E-ISSN: 3041-8798 Director in Charge: Dr. Seyed Ali Darbani Editor-in-chief: Dr. Shokoh Navabinejad Associate editor: Dr. Seyed Ali Darbani Owner: Iranian Association for Women's Studies Publisher: KMAN Publication Inc. (KMANPUB) Contact Email: aftjournal@gmail.com / aftjournal@kmanpub.com Open Access: YES Applied Family Therapy Journal is a scientific open access double-blind anonymous open (since 2024) peer-reviewed 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 Iranian Association for Women's Studies and KMAN Publication Inc. (KMANPUB). Since 2024, this journal only accepts and publishes articles in English and no longer publishes Persian articles. The research fields and topics within the activity framework of AFTJ include: Family counseling and couple therapy Women counseling Children and adolescent counseling Rehabilitation counseling in the field of family Marriage and youth counseling Divorce counseling Children and adolescent counseling Counseling about addiction in the family 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)
Support Vector Machine Classification of High- and Low-Functioning Families Based on Communication, Adaptability, Emotional Security, and Conflict Management
Objective: The present study aimed to develop and evaluate a Support Vector Machine (SVM) classification model capable of distinguishing high-functioning and low-functioning families based on family communication, adaptability, emotional security, and conflict management. Methods and Materials: This cross-sectional predictive study was conducted among 796 families residing in Portugal. Participants were recruited using a stratified multistage sampling procedure from urban and suburban regions. Family functioning served as the target classification variable and was assessed using the Family Assessment Device. Predictor variables included family communication measured by the Family Communication Scale, family adaptability assessed through the Family Adaptability and Cohesion Evaluation Scales IV, emotional security measured by the Security in the Family System Scale, and conflict management evaluated using the Conflict Resolution Styles Inventory. Data preprocessing included standardization, missing-value imputation, and feature preparation. Families were classified into high-functioning and low-functioning groups according to established Family Assessment Device cutoff criteria. An SVM classifier with radial basis function and linear kernels was developed and optimized using five-fold cross-validation and grid search procedures. Model performance was evaluated using accuracy, precision, recall, specificity, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrix analyses, permutation importance, and SHAP analyses. Findings: All predictor variables demonstrated significant positive associations with family functioning. The optimized SVM model achieved a testing accuracy of 89.37%, precision of 0.88, recall of 0.89, specificity of 0.88, and an F1-score of 0.88. Receiver operating characteristic analysis indicated excellent discriminative performance with an AUC of 0.93. Feature importance analyses revealed that emotional security was the strongest predictor of family functioning, accounting for 32.7% of model contribution, followed by family communication (28.4%), family adaptability (21.9%), and conflict management (17.0%). SHAP analyses further confirmed that higher levels of all four predictors increased the probability of classification into the high-functioning family group. Conclusion: The findings demonstrate that communication, adaptability, emotional security, and conflict management constitute powerful indicators of family functioning and can be effectively utilized within machine learning frameworks to classify families with high accuracy. Emotional security emerged as the most influential predictor, highlighting its central role in healthy family systems. The results support the application of SVM-based predictive models as valuable tools for family assessment, early identification of at-risk families, and the development of targeted preventive and intervention programs.
Copyright (c) 2026 Petra Nováková (Author); Tiago Figueiredo (Corresponding Author); Andre McKenzie (Author)
CatBoost Modeling of Parenting Effectiveness Based on Mindfulness, Emotional Intelligence, Psychological Flexibility, and Reflective Functioning
Objective: The present study aimed to develop and evaluate a CatBoost machine learning model for predicting parenting effectiveness based on mindfulness, emotional intelligence, psychological flexibility, and reflective functioning among parents in Thailand. Methods and Materials: This cross-sectional predictive study was conducted among 742 parents recruited from Bangkok, Chiang Mai, Phuket, Khon Kaen, and Nakhon Ratchasima, Thailand. Participants completed the Parenting Sense of Competence Scale, Five Facet Mindfulness Questionnaire, Wong and Law Emotional Intelligence Scale, Acceptance and Action Questionnaire-II, and Reflective Functioning Questionnaire. Following data screening and preprocessing procedures, a CatBoost machine learning algorithm was implemented to predict parenting effectiveness. The dataset was divided into training (80%) and testing (20%) subsets, and hyperparameter optimization was performed using five-fold cross-validation. Model performance was evaluated using the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP (Shapley Additive Explanations) analyses were additionally conducted to examine feature importance and interpret predictor contributions. Findings: The results revealed significant positive associations between parenting effectiveness and mindfulness (r = .69, p < .001), emotional intelligence (r = .72, p < .001), psychological flexibility (r = .65, p < .001), and reflective functioning (r = .67, p < .001). The CatBoost model demonstrated strong predictive performance, explaining 84.2% of the variance in parenting effectiveness in the training dataset (R² = .842) and 81.6% in the testing dataset (R² = .816), with a cross-validation R² of .808. Feature importance analysis indicated that emotional intelligence was the strongest predictor (32.8%), followed by mindfulness (28.6%), reflective functioning (21.7%), and psychological flexibility (16.9%). SHAP analyses confirmed that higher levels of all four predictors contributed positively to parenting effectiveness predictions, with emotional intelligence exerting the greatest overall influence on model outputs. Conclusion: The findings demonstrate that parenting effectiveness is strongly influenced by emotional, cognitive, and self-regulatory psychological capacities. Emotional intelligence emerged as the most important determinant, followed by mindfulness, reflective functioning, and psychological flexibility. The high predictive accuracy of the CatBoost model highlights the utility of machine learning approaches for understanding complex parenting processes and identifying key psychological resources that may serve as targets for intervention. Strengthening these capacities may enhance parenting effectiveness and contribute to healthier family functioning and child development outcomes.
Copyright (c) 2026 Selin Aydemir (Author); Nattaporn Chaisiri (Corresponding Author); Andrej Kováčik (Author)
Explainable LightGBM Analysis of Adolescent Family Adjustment Based on Parenting Style, Family Communication, Emotional Security, and Self-Esteem
Objective: The present study aimed to predict adolescent family adjustment based on parenting style, family communication, emotional security, and self-esteem using an Explainable Light Gradient Boosting Machine (LightGBM) model and SHapley Additive exPlanations (SHAP) analysis among Colombian adolescents. Methods and Materials: This cross-sectional predictive study was conducted among 1,248 adolescents aged 13 to 18 years recruited from public and private secondary schools in Colombia through multistage cluster sampling. Data were collected using standardized measures of family adjustment, parenting style, family communication, emotional security, and self-esteem. Following data preprocessing and quality assessment, the dataset was randomly divided into training (80%) and testing (20%) subsets. A LightGBM algorithm was developed to predict family adjustment scores, and model hyperparameters were optimized using five-fold cross-validation. 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). To enhance model interpretability and identify the relative importance of predictors, SHAP analyses were performed to examine both global and local feature contributions. Findings: The Explainable LightGBM model demonstrated excellent predictive performance, accounting for 89.2% of the variance in family adjustment in the training dataset and 84.6% in the testing dataset (R² = .892 and .846, respectively). Cross-validation analyses confirmed model stability (Mean R² = .839, SD = .021). SHAP-based feature importance analyses revealed that emotional security was the strongest predictor of adolescent family adjustment, accounting for 34.8% of total predictive importance, followed by family communication (29.3%), authoritative parenting style (22.7%), and self-esteem (13.2%). SHAP summary analyses further indicated that higher levels of emotional security, more effective family communication, stronger authoritative parenting practices, and greater self-esteem were consistently associated with higher predicted family adjustment scores. The explainable machine learning framework successfully identified both the magnitude and direction of predictor contributions, highlighting the predominance of family relational factors in explaining adjustment outcomes. Conclusion: The findings demonstrate that adolescent family adjustment can be accurately predicted through explainable machine learning approaches and that emotional security, family communication, authoritative parenting style, and self-esteem are significant contributors to adjustment outcomes.
