Research and Practice in Couple Therapy

The present study aimed to develop and evaluate machine learning models for the classification of high-conflict couples using psychological characteristics, relational functioning indicators, and behavioral interaction features while identifying the most influential predictors of relationship conflict status. This cross-sectional predictive study was conducted among 624 couples (N = 1,248 individuals) recruited from multiple urban and suburban regions of Malaysia. Participants completed a comprehensive assessment battery measuring depression, anxiety, stress, attachment insecurity, emotional intimacy, dyadic adjustment, and conflict behaviors. In addition, couples participated in structured conflict discussion tasks that were coded for behavioral interaction features, including criticism, defensiveness, contempt, stonewalling, positive affect, and conflict resolution attempts. Following data preprocessing procedures, including normalization, missing-value imputation, and feature engineering, the dataset was divided into training and testing subsets using stratified sampling. Several supervised machine learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, Artificial Neural Network, and Extreme Gradient Boosting (XGBoost), were trained and compared. Model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) analyses were conducted to determine feature importance and model interpretability. All machine learning models demonstrated satisfactory predictive performance; however, ensemble learning algorithms significantly outperformed traditional approaches. XGBoost emerged as the best-performing model, achieving an accuracy of 94.1%, precision of 93.7%, recall of 93.3%, F1-score of 93.5%, and an AUC-ROC value of 0.978. Feature importance analyses revealed that dyadic adjustment, emotional intimacy, criticism frequency, psychological aggression, attachment anxiety, positive affect, defensiveness frequency, and stress were the strongest contributors to classification accuracy. High-conflict couples exhibited significantly higher levels of psychological distress, attachment insecurity, aggression, and dysfunctional communication behaviors, whereas low-conflict couples demonstrated greater emotional intimacy, relationship satisfaction, positive affect, and constructive conflict resolution patterns. The confusion matrix further indicated high sensitivity and specificity, confirming the robustness and generalizability of the classification model. The findings demonstrate that machine learning approaches can accurately distinguish high-conflict couples from low-conflict couples by integrating psychological, relational, and behavioral interaction variables. The results highlight the multidimensional nature of relationship conflict and suggest that relational functioning indicators and observed communication behaviors represent particularly powerful predictors of conflict status. Machine learning-based assessment frameworks may provide valuable tools for early identification, risk assessment, personalized intervention planning, and the development of data-driven approaches to couple therapy and relationship education.

The present study aimed to examine the mediating roles of forgiveness and trust in the relationship between infidelity trauma and marital adjustment among married adults in the United States. This cross-sectional correlational study was conducted among 624 married adults in the United States who had experienced emotional and/or sexual infidelity by their spouse within the previous five years. Participants were recruited through online relationship-support communities, social media platforms, and community organizations. Data were collected using the Infidelity Trauma Scale, the Transgression-Related Interpersonal Motivations Inventory (TRIM-18), the Dyadic Trust Scale, and the Revised Dyadic Adjustment Scale. Structural equation modeling (SEM) was employed to examine the direct and indirect relationships among study variables. Confirmatory factor analysis was performed to evaluate the measurement model, and model fit was assessed using χ²/df, CFI, TLI, GFI, RMSEA, and SRMR indices. Indirect effects were tested using bias-corrected bootstrapping with 5,000 resamples and 95% confidence intervals. Structural equation modeling indicated that the proposed model demonstrated excellent fit to the data (χ²/df = 2.34, CFI = 0.951, TLI = 0.943, GFI = 0.924, RMSEA = 0.046, SRMR = 0.044). Infidelity trauma significantly predicted lower forgiveness (β = -0.61, p < .001), lower trust (β = -0.49, p < .001), and poorer marital adjustment (β = -0.24, p < .001). Forgiveness positively predicted trust (β = 0.39, p < .001) and marital adjustment (β = 0.27, p < .001), while trust emerged as the strongest predictor of marital adjustment (β = 0.56, p < .001). Bootstrap analyses revealed significant indirect effects through forgiveness (β = -0.16, 95% CI [-0.23, -0.10]), trust (β = -0.27, 95% CI [-0.36, -0.19]), and the sequential pathway of forgiveness and trust (β = -0.13, 95% CI [-0.18, -0.08]). The final model explained 68.4% of the variance in marital adjustment. The findings indicate that infidelity trauma undermines marital adjustment both directly and indirectly through diminished forgiveness and trust. Trust emerged as the most influential mechanism linking betrayal-related trauma to marital functioning, while forgiveness facilitated adjustment both independently and through its contribution to trust restoration. These results highlight the importance of targeting forgiveness and trust in therapeutic interventions designed to promote recovery and relationship resilience following marital infidelity.

