Research and Practice in Couple Therapy

The present study aimed to examine the direct and indirect effects of childhood emotional neglect on adult romantic relationship quality through the mediating roles of attachment avoidance and emotional intimacy among adults involved in committed romantic relationships. This cross-sectional correlational study was conducted among 524 adults residing in Canada who were currently involved in committed romantic relationships. Participants were recruited from major Canadian urban centers through online platforms, community organizations, and university research networks. Data were collected using the Emotional Neglect subscale of the Childhood Trauma Questionnaire–Short Form (CTQ-SF), the Avoidance dimension of the Experiences in Close Relationships–Revised Questionnaire (ECR-R), the Emotional Intimacy Scale (EIS), and the Couples Satisfaction Index (CSI-32). Structural equation modeling (SEM) was employed to examine the hypothesized relationships among childhood emotional neglect, attachment avoidance, emotional intimacy, and romantic relationship quality. Confirmatory factor analysis was conducted to evaluate the measurement model, and mediation effects were assessed using a bias-corrected bootstrap procedure with 5,000 resamples. Statistical analyses were performed using SPSS version 29 and AMOS version 29. The structural equation model demonstrated excellent fit to the data (χ²/df = 2.08, CFI = 0.96, TLI = 0.95, GFI = 0.92, AGFI = 0.90, RMSEA = 0.045, SRMR = 0.046). Childhood emotional neglect significantly predicted attachment avoidance (β = 0.61, p < .001), emotional intimacy (β = -0.18, p < .001), and romantic relationship quality (β = -0.15, p < .001). Attachment avoidance significantly predicted emotional intimacy (β = -0.63, p < .001) and relationship quality (β = -0.36, p < .001). Emotional intimacy positively predicted romantic relationship quality (β = 0.54, p < .001). Bootstrap analyses revealed significant indirect effects of childhood emotional neglect on emotional intimacy through attachment avoidance (β = -0.38, p < .001) and on relationship quality through both attachment avoidance and emotional intimacy (β = -0.21, p < .001). The model explained 37% of the variance in attachment avoidance, 54% of the variance in emotional intimacy, and 69% of the variance in romantic relationship quality. The findings indicate that childhood emotional neglect exerts enduring negative effects on adult romantic relationship quality both directly and indirectly through increased attachment avoidance and reduced emotional intimacy. Attachment avoidance emerged as a key developmental mechanism linking early emotional deprivation to difficulties in emotional closeness and relationship functioning. Emotional intimacy served as a significant protective factor associated with higher relationship quality. These results highlight the importance of attachment-based and intimacy-focused interventions for individuals with histories of childhood emotional neglect and contribute to a deeper understanding of the developmental pathways connecting early adverse experiences to adult romantic relationship outcomes.

The present study aimed to examine the mediating role of dyadic coping in the relationship between perceived partner responsiveness and marital satisfaction among married adults in Canada using structural equation modeling. This cross-sectional correlational study was conducted among 642 married adults residing in various provinces of Canada. Participants were recruited through community organizations, counseling centers, and online platforms using voluntary participation procedures. Data were collected using the Perceived Partner Responsiveness Scale (PPRS), the Dyadic Coping Inventory (DCI), and the Couples Satisfaction Index (CSI-32). Descriptive statistics, Pearson correlation analyses, confirmatory factor analysis, and structural equation modeling were performed using SPSS 29 and AMOS 29. The measurement model was evaluated through multiple goodness-of-fit indices, and mediation effects were tested using bootstrap procedures with 5,000 resamples and bias-corrected confidence intervals. The results demonstrated significant positive associations among all study variables. Perceived partner responsiveness was positively associated with dyadic coping (β = 0.72, p < .001) and marital satisfaction (β = 0.27, p < .001). Dyadic coping was also positively associated with marital satisfaction (β = 0.58, p < .001). The structural model demonstrated excellent fit to the data (χ²/df = 2.13, CFI = .96, TLI = .95, IFI = .96, GFI = .92, RMSEA = .042, SRMR = .046). Bootstrap analyses indicated a significant indirect effect of perceived partner responsiveness on marital satisfaction through dyadic coping (β = 0.42, p < .001), confirming partial mediation. The final model explained 52% of the variance in dyadic coping and 68% of the variance in marital satisfaction, indicating substantial explanatory power. The findings indicate that perceived partner responsiveness contributes significantly to marital satisfaction both directly and indirectly through dyadic coping. Couples who perceive greater understanding, validation, and emotional support from their partners are more likely to engage in adaptive collaborative coping processes, which in turn enhance marital satisfaction. These results support contemporary relational theories emphasizing the importance of interpersonal responsiveness and dyadic adaptation in marital functioning and suggest that interventions targeting responsive communication and dyadic coping skills may promote healthier and more satisfying marital relationships.

