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)
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)
Identifying Predictors of Treatment Dropout Using ML Analysis of Motivational Interviewing Processes and Attachment‑Related Avoidance
Objective: This study aimed to utilize advanced machine learning algorithms to evaluate whether baseline attachment-related avoidance and specific linguistic frequencies of client change talk and sustain talk during early Motivational Interviewing sessions can reliably predict subsequent outpatient psychotherapeutic treatment dropout. Methods and Materials: The study employed a prospective, longitudinal observational design with a sample of N=485 adults from Mexico City. Dropout was defined as missing three consecutive sessions or mutually agreeing to terminate prematurely. Data collection included baseline demographics, the Experiences in Close Relationships-Revised questionnaire to assess attachment-related avoidance, and Motivational Interviewing Skill Code ratings derived from the audio-recorded and transcribed first two therapy sessions. Machine learning analysis incorporated k-nearest neighbors imputation, Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, and evaluated Random Forest, Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost) classifiers using stratified 10-fold cross-validation. SHapley Additive exPlanations (SHAP) values were utilized for model interpretability. Findings: Results indicated that 157out of 485participants (32.4%) dropped out of treatment. Bivariate analyses revealed that dropouts had significantly higher attachment-related avoidance (M=4.25) compared to completers (M=3.15). During early sessions, the dropout cohort exhibited significantly more Sustain Talk (M=24.6 vs. M=15.2) and less Change Talk (M=19.8 vs. M=31.4), alongside receiving fewer complex reflections and lower empathy ratings from therapists. The XGBoost model demonstrated superior predictive performance (AUC=0.87), followed by Random Forest (AUC=0.84) and SVM (AUC=0.78). SHAP analyses identified attachment avoidance and patient sustain talk as the strongest overall predictors, highlighting a critical interaction effect where highly avoidant patients receiving fewer therapist complex reflections exhibited a compounding probability of dropout. Conclusion: Machine learning algorithms can accurately predict treatment dropout from early clinical interactions, emphasizing that patient retention fundamentally depends on the complex interplay between severe attachment-related avoidance and the therapist’s technical proficiency in managing resistance.
Copyright (c) 2026 Moriel Davila Tenorio (Corresponding Author); Jaime Coello-Bravo , Micaela Reyes Zevallos (Author)
Machine-Learning Prediction of Therapeutic Alliance Quality from Client Readiness to Change and Affective Dysregulation
Objective: The aim of this study is to utilize and compare advanced machine-learning algorithms to predict the early quality of the therapeutic alliance based on a multidimensional assessment of client readiness to change and specific facets of affective dysregulation. Methods and Materials: A prospective observational design was utilized, involving n=452treatment-seeking adults initiating outpatient psychotherapy in Canada. Baseline predictor variables were collected prior to the first therapy session using the University of Rhode Island Change Assessment (URICA) and the Difficulties in Emotion Regulation Scale (DERS). The criterion variable, therapeutic alliance quality, was measured after the fourth session using the Working Alliance Inventory (WAI). Missing data (<2.5%┤) were handled via multiple imputation. The dataset was standardized and split into an 80%training set (n=361) and a 20%testing set (n=91). Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF) models were trained and compared using randomized grid search with 10-fold cross-validation. Findings: The sample had a mean age of M=34.6(SD=11.2) years. Preliminary analyses indicated that the WAI Total Score correlated positively with URICA (r=.46,p<.01) and negatively with DERS (r=-.52,p<.01). On the testing set, the Random Forest model demonstrated the highest predictive accuracy (R^2=.67, RMSE =7.15), outperforming the MLP (R^2=.56, RMSE =8.22) and SVR (R^2=.48, RMSE =8.95) algorithms. SHAP feature importance analysis of the RF model revealed that the strongest negative predictors of the alliance were the DERS Strategies (M.Abs.SHAP=2.84) and Goals (M.Abs.SHAP=2.45) subscales. The URICA Action stage (M.Abs.SHAP=2.10) and Precontemplation stage (M.Abs.SHAP=1.95) emerged as the most significant positive and negative motivational predictors, respectively. Conclusion: Advanced machine-learning models can accurately forecast the early quality of the therapeutic alliance, computationally demonstrating that specific baseline deficits in emotion regulation and distinct motivational stages are fundamental determinants of the therapeutic bond.
