrami
creator: Seyedeh Olia Emadian
rights: Copyright (c) 2026 Fatemeh Abbaszadeh (Author); Seyedeh Hamideh Salim Bahrami (Corresponding Author); Seyedeh Olia Emadian (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 11

Identifying Factors Associated with the Occurrence of Marital Infidelity: A Systematic Review
type: item
description: 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, so…
subject: Articles
  topic: section
subject: Marital infidelity
  topic: keywords
subject: extramarital relationships
  topic: keywords
subject: marital satisfaction
  topic: keywords
subject: psychological factors
  topic: keywords
subject: systematic review
  topic: keywords
creator: Solmaz Saeidi Haghi
creator: Yaser Madani
creator: Keyvan Salehi
creator: Ahmad Behpajooh
rights: Copyright (c) 2026 Solmaz Saeidi Haghi (Author); Yaser Madani (Corresponding Author); Keyvan Salehi, Ahmad Behpajooh (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 15

Artificial Intelligence-Based Assessment and Intervention for Specific Learning Disabilities: A Systematic Review
type: item
description: 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, esp…
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: specific learning disabilities
  topic: keywords
subject: dyslexia
  topic: keywords
subject: dyscalculia
  topic: keywords
subject: dysgraphia
  topic: keywords
subject: machine learning
  topic: keywords
subject: adaptive intervention
  topic: keywords
subject: systematic review
  topic: keywords
creator: Azizreza Ghasemzadeh
rights: Copyright (c) 2026 Azizreza Ghasemzadeh (Corresponding Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 17

A Structural Equation Analysis of Parental Psychological Control, Adolescent Self-Esteem, Emotional Intelligence, and Internalizing Symptoms
type: item
description: 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 in…
subject: Articles
  topic: section
subject: Parental psychological control
  topic: keywords
subject: self-esteem
  topic: keywords
subject: emotional intelligence
  topic: keywords
subject: internalizing symptoms
  topic: keywords
subject: adolescents
  topic: keywords
subject: structural equation modeling
  topic: keywords
creator: Marien Bremmer
creator: Jim Selli
creator: Annelle Termes-Rife
rights: Copyright (c) 2026 Marien Bremmer (Corresponding Author); Jim Selli , Annelle Termes-Rife (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 15

Explainable XGBoost Models for Predicting Generalized Anxiety Disorder from Digital Mental Health Indicators
type: item
description: 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 mo…
subject: Articles
  topic: section
subject: Generalized anxiety disorder
  topic: keywords
subject: XGBoost
  topic: keywords
subject: explainable artificial intelligence
  topic: keywords
subject: SHAP
  topic: keywords
subject: digital mental health
  topic: keywords
subject: digital phenotyping
  topic: keywords
subject: smartphone use
  topic: keywords
rights: Copyright (c) 2026
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 14

The Relationship Between Cyberbullying and Suicidal Ideation in Adolescent Girls The Mediating Role of Perceived Maternal Acceptance and Perceived Burdensomeness
type: item
description: 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 p…
subject: Articles
  topic: section
subject: Cyberbullying
  topic: keywords
subject: Suicidal Ideation
  topic: keywords
subject: Perceived Maternal Acceptance
  topic: keywords
subject: Perceived Burdensomeness
  topic: keywords
subject: Adolescent Girls
  topic: keywords
subject: Structural Equation Modeling.
  topic: keywords
creator: Kiana Naghash Souratgar
creator: Hadis Aziziani Khoshnami
creator: Mehran Haidari Nia
rights: Copyright (c) 2026 Kiana Naghash Souratgar (Corresponding Author); Hadis Aziziani Khoshnami, Mehran Haidari Nia (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 13

Modeling the Psychological Determinants of Digital Addiction: The Mediating Roles of Loneliness, Self-Control, and Emotional Dysregulation
type: item
description: 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 loneli…
subject: Articles
  topic: section
subject: Digital addiction
  topic: keywords
subject: loneliness
  topic: keywords
subject: self-control
  topic: keywords
subject: emotional dysregulation
  topic: keywords
subject: structural equation modeling
  topic: keywords
subject: Canada
  topic: keywords
creator: Danielle Laprade
creator: Babak Nazarpour
creator: Frederick Stanley
rights: Copyright (c) 2026 Danielle Laprade , Babak Nazarpour (Author); Frederick Stanley (Corresponding Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 14