The present study aimed to investigate the effectiveness of Emotionally Focused Couple Therapy (EFCT) on attachment injuries, dyadic trust, and marital forgiveness among couples experiencing distress related to emotional or sexual infidelity. This study employed a quasi-experimental design with pretest, posttest, and three-month follow-up assessments using an experimental and a waitlist control group. The population consisted of couples seeking counseling services for infidelity-related relationship problems in Canada during 2025–2026. Following screening procedures, 60 couples (120 individuals) who met the inclusion criteria were selected and randomly assigned to either the experimental group (30 couples) or the control group (30 couples). Participants in the experimental group received twelve weekly 90-minute sessions of Emotionally Focused Couple Therapy, whereas the control group received no intervention during the study period. Data were collected using the Attachment Injury Resolution Scale, the Dyadic Trust Scale, and the Marital Forgiveness Scale. The data were analyzed using repeated-measures multivariate analysis of variance and Bonferroni post hoc comparisons in SPSS version 29. The results of repeated-measures multivariate analysis of variance revealed significant effects of time, group, and Time × Group interaction for all dependent variables. Significant interaction effects were found for attachment injuries (F = 103.84, p < .001, η² = .66), dyadic trust (F = 95.76, p < .001, η² = .62), and marital forgiveness (F = 129.86, p < .001, η² = .72), indicating that participants receiving EFCT experienced significantly greater improvements than those in the control group. Bonferroni pairwise comparisons demonstrated significant reductions in attachment injuries and significant increases in dyadic trust and marital forgiveness from pretest to posttest and from pretest to follow-up (p < .001). No significant differences were observed between posttest and follow-up scores, indicating maintenance of treatment gains across the follow-up period. The findings indicate that Emotionally Focused Couple Therapy is an effective intervention for couples experiencing infidelity-related distress. By addressing attachment-related vulnerabilities and promoting emotionally corrective interactions, EFCT significantly reduced attachment injuries while enhancing dyadic trust and marital forgiveness. The sustained improvements observed at follow-up suggest that the intervention facilitates enduring changes in relational functioning and supports long-term recovery following experiences of betrayal.

The present study aimed to investigate the effectiveness of Emotionally Focused Couple Therapy (EFCT) on attachment injuries, dyadic trust, and marital forgiveness among couples experiencing distress related to emotional or sexual infidelity. This study employed a quasi-experimental design with pretest, posttest, and three-month follow-up assessments using an experimental and a waitlist control group. The population consisted of couples seeking counseling services for infidelity-related relationship problems in Canada during 2025–2026. Following screening procedures, 60 couples (120 individuals) who met the inclusion criteria were selected and randomly assigned to either the experimental group (30 couples) or the control group (30 couples). Participants in the experimental group received twelve weekly 90-minute sessions of Emotionally Focused Couple Therapy, whereas the control group received no intervention during the study period. Data were collected using the Attachment Injury Resolution Scale, the Dyadic Trust Scale, and the Marital Forgiveness Scale. The data were analyzed using repeated-measures multivariate analysis of variance and Bonferroni post hoc comparisons in SPSS version 29. The results of repeated-measures multivariate analysis of variance revealed significant effects of time, group, and Time × Group interaction for all dependent variables. Significant interaction effects were found for attachment injuries (F = 103.84, p < .001, η² = .66), dyadic trust (F = 95.76, p < .001, η² = .62), and marital forgiveness (F = 129.86, p < .001, η² = .72), indicating that participants receiving EFCT experienced significantly greater improvements than those in the control group. Bonferroni pairwise comparisons demonstrated significant reductions in attachment injuries and significant increases in dyadic trust and marital forgiveness from pretest to posttest and from pretest to follow-up (p < .001). No significant differences were observed between posttest and follow-up scores, indicating maintenance of treatment gains across the follow-up period. The findings indicate that Emotionally Focused Couple Therapy is an effective intervention for couples experiencing infidelity-related distress. By addressing attachment-related vulnerabilities and promoting emotionally corrective interactions, EFCT significantly reduced attachment injuries while enhancing dyadic trust and marital forgiveness. The sustained improvements observed at follow-up suggest that the intervention facilitates enduring changes in relational functioning and supports long-term recovery following experiences of betrayal.