Copyright (c) 2026 Christopher Brown , Lucas Pires (Author); Terri L. Benskin (Corresponding Author)
Modeling Social Anxiety, Attentional Bias, and Metacognitive Beliefs with Supervised Learning Algorithms
Objective: The objective of this study was to utilize supervised machine learning algorithms to model and predict the severity of social anxiety based on the complex, non-linear interplay between multidimensional metacognitive beliefs and objective chronometric measures of attentional bias. Methods and Materials: A cross-sectional, predictive study design was employed with 854adult participants (mean age 34.52years) recruited from the USA via online crowdsourcing. Social anxiety was assessed using the Liebowitz Social Anxiety Scale (LSAS), and metacognitive beliefs were measured via the Metacognitions Questionnaire-30 (MCQ-30). Attentional bias was quantified using a computerized visual dot-probe paradigm, measuring reaction times in milliseconds. Following preprocessing and k-nearest neighbors imputation for missing values (approximately 2%), the data were split into an 80%training and 20%testing set. Supervised learning algorithms, including Random Forest, Support Vector Machines, and Gradient Boosting Regressors, were trained using 10-fold cross-validation. Model evaluation relied on R^2, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), while feature importance was interpreted using SHapley Additive exPlanations (SHAP) values. Findings: On the unseen testing set (n=171), the Gradient Boosting Regressor emerged as the optimal model, demonstrating superior predictive performance (R^2=.68, RMSE=13.65, MAE=10.42) compared to the Random Forest Regressor (R^2=.63, RMSE=14.68, MAE=11.55) and the Support Vector Machine (R^2=.55, RMSE=16.18, MAE=12.89). Feature importance analysis using SHAP values indicated that Negative Beliefs about uncontrollability and danger had the highest predictive influence (Mean ∣SHAP∣=6.84), closely followed by the Attentional Bias Score (Mean ∣SHAP∣=4.72) and the Need to Control thoughts (Mean ∣SHAP∣=3.55). Furthermore, SHAP dependence plots revealed a distinct non-linear interaction wherein the predictive impact of attentional bias was exponentially magnified in the presence of high negative metacognitive beliefs. Conclusion: Advanced machine learning algorithms successfully captured the complex architecture of social anxiety, demonstrating that clinical severity is predominantly driven by a synergistic, non-linear interaction between severe negative metacognitions and bottom-up visual hyper-vigilance toward threat.
Copyright (c) 2026 Josep Bosch Borras (Corresponding Author); Zarley Mett , Marlenne Reynoso-Calderon (Author)
A Machine-Learning Approach to Modeling Cognitive Flexibility, Stress Reactivity, and Executive Function in Clinical Populations
Objective: The objective of this study was to develop a machine-learning model integrating behavioral measures of executive function, cognitive flexibility, and psychophysiological stress reactivity to predict and classify clinical symptomatology in individuals with Major Depressive Disorder, Generalized Anxiety Disorder, and Post-Traumatic Stress Disorder. Methods and Materials: A cross-sectional design was utilized with a sample of participants, comprising Healthy Controls ( ) and individuals diagnosed with Major Depressive Disorder ( ), Generalized Anxiety Disorder ( ), and Post-Traumatic Stress Disorder ( ). Participants completed a comprehensive three-hour assessment protocol including neurocognitive tasks (Wisconsin Card Sorting Test, Stroop Color-Word Test, Trail Making Test), a modified social-evaluative stress task with concurrent psychophysiological recordings (electrocardiography for heart rate variability [HRV], salivary cortisol), and clinical self-report questionnaires. Machine learning models (Random Forest, Support Vector Machine, and XGBoost) were trained on of the data to classify clinical versus control groups, utilizing hyperparameter tuning and SHAP (SHapley Additive exPlanations) values for feature interpretation. Findings: The XGBoost algorithm achieved the highest predictive performance in classifying clinical versus control groups, yielding an AUC-ROC of . Traditional linear analyses indicated that clinical cohorts exhibited significantly greater symptom severity and poorer cognitive performance, including elevated perseverative errors on the WCST and compromised Trail Making Test performance. Physiologically, cortisol reactivity ( ) was blunted in the PTSD group and exaggerated in the MDD group, while baseline HRV was significantly lower across all clinical cohorts. SHAP feature importance analysis revealed that the TMT Part B-A difference score was the most crucial predictor of clinical classification, followed closely by cortisol reactivity ( ), baseline anxiety, and baseline HRV. Conclusion: Advanced machine-learning algorithms effectively capture the complex, non-linear interactions between cognitive flexibility and autonomic stress dysregulation, underscoring the necessity of integrated, multidimensional approaches for accurate psychiatric classification.
