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)
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)
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)
Exploring Dimensions of Trauma-Linked Somatic Complaints in Refugees: A Qualitative Study in Canada
Objective: This study aimed to explore the dimensions of trauma-linked somatic complaints among refugees, focusing on how trauma is embodied, expressed, and managed in the context of displacement. Methods and Materials: A qualitative research design was employed, using semi-structured in-depth interviews with 17 refugees residing in Canada. Participants were recruited through community networks and refugee support organizations. Interviews, lasting 60–90 minutes, were conducted with the aid of professional interpreters where necessary and continued until theoretical saturation was reached. All interviews were audio-recorded, transcribed verbatim, and analyzed using NVivo 14 software. A thematic analysis approach was applied, involving iterative coding, categorization, and theme development to capture the range of trauma-linked somatic experiences. Findings: Four overarching themes emerged from the analysis: (1) Bodily manifestations of trauma, including chronic pain, gastrointestinal distress, sleep disturbances, neurological symptoms, cardiovascular complaints, and persistent fatigue; (2) Psychological-somatic linkages, where anxiety, depression, flashbacks, and emotional suppression were embodied through physical discomforts; (3) Sociocultural and contextual influences, including cultural idioms of distress, stigma, access barriers to healthcare, post-migration stressors, and the role of community and faith; and (4) Coping and healing strategies, ranging from medical help-seeking and alternative remedies to mindfulness, community-based support, resilience-building, and avoidance. Participants’ narratives highlighted the embodied nature of trauma and the interplay between pre-migration trauma and post-migration challenges in shaping somatic complaints. Conclusion: The study underscores that trauma-linked somatic complaints among refugees are complex, multifaceted, and shaped by cultural and contextual factors. Recognizing these complaints as legitimate trauma expressions is crucial for trauma-informed healthcare and fair asylum adjudication. Integrating medical, psychological, and social approaches, alongside culturally sensitive and community-based interventions, is essential to address the embodied dimensions of refugee trauma.
Copyright (c) 2026 Przemyslaw Wandzilak (Author); Onur Isci (Corresponding Author); Paulo Castro-Medina (Author)
Job Insecurity and Turnover Intention: The Moderating Role of Psychological Capital among Nigerian Employees
Objective: This study aimed to examine the relationship between job insecurity and turnover intention and to determine whether psychological capital moderates this relationship among employees in Nigeria. Methods and Materials: A descriptive correlational design was employed with a sample of 410 full-time employees from diverse public and private organizations in Nigeria. The sample size was determined using Morgan and Krejcie’s table to ensure adequate statistical power. Data were collected via standardized instruments: the Job Insecurity Scale, the Psychological Capital Questionnaire (PCQ-24), and the Turnover Intention Scale (TIS-6). Statistical analyses were performed using SPSS-27 for descriptive and bivariate correlation analyses and AMOS-21 for Structural Equation Modeling (SEM). Model fit was evaluated using χ²/df, CFI, TLI, GFI, AGFI, and RMSEA indices. Findings: Job insecurity was positively and significantly associated with turnover intention (r = .56, p < .001), while psychological capital was negatively correlated with both job insecurity (r = –.41, p < .001) and turnover intention (r = –.48, p < .001). SEM indicated good model fit (χ² = 124.87, df = 61, χ²/df = 2.05, CFI = .96, TLI = .95, GFI = .94, AGFI = .91, RMSEA = .051). Job insecurity had a strong direct positive effect on turnover intention (β = .53, p < .001), while psychological capital showed a significant negative direct effect (β = –.42, p < .001). The interaction effect was significant (β = –.16, p = .001), confirming that higher psychological capital attenuates the impact of job insecurity on turnover intention. Conclusion: These findings highlight the critical buffering role of psychological capital in reducing turnover intention under job insecurity. Developing employees’ hope, resilience, optimism, and self-efficacy can serve as an effective retention strategy, especially in economically volatile environments.