The present study aimed to predict dyadic adjustment among couples using a multimodal framework incorporating psychological symptoms, communication quality, sexual intimacy, and perceived support. This cross-sectional predictive study was conducted among 624 individuals representing 312 couples residing in Mexico. Participants were recruited through community organizations, healthcare settings, counseling centers, and online platforms. Data were collected using the Dyadic Adjustment Scale (DAS), Brief Symptom Inventory-18 (BSI-18), Communication Patterns Questionnaire–Short Form (CPQ-SF), Personal Assessment of Intimacy in Relationships Scale (PAIR), and the Multidimensional Scale of Perceived Social Support (MSPSS). Descriptive statistics, Pearson correlation analyses, hierarchical multiple regression, and structural equation modeling (SEM) were performed using SPSS 29 and AMOS 29. Model fit was evaluated using χ²/df, CFI, TLI, GFI, SRMR, and RMSEA indices. Correlation analyses indicated that dyadic adjustment was negatively associated with psychological symptoms (r = -.58, p < .001) and positively associated with communication quality (r = .74, p < .001), sexual intimacy (r = .68, p < .001), and perceived support (r = .61, p < .001). Hierarchical multiple regression analysis revealed that psychological symptoms (β = -.24, p < .001), communication quality (β = .43, p < .001), sexual intimacy (β = .29, p < .001), and perceived support (β = .18, p < .001) significantly predicted dyadic adjustment, collectively explaining 70.9% of the variance (R² = .709, p < .001). Structural equation modeling demonstrated excellent model fit (χ²/df = 2.20, CFI = .967, TLI = .961, GFI = .948, SRMR = .039, RMSEA = .044). Standardized path coefficients confirmed significant direct effects of psychological symptoms (β = -.27, p < .001), communication quality (β = .46, p < .001), sexual intimacy (β = .31, p < .001), and perceived support (β = .20, p < .001) on dyadic adjustment. The final model explained 73.4% of the variance in dyadic adjustment. The findings demonstrate that dyadic adjustment is a multidimensional relational outcome shaped by psychological, communicative, sexual, and social factors. Communication quality emerged as the strongest predictor of relationship functioning, followed by sexual intimacy, psychological symptoms, and perceived support. These results support systemic and dyadic perspectives of relationship functioning and suggest that interventions targeting communication skills, emotional well-being, intimacy enhancement, and support mobilization may substantially improve couple adjustment and relational resilience.

The present study aimed to examine the effectiveness of Trauma-Informed Couple Therapy (TICT) in improving attachment security, emotional safety, and intimate partner responsiveness among couples with childhood trauma histories. This quasi-experimental study employed a pre-test, post-test, and three-month follow-up design with an experimental group and a waitlist control group. The research was conducted in Canada among 52 couples (104 individuals) with documented childhood trauma histories who were recruited from community counseling centers and mental health clinics. Participants were assigned to either an experimental group (26 couples) receiving Trauma-Informed Couple Therapy or a control group (26 couples) receiving no intervention during the study period. Data were collected using the Experiences in Close Relationships-Revised Questionnaire (ECR-R) to assess attachment security, the Emotional Safety Scale for Couples (ESSC), and the Perceived Partner Responsiveness Scale (PPRS). The intervention consisted of twelve weekly 90-minute sessions integrating attachment-based, trauma-informed, emotionally focused, and relational resilience principles. Data were analyzed using repeated-measures analysis of variance and Bonferroni post hoc comparisons in SPSS version 29. The results of repeated-measures analysis of variance revealed significant Time × Group interaction effects for attachment security, F(2, 204) = 62.47, p < .001, η² = .380; emotional safety, F(2, 204) = 71.66, p < .001, η² = .413; and intimate partner responsiveness, F(2, 204) = 68.19, p < .001, η² = .401. Significant main effects of time and group were also observed across all outcome variables (p < .001). Bonferroni pairwise comparisons demonstrated significant improvements from pre-test to post-test and from pre-test to follow-up for attachment security, emotional safety, and intimate partner responsiveness in the experimental group (p < .001). No significant differences emerged between post-test and follow-up scores (p > .05), indicating maintenance of treatment gains over the three-month follow-up period. The large effect sizes obtained across all dependent variables suggest substantial intervention-related improvements in relational functioning among couples with childhood trauma histories. The findings indicate that Trauma-Informed Couple Therapy is an effective intervention for enhancing attachment security, emotional safety, and intimate partner responsiveness among couples affected by childhood trauma. By addressing trauma-related attachment disruptions, fostering emotionally safe interactions, and strengthening responsive relational processes, the intervention contributed to meaningful and sustained improvements in couple functioning. These results support the integration of trauma-informed and attachment-based approaches within couple therapy and highlight the importance of addressing the interpersonal consequences of childhood trauma in clinical practice.