The present study aimed to predict work–family conflict and marital satisfaction among married employees using machine learning algorithms based on job stress, emotional exhaustion, dyadic coping, and partner support. This cross-sectional predictive study was conducted among 584 married employees recruited from governmental and private organizations in Tehran, Iran, through multistage cluster sampling. Participants completed the Work–Family Conflict Scale, Revised Dyadic Adjustment Scale, Job Stress Scale, Emotional Exhaustion subscale of the Maslach Burnout Inventory, Dyadic Coping Inventory, and Spousal Support Scale. Following data preprocessing, normalization, and missing-value treatment, the dataset was divided into training (80%) and testing (20%) subsets. Several machine learning algorithms, including Multiple Linear Regression, Support Vector Regression, Random Forest Regression, Gradient Boosting Regression, Artificial Neural Networks, and Extreme Gradient Boosting (XGBoost), were implemented. Model performance was evaluated using the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Feature importance analyses were conducted using permutation importance and SHAP techniques. Correlation analyses revealed that work–family conflict was positively associated with job stress (r = 0.68, p < .001) and emotional exhaustion (r = 0.64, p < .001), while negatively associated with dyadic coping (r = -0.49, p < .001), partner support (r = -0.56, p < .001), and marital satisfaction (r = -0.62, p < .001). Marital satisfaction demonstrated significant positive relationships with dyadic coping (r = 0.73, p < .001) and partner support (r = 0.76, p < .001). Among all predictive models, XGBoost demonstrated the highest accuracy. The optimal model explained 81% of the variance in work–family conflict (R² = 0.81, RMSE = 5.14) and 84% of the variance in marital satisfaction (R² = 0.84, RMSE = 4.37). Feature importance analyses indicated that job stress and emotional exhaustion were the strongest predictors of work–family conflict, whereas partner support and dyadic coping emerged as the most influential predictors of marital satisfaction. The findings demonstrate that machine learning approaches can accurately predict work–family conflict and marital satisfaction using a combination of occupational and relational variables. Job stress and emotional exhaustion substantially increase vulnerability to work–family conflict, whereas dyadic coping and partner support function as powerful protective factors that enhance marital satisfaction. These results underscore the importance of strengthening both workplace well-being and couple-based coping resources to promote healthier marital relationships and better adaptation to work–family demands.

The objective of this study was to develop and evaluate machine learning models capable of classifying adult attachment styles using multimodal dyadic behavioral, emotional, and physiological indicators derived from real-time couple interactions. A cross-sectional observational design was implemented with adult romantic couples recruited from community settings in Canada. Both partners in each dyad participated in standardized interaction tasks designed to elicit attachment-relevant behaviors, including conflict discussion and support-seeking exchanges. Adult attachment styles were assessed using validated self-report measures and used as supervised learning labels. Multimodal data were collected, including behavioral coding of dyadic interactions, self-reported emotional responses, physiological indices of autonomic regulation, and paralinguistic and facial-expression features extracted from audio–video recordings. Machine learning pipelines incorporated data preprocessing, feature extraction at the dyadic level, dimensionality reduction, and model training using multiple classification algorithms. Stratified dyad-level cross-validation and hyperparameter optimization were applied to ensure robust generalization and prevent data leakage. Non-linear and ensemble-based models significantly outperformed linear classifiers in attachment style prediction, with neural network and gradient boosting models achieving the highest accuracy and area under the receiver operating characteristic curve. Dyadic emotional synchrony and observed behavioral responsiveness emerged as the strongest predictors of attachment style classification, followed by self-reported attachment dimensions. Physiological and paralinguistic indicators provided incremental predictive value when integrated with behavioral features. Cross-validation analyses demonstrated high stability across folds, and misclassification patterns primarily occurred between theoretically adjacent attachment styles, indicating construct-consistent overlap rather than random error. The findings demonstrate that adult attachment styles can be accurately classified using machine learning models trained on multimodal dyadic interaction data, supporting a relational and interaction-based conceptualization of attachment. This approach offers theoretical advances in attachment research and practical implications for objective assessment and intervention planning in couple and relational contexts.