Copyright (c) 2026 Melissa Lemke (Author); Merin Varghese (Corresponding Author); Wendy Puffer (Author)
Avoidance as a Mediating Mechanism Between Maladaptive Coping and PTSD Symptoms
Objective: This study aimed to investigate whether avoidance mediates the relationship between maladaptive coping strategies and posttraumatic stress disorder (PTSD) symptoms among Canadian adults. Methods and Materials: A descriptive correlational design was employed with a sample of 400 adults recruited from Canada, determined based on Morgan and Krejcie’s sample size table. Data were collected using the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), the Brief COPE maladaptive subscales, and the Impact of Event Scale–Avoidance subscale (IES-A). Statistical analyses were conducted using SPSS version 27 for descriptive and correlational analyses, and AMOS version 21 for structural equation modeling (SEM). Pearson correlation coefficients were computed to examine bivariate relationships, and SEM was applied to test the hypothesized mediation model. Model fit was assessed using multiple indices including χ²/df, GFI, AGFI, CFI, TLI, and RMSEA. Findings: Pearson correlations revealed significant positive associations between maladaptive coping and PTSD symptoms (r = .52, p < .001), maladaptive coping and avoidance (r = .44, p < .001), and avoidance and PTSD symptoms (r = .47, p < .001). The structural model demonstrated good fit to the data (χ² = 124.86, df = 58, χ²/df = 2.15, GFI = .94, AGFI = .91, CFI = .96, TLI = .95, RMSEA = .053). Path analysis showed that maladaptive coping had a direct effect on PTSD symptoms (β = .36, p < .001), and an indirect effect via avoidance (β = .11, p < .001). The total effect of maladaptive coping on PTSD symptoms was β = .47 (p < .001), confirming the partial mediating role of avoidance. Conclusion: The findings highlight avoidance as a significant mechanism through which maladaptive coping contributes to PTSD symptom severity. These results underscore the need for interventions that reduce avoidance behaviors and promote adaptive coping strategies to mitigate posttraumatic stress.
Copyright (c) 2026 Matthew McNally, Donal O’Connor (Author); Kam Sheng Yu (Corresponding Author); Ikeagwu Ugbeh-Johnson, Aleiia J.G. Pinto (Author)
Attachment Anxiety as a Mediator Between Childhood Neglect and Intimate Partner Violence Victimization
Objective: This study aimed to examine whether attachment anxiety mediates the relationship between childhood neglect and intimate partner violence (IPV) victimization among adults in Canada. Methods and Materials: A descriptive correlational design was employed with a sample of 412 adults recruited from Canada, selected based on the Morgan and Krejcie sample size table. Standardized measures were used, including the Childhood Trauma Questionnaire–Short Form (CTQ-SF) for childhood neglect, the Experiences in Close Relationships–Revised (ECR-R) for attachment anxiety, and the Revised Conflict Tactics Scales (CTS2) for IPV victimization. Data were analyzed using SPSS version 27 and AMOS version 21. Pearson correlation analysis was performed to assess bivariate associations among variables, while Structural Equation Modeling (SEM) was used to test the hypothesized mediation model. Model fit was evaluated using χ²/df, GFI, AGFI, CFI, TLI, and RMSEA indices. Findings: Results revealed significant positive correlations between childhood neglect and attachment anxiety (r = .46, p < .001), childhood neglect and IPV victimization (r = .39, p < .001), and attachment anxiety and IPV victimization (r = .52, p < .001). The SEM analysis demonstrated good model fit (χ²/df = 2.01, GFI = .94, AGFI = .91, CFI = .96, TLI = .95, RMSEA = .049). Path analysis indicated that childhood neglect significantly predicted attachment anxiety (β = .46, p < .001), and attachment anxiety predicted IPV victimization (β = .41, p < .001). Childhood neglect also directly predicted IPV victimization (β = .22, p < .01). Mediation analysis confirmed that attachment anxiety partially mediated the relationship, with a significant indirect effect (β = .19, p < .001). Conclusion: These findings suggest that attachment anxiety is a key psychological mechanism linking childhood neglect to IPV victimization.