Copyright (c) 2026 Marco Quispe (Author); Andrés Mejía (Corresponding Author)
Factors Influencing the Decision to Have Children in Iranian Military Families: A Qualitative Study
Objective: The present study was conducted to explain the dynamics of military families and the factors influencing childbearing within the context of Iranian society. Methods and Materials: This study was conducted using a qualitative approach based on grounded theory, employing the Strauss and Corbin paradigmatic model. The participants were selected from military families residing in Tehran, Iran. Data were collected through semi-structured interviews using purposive sampling with 18 members of military families. The data were analyzed in three stages of coding (open, axial, and selective) and categorized into main themes. Findings: Analysis of the participants’ interviews led to the identification of 13 selective categories, 38 axial categories, and 156 subcategories. Childbearing among military families is influenced by numerous inhibiting and encouraging factors, including economic costs, family support networks, generational continuity and national authority, commitment to the leadership discourse system, faith-based action, parents’ psychological capacity, parenting experience, organizational and occupational structure, coherence of population policies, transformation of contemporary lifestyles, and institutionalization of structural support. Conclusion: The findings of the study can assist policymakers and social planners in shifting their focus from general factors toward key and realistic components involved in the childbearing decision-making process and in designing supportive policies aimed at increasing family satisfaction and enhancing parents’ inclination and motivation toward childbearing.
Copyright (c) 2025 Nima Saberi (Author); Davoud Nodehi (Corresponding Author); Ahmad Salami (Author)
LightGBM-Based Prediction of Marital Commitment Using Sexual Satisfaction, Trust, Emotional Intimacy, and Attachment Security
Objective: The present study aimed to develop and evaluate a Light Gradient Boosting Machine (LightGBM) model for predicting marital commitment based on sexual satisfaction, trust, emotional intimacy, and attachment security among married adults in Argentina. Methods and Materials: This cross-sectional predictive study was conducted among 1,248 married adults residing in Argentina. Participants were selected using a stratified convenience sampling method from major urban areas, including Buenos Aires, Córdoba, Rosario, and Mendoza. Data were collected using standardized measures of marital commitment, sexual satisfaction, trust, emotional intimacy, and attachment security. Following data preprocessing, including missing-value imputation and data quality screening, the dataset was randomly divided into training (80%) and testing (20%) subsets. A LightGBM algorithm was implemented to predict marital commitment. Hyperparameter optimization was conducted using five-fold cross-validation and grid-search procedures. Model performance was evaluated using the coefficient of determination (R²), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Feature importance analysis and SHapley Additive exPlanations (SHAP) were employed to determine the relative contribution of each predictor and enhance model interpretability. Findings: Correlation analyses revealed significant positive associations between marital commitment and emotional intimacy (r = .79, p < .001), trust (r = .76, p < .001), sexual satisfaction (r = .71, p < .001), and attachment security (r = .68, p < .001). The LightGBM model demonstrated excellent predictive performance, explaining 88.4% of the variance in the training dataset and 84.2% in the testing dataset (R² = .842). Error metrics indicated high prediction accuracy (Testing RMSE = 5.38, MAE = 4.09, MAPE = 5.91%). Feature importance analysis showed that emotional intimacy was the strongest predictor (34.7%), followed by trust (29.3%), sexual satisfaction (22.2%), and attachment security (13.8%). SHAP analysis confirmed the dominant influence of emotional intimacy and trust on model predictions. Conclusion: The findings demonstrate that marital commitment can be predicted with high accuracy using sexual satisfaction, trust, emotional intimacy, and attachment security. Emotional intimacy and trust emerged as the most influential predictors, highlighting the central role of emotional connection and relational security in sustaining committed marital relationships. The study further illustrates the utility of machine learning approaches, particularly LightGBM, for advancing predictive research in family and relationship psychology.
Explainable Artificial Intelligence (XAI)
Copyright (c) 2026 Nikola Petrović, Nicolás Barrenechea, Ádám Székely (Author)
Random Forest Prediction of Family Functioning Based on Emotional Availability, Family Communication, Psychological Well-Being, and Coping Self-Efficacy
Objective: The present study aimed to develop and evaluate a Random Forest machine learning model for predicting family functioning based on emotional availability, family communication, psychological well-being, and coping self-efficacy among Canadian adults. Methods and Materials: This cross-sectional predictive study was conducted among 768 adults residing in Canada who were recruited through community organizations, social media platforms, and online advertisements. Data were collected using the Family Assessment Device (FAD), Brief Emotional Availability Scale (BEAS), Family Communication Scale (FCS), Ryff’s Psychological Well-Being Scale (PWB), and the Coping Self-Efficacy Scale (CSE). Following data screening and preprocessing procedures, descriptive statistics and Pearson correlations were calculated. The dataset was randomly divided into training (80%) and testing (20%) subsets. A Random Forest regression model was developed using five-fold cross-validation and hyperparameter optimization procedures. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). Feature importance and SHAP (Shapley Additive Explanations) analyses were employed to determine the relative contribution of each predictor variable. Findings: Significant associations were observed between family functioning and all predictor variables. Family communication demonstrated the strongest negative correlation with family dysfunction (r = -.76, p < .001), followed by emotional availability (r = -.71, p < .001), psychological well-being (r = -.68, p < .001), and coping self-efficacy (r = -.64, p < .001). The Random Forest model achieved strong predictive performance, yielding an R² of .847 on the testing dataset and a cross-validation R² of .836. Feature importance analysis identified family communication (37.2%) as the most influential predictor, followed by emotional availability (28.6%), psychological well-being (20.4%), and coping self-efficacy (13.8%). Permutation importance and SHAP analyses confirmed the stability of this predictor hierarchy and revealed that communication and emotional availability exerted the greatest impact on model predictions. Conclusion: The findings indicate that family functioning can be predicted with high accuracy using emotional availability, family communication, psychological well-being, and coping self-efficacy. Relational factors, particularly communication quality and emotional availability, emerged as the most influential determinants of family functioning. The results support the application of machine learning approaches in family psychology and highlight key targets for interventions designed to strengthen family relationships and promote adaptive family functioning.
XGBoost-Based Prediction of Family Resilience from Psychological Flexibility, Social Support, Family Cohesion, and Emotional Regulation
Objective: The present study aimed to predict family resilience based on psychological flexibility, social support, family cohesion, and emotional regulation using the XGBoost machine learning algorithm and to determine the relative importance of these predictors in explaining resilience outcomes among Canadian adults. Methods and Materials: This cross-sectional predictive study was conducted among 812 adults residing in Canada. Participants were recruited through community organizations, family support centers, and online platforms using a stratified convenience sampling method. Data were collected using the Family Resilience Assessment Scale (FRAS), Acceptance and Action Questionnaire-II (AAQ-II), Multidimensional Scale of Perceived Social Support (MSPSS), Family Adaptability and Cohesion Evaluation Scales IV (FACES-IV), and Emotion Regulation Questionnaire (ERQ). Following data preprocessing and standardization procedures, the dataset was divided into training (80%) and testing (20%) subsets. An Extreme Gradient Boosting (XGBoost) model was developed and optimized using five-fold cross-validation and grid-search hyperparameter tuning. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean squared error (MSE). Feature importance and SHAP (Shapley Additive Explanations) analyses were performed to determine the relative contribution of each predictor. Findings: The XGBoost model demonstrated excellent predictive performance, achieving an R² of 0.867 on the testing dataset, indicating that 86.7% of the variance in family resilience was explained by the predictors. Family resilience was positively correlated with psychological flexibility (r = .68, p < .001), social support (r = .72, p < .001), family cohesion (r = .79, p < .001), and emotional regulation (r = .64, p < .001). Feature importance analysis revealed that family cohesion was the strongest predictor (34.8%), followed by social support (28.7%), psychological flexibility (21.5%), and emotional regulation (15.0%). SHAP analyses confirmed these findings and demonstrated that higher levels of each predictor contributed positively to resilience predictions, with family cohesion exerting the largest overall influence. Conclusion: The findings indicate that family resilience can be predicted with high accuracy using psychological flexibility, social support, family cohesion, and emotional regulation. Family cohesion emerged as the most influential determinant, highlighting the critical role of strong family relationships in fostering adaptive functioning. The study demonstrates the utility of machine learning approaches, particularly XGBoost, for identifying key resilience factors and provides evidence supporting multidimensional models of family resilience that integrate individual, relational, and social protective resources.