Explainable XGBoost Models for Predicting Burnout among Mental Health Professionals Using Compassion Fatigue, Psychological Capital, Self-Care, and Occupational Stress
type: item
description: 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 compassio…
subject: Articles
  topic: section
subject: Burnout
  topic: keywords
subject: Mental Health Professionals
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Compassion Fatigue
  topic: keywords
subject: Psychological Capital
  topic: keywords
subject: Self-Care
  topic: keywords
subject: Occupational Stress
  topic: keywords
creator: Abdul-Wahab Inusah
creator: Ting Yang
creator: Mohammad Rahman Washim
creator: Mohamad Mustapa
rights: Copyright (c) 2026 Abdul-Wahab Inusah , Ting Yang (Author); Mohammad Rahman Washim (Corresponding Author); Mohamad Mustapa (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 15

Random Forest Classification of Suicide Risk Using Hopelessness, Defeat, Entrapment, and Psychological Pain
type: item
description: 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 wa…
subject: Articles
  topic: section
subject: Suicide risk
  topic: keywords
subject: Random Forest
  topic: keywords
subject: hopelessness
  topic: keywords
subject: defeat
  topic: keywords
subject: entrapment
  topic: keywords
subject: psychological pain
  topic: keywords
subject: machine learning
  topic: keywords
subject: Morocco
  topic: keywords
creator: Nabil Mouhajir
creator: Mohammed Abubaker
creator: Thiago Saab
rights: Copyright (c) 2026 Nabil Mouhajir (Corresponding Author); Mohammed Abubaker, Thiago Saab (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 14

Predicting Major Depressive Disorder Using Random Forest Models Based on Psychological, Behavioral, and Lifestyle Indicators
type: item
description: 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…
subject: Articles
  topic: section
subject: Major Depressive Disorder
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Depression Prediction
  topic: keywords
subject: Psychological Indicators
  topic: keywords
subject: Lifestyle Behaviors
  topic: keywords
subject: Sleep Quality
  topic: keywords
subject: Mental Health Screening
  topic: keywords
creator: Liam Wooderson
creator: Milena Fini
creator: Mariusz Szypa
rights: Copyright (c) 2026 Liam Wooderson (Author); Milena Fini (Corresponding Author); Mariusz Szypa (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 15

Modeling Emotional Eating through Self-Compassion, Body Dissatisfaction, Emotion Regulation, and Psychological Distress
type: item
description: 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 emotion…
subject: Articles
  topic: section
subject: Emotional eating
  topic: keywords
subject: Self-compassion
  topic: keywords
subject: Body dissatisfaction
  topic: keywords
subject: Emotion regulation
  topic: keywords
subject: Psychological distress
  topic: keywords
subject: Structural equation modeling
  topic: keywords
rights: Copyright (c) 2026
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 15

A Cross-Cultural Structural Equation Model of Social Anxiety: The Roles of Intolerance of Uncertainty, Fear of Negative Evaluation, and Self-Compassion
type: item
description: 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 n…
subject: Articles
  topic: section
subject: Social Anxiety
  topic: keywords
subject: Intolerance of Uncertainty
  topic: keywords
subject: Fear of Negative Evaluation
  topic: keywords
subject: Self-Compassion
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
subject: Cross-Cultural Psychology
  topic: keywords
creator: Merve Okan
creator: Cheng-Xiong Lu
rights: Copyright (c) 2026 Merve Okan (Corresponding Author); Cheng-Xiong Lu (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 14