Copyright (c) 2026 Iheanyi Iwueze (Author); Manuel Vaultier (Corresponding Author)
Comparison of the Effectiveness of Short-Term Psychodynamic Therapy and an Integrated Acceptance and Commitment–Based Therapy with Schema Therapy on Improving Interpersonal Relationships and Controlling Explosive Anger in Insecurely Attached Nurses with Anxiety Symptoms
Objective: The objective of this study was to compare the effectiveness of short-term psychodynamic therapy and an integrated acceptance and commitment–based therapy with schema therapy on improving interpersonal relationships and controlling explosive anger in insecurely attached nurses with anxiety symptoms. Methods and Materials: This study employed a quasi-experimental design with pretest, posttest, and one-month follow-up, including a control group. The statistical population consisted of nurses working in intensive care, coronary care, emergency, and oncology wards of Imam Reza Hospital in Mashhad during the second half of 2024. Following a multistage screening process based on the Kian-Zadeh Adult Attachment Styles Questionnaire and the anxiety subscale of the DASS-21, 30 nurses with insecure attachment styles and elevated anxiety symptoms were selected and randomly assigned to three groups: short-term psychodynamic therapy, integrated acceptance and commitment–based therapy with schema therapy, and a wait-list control group (n = 10 per group). Each intervention was delivered in eight 70-minute sessions over four weeks. Data were collected using validated measures of interpersonal relationships and explosive anger at pretest, posttest, and follow-up. Data analysis was conducted using univariate and multivariate analyses of covariance and Bonferroni post hoc tests in SPSS-V22. Findings: Multivariate analysis of covariance revealed significant group effects for both interpersonal relationships and explosive anger at posttest and follow-up (p < .01). Bonferroni comparisons showed that both intervention groups significantly outperformed the control group on all outcomes. Short-term psychodynamic therapy demonstrated a significantly greater reduction in explosive anger at posttest compared to the integrated intervention (p < .05), while the integrated acceptance and commitment–based therapy with schema therapy showed a stronger effect on interpersonal relationships at posttest (p < .01). Differences between the two intervention groups were no longer significant at follow-up. Conclusion: Both short-term psychodynamic therapy and integrated acceptance and commitment–based therapy with schema therapy are effective interventions for improving interpersonal relationships and reducing explosive anger in insecurely attached nurses with anxiety symptoms, with differential short-term advantages across outcome domains.
Copyright (c) 2026 Fatemeh Hasannejad (Author); Fatemeh Shahabizadeh (Corresponding Author); Jalil Jarrahi Feriz (Author)
Improving Employability Skills Acquisition of Visually Impaired Students Through Inclusive Career Coaching Progrmme
Objective: This study examined the effectiveness of an inclusive career coaching programme in improving employability skills acquisition among students with visual impairment in Nigerian tertiary institutions. Methods and Materials: A quasi-experimental design was employed involving 25 visually impaired students purposively sampled from public tertiary institutions in Enugu State, Nigeria. Participants were assigned to either a treatment group (n = 12) receiving the inclusive career coaching programme or a control group (n = 13). Employability skills were assessed at pretest, posttest, and follow-up using a validated and reliable instrument. Data were analyzed using descriptive statistics and analysis of covariance (ANCOVA), controlling for baseline scores and demographic variables. Findings: Descriptive statistics indicated marked improvement in employability skills for the treatment group from pretest (M = 35.75, SD = 5.72) to posttest (M = 56.08, SD = 4.27) and follow-up (M = 62.17, SD = 3.19), while the control group showed minimal change. ANCOVA results revealed statistically significant effects of the intervention on posttest (F(1,8) = 156.43, p < .001, η²p = 0.951) and follow-up employability scores (F(1,8) = 195.84, p < .001, η²p = 0.961), indicating both immediate and sustained gains. Gender and age did not significantly moderate the programme’s effectiveness. Conclusion: The inclusive career coaching programme was highly effective in enhancing employability skills among students with visual impairment. These findings suggest that structured, inclusive interventions can foster skill acquisition, improve self-efficacy, and promote socioeconomic participation for this population.