Copyright (c) 2026 Tendai Chikore (Author); Olivia Macpherson (Corresponding Author); Pascal Reinert (Author)
Modeling Coparenting Quality Based on Marital Satisfaction, Emotional Intelligence, Parenting Alliance, and Psychological Flexibility
Objective: The present study aimed to develop and test a structural model of coparenting quality based on marital satisfaction, emotional intelligence, parenting alliance, and psychological flexibility among Canadian parents. Methods and Materials: This study employed a cross-sectional correlational design using structural equation modeling (SEM). The study population consisted of married or cohabiting parents residing in Canada who had at least one child between the ages of 3 and 16 years. A total of 624 parents were recruited through community organizations, educational institutions, parenting networks, and online platforms using convenience sampling. Data were collected using the Coparenting Relationship Scale (CRS), Revised Dyadic Adjustment Scale (RDAS), Wong and Law Emotional Intelligence Scale (WLEIS), Parenting Alliance Measure (PAM), and Acceptance and Action Questionnaire-II (AAQ-II). Descriptive statistics, Pearson correlation analysis, confirmatory factor analysis, and structural equation modeling were conducted using SPSS 29 and AMOS 29. Model fit was evaluated using χ²/df, CFI, TLI, GFI, AGFI, RMSEA, and SRMR indices. Findings: The results revealed significant positive correlations among all study variables. The proposed structural model demonstrated excellent fit to the data (χ²/df = 2.41, CFI = .96, TLI = .95, GFI = .94, AGFI = .92, RMSEA = .048, SRMR = .041). Structural path analysis indicated that parenting alliance was the strongest predictor of coparenting quality (β = .42, p < .001), followed by marital satisfaction (β = .34, p < .001), psychological flexibility (β = .18, p < .001), and emotional intelligence (β = .15, p < .001). Collectively, the predictor variables explained 68% of the variance in coparenting quality (R² = .68), demonstrating substantial explanatory power. Conclusion: The findings indicate that coparenting quality is significantly influenced by both relational and psychological factors. Parenting alliance and marital satisfaction emerged as particularly influential determinants, while emotional intelligence and psychological flexibility contributed additional explanatory value.
Copyright (c) 2026 Yanelis Batista (Author); Chloe Bennett (Corresponding Author); Ricardo Escobar (Author)
Modeling Family Quality of Life Based on Economic Pressure, Family Adaptability, Hope, and Emotional Support: The Mediating Role of Psychological Well-Being
Objective: The present study aimed to develop and test a structural model of family quality of life based on economic pressure, family adaptability, hope, and emotional support, while examining the mediating role of psychological well-being among married adults in Canada. Methods and Materials: This study employed a cross-sectional correlational design using structural equation modeling (SEM). The study population consisted of married adults residing in Canada. A total of 612 individuals were recruited through community organizations and online platforms, of whom 587 participants met the inclusion criteria and were retained for analysis. Data were collected using standardized instruments measuring family quality of life, economic pressure, family adaptability, hope, emotional support, and psychological well-being. Descriptive statistics, Pearson correlation analyses, confirmatory factor analysis, and structural equation modeling were conducted using SPSS 29 and AMOS 29. The significance of indirect effects was assessed through bootstrap analysis with 5,000 resamples. Findings: The measurement model demonstrated excellent fit to the data (χ²/df = 2.41, CFI = .956, TLI = .949, RMSEA = .049, SRMR = .042). The structural model also exhibited satisfactory fit indices (χ²/df = 2.53, CFI = .951, TLI = .947, RMSEA = .051, SRMR = .044). Economic pressure significantly and negatively predicted psychological well-being (β = −.28, p < .001) and family quality of life (β = −.21, p < .001). Family adaptability positively predicted psychological well-being (β = .24, p < .001) and family quality of life (β = .18, p < .001). Hope showed significant positive effects on psychological well-being (β = .35, p < .001) and family quality of life (β = .16, p < .001). Emotional support positively predicted psychological well-being (β = .27, p < .001) and family quality of life (β = .14, p = .001). Psychological well-being emerged as the strongest direct predictor of family quality of life (β = .49, p < .001). Bootstrap analyses confirmed significant indirect effects of all predictor variables on family quality of life through psychological well-being (p < .001). The model explained 69% of the variance in psychological well-being and 74% of the variance in family quality of life. Conclusion: The findings indicate that family quality of life is shaped by the combined influence of economic, familial, and psychosocial factors. Psychological well-being plays a central mediating role, transforming the effects of economic pressure, family adaptability, hope, and emotional support into family quality-of-life outcomes. Interventions aimed at strengthening psychological well-being, fostering hope, enhancing emotional support, and promoting adaptive family functioning may substantially improve family quality of life while mitigating the negative effects of economic stress.
Copyright (c) 2026 Mikko Salonen (Author); Liam Desrosiers (Corresponding Author); Stefan Leitner (Author)
The Effect of Intergenerational Trauma on Marital Stability: The Mediating Roles of Cognitive Emotion Regulation, Trust, and Intimacy
Objective: The present study aimed to examine the effect of intergenerational trauma on marital stability among married adults in Canada and to investigate the mediating roles of cognitive emotion regulation, trust, and intimacy in explaining this relationship. Methods and Materials: This study employed a cross-sectional correlational design using structural equation modeling (SEM). The study population consisted of married adults residing in Canada. A total of 642 participants were recruited through community organizations, counseling centers, social media platforms, and online advertisements, of whom 618 completed questionnaires were retained for analysis after data screening. Participants completed the Intergenerational Trauma Scale, Cognitive Emotion Regulation Questionnaire (CERQ), Dyadic Trust Scale, Personal Assessment of Intimacy in Relationships (PAIR), and Marital Stability Scale. Data were analyzed using SPSS 29 and AMOS 29. Descriptive statistics, Pearson correlation analyses, confirmatory factor analysis, and structural equation modeling were performed. The significance of indirect effects was assessed using bootstrapping procedures with 5,000 resamples and 95% confidence intervals. Findings: The structural model demonstrated excellent fit to the data (χ²/df = 2.37, CFI = 0.961, TLI = 0.955, RMSEA = 0.047, SRMR = 0.039). Intergenerational trauma negatively predicted cognitive emotion regulation (β = -0.48, p < .001), trust (β = -0.35, p < .001), intimacy (β = -0.28, p < .001), and marital stability (β = -0.21, p < .001). Cognitive emotion regulation positively predicted trust (β = 0.39, p < .001), intimacy (β = 0.26, p < .001), and marital stability (β = 0.18, p < .001). Trust significantly predicted intimacy (β = 0.51, p < .001) and marital stability (β = 0.29, p < .001). Intimacy emerged as the strongest direct predictor of marital stability (β = 0.46, p < .001). Bootstrapping analyses confirmed significant indirect effects of intergenerational trauma on marital stability through cognitive emotion regulation, trust, and intimacy (β = -0.42, p < .001), indicating partial mediation. The model explained 71% of the variance in marital stability. Conclusion: The findings demonstrate that intergenerational trauma exerts a substantial negative influence on marital stability through its detrimental effects on emotional regulation, interpersonal trust, and intimacy.