The present study aimed to test a dyadic model examining the direct and indirect associations among romantic jealousy, reassurance seeking, partner monitoring, and relationship instability by simultaneously evaluating actor and partner effects within romantic couples. This cross-sectional correlational study was conducted among 412 romantic couples (N = 824 individuals) residing in the United States. Participants were recruited through online platforms, community organizations, and university participant pools and were required to be at least 18 years old and involved in a committed romantic relationship for a minimum of six months. Data were collected using the Multidimensional Jealousy Scale, Reassurance-Seeking Scale, Partner Monitoring Scale, and Marital Instability Index. Descriptive statistics, reliability analyses, and Pearson correlations were performed using SPSS version 29. Dyadic relationships among variables were examined through the Actor–Partner Interdependence Model (APIM) within a structural equation modeling framework using AMOS version 29. Model fit was evaluated using χ²/df, CFI, TLI, RMSEA, and SRMR indices, and indirect effects were tested through bootstrapping procedures with 5,000 resamples. Results indicated significant positive associations among romantic jealousy, reassurance seeking, partner monitoring, and relationship instability (all p < .001). The proposed dyadic structural model demonstrated excellent fit to the data (χ²/df = 2.24, CFI = .967, TLI = .961, RMSEA = .039, SRMR = .041). Actor effects revealed that romantic jealousy significantly predicted reassurance seeking (β = .53, p < .001), partner monitoring (β = .49, p < .001), and relationship instability (β = .17, p < .001). Reassurance seeking (β = .21, p < .001) and partner monitoring (β = .42, p < .001) significantly predicted relationship instability. Significant partner effects were also observed, indicating that one partner’s jealousy predicted the other partner’s monitoring behaviors (β = .22, p < .001) and relationship instability (β = .18, p = .001), while partner monitoring predicted the partner’s instability (β = .25, p < .001). Bootstrapping analyses confirmed significant indirect effects of jealousy on relationship instability through reassurance seeking (β = .11, p < .001) and partner monitoring (β = .21, p < .001). The findings support a comprehensive dyadic model in which romantic jealousy contributes to relationship instability both directly and indirectly through reassurance-seeking and partner-monitoring behaviors. The results highlight the importance of considering emotional insecurity as an interpersonal process that affects both members of a romantic dyad. Reassurance seeking and monitoring behaviors appear to function as key mechanisms through which jealousy undermines relationship stability. These findings underscore the value of dyadic perspectives in relationship research and suggest that interventions targeting insecurity, excessive reassurance seeking, and partner surveillance may enhance relationship functioning and long-term stability.

The present study aimed to investigate the effectiveness of Solution-Focused Couple Therapy (SFCT) on hope, dyadic coping, and relationship resilience among couples experiencing economic stress. This study employed a quasi-experimental design with pretest, posttest, and two-month follow-up assessments using a control group. The statistical population consisted of married couples experiencing significant economic stress in Toronto, Canada. Forty couples (80 individuals) meeting the inclusion criteria were selected through purposive sampling and randomly assigned to an experimental group (20 couples) and a control group (20 couples). The experimental group participated in eight weekly 90-minute sessions of Solution-Focused Couple Therapy, while the control group received no intervention during the study period. Data were collected using the Adult Hope Scale, the Dyadic Coping Inventory, and the Relationship Resilience Assessment Scale. Data analysis was conducted using repeated-measures analysis of variance and Bonferroni post hoc tests in SPSS version 29. The results of repeated-measures analysis of variance revealed significant effects of time, group, and Time × Group interaction for all dependent variables. For hope, significant effects were observed for time (F = 51.84, p < .001), group (F = 42.27, p < .001), and Time × Group interaction (F = 64.18, p < .001, η² = .451). Dyadic coping also demonstrated significant time (F = 58.73, p < .001), group (F = 47.91, p < .001), and interaction effects (F = 71.26, p < .001, η² = .477). Similarly, relationship resilience showed significant effects for time (F = 62.54, p < .001), group (F = 50.16, p < .001), and Time × Group interaction (F = 75.83, p < .001, η² = .493). Bonferroni comparisons indicated significant improvements from pretest to posttest and from pretest to follow-up for all variables (p < .001), while no significant differences were found between posttest and follow-up assessments (p > .05), indicating maintenance of treatment gains over time. The findings demonstrate that Solution-Focused Couple Therapy is an effective intervention for enhancing hope, dyadic coping, and relationship resilience among couples facing economic stress. By emphasizing strengths, future-oriented goals, and collaborative problem-solving, the intervention enabled couples to develop adaptive psychological and relational resources that persisted beyond the treatment period. These results support the application of solution-focused approaches as an evidence-based strategy for promoting relational well-being and resilience among couples confronted with financial adversity.

