Journal of Psychosociological Research in Family and Culture
JPRFC publishes four issues per year, with occasional special issues coming in addition. E-ISSN: 3041-8550 Director in Charge: M.B. Jafari Editor-in-chief: Dr. Mehdi Rostami Owner: KMAN Research Institute Publisher: KMAN Publication Inc. (KMANPUB) Email: jprfc@kmanpub.com Open Access: YES JPRFC covers a wide range of topics related to family and culture, such as psychology, sociology, anthropology, and more. The journal provides an advantageous resource for professionals and scholars in these fields, as it offers a platform for publishing cutting-edge research and innovative ideas. The journal is committed to publishing articles that make significant contributions to the fields of family and culture, and that have practical implications for professionals working in these areas. Overall, JPRCF and Culture is an excellent addition to the academic community. With its emphasis on quality research, meticulous peer-review process, and commitment to open access, the journal is well-positioned to serve as a leading resource for professionals and scholars in the fields of family and culture. 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)
Identifying Cultural Patterns in Parenting Beliefs Through Clustering Algorithms
Objective: The present study aimed to identify latent cultural patterns in parenting beliefs by applying clustering algorithms to multidimensional data on parental attitudes and cultural orientations. Methods and Materials: This study employed a quantitative, cross-sectional design with a data-driven analytical approach. The sample consisted of 412 parents from Georgia, selected through stratified random sampling to ensure representation across demographic strata. Data were collected using standardized instruments assessing parenting beliefs, including dimensions such as parental authority, autonomy support, emotional socialization, and behavioral control, alongside cultural orientation constructs such as collectivism, individualism, power distance, and uncertainty avoidance. After data preprocessing, clustering analyses were conducted using K-means and hierarchical methods. The optimal number of clusters was determined using the Elbow method, Silhouette coefficient, and Davies–Bouldin index. Principal component analysis (PCA) was also employed to enhance interpretability and visualize cluster separation. Statistical preprocessing and descriptive analyses were performed using IBM SPSS, while machine learning analyses were implemented in Python. Findings: Clustering results revealed a statistically meaningful three-cluster solution with acceptable validity indices (Silhouette = 0.53; Davies–Bouldin = 0.81), indicating moderate-to-strong separation between clusters. The first cluster demonstrated significantly higher levels of parental authority, behavioral control, and collectivist orientation, reflecting a traditional-authoritarian profile. The second cluster exhibited significantly higher autonomy support, emotional socialization, and individualism, representing an autonomy-supportive profile. The third cluster showed moderate levels across all variables, indicating a hybrid or integrative parenting belief pattern. Principal component analysis confirmed clear spatial differentiation among clusters, supporting the robustness and interpretability of the classification model. Conclusion: The findings demonstrate that parenting beliefs are structured into distinct cultural profiles that reflect varying combinations of authority, autonomy, and cultural orientation. The identification of traditional, autonomy-supportive, and hybrid parenting patterns highlights the complexity and heterogeneity of parenting beliefs within a single cultural context. These results underscore the value of machine learning approaches in uncovering latent cultural structures and provide a foundation for culturally informed research and intervention in family and developmental studies.
Copyright (c) 2025 Naledi Molefe (Author); Tamar Gelashvili (Corresponding Author); Shantel Rowe (Author)
Machine Learning-Based Classification of Cultural Beliefs and Mental Health Attitudes
Objective: The present study aimed to classify cultural beliefs and mental health attitudes using machine learning techniques and to identify the most influential psychosocial predictors within an Armenian population. Methods and Materials: This cross-sectional, descriptive–analytical study was conducted on a sample of 412 adults from Armenia selected through stratified random sampling. Data were collected using standardized instruments assessing cultural beliefs (including individualism, collectivism, traditionalism, and authority orientation) and mental health attitudes (including stigma, help-seeking attitudes, emotional openness, and beliefs about mental illness), along with a demographic questionnaire. Data preprocessing included normalization, missing data imputation, and categorical encoding. Feature selection was performed using correlation-based filtering and recursive feature elimination. Supervised machine learning models, including Support Vector Machine, Random Forest, and Gradient Boosting, were implemented to classify participants based on their psychosocial profiles. Model evaluation was conducted using 10-fold cross-validation, with performance assessed via accuracy, precision, recall, F1-score, and AUC-ROC metrics. Findings: Inferential results indicated significant associations between cultural belief dimensions and mental health attitudes, with collectivism, traditionalism, and authority orientation positively predicting mental health stigma, while individualism significantly predicted help-seeking attitudes and emotional openness. Machine learning analyses revealed that the Gradient Boosting model demonstrated superior classification performance (accuracy = 0.902, AUC = 0.947) compared to Random Forest and Support Vector Machine models. Feature importance analysis showed that mental health stigma, help-seeking attitudes, and collectivism were the most influential predictors in distinguishing participant profiles. Correlation patterns further confirmed that higher stigma was significantly associated with lower help-seeking and emotional openness, while culturally embedded belief systems significantly shaped mental health perceptions. Conclusion: The findings demonstrate that cultural beliefs play a critical role in shaping mental health attitudes and that machine learning techniques provide a robust framework for accurately classifying these complex relationships. The prominence of stigma-related constructs highlights the need for culturally sensitive interventions aimed at reducing stigma and promoting adaptive help-seeking behaviors. Integrating computational approaches with cultural psychology offers valuable insights for targeted mental health strategies and policy development.