Copyright (c) 2026 Jone Vakacegu (Author); Nathaniel Brooks (Corresponding Author)
A Causal Model of Parenting Stress Based on Child Behavioral Problems, Parental Perfectionism, Psychological Inflexibility, and Family Conflict: The Mediating Role of Parental Self-Compassion
Objective: The present study aimed to develop and test a causal model of parenting stress based on child behavioral problems, parental perfectionism, psychological inflexibility, and family conflict, with parental self-compassion serving as a mediating variable among Canadian parents. Methods and Materials: This study employed a cross-sectional correlational design using structural equation modeling (SEM). The statistical population consisted of Canadian parents with at least one child aged 6–16 years. Using purposive sampling, 620 parents were recruited from schools, community family centers, parenting organizations, and online parenting networks across several Canadian provinces. Data were collected using the Parenting Stress Index–Short Form (PSI-SF), Strengths and Difficulties Questionnaire (SDQ), Multidimensional Perfectionism Scale (MPS), Acceptance and Action Questionnaire-II (AAQ-II), Family Environment Scale (FES–Conflict Subscale), and Self-Compassion Scale (SCS). Data were analyzed using SPSS version 27 and AMOS version 24. Descriptive statistics, Pearson correlation coefficients, confirmatory factor analysis, and structural equation modeling were conducted. The mediating role of parental self-compassion was examined using bootstrap analysis with 5,000 resamples and 95% bias-corrected confidence intervals. Findings: The proposed structural model demonstrated satisfactory fit to the data (χ²/df = 2.58, CFI = .95, TLI = .95, IFI = .95, GFI = .92, RMSEA = .051). Child behavioral problems (β = .24, p < .001), parental perfectionism (β = .17, p < .001), psychological inflexibility (β = .31, p < .001), and family conflict (β = .22, p < .001) had significant positive direct effects on parenting stress. Child behavioral problems (β = -.16, p < .001), parental perfectionism (β = -.23, p < .001), psychological inflexibility (β = -.41, p < .001), and family conflict (β = -.19, p < .001) significantly predicted lower parental self-compassion. Parental self-compassion had a significant negative effect on parenting stress (β = -.46, p < .001). Bootstrap analyses confirmed significant indirect effects of all predictor variables on parenting stress through parental self-compassion (p < .001). The model explained 68% of the variance in parenting stress and 57% of the variance in parental self-compassion. Conclusion: The findings indicate that parenting stress is influenced by a complex interplay of child-related, parent-related, and family-system factors. Child behavioral problems, parental perfectionism, psychological inflexibility, and family conflict increase parenting stress both directly and indirectly through reductions in parental self-compassion. Among the predictors, psychological inflexibility emerged as the strongest risk factor, whereas parental self-compassion functioned as a significant protective mechanism. These results highlight the importance of interventions targeting self-compassion, psychological flexibility, and family relationship quality to reduce parenting stress and promote parental well-being.
Copyright (c) 2026 Keisuke Mori (Author); Ethan Holloway (Corresponding Author); Erik Lundqvist (Author)
Comparing the Effectiveness of Cognitive-Behavioral Couple Therapy and Emotionally Focused Couple Therapy on Romantic Relationship Perfectionism in Couples with Conflict
Objective: The present study aimed to compare the effectiveness of cognitive-behavioral couple therapy and emotionally focused couple therapy on romantic relationship perfectionism among couples experiencing marital conflict. Methods and Materials: The present study employed a quasi-experimental pretest–posttest design with a control group and a three-month follow-up period. The statistical population consisted of all couples referring to counseling centers in Districts 21 and 22 of Tehran in 2025. Using purposive sampling and G*Power software, 45 couples meeting the inclusion criteria were selected and randomly assigned into two experimental groups and one control group, with 15 couples in each group. Data collection instruments included the Romantic Relationship Perfectionism Scale developed by Matte and Lafontaine (2012) and the Marital Conflict Questionnaire developed by Sanaei and Barati (1999). The first experimental group received cognitive-behavioral couple therapy based on Dattilio’s (2009) protocol in 10 sessions of 90 minutes, while the second experimental group received emotionally focused couple therapy based on Johnson’s (2012) model in 9 sessions of 90 minutes. The control group received no intervention. Data were analyzed using mixed repeated-measures analysis of variance in IBM SPSS Statistics version 18. Findings: The results of mixed repeated-measures analysis of variance demonstrated significant effects of group, treatment stages, and the interaction between group and treatment stages on self-oriented and other-oriented romantic relationship perfectionism (p < .001). Both cognitive-behavioral couple therapy and emotionally focused couple therapy significantly reduced romantic relationship perfectionism compared to the control group. The Bonferroni post-hoc test indicated significant differences between pretest and posttest scores as well as between pretest and follow-up scores (p < .001), while no significant differences were observed between posttest and follow-up stages, indicating the stability of treatment effects. Furthermore, Tukey post-hoc comparisons revealed that cognitive-behavioral couple therapy was significantly more effective than emotionally focused couple therapy in reducing both self-oriented and other-oriented perfectionism (p < .05). Conclusion: The findings suggest that both cognitive-behavioral couple therapy and emotionally focused couple therapy are effective interventions for reducing romantic relationship perfectionism among couples with marital conflict; however, cognitive-behavioral couple therapy demonstrated greater effectiveness. These results highlight the importance of targeting maladaptive cognitive patterns, unrealistic relational expectations, and emotional interactional processes in therapeutic interventions for distressed couples.
Copyright (c) 2025 Davoud Zamani Khormandichali (Author); Asghar Noruzi (Corresponding Author); Kolsoum Akbarnataj Bisheh (Author)
Investigating the Effectiveness of Schema Therapy on Intolerance of Uncertainty, Rejection Sensitivity, and Self-Critical Rumination in Women Victims of Emotional Abuse
Objective: The present study aimed to investigate the effectiveness of schema therapy on intolerance of uncertainty, rejection sensitivity, and self-critical rumination among women victims of emotional abuse. Methods and Materials: In terms of purpose, this study was applied research, and in terms of design, it was a quasi-experimental study employing a pretest–posttest design with follow-up and a control group. The statistical population consisted of all women victims of emotional abuse who referred to counseling centers, psychological clinics, and support centers for victims of unhealthy relationships in Tehran in 2025. The research sample was selected using purposive and convenience sampling methods and included 30 women who, after meeting the inclusion criteria and completing informed consent forms, were randomly assigned to an experimental group (n = 15) and a control group (n = 15). The experimental group received schema therapy intervention during 10 weekly 60-minute sessions, whereas the control group received no intervention during this period. Data were collected using the Intolerance of Uncertainty Questionnaire (Carleton et al., 2007), the Rejection Sensitivity Scale (Downey & Feldman, 1996), and the Self-Critical Rumination Questionnaire (Smart et al., 2016). The instruments demonstrated adequate validity and reliability in both Iranian and international samples. Data were collected at three stages, including pretest, posttest, and follow-up, and were analyzed using repeated-measures analysis of variance in IBM SPSS Statistics. Findings: The findings indicated that schema therapy significantly reduced intolerance of uncertainty, rejection sensitivity, and self-critical rumination in the experimental group compared with the control group (p < .001), and these improvements remained stable at the follow-up stage. Furthermore, the large effect sizes and complete statistical power indicated the strong and robust effectiveness of the intervention. Conclusion: Based on the findings, it can be concluded that schema therapy is an effective approach for reducing the cognitive-emotional vulnerabilities of women victims of emotional abuse and may be implemented as an empowering psychological intervention in counseling and support service centers.
Copyright (c) 2025 Zivar Safdari Ghooshchi (Author)
Predicting Tendency Toward Risky Behaviors Based on Emotion Regulation Difficulties and Mentalization in Children of Divorce: The Mediating Role of Post Traumatic Growth and the Moderating Role of Family Communication Patterns
Objective: The present study aimed to predict tendencies toward risky behaviors based on difficulties in emotion regulation and mentalization in children of divorce, with the mediating role of post traumatic growth and the moderating role of family communication patterns. Methods and Materials: This descriptive–correlational study using path analysis was conducted in 2025. The statistical population consisted of children of divorce aged 15–18 years in Babol, from whom 300 participants were selected through purposive sampling. Data were collected using the Iranian Adolescents Risk Taking Scale (2011), Difficulties in Emotion Regulation Scale (2004), Mentalization Questionnaire (2018), Post Traumatic Growth Inventory (1996), and Family Communication Patterns Questionnaire (1990). Data were analyzed using Pearson correlation coefficients, path analysis, moderation analysis, and bootstrap procedures in SPSS and AMOS. Findings: The results indicated that difficulties in emotion regulation had a significant positive direct effect on risky behavior tendencies (β = 0.41, p < .001), whereas mentalization (β = −0.36, p < .001), post traumatic growth (β = −0.29, p < .001), and family communication patterns (β = −0.31, p < .001) had significant negative direct effects. Furthermore, family communication patterns significantly moderated the relationship between difficulties in emotion regulation and risky behaviors (β = −0.18, p < .001), as well as the relationship between mentalization and risky behaviors (β = −0.15, p < .01). Bootstrap results also confirmed the mediating role of post traumatic growth in the relationship between difficulties in emotion regulation and risky behaviors (β = 0.12, 95% CI [0.06, 0.19]) and between mentalization and risky behaviors (β = −0.12, 95% CI [−0.18, −0.05]). The proposed model demonstrated good fit to the data (CFI = 0.95, TLI = 0.94, RMSEA = 0.054). Conclusion: The findings suggest that children of divorce with greater difficulties in emotion regulation are more likely to engage in risky behaviors, whereas higher levels of mentalization, post traumatic growth, and constructive family communication patterns serve as protective factors.