Copyright (c) 2026 Ana Amelia Coder , Trung-Hieu Ngo (Author); Joshua Sutton (Corresponding Author)
Cognitive Reappraisal as a Mediating Mechanism Between Mindfulness and Stress Reduction
Objective: The objective of this study was to examine whether cognitive reappraisal mediates the relationship between mindfulness and stress reduction among Canadian adults. Methods and Materials: A descriptive correlational design was applied with a sample of 399 participants selected according to the Morgan and Krejcie table. Standardized instruments were used to measure mindfulness (Five Facet Mindfulness Questionnaire), cognitive reappraisal (Cognitive Emotion Regulation Questionnaire – Reappraisal subscale), and stress (Perceived Stress Scale). Data were analyzed using SPSS version 27 for descriptive statistics and Pearson correlations, followed by Structural Equation Modeling (SEM) in AMOS version 21 to test the hypothesized mediating model. Model fit was evaluated using χ², GFI, AGFI, CFI, TLI, and RMSEA indices. Findings: Pearson correlation results indicated that mindfulness was significantly and positively associated with cognitive reappraisal (r = .47, p < .001) and negatively related to perceived stress (r = –.54, p < .001). Cognitive reappraisal was also significantly and negatively associated with stress (r = –.39, p < .001). The structural model demonstrated excellent fit (χ²/df = 1.92, GFI = .94, AGFI = .91, CFI = .97, TLI = .96, RMSEA = .048). SEM results confirmed significant direct paths from mindfulness to reappraisal (β = .45, p < .001), reappraisal to stress (β = –.23, p = .002), and mindfulness to stress (β = –.36, p < .001). The indirect effect of mindfulness on stress through reappraisal was also significant (β = –.10, p = .004). Overall, the total effect of mindfulness on stress reduction was strong (β = –.46, p < .001). Conclusion: The findings demonstrate that cognitive reappraisal partially mediates the effect of mindfulness on stress reduction, confirming that mindfulness not only directly lowers stress but also exerts indirect effects by enhancing adaptive emotion regulation strategies.
Copyright (c) 2026 Sue J Critchley , Curry Bordelon (Author); Molly Schwarzenberger (Corresponding Author)
Identifying Indicators of Treatment Satisfaction in Online Therapy Platforms: A Qualitative Study in Taiwan
Objective: This study aimed to identify the core indicators of treatment satisfaction in online therapy platforms from the perspectives of clients in Taiwan. Methods and Materials: A qualitative research design was employed using semi-structured, in-depth interviews. Twenty-two participants (12 women, 10 men), all of whom had engaged in online therapy within the previous year, were purposively recruited. Data collection continued until theoretical saturation was achieved. Interviews were conducted via video call or telephone, lasting between 45 and 70 minutes, and were transcribed verbatim. Thematic analysis was performed using NVivo 14 software, with iterative coding and regular team discussions to ensure reliability and conceptual clarity. Findings: Analysis yielded four major categories of satisfaction indicators. The first, Platform Accessibility and Usability, included ease of access, intuitive navigation, reliability, flexible scheduling, transparent cost structures, and privacy protections. The second, Therapist–Client Relationship, encompassed communication quality, professional competence, trust, rapport building, and responsiveness. The third, Perceived Therapeutic Outcomes, reflected emotional support, cognitive reframing, behavioral improvements, goal attainment, symptom reduction, and long-term benefits. The fourth, Overall User Experience and Satisfaction, involved convenience, perceived value, continuity of care, cultural and language fit, peer recommendation, and comparisons with traditional therapy. Conclusion: The study highlights that treatment satisfaction in online therapy platforms is a multidimensional construct shaped by both digital infrastructure and therapeutic relationships. By integrating technological reliability, therapist empathy, outcome visibility, and cultural resonance, platforms can foster higher satisfaction and sustained engagement.