Copyright (c) 2026 Liziana Onuigbo (Author); Mkpoikanke Sunday Otu (Corresponding Author)
Designing a Mathematics Concepts Educational Package for Students with Mathematics Learning Disability in Sari City
Objective: The primary objective of this study was to design, develop, and validate an evidence-based educational package for teaching foundational mathematics concepts to elementary students with mathematics learning disabilities in Sari city. Methods and Materials: This study employed a qualitative research design utilizing thematic analysis. The participant pool consisted of mathematics and special education specialists, selected through purposive sampling. Interviews continued until theoretical saturation was achieved after 12 participants, though 15 experts were ultimately interviewed to ensure absolute confidence. Data were collected using pre-designed, semi-structured audio-recorded interviews. The transcribed texts were analyzed utilizing MAXQDA software through a multi-stage coding process to identify overarching, organizing, and basic themes. To assess the validity and credibility of the designed package, quantitative metrics including the Content Validity Ratio (CVR) and the Content Validity Index (CVI) were calculated based on the expert panel’s ratings. Findings: The thematic analysis yielded a structured, 10-session educational package emphasizing concrete-to-abstract number teaching, practical multisensory activities, and the reduction of math-related anxiety. Key overarching themes extracted included number conceptual understanding, counting skills, comparative numerical relationships, four operations, and motivation/self-efficacy. The quantitative validation confirmed the package’s high content validity. Depending on the session, CVR values were calculated at 0.60 (12 experts), 0.73 (13 experts), and 0.87 (14 experts). Furthermore, the item-level CVI (CVI-I) scores across the 10 sessions ranged from 0.80 to a peak of 0.93 (in the ninth session), with the scale-level CVI (CVI-S) for the first session established at 0.848. Conclusion: The meticulously designed 10-session educational package is a highly valid, systematic framework. By combining concrete multisensory activities with targeted strategies to reduce emotional pressure, it provides educators with an effective, evidence-based tool to improve the mathematical self-efficacy and conceptual foundations of students with learning disabilities.
Copyright (c) 2026 Mohammad Yazdanian (Author); Fereshteh Baezzat (Corresponding Author); Soheila Qomian (Author)
Why Do Romantic Relationships Start Early and End Quickly? Examining the Pattern of Unstable Relationships Among Generation Z
Objective: This study aimed to identify and model the psychological, behavioral, and digitally mediated factors that explain why romantic relationships among Generation Z tend to initiate rapidly yet remain unstable. Methods and Materials: A cross-sectional study was conducted with a sample of Generation Z young adults residing in Toronto, Canada, all of whom had experienced at least one romantic relationship in recent years. Data were collected using a comprehensive online questionnaire assessing demographic characteristics, relationship histories, attachment-related variables, emotional regulation, and digital relational practices such as dating-app use and social media monitoring. A machine learning framework was employed to analyze the data, including supervised classification algorithms and unsupervised clustering techniques. Multiple models were trained and validated using cross-validation procedures, and explainability methods were applied to identify the most influential predictors of relationship instability. Findings: Inferential results indicated that non-linear machine learning models significantly outperformed linear models in predicting relationship instability. Rapid emotional attachment, attachment anxiety, fear of abandonment, intensive dating-app use, and frequent social media monitoring emerged as the strongest predictors of unstable romantic trajectories. Clustering analyses revealed distinct relational profiles, including accelerated–fragile, digitally entangled, avoidant–episodic, and relatively stable patterns, with the majority of participants falling into instability-prone profiles. Protective effects were observed for indicators of secure attachment and offline relational support. Conclusion: The findings demonstrate that unstable romantic relationships among Generation Z are best understood as outcomes of interacting psychological vulnerabilities and digitally shaped relational practices rather than isolated individual deficits. Early emotional acceleration combined with constant digital comparison creates conditions under which relationships form quickly but lack durability.