This study aimed to develop and interpret machine learning models capable of identifying high-conflict couples at elevated risk for intimate partner violence by integrating multidimensional dyadic, psychological, and relational data. A cross-sectional predictive design was employed with a sample of 368 high-conflict heterosexual couples recruited from counseling and community support settings in Italy. Partners independently completed validated self-report measures assessing conflict dynamics, attachment orientations, emotional regulation, perceived stress, jealousy, relationship satisfaction, and intimate partner violence risk, alongside demographic information. Dyadic data were preprocessed and structured to preserve partner-level and couple-level information. Multiple supervised machine learning algorithms, including regularized logistic regression, support vector machines, random forest, and gradient boosting, were trained and evaluated using stratified cross-validation. Model interpretability was examined using explainable artificial intelligence techniques based on feature attribution. Ensemble-based models outperformed linear and kernel-based approaches, with the gradient boosting model demonstrating the highest predictive accuracy and discrimination (accuracy = 0.88; AUC = 0.94). Sensitivity to high-risk classifications was robust across ensemble models, indicating effective identification of couples at elevated risk. Feature importance analyses revealed that conflict escalation, anger dysregulation, attachment anxiety, perceived stress, and jealousy intensity were the strongest contributors to risk classification, while lower relationship satisfaction showed a smaller but meaningful effect. The results indicated that nonlinear interactions among relational and emotional variables substantially enhanced predictive performance. The findings demonstrate that explainable machine learning models can reliably identify high-conflict couples at risk for intimate partner violence by capturing complex dyadic interaction patterns. Integrating such models into preventive and clinical contexts may support earlier detection, targeted intervention, and ethically informed decision-making, complementing traditional assessment approaches.

The objective of this study was to develop and evaluate an artificial intelligence–based model capable of predicting intimacy decline in romantic couples using dyadic attachment orientations and individual and shared stress patterns. The study employed a longitudinal, correlational design with a predictive modeling framework. Married and long-term cohabiting couples from Turkey participated as dyads, with both partners independently completing validated self-report measures of attachment anxiety and avoidance, perceived individual stress, dyadic stress, and relational intimacy. Data were collected at baseline and at a six-month follow-up to capture changes in intimacy over time. After data preprocessing and dyadic feature construction, multiple supervised machine learning algorithms were trained and validated using cross-validation procedures to predict intimacy decline as both a categorical and continuous outcome. The predictive models demonstrated strong performance, with ensemble-based algorithms achieving the highest classification accuracy and area under the curve values in distinguishing couples with declining intimacy from those with stable intimacy. Inferential feature analyses indicated that attachment anxiety, attachment avoidance, dyadic stress, and interaction terms between attachment insecurity and stress were the most influential predictors. Models incorporating dyadic discrepancy indicators consistently outperformed those based solely on individual-level features, indicating significant partner interdependence effects. Higher combined levels of attachment insecurity and stress were associated with greater magnitude of intimacy decline over time. The findings indicate that intimacy decline can be accurately predicted using AI-based models that integrate dyadic attachment and stress variables, supporting the view that intimacy erosion emerges from complex, interactive relational processes. These results highlight the potential of artificial intelligence to inform early identification and prevention strategies in couple and family interventions.