Copyright (c) 2026 Dandan Ma (Corresponding Author); jing Wei (Author)
Identifying Core Indicators of Relapse in Clients Undergoing Cognitive Behavioral Therapy for Depression: A Qualitative Study
Objective: The objective of this study was to explore and identify the core indicators of relapse in clients who have completed cognitive behavioral therapy (CBT) for depression. Methods and Materials: This qualitative study employed a thematic analysis approach based on semi-structured interviews. Twenty-one participants (12 female, 9 male) aged between 23 and 54 years, all of whom had previously undergone a full course of CBT for depression, were recruited from mental health centers in Turkey. Participants were selected through purposive sampling and interviews were continued until theoretical saturation was reached. Data collection involved individual interviews lasting 45–75 minutes, which were audio-recorded, transcribed verbatim, and supplemented with field notes. NVivo 14 software was used to assist with systematic coding, organization, and analysis of the data. Findings: Thematic analysis revealed three overarching categories of relapse indicators. The first, emotional and cognitive triggers, included rumination, negative thinking, cognitive distortions, emotional dysregulation, and loss of therapy skills application. The second, behavioral and lifestyle indicators, encompassed withdrawal, disturbances in sleep and appetite, decline in daily functioning, maladaptive coping strategies, and abandonment of healthy routines. The third, social and interpersonal factors, involved family and relationship strain, lack of social support, stigma and shame, work and academic pressures, and interpersonal sensitivity. Conclusion: Relapse in depression following CBT is a multifaceted process involving the interplay of cognitive-emotional vulnerabilities, behavioral patterns, and social stressors. Early identification of these indicators, combined with booster interventions, aftercare programs, and personalized relapse-prevention planning, may enhance the sustainability of treatment gains.
Copyright (c) 2026 Kemal Gökçay (Corresponding Author); Murat Ceyhan (Author)
Indicators of Burnout and Recovery in Early-Career Psychologists: A Thematic Analysis
Objective: The objective of this study was to explore the indicators of burnout and the strategies of recovery among early-career psychologists, with a focus on their lived experiences within the Irish professional context. Methods and Materials: This qualitative study employed a thematic analysis design. Twenty early-career psychologists practicing in Ireland participated in semi-structured interviews, selected through purposive sampling. Participants represented diverse professional settings including public health services, private practice, community organizations, and academic contexts. Data collection continued until theoretical saturation was achieved. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase framework, supported by NVivo 14 software for data management and coding. Findings: Analysis revealed four overarching themes: (1) Indicators of Burnout, including emotional exhaustion, cognitive strain, physical symptoms, behavioral withdrawal, loss of motivation, and cynicism; (2) Work-Related Stressors, such as excessive caseloads, limited autonomy, organizational climate challenges, career insecurity, peer competition, and inadequate supervision; (3) Coping and Recovery Strategies, encompassing self-care practices, professional and social support, boundary setting, skill development, and meaning-making processes; and (4) Facilitators of Resilience and Growth, including personal traits, supportive work environments, professional identity, work-life integration, positive client outcomes, and continuous development. Illustrative participant quotations highlighted the interplay between strain and recovery, underscoring both vulnerabilities and strengths in early-career practice. Conclusion: The findings demonstrate that early-career psychologists face significant burnout risks shaped by organizational and systemic stressors, but also employ diverse coping mechanisms and resilience-building strategies. Recovery from burnout is not only possible but often accompanied by professional growth when adequate supports are in place. These results highlight the need for organizational policies, training programs, and supervisory practices that foster resilience, enhance professional identity, and promote sustainable career development for emerging psychologists.