Copyright (c) 2025 Zuzana Kováčová (Author); Mariam Grigoryan (Corresponding Author)
Predicting Family Stability Using Socioeconomic and Cultural Variables via Machine Learning
Objective: The present study aimed to predict family stability based on socioeconomic and cultural variables using advanced machine learning models. Methods and Materials: This study employed a cross-sectional predictive-correlational design involving 428 participants from diverse urban and semi-urban regions of Ecuador selected through multistage cluster sampling. Data were collected using standardized instruments, including the Family Adaptability and Cohesion Evaluation Scale IV (FACES IV) to measure family stability and its subcomponents, alongside structured measures of socioeconomic status and culturally adapted scales assessing collectivism, traditionalism, gender role beliefs, and intergenerational norms. Data preprocessing included handling missing values, normalization, and categorical encoding. Machine learning algorithms, including Random Forest, Support Vector Machines, Gradient Boosting, and Artificial Neural Networks, were implemented using Python-based analytical frameworks. Model performance was evaluated through 10-fold cross-validation, with accuracy, precision, recall, F1-score, and AUC-ROC as key performance indicators. Feature importance analysis was conducted to identify the most influential predictors of family stability. Findings: The results indicated that Gradient Boosting achieved the highest predictive performance (AUC = 0.93; accuracy = 0.88), followed by Artificial Neural Networks (AUC = 0.92), Random Forest (AUC = 0.91), and Support Vector Machines (AUC = 0.88). Communication quality emerged as the strongest predictor of family stability, followed by socioeconomic status, family cohesion, and cultural collectivism. Significant positive associations were observed between family stability and communication (r = 0.67), socioeconomic status (r = 0.48), and collectivism (r = 0.42), indicating the combined influence of relational, structural, and cultural dimensions in shaping family outcomes. Conclusion: The findings demonstrate that family stability is a multidimensional construct influenced by the interaction of socioeconomic conditions, cultural values, and relational processes, with machine learning models providing robust predictive capabilities. The prominence of communication quality highlights the critical role of intra-family dynamics, while the contribution of socioeconomic and cultural variables underscores the need for integrative and context-sensitive approaches in both research and intervention.