Stacked Generalization Model for Predicting Happiness: Positive Affect, Gratitude, Social Connectedness, and Psychological Flexibility
type: item
description: 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 …
subject: Articles
  topic: section
subject: Happiness
  topic: keywords
subject: Stacked Generalization
  topic: keywords
subject: Positive Affect
  topic: keywords
subject: Gratitude
  topic: keywords
subject: Social Connectedness
  topic: keywords
subject: Psychological Flexibility
  topic: keywords
creator: Merve Okan
creator: Cheng-Xiong Lu
creator: Laura Ros-García
creator: Wilmer Antonio Hernandez
rights: Copyright (c) 2026 Merve Okan (Author); Cheng-Xiong Lu , Laura Ros-García (Corresponding Author); Wilmer Antonio Hernandez (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 10

Explainable Boosting Machine (EBM) for Decision-Making Competence: Cognitive Reflection, Risk Perception, Emotional Bias, and Uncertainty Tolerance
type: item
description: 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 effect…
subject: Articles
  topic: section
subject: Decision-Making Competence
  topic: keywords
subject: Explainable Boosting Machine
  topic: keywords
subject: Cognitive Reflection
  topic: keywords
subject: Risk Perception
  topic: keywords
subject: Emotional Bias
  topic: keywords
subject: Uncertainty Tolerance
  topic: keywords
creator: Juan José Ripoll Escartí
creator: Puffy Soundy
creator: Oronde Campbell
creator: Guangteng Liu
creator: Taraneh Bahri
rights: Copyright (c) 2026 Juan José Ripoll Escartí (Corresponding Author); Puffy Soundy , Oronde Campbell (Author); Guangteng Liu (Corresponding Author); Taraneh Bahri (Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 11

Bayesian Network Modeling of Marital Conflict: Attachment Styles, Communication Patterns, Emotional Reactivity, and Cognitive Distortions
type: item
description: 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.…
subject: Articles
  topic: section
subject: Marital Conflict
  topic: keywords
subject: Bayesian Network Modeling
  topic: keywords
subject: Attachment Styles
  topic: keywords
subject: Emotional Reactivity
  topic: keywords
subject: Cognitive Distortions
  topic: keywords
subject: Communication Patterns
  topic: keywords
creator: Kieran L. O'Meara
creator: Stuart Giles-Haigh
creator: Antonella Panebianco
creator: Richard Marques
rights: Copyright (c) 2026 Kieran L. O'Meara , Stuart Giles-Haigh , Antonella Panebianco (Author); Richard Marques (Corresponding Author)
date: 2026-07-01
publication date: 2026-07-01
starting page: 1
ending page: 11

Predicting Emotional Dysregulation Using Machine Learning: The Role of Digital Rumination, Sleep Variability, Cognitive Load Reactivity, and Social Micro-Withdrawal
type: item
description: 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 el…
subject: Articles
  topic: section
subject: Emotional Dysregulation
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Digital Rumination
  topic: keywords
subject: Sleep Variability
  topic: keywords
subject: Cognitive Load Reactivity
  topic: keywords
subject: Social Micro-Withdrawal
  topic: keywords
creator: Hayato Ichikawa
creator: Shimoguri Hasegawa
creator: Oleksandr Panchenko
creator: Vitalii Stankevych
rights: Copyright (c) 2026 Hayato Ichikawa (Author); Shimoguri Hasegawa (Corresponding Author); Oleksandr Panchenko, Vitalii Stankevych (Author)
date: 2026-04-28
publication date: 2026-04-28
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.131

Predicting Therapist Effectiveness by Empathy Accuracy, Session Synchrony, Linguistic Alignment, and Reflective Depth: A Machine Learning Analysis
type: item
description: 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: T…
subject: Articles
  topic: section
subject: Therapist effectiveness
  topic: keywords
subject: machine learning
  topic: keywords
subject: empathy accuracy
  topic: keywords
subject: session synchrony
  topic: keywords
subject: linguistic alignment
  topic: keywords
subject: reflective depth
  topic: keywords
subject: psychotherapy process
  topic: keywords
creator: Anastasios Kyrillidis
creator: Jacqueline Woolley
creator: Julie Larson
rights: Copyright (c) 2026 Anastasios Kyrillidis (Corresponding Author); Jacqueline Woolley , Julie Larson (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.5303