Copyright (c) 2026 Seyed Milad Saadati (Corresponding Author); Armin Khadem Fini (Author)
Being-With-Algorithm: Psychotherapists’ Lived Experiences of Artificial Intelligence and the Therapeutic Relationship
Objective: The present study aimed to explore psychotherapists’ lived experiences of artificial intelligence and its perceived influence on the therapeutic relationship within contemporary psychotherapy practice. Methods and Materials: This study employed a qualitative phenomenological design using Interpretative Phenomenological Analysis (IPA). The participants consisted of 20 psychotherapists practicing in Tehran, Iran, selected through purposive sampling with maximum variation based on therapeutic orientation, years of experience, and familiarity with artificial intelligence technologies. Inclusion criteria included a minimum of three years of clinical experience and prior exposure to AI-assisted therapeutic or professional tools. Data were collected through semi-structured in-depth interviews lasting between 60 and 95 minutes. Interview questions focused on therapists’ experiences regarding empathy, therapeutic presence, ethical concerns, professional identity, emotional authenticity, and human-AI interaction in psychotherapy. Interviews were audio-recorded, transcribed verbatim, and analyzed using iterative phenomenological coding procedures. Strategies including member checking, peer review, reflexive memo writing, and audit trail documentation were employed to enhance trustworthiness and credibility. Findings: The findings revealed that psychotherapists experienced artificial intelligence through a complex coexistence of acceptance, ambivalence, and existential concern. Five superordinate themes emerged from the data: AI as a clinical companion, threats to therapeutic presence, ethical and professional ambivalence, reconfiguration of therapist identity, and human-AI coexistence in therapy. Participants acknowledged the usefulness of AI for administrative efficiency, cognitive support, and clinical organization while simultaneously expressing concerns regarding emotional dehumanization, diminished empathic attunement, weakened authenticity, and ethical uncertainty. Therapists emphasized that empathy, emotional resonance, and relational presence were fundamentally human dimensions that could not be authentically replicated by algorithmic systems. The emergence of AI also prompted deeper reflection on the uniqueness of human therapeutic engagement and the preservation of relational centrality within psychotherapy. Conclusion: The findings suggest that psychotherapists perceive artificial intelligence as both a valuable professional resource and a potential challenge to the relational foundations of psychotherapy. Although AI technologies may enhance efficiency and accessibility, therapists continue to regard authentic human connection, emotional attunement, and existential understanding as irreplaceable components of therapeutic healing. The future integration of AI into psychotherapy therefore requires careful ethical regulation, professional adaptation, and preservation of human-centered therapeutic values.
Copyright (c) 2026 Mojdeh Monadi (Author); Rezgar Mohammadi (Corresponding Author); Ahmadreza Kiani (Author)
The Effectiveness of an Integrated Acceptance and Commitment Therapy and Compassion-Focused Therapy Intervention on Resilience and Anger Control in Women with Marital Conflict
Objective: The present study aimed to investigate the effectiveness of an integrated intervention based on Acceptance and Commitment Therapy (ACT) and Compassion-Focused Therapy (CFT) on resilience and anger control among women experiencing marital conflict. Methods and Materials: The present study employed a quasi-experimental design with pretest–posttest measurements and a control group. The statistical population consisted of married women experiencing marital conflict in the city of Behbahan. Using convenience sampling and based on predetermined inclusion criteria, 60 participants were selected and randomly assigned to an experimental group (n = 30) and a control group (n = 30). The experimental group received an integrated intervention combining Acceptance and Commitment Therapy and Compassion-Focused Therapy in ten 2-hour weekly sessions, while the control group received no intervention. Data were collected using the Marital Conflict Questionnaire developed by Sanaei, Barati, and Bustani, the Connor–Davidson Resilience Scale, and the Spielberger State–Trait Anger Expression Inventory (STAXI-2). Data analysis was conducted using Multivariate Analysis of Covariance (MANCOVA) in SPSS version 21. Findings: The results of Multivariate Analysis of Covariance indicated a significant difference between the experimental and control groups in at least one of the dependent variables (F = 13.87, p < 0.0001). The findings further demonstrated that the integrated intervention significantly increased resilience among women with marital conflict (F = 24.13, p < 0.0001). In addition, the intervention significantly improved anger control in the experimental group compared with the control group (F = 90.34, p < 0.0001). The reported effect sizes indicated substantial intervention effectiveness for both resilience and anger control outcomes. Conclusion: The findings suggest that integrating Acceptance and Commitment Therapy with Compassion-Focused Therapy is effective in enhancing resilience and improving anger control among women experiencing marital conflict. The intervention appears to strengthen psychological flexibility, emotional regulation, self-compassion, and adaptive coping capacities, thereby reducing maladaptive emotional reactions associated with marital distress. This integrated therapeutic approach may therefore serve as an effective psychological intervention for women facing relational and emotional difficulties within marital relationships.