Copyright (c) 2025 Seyedeh Nafiseh Mohseni Mansour (Author); Nasrin Kadkhoda (Corresponding Author); Bita Khelghati, Seyedeh Mahsan Miri , Mehrnoosh Ranjbar (Author)
Comparison of the Effects of Reality Therapy and Cognitive Behavioral Therapy on the Resilience and Adjustment of Family Caregivers of Children with Autism
Objective: The present study aimed to compare the effectiveness of reality therapy and cognitive behavioral therapy on resilience and adjustment among family caregivers of children with autism spectrum disorder. Methods and Materials: This study was conducted using a quasi-experimental design with a pretest–posttest format and a control group. The statistical population included all family caregivers of children with autism spectrum disorder who were members of the Tehran branch of the Iran Autism Association. A total of 45 participants were selected through purposive sampling and assigned into three groups, including a reality therapy group (n = 15), a cognitive behavioral therapy group (n = 15), and a control group (n = 15). The experimental groups participated in eight weekly group intervention sessions, while the control group received no intervention. Data were collected using the Connor–Davidson Resilience Scale and the Sinha and Singh Adjustment Questionnaire. The obtained data were analyzed using repeated measures multivariate analysis of variance in IBM SPSS Statistics. Findings: The results demonstrated that both reality therapy and cognitive behavioral therapy significantly improved resilience and adjustment among family caregivers of children with autism spectrum disorder compared with the control group. The repeated measures analysis of variance indicated significant effects of time and the interaction between time and group for resilience and adjustment (p < 0.001). Bonferroni post hoc comparisons further revealed significant differences between each experimental group and the control group in posttest scores. However, no statistically significant difference was observed between the reality therapy and cognitive behavioral therapy groups regarding resilience and adjustment outcomes (p > 0.05), indicating that both interventions were equally effective. Conclusion: The findings suggest that both reality therapy and cognitive behavioral therapy are effective interventions for improving resilience and adjustment among family caregivers of children with autism spectrum disorder.
Copyright (c) 2025 Haniyeh Hossein Shiroudi (Author); Alireza Pirkhaefi (Corresponding Author)
The Effectiveness of Metacognitive Training on Distress Tolerance and Cognitive Dysregulation in Mothers of Children with Intellectual Disabilities
Objective: The present study aimed to investigate the effectiveness of metacognitive training on distress tolerance and cognitive dysregulation in mothers of children with intellectual disabilities. Methods and Materials: This study was an applied quasi-experimental research with a pretest–posttest design and a two-month follow-up including a control group. The statistical population consisted of all mothers of children with intellectual disabilities in Tabriz in 2025, from whom 30 participants were selected using purposive and convenience sampling methods. After meeting the inclusion criteria and providing informed consent, participants were randomly assigned to an experimental group (n = 15) and a control group (n = 15). The experimental group received 12 weekly sessions of metacognitive training based on Wells’ protocol (2017), each lasting 60 minutes, while the control group received no intervention. Data were collected using the Distress Tolerance Questionnaire (Simons & Gaher, 2005) and the Cognitive Dysregulation Questionnaire (Mason et al., 1995), administered at pretest, posttest, and follow-up stages. Data were analyzed using repeated measures analysis of variance (ANOVA) in SPSS version 27. Findings: The results of repeated measures ANOVA indicated significant effects of time, group, and time × group interaction on distress tolerance (F = 195.32, p < .001, η² = 0.875; F = 45.94, p < .001, η² = 0.621; F = 71.43, p < .001, η² = 0.718) and cognitive dysregulation (F = 280.98, p < .001, η² = 0.909; F = 30.82, p < .001, η² = 0.524; F = 98.78, p < .001, η² = 0.779). Multivariate analysis of variance also confirmed a significant main effect of time (F = 31.86, p < .001, η² = 0.485) and a significant time × group interaction (F = 44.92, p < .001, η² = 0.581). Bonferroni post hoc tests showed significant differences between pretest and posttest and between pretest and follow-up (p < .001), while no significant differences were observed between posttest and follow-up (p = 1.00), indicating stability of effects. Conclusion: Metacognitive training is an effective and stable intervention for increasing distress tolerance and reducing cognitive dysregulation in mothers of children with intellectual disabilities, and its effects remain sustained over time.
mothers of children with intellectual disabilities
Copyright (c) 2025 Khadijeh Zobeidizad (Author); Fahimeh Sattari (Corresponding Author)
Predicting Parenting Styles in Parents of Children with Intellectual Disabilities Based on Emotional Self-Regulation, Integrative Self-Knowledge, and Resilience
Objective: The present study aimed to predict parenting styles among parents of children with intellectual disabilities based on emotional self-regulation, integrative self-knowledge, and resilience. Methods and Materials: This study employed a descriptive correlational design. The statistical population consisted of parents of children with intellectual disabilities in Kermanshah in 2025. A multistage cluster sampling method was used to select participants from rehabilitation and occupational therapy centers, resulting in a final sample of 315 parents who met the inclusion criteria. Data were collected using standardized instruments, including the Difficulties in Emotion Regulation Scale, the Parenting Styles Questionnaire, the Connor–Davidson Resilience Scale, and the Integrative Self-Knowledge Questionnaire. Data analysis was conducted using Pearson correlation and multiple regression analyses in SPSS version 26. Findings: The results indicated that emotional self-regulation, integrative self-knowledge, and resilience significantly predicted parenting styles. Emotional self-regulation (β = -0.485, p < .001), integrative self-knowledge (β = -0.141, p < .05), and resilience (β = -0.131, p < .05) negatively predicted authoritarian parenting, with the model explaining 25.5% of the variance (R² = 0.255). For permissive parenting, emotional self-regulation (β = -0.229, p < .001) and integrative self-knowledge (β = -0.204, p < .001) were significant negative predictors, while resilience was not significant, and the model explained 15.4% of the variance (R² = 0.154). In predicting authoritative parenting, integrative self-knowledge (β = 0.194, p < .01) and resilience (β = 0.296, p < .001) were significant positive predictors, while emotional self-regulation was not significant (β = 0.092, p > .05), with the model explaining 10% of the variance (R² = 0.100). Correlation analyses also revealed significant relationships among the study variables. Conclusion: The findings suggest that emotional self-regulation, integrative self-knowledge, and resilience play important roles in shaping parenting styles among parents of children with intellectual disabilities, with adaptive psychological resources promoting authoritative parenting and reducing maladaptive styles.
Copyright (c) 2025 Sadaf Bakhti, Jahangir Karami (Author)
The Effectiveness of Acceptance and Commitment Therapy on Marital Inhibition and Emotional Intimacy among Women in Second Marriages
Objective: The present study aimed to determine the effectiveness of Acceptance and Commitment Therapy (ACT) on marital inhibition and emotional intimacy among women in second marriages. Methods and Materials: This study employed a quasi-experimental method using a pretest-posttest design with a control group. The statistical population consisted of women in second marriages who referred to counseling centers in Tehran in 2026. Using convenience sampling, 28 participants were selected and randomly assigned into experimental (n = 14) and control (n = 14) groups. Data were collected using the Marital Inhibition Questionnaire and the Emotional Intimacy Questionnaire. The experimental group participated in eight weekly 90-minute sessions of Acceptance and Commitment Therapy, whereas the control group received no intervention. Data were analyzed using analysis of covariance (ANCOVA) in IBM SPSS Statistics version 27 at a significance level of 0.05. Findings: The findings indicated that Acceptance and Commitment Therapy significantly reduced marital inhibition and increased emotional intimacy among women in second marriages. The results of covariance analysis for marital inhibition demonstrated a significant difference between the experimental and control groups in the posttest phase (F = 9.412, effect size = 0.148). In addition, the covariance analysis results for emotional intimacy revealed a significant difference between the two groups following the intervention (F = 13.845, effect size = 0.193). The posttest mean scores showed that marital inhibition decreased and emotional intimacy increased in the experimental group compared with the control group. Conclusion: Based on the findings, Acceptance and Commitment Therapy was effective in improving emotional intimacy and reducing marital inhibition among women in second marriages. By increasing psychological flexibility, emotional acceptance, and adaptive interpersonal functioning, ACT may help remarried women establish healthier emotional relationships and reduce maladaptive controlling behaviors within marital interactions.