Copyright (c) 2025 Victor Rusu (Author); Daniela Cevallos (Corresponding Author); Elena Stojanovska (Author)
A Predictive Study of Family Adaptability Based on Cultural and Economic Indicators
Objective: The present study aimed to predict family adaptability based on cultural orientation and economic indicators using both statistical and machine learning approaches in a Canadian population. Methods and Materials: This study employed a quantitative, cross-sectional, descriptive–correlational design. The statistical population consisted of married adults residing in Canada in 2025, from which 486 participants were selected using a stratified random sampling method based on the Krejcie and Morgan table. Data were collected using standardized instruments, including the Family Adaptability subscale of the Family Adaptability and Cohesion Evaluation Scales IV (FACES IV), a Cultural Orientation Scale assessing individualism, collectivism, traditionalism, and acculturation, and a socioeconomic questionnaire measuring income index, financial stability, and economic pressure. The reliability and validity of all instruments were confirmed in prior research and re-evaluated in the present study. Data analysis was conducted using IBM SPSS Statistics (version 27) for descriptive statistics, Pearson correlations, and multiple regression analysis. Additionally, machine learning models including Random Forest, Support Vector Machine, and Gradient Boosting were implemented in Python using Scikit-learn to enhance predictive accuracy. Findings: The results revealed that collectivism (β = 0.31, p < 0.001), financial stability (β = 0.36, p < 0.001), acculturation (β = 0.18, p < 0.001), and individualism (β = 0.12, p = 0.007) were significant positive predictors of family adaptability, whereas economic pressure (β = -0.29, p < 0.001) was a significant negative predictor. The regression model explained 46% of the variance in family adaptability (R² = 0.46, p < 0.001). Machine learning results indicated that the Gradient Boosting model achieved the highest predictive performance (R² = 0.74), followed by Random Forest (R² = 0.71) and Support Vector Machine (R² = 0.66). Feature importance analysis identified financial stability and economic pressure as the most influential predictors. Conclusion: The findings demonstrate that both cultural and economic factors play a critical and complementary role in shaping family adaptability, with economic conditions exerting particularly strong effects. The integration of machine learning approaches provided enhanced predictive accuracy and revealed complex relationships among variables, highlighting the importance of multidimensional and data-driven approaches in family research. These results underscore the need for interventions and policies that simultaneously address financial stability and cultural integration to promote adaptive family functioning.
Copyright (c) 2025 Andrej Kováčik (Author); Liam Desrosiers (Corresponding Author); Marc Puigdemont (Author)
Cross-Cultural Prediction of Norm Internalization Using Conformity Pressure, Moral Disengagement, and Social Learning
Objective: The present study aimed to predict norm internalization across culturally diverse individuals based on conformity pressure, moral disengagement, and social learning using an integrated statistical and machine learning approach. Methods and Materials: This study employed a descriptive–correlational design with a predictive modeling framework. The sample consisted of 512 participants from Canada, selected through stratified random sampling to ensure cultural diversity. Data were collected using standardized instruments measuring norm internalization, conformity pressure, moral disengagement, and social learning. Demographic variables were also assessed to control for potential confounding effects. Data analysis was conducted using both classical statistical techniques, including Pearson correlation and multiple regression analysis, and advanced machine learning models such as Random Forest, Support Vector Machine, and Gradient Boosting. Model performance was evaluated using cross-validation procedures and metrics including R², RMSE, and MAE. Findings: The results indicated that conformity pressure (β = 0.24, p < 0.001) and social learning (β = 0.31, p < 0.001) significantly and positively predicted norm internalization, while moral disengagement (β = -0.38, p < 0.001) was a significant negative predictor. The regression model explained a substantial proportion of variance in norm internalization (R² = 0.41). Machine learning analyses revealed that the Gradient Boosting model achieved the highest predictive performance (R² = 0.47), outperforming Random Forest and Support Vector Machine models. Feature importance analysis consistently identified moral disengagement as the most influential predictor, followed by social learning and conformity pressure. Conclusion: The findings demonstrate that norm internalization is a multifaceted process influenced by the interaction of social influence and cognitive moral mechanisms. Social learning and conformity pressure facilitate the internalization of norms, whereas moral disengagement undermines it. The integration of machine learning methods enhances predictive accuracy and reveals complex non-linear relationships among variables. These results highlight the importance of addressing both social and cognitive dimensions in interventions aimed at promoting ethical behavior and strengthening social cohesion in culturally diverse contexts.