LSTM-Based Longitudinal Prediction of Psychological Resilience: The Role of Self-Compassion, Meaning in Life, Cognitive Reappraisal, and Social Support
type: item
description: 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 …
subject: Articles
  topic: section
subject: psychological resilience
  topic: keywords
subject: self-compassion
  topic: keywords
subject: meaning in life
  topic: keywords
subject: cognitive reappraisal
  topic: keywords
subject: social support
  topic: keywords
creator: Kamal Darabi
creator: Mark L. Wightman
creator: Pieter Muysken
creator: Nikolaos G. Sarafianos
rights: Copyright (c) 2026 Kamal Darabi (Corresponding Author); Mark L. Wightman , Pieter Muysken , Nikolaos G. Sarafianos (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 10
doi: 10.61838/kman.jarac.5237

Artificial Intelligence Modeling of Risk-Taking Behavior: Contributions of Sensation Seeking, Delay Discounting, Emotional Dysregulation, and Peer Influence
type: item
description: 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 …
subject: Articles
  topic: section
subject: risk-taking behavior
  topic: keywords
subject: sensation seeking
  topic: keywords
subject: delay discounting
  topic: keywords
subject: emotional dysregulation
  topic: keywords
subject: peer influence
  topic: keywords
creator: Ralph McCallum
creator: Camélia Pelletier Paquette
creator: Jonathan E. Rosenberg
creator: Gustavo Blais-Rochette
rights: Copyright (c) 2026 Ralph McCallum (Corresponding Author); Camélia Pelletier Paquette , Jonathan E. Rosenberg , Gustavo Blais-Rochette (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 10
doi: 10.61838/kman.jarac.5278

AI-Based Prediction of Social Anxiety: Interactions Among Intolerance of Uncertainty, Self-Focused Attention, Safety Behaviors, and Fear of Evaluation
type: item
description: 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 …
subject: Articles
  topic: section
subject: Social Anxiety
  topic: keywords
subject: Intolerance of Uncertainty
  topic: keywords
subject: Self-Focused Attention
  topic: keywords
subject: Safety Behaviors
  topic: keywords
subject: Fear of Evaluation
  topic: keywords
creator: Kathleen M Shanahan
creator: Pablo Luciano Cutuli
creator: Laura Stephens
rights: Copyright (c) 2026 Kathleen M Shanahan (Corresponding Author); Pablo Luciano Cutuli , Laura Stephens (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 9
doi: 10.61838/kman.jarac.5260

Modeling Internet Addiction Severity Using Deep Learning: Effects of Impulsivity, Emotional Dysregulation, Reward Sensitivity, and Fear of Missing Out
type: item
description: 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 construc…
subject: Articles
  topic: section
subject: Internet addiction
  topic: keywords
subject: deep learning
  topic: keywords
subject: impulsivity
  topic: keywords
subject: emotional dysregulation
  topic: keywords
subject: reward sensitivity
  topic: keywords
subject: fear of missing out
  topic: keywords
subject: predictive modeling
  topic: keywords
creator: Mario Martinez-Reviriego
creator: Sara Sandín Vázquez
creator: Goral Gondnale
rights: Copyright (c) 2026 Mario Martinez-Reviriego (Corresponding Author); Sara Sandín Vázquez , Goral Gondnale (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 10
doi: 10.61838/kman.jarac.5247

Predicting Digital Burnout Using Machine Learning: The Role of Cognitive Flexibility, Emotional Regulation, Social Comparison, and Online Disinhibition
type: item
description: 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 pr…
subject: Articles
  topic: section
subject: digital burnout
  topic: keywords
subject: machine learning
  topic: keywords
subject: cognitive flexibility
  topic: keywords
subject: emotional regulation
  topic: keywords
subject: social comparison
  topic: keywords
subject: online disinhibition
  topic: keywords
creator: Marianne Rathje Lennert
creator: Guangjie Zheng
creator: Riley Tedrow
rights: Copyright (c) 2026 Marianne Rathje Lennert (Corresponding Author); Guangjie Zheng , Riley Tedrow (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 10
doi: 10.61838/kman.jarac.5243