Copyright (c) 2025 Fatemeh Abedi Firouzjaei (Author); Mina Sadat Abdollahizade (Corresponding Author); Azadeh Armoon , Sohila Karimi , Maryam Ahmadimoghadam (Author)
Comparison of Compassion-Based Positive Couple Therapy and Emotionally Focused Couple Therapy on Emotional Experience Toward Spouse Among Young Couples in Tehran
Objective: The present study aimed to compare the effectiveness of compassion-based positive couple therapy and emotionally focused couple therapy on emotional experience toward spouse among young couples in Tehran. Methods and Materials: This applied study employed a semi-experimental pretest–posttest design with a control group and a two-month follow-up phase. The statistical population consisted of young couples referring to counseling and psychological service clinics in District 5 of Tehran between July 2024 and March 2025. Using purposive sampling, 45 couples were selected and assigned into two experimental groups and one control group, with 15 couples in each group. The first experimental group received compassion-based positive couple therapy, while the second experimental group participated in emotionally focused couple therapy. Both interventions were implemented in fifteen two-hour sessions conducted twice weekly. The control group remained on a waiting list and received no intervention during the study period. Data were collected using the Emotional Experience Toward Spouse Questionnaire developed by Yusefi and Ghaffaralehi. Data analysis was performed using SPSS-26 through descriptive statistics, repeated measures analysis of variance, multivariate analysis of variance, and Bonferroni post hoc tests. Findings: The results indicated significant effects of group, time, and group-by-time interaction on positive and negative emotional experiences toward spouse (p < .001). Both compassion-based positive couple therapy and emotionally focused couple therapy significantly increased positive emotional experiences and reduced negative emotional experiences compared with the control group. Bonferroni post hoc comparisons demonstrated that the intervention groups achieved significantly better outcomes than the control group at the posttest stage (p < .001). Pairwise comparisons further revealed significant improvements from pretest to posttest and from pretest to follow-up in both intervention groups, while no significant differences were observed between posttest and follow-up stages, indicating the stability of treatment effects over time. Conclusion: The findings suggest that both compassion-based positive couple therapy and emotionally focused couple therapy are effective interventions for improving emotional experiences toward spouse among young couples. These approaches appear to strengthen emotional intimacy, emotional responsiveness, and adaptive emotional interactions while reducing negative emotional experiences and relational distress. Therefore, the application of emotion-centered and compassion-oriented therapeutic interventions may play an important role in promoting marital well-being and emotional stability among couples.
Compassion-Based Positive Couple Therapy
Copyright (c) 2025 Fatemeh Mehdipour (Author); Naser Amini (Corresponding Author); Marjan Albehbahani (Author)
Algorithmic Modeling of Caregiver Systemic Burden in Chronic Illness by Intersecting Partner Neuroticism and Dyadic Coping Mechanisms
Objective: To develop an algorithmic model predicting caregiver systemic burden by evaluating the complex, non-linear intersections between a care-recipient’s neuroticism and the couple’s dyadic coping mechanisms. Methods and Materials: A cross-sectional, dyadic study design was utilized, comprising 214 caregiver/care-recipient dyads managing chronic illness in Yerevan, Armenia. Psychometric data were collected using the Zarit Burden Interview (ZBI) for caregiver burden, the Big Five Inventory-2 (BFI-2) for care-recipient neuroticism, and the Dyadic Coping Inventory (DCI) for dyadic coping. Predictive modeling was conducted using a Gradient Boosting Machine (GBM) algorithm with the dataset partitioned into training ( ) and testing ( ) subsets, while SHapley Additive exPlanations (SHAP) were employed to extract feature importance and interaction effects. Findings: The GBM model exhibited strong predictive performance, accounting for of the variance in caregiver systemic burden on the test set ( ), with a Root Mean Square Error ( ) of . SHAP analyses identified care-recipient neuroticism as the most potent predictor of burden (Mean ), followed by caregiver engagement in negative dyadic coping (Mean ) and illness duration (Mean ). Interaction analyses revealed that the detrimental impact of partner neuroticism was exponentially amplified by negative dyadic coping and significantly buffered by active supportive dyadic coping. Conclusion: Algorithmic modeling effectively demonstrates that caregiver systemic burden is a deeply interdependent phenomenon, highlighting that the psychological toll of a partner’s neuroticism is highly conditional upon the couple’s underlying dyadic coping architecture.
Copyright (c) 2026 Aishath Naseem (Author); Arman Sargsyan (Corresponding Author); Zuzana Hrivnáková (Author)
A Random Forest Analysis Predicting Intergenerational Attachment Insecurity from Parental Reflective Functioning and Childhood Trauma Phenotypes
Objective: The primary objective of this study was to utilize a Random Forest machine learning approach to predict the variance in intergenerational attachment insecurity using distinct childhood trauma phenotypes and multidimensional facets of parental reflective functioning. Methods and Materials: A cross-sectional, correlational, and predictive design was employed with a community sample of Romanian primary caregivers of children aged to years. Data were collected via self-report instruments, including the Childhood Trauma Questionnaire (CTQ) to assess five trauma phenotypes, the Parental Reflective Functioning Questionnaire (PRFQ) to evaluate mentalizing dimensions, and the Experiences in Close Relationships-Revised (ECR-R) alongside the Child Attachment Questionnaire (CAQ) to index intergenerational attachment insecurity. During data preprocessing, missing values were resolved using K-nearest neighbors imputation, and independent variables were subjected to Z-score standardization. A Random Forest regression algorithm was trained on an data split and evaluated on the remaining testing set, with feature importance mathematically derived via the mean decrease in node impurity. Findings: Descriptive statistics indicated that emotional neglect was the most frequently endorsed historical trauma phenotype ( , ), while participants generally demonstrated moderate to high levels of interest and curiosity in their children’s mental states ( , ). The standardized outcome variable of intergenerational attachment insecurity ( , ) showed significant positive bivariate correlations with emotional abuse ( , ) and emotional neglect ( , ). Feature importance extraction from the Random Forest model identified emotional neglect and emotional abuse as the most powerful distal trauma predictors. Among proximal psychological mechanisms, maladaptive pre-mentalizing modes emerged as the strongest positive predictor of insecurity ( , ), whereas adaptive reflective capacities—specifically interest and curiosity ( , ) and certainty about mental states ( , )—functioned as significant negative predictors. Conclusion: Advanced machine learning successfully mapped the non-linear pathways of relational risk, revealing that historical emotional maltreatment and current pre-mentalizing deficits are the primary drivers of intergenerational attachment insecurity, while adaptive reflective functioning serves as a crucial protective buffer.