Copyright (c) 2026 Ali Babaei (Corresponding Author); Fatemeh Dadvand (Author)
Investigating the Causal Relationship Between Achievement Motivation and Academic Optimism with Grit, Mediated by Self-Regulation
Objective: The present study aimed to examine the structural relationships between achievement motivation and academic optimism with grit, considering the mediating role of academic self-regulation among university students. Methods and Materials: This study employed a descriptive correlational design using structural equation modeling (SEM). The statistical population consisted of undergraduate students enrolled during the 2019–2020 academic year at Shahid Chamran University of Ahvaz, from which 230 participants were selected through convenience sampling. Data were collected using four standardized instruments: the Achievement Motivation Questionnaire (Hermans), the Academic Optimism Scale (Tschannen-Moran et al.), the Academic Self-Regulated Learning Strategies Questionnaire (Zimmerman & Martinez-Pons), and the Grit Scale (Duckworth et al.). The validity of the instruments was assessed through confirmatory factor analysis, and reliability was confirmed using Cronbach’s alpha coefficients. Data analysis was conducted using SPSS version 24 and AMOS version 24, and the bootstrap method was applied to test indirect effects. Findings: The results of SEM indicated that achievement motivation had a significant direct effect on academic self-regulation and grit. Academic self-regulation also had a significant direct effect on grit. Academic optimism showed a significant direct effect on grit but did not have a significant direct effect on academic self-regulation. Furthermore, achievement motivation had a significant indirect effect on grit through academic self-regulation, confirming the mediating role of self-regulation. However, the indirect effect of academic optimism on grit through academic self-regulation was not statistically significant. Overall, the final model demonstrated an acceptable fit to the data based on standard fit indices. Conclusion: The findings highlight the central role of achievement motivation in enhancing both self-regulated learning and grit, as well as the importance of self-regulation as a key mechanism linking motivation to perseverance.
Copyright (c) 2026 Marzieh Parsai Moghadam (Author); Mehdi Khanjani (Corresponding Author); Ahmad Borjali (Author)
Factors Influencing Psychological Flexibility in Adults With Generalized Anxiety Disorder: A Qualitative Study
Objective: The aim of this study was to explore the factors influencing psychological flexibility in adults diagnosed with Generalized Anxiety Disorder (GAD). Methods and Materials: This study employed a qualitative design using semi-structured interviews with 33 adults clinically diagnosed with GAD and residing in the United States. Participants were recruited through purposive sampling, and data collection continued until theoretical saturation was reached. Interviews lasted 45–70 minutes and were conducted either face-to-face or via secure online platforms. All interviews were audio-recorded, transcribed verbatim, and analyzed thematically using NVivo 14 software. To enhance rigor, multiple researchers coded the data, discrepancies were resolved through discussion, and reflexive memos were maintained throughout the process. Findings: Analysis revealed three overarching themes with multiple subthemes. The first theme, cognitive–emotional regulation, included maladaptive thought patterns, emotional awareness deficits, avoidance versus acceptance struggles, lack of self-compassion, and attempts at cognitive reframing. The second theme, interpersonal and social contexts, encompassed the role of social support, interpersonal conflicts, role expectations, and experiences of stigma. The third theme, coping strategies and resources, highlighted the use of mind–body practices, behavioral adaptations, professional help-seeking, lifestyle adjustments, values orientation, and resilience-building. Participant quotations illustrated the tension between inflexibility (e.g., catastrophic thinking, self-criticism, concealment of symptoms) and adaptive coping strategies (e.g., mindfulness practices, values-driven actions). Conclusion: This study provides an in-depth understanding of the multifaceted factors influencing psychological flexibility in adults with GAD. Findings underscore the interplay between cognitive-emotional processes, interpersonal dynamics, and coping resources, suggesting that interventions should not only target individual-level mechanisms such as acceptance and reappraisal but also address relational support and stigma reduction. Enhancing flexibility through mindfulness, self-compassion, and values-based strategies may improve resilience and functioning in this population.
Copyright (c) 2026 Maura McDonnell (Author); Erwin A. William (Corresponding Author); Chidinma Chikwe (Author)