Identifying Predictors of Therapy Responsiveness from Meta-Emotion Beliefs and Cognitive Flexibility Using ML
type: item
description: 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 h…
subject: Articles
  topic: section
subject: Therapy Responsiveness
  topic: keywords
subject: Meta-Emotion
  topic: keywords
subject: Cognitive Flexibility
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Musli Salleh Sahimi
creator: Noorhayati Saharuddin
creator: Ahmad Ibrahim Ahmad
creator: Nora Syed Alwi
rights: Copyright (c) 2026 Musli Salleh Sahimi , Noorhayati Saharuddin (Author); Ahmad Ibrahim Ahmad (Corresponding Author); Nora Syed Alwi (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.5191

Detecting Post-Traumatic Stress Risk via ML Analysis of Dissociative Tendencies and Arousal Dysregulation
type: item
description: 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.
subject: Articles
  topic: section
subject: Post-Traumatic
  topic: keywords
subject: Stress Disorder
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Dissociation
  topic: keywords
subject: Arousal Dysregulation
  topic: keywords
creator: Hadassah N. Griffin
creator: Spyridon Derdemezis
creator: Guillermo Umpiérrez-Failache
creator: Charlotte Wray
rights: Copyright (c) 2026 Hadassah N. Griffin (Corresponding Author); Spyridon Derdemezis , Guillermo Umpiérrez-Failache , Charlotte Wray (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.5238

Machine‑Learning Prediction of Academic Stress from Working Memory Load and Neuroticism Facets
type: item
description: 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.
subject: Articles
  topic: section
subject: Academic Stress
  topic: keywords
subject: Working Memory
  topic: keywords
subject: Neuroticism
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Students
  topic: keywords
creator: Belinda Maree Brucki
creator: Letícia Lima de Souza
rights: Copyright (c) 2026 Belinda Maree Brucki (Corresponding Author); Letícia Lima de Souza (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.5166

Modeling Generalized Anxiety Disorder from Intolerance of Uncertainty and Attentional Control Using AI‑Driven Feature Selection
type: item
description: 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 pr…
subject: Articles
  topic: section
subject: Generalized Anxiety Disorder
  topic: keywords
subject: Intolerance of Uncertainty
  topic: keywords
subject: Attentional Control
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Merik Voswinkel
creator: Sahar Khodaei
creator: Kari Heffner-Solimeo
rights: Copyright (c) 2026 Merik Voswinkel (Corresponding Author); Sahar Khodaei , Kari Heffner-Solimeo (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 12
doi: 10.61838/kman.jarac.5210

A Predictive ML Model of Self‑Compassion, Shame‑Proneness, and Emotion Regulation Strategies in Counseling Outcomes
type: item
description: 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…
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Self-Compassion
  topic: keywords
subject: Shame-Proneness
  topic: keywords
subject: Emotion Regulation
  topic: keywords
creator: Padma Dolderer
creator: Lilit Hayrapetyan
creator: Nathan C. Mathewson
creator: Andrea Campostrini
rights: Copyright (c) 2026 Lucia Galisova , Lilit Hayrapetyan (Author); Nathan C. Mathewson (Corresponding Author); Andrea Campostrini (Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.5205

Predicting Obsessive–Compulsive Symptom Severity Using Models of Cognitive Inhibition and Perfectionism Dimensions
type: item
description: 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 fundam…
subject: Articles
  topic: section
subject: Obsessive-Compulsive Disorder
  topic: keywords
subject: Cognitive Inhibition
  topic: keywords
subject: Perfectionism
  topic: keywords
subject: Thought Suppression
  topic: keywords
subject: Response Inhibition
  topic: keywords
creator: Matthew Crawford-Flett
creator: Rachel Odunlami
creator: George Greenfield
creator: Lucia Galisova
rights: Copyright (c) 2026 Rachel Odunlami , Matthew Crawford-Flett , George Greenfield (Author); Lucia Galisova (Corresponding Author)
date: 2026-04-01
publication date: 2026-04-01
starting page: 1
ending page: 11
doi: 10.61838/kman.jarac.5198