Copyright (c) 2026 Miriam Hochberger (Author); Andrei Dumitrescu (Corresponding Author); Lucía Bentancor (Author)
Uncovering Nonlinear Dynamics in Intergenerational Anxiety: Support Vector Regression of Parental Psychological Control, Child Behavioral Inhibition, and Family Climate
Objective: To utilize Support Vector Regression to uncover the complex, nonlinear dynamics and threshold effects characterizing the relationship between parental psychological control, child behavioral inhibition, family climate, and the manifestation of intergenerational anxiety symptoms in children. Methods and Materials: This study employed a cross-sectional design encompassing 482 parent-child dyads recruited from Kenya. Primary data collection instruments included the Psychological Control Scale, the Behavioral Inhibition Questionnaire, the Family Environment Scale, and the Screen for Child Anxiety Related Emotional Disorders (SCARED). To analyze the multidimensional data, a Support Vector Regression model utilizing a Radial Basis Function kernel was implemented. Model hyperparameters were optimized via 10-fold cross-validation, and individual predictor contributions were evaluated using permutation feature importance. Findings: The Support Vector Regression model demonstrated superior predictive capability, explaining of the variance ( ) in child anxiety, significantly outperforming standard Multiple Linear Regression ( ). Permutation analysis identified child behavioral inhibition as the most critical predictor, accounting for a increase in model error when neutralized, followed closely by parental psychological control ( ), family conflict ( ), and family cohesion ( ). Furthermore, partial dependence profiles revealed a critical toxicity threshold: child anxiety spiked exponentially only when parental psychological control exceeded standard deviations above the mean, while high family conflict acted as a synergistic non-linear amplifier specifically for highly inhibited youth. Conclusion: The intergenerational transmission of anxiety is fundamentally governed by complex, non-linear tipping points and synergistic temperamental vulnerabilities that are most accurately captured and understood through advanced machine learning models.
Copyright (c) 2026 Andrés Benítez (Author); Mercy Atieno (Corresponding Author); Elpida Christou (Author)
Development of a Neuropsychological Questionnaire for Marital Conflicts and Examination of Its Psychometric Properties in Iran
Objective: The aim of the present study was to develop a neuropsychological questionnaire for marital conflicts and to examine its psychometric properties in Iran. Methods and Materials: This study was conducted in two phases: pilot and final. In the pilot phase, the initial questionnaire was administered to 81 couples experiencing marital conflict, and its structure was examined using exploratory factor analysis. In the final phase, the revised version was administered to 318 couples with marital conflict and 112 married individuals without severe conflict. To assess convergent validity, the Barati and Sanaei Marital Conflict Questionnaire and the ENRICH Marital Satisfaction Questionnaire were used. Data analysis was conducted using reliability indices, construct validity, convergent and discriminant validity, and confirmatory factor analysis. Findings: The results indicated that the questionnaire has a five-factor structure including the prefrontal cortex, anterior cingulate gyrus, deep limbic system, basal ganglia, and temporal cortex. Correlation coefficients with the marital conflict scale ranged from 0.58 to 0.62, and correlations with marital satisfaction were negative and significant. Additionally, significant differences were observed between couples with and without conflict across all components (p < 0.01). Conclusion: The neuropsychological marital conflict questionnaire demonstrated satisfactory validity and reliability and can be used as a culturally adapted and valid instrument for assessing the neuropsychological dimensions of marital conflicts in research and clinical interventions.
Copyright (c) 2025 Malihe Nasr Esfahani (Author); Mojtaba Tamadoni (Corresponding Author); Mastooreh Sedaghat (Author)
Algorithmic Differentiation of Parenting Phenotypes: Clustering Parental Burnout, Psychological Flexibility, and Their Effects on Child Effortful Control
Objective: This study aimed to differentiate parenting phenotypes via algorithmic clustering of parental burnout and psychological flexibility, and to examine their subsequent impact on child effortful control. Methods and Materials: A cross-sectional, quantitative study was conducted with primary caregivers of children aged to years in Colombia. Data were collected via a secure online survey utilizing validated instruments: the Parental Burnout Assessment (PBA), the Parental Acceptance and Action Questionnaire (PAAQ; reverse-coded), and the Effortful Control scale from the parent-reported Child Behavior Questionnaire (CBQ). A two-step cluster analysis (hierarchical with Ward’s method followed by K-means) was applied to standardized -scores of the PBA and PAAQ to determine parenting profiles. Group distinctiveness and effects on child effortful control were analyzed using MANOVA and ANCOVA, controlling for parent age and child gender, followed by post-hoc Tukey’s HSD tests. Findings: The clustering algorithm yielded an optimal three-phenotype solution (average silhouette width = ): Adaptive ( , low burnout, high flexibility), Strained ( , high burnout, low flexibility), and Vulnerable ( , moderate scores). A MANOVA confirmed significant multivariate distinctiveness between the clusters (Wilks’ , , , ). ANCOVA revealed a significant main effect of parenting phenotype on child effortful control ( , , ). Children of the Adaptive Phenotype exhibited the highest effortful control ( ), scoring significantly higher than both the Vulnerable ( ) and Strained ( ) phenotypes ( for all pairwise comparisons). Conclusion: Distinct algorithmic parenting phenotypes significantly predict variations in child effortful control, highlighting the critical influence of parental psychological states on child self-regulation.
Copyright (c) 2026 Laura Paredes (Author); Andrés Mejía (Corresponding Author)
Unsupervised Clustering of Intimate Partner Violence Typologies: Analyzing Communication Deviance, Attachment Anxiety, and Trait Impulsivity via Machine Learning
Objective: This study aimed to identify distinct typologies of intimate partner violence (IPV) risk by analyzing the interplay of communication deviance, attachment anxiety, and trait impulsivity using unsupervised machine learning within a community sample. Methods and Materials: A cross-sectional quantitative design was utilized with a sample of individuals from heterosexual couples in Poland. Data were collected via online self-report measures, including the Revised Conflict Tactics Scales (CTS2), the Communication Deviance Scale, the Experiences in Close Relationships-Revised (ECR-R) for attachment anxiety, and the Barratt Impulsiveness Scale (BIS-11). A k-means clustering algorithm was applied to the standardized psychological variables to derive typologies, which were subsequently profiled and validated against IPV severity using one-way ANOVAs. Findings: The k-means algorithm yielded a robust three-cluster solution. Cluster 1, the “Regulated/Secure” group ( , ), exhibited low scores across all three risk factors and reported minimal IPV. Cluster 2, the “Anxious-Impulsive” group ( , ), displayed elevated attachment anxiety and trait impulsivity alongside moderate communication deviance, corresponding to significantly higher rates of psychological and physical aggression. Cluster 3, the “Dysregulated/Deviant” group ( , ), demonstrated the highest scores across all three psychological constructs and reported the most severe, multifaceted IPV, including significantly elevated levels of sexual coercion and physical injury. Conclusion: The identification of these distinct psychological risk profiles highlights the fundamental heterogeneity of IPV perpetration and underscores the critical necessity for tailored, person-centered screening and intervention strategies.
Copyright (c) 2026 Verónica Escalante (Author); Mateusz Kaczmarek (Corresponding Author); Natalia Caballero (Author)
Forecasting Internalizing Psychopathology in Adolescents: A Gradient Boosting Approach to Parental Expressed Emotion, Adverse Childhood Experiences (ACEs), and Cognitive Reappraisal
Objective: The objective of this study was to prospectively forecast adolescent internalizing psychopathology over an 18-month period by modeling the complex, non-linear interactions between cumulative adverse childhood experiences (ACEs), parental expressed emotion, and habitual cognitive reappraisal using a machine learning framework. Methods and Materials: This prospective longitudinal study utilized a sample of German adolescent-parent dyads. Baseline (T1) predictors included cumulative trauma assessed via the Adverse Childhood Experiences Questionnaire (ACE-Q), habitual emotion regulation measured by the Emotion Regulation Questionnaire (ERQ), and Parental Expressed Emotion (criticism and emotional over-involvement) derived from the Five-Minute Speech Sample (FMSS). The primary outcome, adolescent internalizing psychopathology at an 18-month follow-up (T2), was assessed using the Youth Self-Report (YSR). Data were analyzed using a gradient boosting regression ensemble, evaluated via an train-test split and 10-fold cross-validation, to capture higher-order non-linear predictive topologies without requiring a priori specification. Findings: The gradient boosting regression model demonstrated robust forecasting accuracy on the unseen test set, successfully predicting a significant portion of the variance in T2 internalizing symptoms ( , ), yielding a coefficient of determination of , with a Mean Absolute Error ( ) of and a Root Mean Square Error ( ) of . Bivariate analyses confirmed foundational relationships, showing positive correlations between ACEs and T2 internalizing symptoms ( ) and protective, negative correlations for cognitive reappraisal ( ). Multivariate feature importance extraction revealed a distinct predictive hierarchy: cumulative ACEs emerged as the single most dominant forecasting variable ( relative importance), followed significantly by parental criticism ( ), cognitive reappraisal ( ), and parental emotional over-involvement ( ). Conclusion: Cumulative childhood trauma and proximal parental criticism serve as the primary structural catalysts for future adolescent internalizing distress, whereas the adolescent’s habitual capacity for cognitive reappraisal offers a vital, quantifiable counterweight for psychological resilience.