The present study aimed to investigate the effectiveness of Narrative Couple Therapy on relational identity reconstruction, marital meaning-making, and post-conflict reconciliation among married couples experiencing relationship distress in Canada. This study employed a quasi-experimental design with pretest, posttest, and three-month follow-up assessments using a control group. The statistical population consisted of married couples who sought services from counseling centers and family therapy clinics in Canada during 2025. Following eligibility screening, 60 couples (120 individuals) were selected and randomly assigned to an experimental group (30 couples) and a control group (30 couples). The experimental group participated in a 12-session Narrative Couple Therapy program conducted weekly, while the control group received no intervention during the study period. Data were collected using the Relational Identity Reconstruction Scale, the Marital Meaning-Making Questionnaire, and the Post-Conflict Reconciliation Inventory. Data were analyzed using repeated-measures analysis of variance and Bonferroni post hoc comparisons in SPSS version 29. The results revealed significant time, group, and time-by-group interaction effects for all study variables. For relational identity reconstruction, a significant interaction effect was observed, F(2,116) = 61.84, p < .001, η² = .52. Marital meaning-making also demonstrated a significant interaction effect, F(2,116) = 72.57, p < .001, η² = .56. Similarly, post-conflict reconciliation showed a significant interaction effect, F(2,116) = 69.44, p < .001, η² = .55. Bonferroni pairwise comparisons indicated significant improvements from pretest to posttest and from pretest to follow-up across all outcome variables (p < .001). No significant differences were found between posttest and follow-up scores, indicating that treatment gains were maintained over time. The findings indicate that Narrative Couple Therapy is an effective intervention for enhancing relational identity reconstruction, strengthening marital meaning-making, and improving post-conflict reconciliation among married couples. By facilitating the reconstruction of relational narratives and promoting collaborative meaning-making processes, this therapeutic approach contributes to enduring improvements in relationship functioning and emotional connection. The stability of treatment effects at follow-up further supports the utility of Narrative Couple Therapy as a sustainable and evidence-based intervention for couples experiencing relational difficulties.

The present study aimed to examine the structural relationships among differentiation of self, conflict resolution styles, emotional intimacy, and marital stability and to investigate the direct and indirect pathways through which these variables influence marital stability among married adults. This study employed a cross-sectional correlational design using structural equation modeling (SEM). The participants consisted of 642 married adults residing in the United States who were selected through online and community-based recruitment methods. Data were collected using the Differentiation of Self Inventory-Revised (DSI-R), the Conflict Resolution Styles Inventory (CRSI), the Emotional Intimacy Scale (EIS), and the Marital Instability Index (MII). Descriptive statistics, Pearson correlation analyses, confirmatory factor analysis, and structural equation modeling were conducted using SPSS 29 and AMOS 29. Model fit was evaluated using multiple goodness-of-fit indices, and indirect effects were tested through bootstrapping procedures with 5,000 resamples. The proposed structural model demonstrated satisfactory fit to the data (χ²/df = 2.57, CFI = 0.948, TLI = 0.942, GFI = 0.918, AGFI = 0.904, RMSEA = 0.050, SRMR = 0.047). Differentiation of self positively predicted positive problem-solving conflict resolution (β = 0.58, p < .001), emotional intimacy (β = 0.41, p < .001), and marital stability (β = 0.24, p < .001), while negatively predicting conflict engagement (β = -0.49, p < .001) and withdrawal (β = -0.53, p < .001). Positive problem solving positively predicted emotional intimacy (β = 0.36, p < .001), whereas conflict engagement (β = -0.21, p < .001) and withdrawal (β = -0.25, p < .001) negatively predicted emotional intimacy. Emotional intimacy emerged as the strongest predictor of marital stability (β = 0.57, p < .001). Significant indirect effects were observed between differentiation of self and marital stability through conflict resolution styles and emotional intimacy (β = 0.33, p < .001). The model explained 58% of the variance in emotional intimacy and 67% of the variance in marital stability. The findings indicate that differentiation of self contributes significantly to marital stability both directly and indirectly through constructive conflict resolution styles and enhanced emotional intimacy. Emotional intimacy functions as a central mechanism linking individual psychological functioning and interpersonal relationship processes to marital stability. Interventions designed to strengthen self-differentiation, improve conflict management skills, and foster emotional intimacy may be effective strategies for promoting stable and satisfying marital relationships.