Copyright (c) 2026 Derek Péloquin (Corresponding Author); Jessica Brassard , Nadereh Saadati, Lawrence Siegel , Mehdi Rostami, Kamdin Parsakia, Nicole H. Garcia (Author)
Examining the Effectiveness of Infra-Low Frequency Neurofeedback on Cognitive and Clinical Components and Brain Signals in Patients with Parkinson’s Disease
Objective: The aim of the present study was to examine the effectiveness of infra-low frequency neurofeedback on cognitive and clinical components and brain signals in patients with Parkinson’s disease. Methods and Materials: The study population consisted of patients with Parkinson’s disease aged 50 to 95 years, with disease severity at stages 2 or 3 according to the Hoehn and Yahr criteria, who were referred to the neurology clinic of Ayatollah Rouhani Hospital in Babol. The final sample included 5 participants. During four phases of the study, participants completed computerized tests from the RehaCom software, the Unified Parkinson’s Disease Rating Scale, and the Montreal Cognitive Assessment. After the second pretest phase, each patient received 20 sessions of 30-minute infra-low frequency neurofeedback intervention based on the Othmer protocol (2017). Brainwave activity was recorded at each phase of the study. Behavioral data were analyzed using the paired-samples t-test, while brain signal data were analyzed using analysis of variance (ANOVA) and post hoc tests. Findings: The findings indicated improvements in motor and cognitive performance of participants at the follow-up stage compared to the pretest. Results from the RehaCom computerized tests demonstrated improvements in selective attention, working memory capacity, and logical reasoning. Additionally, the absolute power of gamma and beta brainwaves decreased, suggesting enhanced cognitive and emotional regulation in participants. Conclusion: The findings of this study indicate that infra-low frequency neurofeedback is effective in improving motor and cognitive functioning in individuals with Parkinson’s disease. Considering the results of the present study and the importance of non-invasive treatments in age-related disorders—particularly Alzheimer’s and Parkinson’s diseases—in enhancing cognitive and motor performance and slowing the progressive course of these disorders, greater attention to novel therapeutic approaches and further research is essential to address existing limitations and gaps in this field.
Copyright (c) 2026 Zahra Fadakaran (Author); Reza Khosrowabadi (Corresponding Author); Payam Saadat (Author)
Workload and Creative Performance: The Buffering Role of Intrinsic Motivation
Objective: This study aimed to examine the effect of workload on creative performance and to test whether intrinsic motivation buffers the potential negative impact of workload among employees in Colombia. Methods and Materials: A descriptive correlational research design was employed with a sample of 397 full-time employees from diverse organizations across Colombia, selected using Morgan and Krejcie’s sample size determination table. Data were collected using validated instruments: the Quantitative Workload Inventory, the Work Preference Inventory–Intrinsic Motivation subscale, and the Employee Creativity Scale. Descriptive statistics, Pearson’s correlation analysis, and structural equation modeling (SEM) were conducted to test the hypothesized relationships. Analyses were performed using SPSS version 27 and AMOS version 21. Model fit was assessed through multiple indices, including χ², degrees of freedom, χ²/df, GFI, AGFI, CFI, TLI, and RMSEA. Findings: Results indicated that workload was significantly and negatively correlated with creative performance (r = –.31, p < .001) and with intrinsic motivation (r = –.27, p = .001), while intrinsic motivation was strongly and positively correlated with creative performance (r = .48, p < .001). SEM analysis confirmed good model fit (χ²/df = 1.74; GFI = .94; CFI = .97; RMSEA = .043) and revealed that workload exerted a significant direct negative effect on creative performance (β = –.29, p < .001) and on intrinsic motivation (β = –.27, p < .001). Intrinsic motivation showed a robust positive effect on creative performance (β = .52, p < .001) and buffered the negative workload–creativity relationship, partially mediating and weakening the total negative impact of workload (β total = –.15, p = .041). Conclusion: Findings highlight that although heavy workload reduces employees’ creativity, fostering intrinsic motivation can protect and sustain innovative performance under job demands. Organizations should design roles and climates that support autonomy, curiosity, and self-driven engagement to maintain creativity despite high workload conditions.
Copyright (c) 2025 Mercedes Narcisa Idrovo, Yirsa Meza Villanueva, Stephanie Flores Carrera (Author)