Copyright (c) 2026 Jean-Claude Niyonsenga (Author); Lena Hoffmann (Corresponding Author); Carlos Menjívar (Author)
Using Deep Learning to Classify Family Functioning Profiles Based on Parental Attachment, Emotion Socialization, and Child Behavioral Outcomes
Objective: The objective of this study was to identify distinct family functioning typologies based on parental attachment, emotion socialization practices, and child behavioral outcomes, and to subsequently develop a deep learning model for the accurate automated classification of these profiles. Methods and Materials: This study utilized a cross-sectional design involving two-parent families from Greece. Data were collected using secure online self-report questionnaires, including the Experiences in Close Relationships-Revised for parental attachment, the Coping with Children’s Negative Emotions Scale for parental emotion socialization, and the Strengths and Difficulties Questionnaire for child behavioral outcomes. Data analysis proceeded in two phases: first, Latent Profile Analysis (LPA) was utilized to identify unobserved family profiles. Second, a Multi-Layer Perceptron (MLP) deep learning model was developed to classify families into these established profiles. The dataset was partitioned into training ( ), validation ( ), and testing ( ) segments to evaluate the model’s predictive performance. Findings: Latent Profile Analysis successfully identified three distinct family functioning profiles: Adaptive Family Functioning ( , ), Anxious-Inconsistent Functioning ( , ), and High-Risk/Avoidant-Punitive Functioning ( , ). The trained Multi-Layer Perceptron deep learning model demonstrated exceptional classification capabilities, achieving an overall accuracy of on the unseen test set. The model yielded high F1-scores for each respective class: for the Adaptive profile, for the Anxious-Inconsistent profile, and for the High-Risk profile, with the few misclassifications occurring primarily between the Anxious-Inconsistent and High-Risk groups. Conclusion: The robust identification of multidimensional family profiles and their highly accurate classification via an artificial neural network provides a powerful, innovative methodological framework to enhance targeted clinical assessments and tailor psychological interventions.
Copyright (c) 2026 Minh Tran Quoc (Author); Nikos Papadakis (Corresponding Author); Kwame Asante (Author)
Machine-Learning Identification of Family Adaptation Patterns During Economic Stress Based on Coping Strategies and Couple Communication
Objective: This study aimed to identify distinct family adaptation patterns during economic stress by applying unsupervised machine-learning to the interplay of coping strategies and couple communication behaviors. Methods and Materials: A quantitative, cross-sectional design was employed with married couples from Malaysia experiencing varying degrees of economic stress. Data were collected via self-report instruments measuring perceived economic pressure, coping mechanisms (problem-focused, emotion-focused, and avoidant), and couple communication patterns (constructive and demand-withdraw). An unsupervised K-Means clustering algorithm was applied to the multidimensional data to segment the couples into naturally occurring adaptation profiles, followed by post-hoc statistical analyses to evaluate the characteristics of each profile and their associations with objective socio-economic factors. Findings: The K-Means clustering analysis ( ) successfully delineated three distinct family adaptation patterns. Cluster , the “Resilient-Constructive Adapters” ( , ), exhibited high problem-focused coping and constructive communication, reporting the lowest perceived economic stress ( , ). Cluster , the “Distressed-Avoidant Adapters” ( , ), was characterized by heavy reliance on avoidant coping and demand-withdraw communication, experiencing the highest levels of economic stress ( , ) and showing a disproportionate representation from the lowest income bracket ( ). Cluster , the “Ambivalent-Emotion-Focused Adapters” ( , ), displayed moderate communication quality with a high reliance on emotion-focused coping and social support seeking, reporting moderate economic stress ( , ). A statistically significant association was found between cluster membership and objective household income ( , ). Conclusion: Unsupervised machine-learning successfully revealed that families navigate financial hardship through interconnected behavioral typologies, highlighting the necessity for tailored interventions that address both objective financial realities and dyadic relationship dynamics to foster resilience.
The Mediating Role of Frustration Tolerance in the Relationship Between Early Maladaptive Schemas, Self-Differentiation, and Marital Conflict in Individuals Applying for Divorce
Objective: The present study aimed to determine the fit of a structural equation model examining the mediating role of frustration tolerance in the relationship between early maladaptive schemas and self-differentiation with marital conflict among individuals applying for divorce. Methods and Materials: This study employed a fundamental, descriptive-correlational design using structural equation modeling (SEM). The statistical population consisted of individuals applying for divorce who had active cases in family counseling centers affiliated with the judiciary in Karaj and Hashtgerd between February 2025 and October 2025. A sample of 250 participants (139 women and 111 men) was selected through convenience sampling. Data were collected using the Revised Marital Conflict Questionnaire (Sanaei et al., 2008), the Young Schema Questionnaire (Young, 2005), the Self-Differentiation Inventory (Skowron & Friedlander, 1998), and the Frustration Discomfort Scale (Harrington, 2005). Data analysis was conducted using SPSS version 27 and AMOS version 26 with maximum likelihood estimation. Findings: The results indicated that the proposed structural model demonstrated an acceptable fit with the data (χ²/df = 1.49, GFI = 0.908, CFI = 0.958, RMSEA = 0.044). Early maladaptive schemas had a positive and significant effect on marital conflict (β = 0.412, p = .001), whereas self-differentiation had a negative and significant effect (β = −0.374, p = .001). Frustration tolerance (operationalized as intolerance) showed a positive and significant relationship with marital conflict (β = 0.424, p = .001). Moreover, frustration tolerance significantly mediated the relationships between early maladaptive schemas and marital conflict (β = 0.157, p = .001) and between self-differentiation and marital conflict (β = −0.138, p = .001). The model explained 57% of the variance in marital conflict (R² = 0.57). Conclusion: The findings suggest that early maladaptive schemas and self-differentiation are significant predictors of marital conflict, both directly and indirectly through frustration tolerance.
Copyright (c) 2025 Samira Hosseini (Author); Said Ali mohammad Mousavi (Corresponding Author); Arezoo Shomalyoskoei (Author)
The Effectiveness of Systemic-Behavioral Couples Therapy (SBCT) in Enhancing Relationship Outcomes Among Spouses of Individuals with Parkinson’s Disease
Objective: This study evaluated the efficacy of Systemic-Behavioral Couples Therapy (SBCT) in improving relational outcomes among spouses of PD patients. Methods and Materials: Employing a quasi-experimental pretest–posttest–follow-up design with a control group, 40 spouses attending the Neurology Specialty Department of Firouzgar Hospital, Tehran (2024) were recruited via purposive sampling and randomly allocated to experimental and control conditions (final N = 24; 12 per group). The experimental group underwent six weekly 45-minute SBCT sessions targeting communication enhancement, cognitive restructuring, and interaction pattern modification. Outcome measures included marital satisfaction (Hudson, 1982), sexual intimacy (Batalani et al., 2010), and family emotional climate (Hillburn, 1964). Data were analyzed using ANCOVA, paired t-tests, and repeated measures ANOVA in SPSS 24, with α = .05. Findings: The SBCT group demonstrated significant improvements across all outcomes compared to the control group (p < .001). Effect sizes were large (η² = .68–.70), and Cohen’s d values were very large to extremely large (2.47–4.33). Reliable Change Index (RCI > 1.96) confirmed clinically meaningful change. Follow-up assessment showed slight attenuation, but post-intervention gains remained substantially higher than baseline. Conclusion: SBCT represents a robust, clinically meaningful intervention to enhance marital satisfaction, sexual intimacy, and family emotional climate in spouses of PD patients. These findings support integrating SBCT into psychosocial support frameworks for families affected by chronic neurodegenerative disorders.
Copyright (c) 2025 Farshid Fathy-Karkaragh (Author); Erfan Azami (Corresponding Author); Esmaeil Sadri Damirchi, Omid Malekpour, Manijeh Firoozi, Gholamali Shahidi (Author)