Copyright (c) 2025 Yanelis Batista (Author); Gabrielle Pelletier (Corresponding Author); Rodrigo Villagra (Author)
Modeling Substance Use Risk via Family Monitoring, Sensation Seeking, and Peer Deviance
Objective: The present study aimed to model substance use risk among adolescents by examining the predictive roles of family monitoring, sensation seeking, and peer deviance within an integrated analytical framework. Methods and Materials: This study employed a quantitative, cross-sectional, correlational design among 472 high school students from urban regions of Ontario, Canada, selected through multistage cluster sampling. Data were collected using standardized self-report instruments, including the Substance Use Risk Profile Scale, the Parental Monitoring Scale, the Brief Sensation Seeking Scale, and a modified Peer Deviance Scale. All instruments demonstrated acceptable validity and reliability in previous research and within the current sample. Data analysis was conducted using IBM SPSS version 27 and AMOS version 24. Descriptive statistics and Pearson correlation coefficients were calculated, followed by multiple regression analysis to examine predictive relationships. Structural equation modeling was used to test the hypothesized model and evaluate direct and indirect effects. Model fit was assessed using standard indices including χ²/df, CFI, TLI, GFI, and RMSEA. Findings: The results indicated significant relationships among all study variables. Family monitoring was negatively associated with substance use risk (β = -0.31, p < 0.001), while sensation seeking (β = 0.36, p < 0.001) and peer deviance (β = 0.43, p < 0.001) showed positive and significant predictive effects, with peer deviance emerging as the strongest predictor. The overall regression model was significant (F(3, 468) = 132.74, p < 0.001), explaining 46% of the variance in substance use risk. Structural equation modeling demonstrated good model fit (χ²/df = 2.41, CFI = 0.95, TLI = 0.94, GFI = 0.93, RMSEA = 0.055). Indirect effects revealed that family monitoring reduced substance use risk through peer deviance, and sensation seeking partially mediated the relationship between peer deviance and substance use risk. Conclusion: The findings highlight the multidimensional nature of adolescent substance use risk, emphasizing the combined influence of familial, personality, and peer-related factors. Family monitoring serves as a protective factor, whereas sensation seeking and peer deviance function as significant risk enhancers. Integrative models that consider both individual dispositions and social contexts provide a more comprehensive understanding of substance use risk. These results underscore the importance of developing prevention and intervention strategies that simultaneously target family dynamics, personality traits, and peer environments to effectively reduce substance use risk among adolescents.
Copyright (c) 2025 Luis Montenegro (Author); Nathaniel Brooks (Corresponding Author); Kareem Mohammed (Author)
Predicting Fertility Intentions Using Cultural Norms, Economic Security, and Relationship Satisfaction via a Machine Learning Approach
Objective: The present study aimed to predict fertility intentions based on cultural norms, economic security, and relationship satisfaction using advanced machine learning techniques. Methods and Materials: This study employed a cross-sectional, predictive-correlational design conducted on 512 adults of reproductive age in South Africa selected through stratified random sampling. Data were collected using standardized instruments including the Cultural Values Scale, Economic Stability Index, Dyadic Adjustment Scale, and a Fertility Intention Scale, all of which demonstrated acceptable validity and reliability in previous studies. Data were analyzed using IBM SPSS-27 for descriptive and correlational analyses, followed by machine learning modeling in Python using Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Network algorithms. Data preprocessing included normalization, missing data imputation, and categorical encoding. Model performance was evaluated using 10-fold cross-validation and metrics including accuracy, precision, recall, F1-score, and area under the ROC curve. Findings: The results indicated significant positive relationships between cultural norms, economic security, relationship satisfaction, and fertility intentions (p < .01). Among predictors, economic security demonstrated the strongest predictive power, followed by relationship satisfaction and cultural norms. Machine learning results showed that the Artificial Neural Network achieved the highest performance (accuracy = 0.868, AUC = 0.926), followed by Gradient Boosting (accuracy = 0.856, AUC = 0.914) and Random Forest (accuracy = 0.842, AUC = 0.901), while Support Vector Machine showed comparatively lower performance (accuracy = 0.801, AUC = 0.862). Feature importance analysis confirmed the dominant role of economic security across all models. Conclusion: The superior performance of machine learning models demonstrates their effectiveness in capturing complex, nonlinear relationships among predictors, offering a powerful approach for understanding and predicting reproductive decision-making.
Copyright (c) 2025 Nikola Petrović (Author); Kabelo Radebe (Corresponding Author)
Machine Learning-Based Identification of Cultural Determinants of Decision-Making: The Role of Risk Perception, Uncertainty Avoidance, and Norm Compliance
Objective: The present study aimed to identify and model the cultural determinants of decision-making using machine learning techniques, with a specific focus on the predictive roles of risk perception, uncertainty avoidance, and norm compliance. Methods and Materials: This study employed a descriptive–correlational design with a machine learning predictive framework. The sample consisted of 412 adult participants from Portugal selected through stratified random sampling to ensure demographic diversity. Data were collected using standardized instruments measuring risk perception, uncertainty avoidance, norm compliance, and decision-making quality. After preprocessing procedures including normalization and handling of missing values, data were analyzed using both traditional statistical methods and advanced machine learning algorithms. Supervised learning models, including Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting, were applied to predict decision-making outcomes. Model performance was evaluated using k-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve. Feature importance analysis was conducted to determine the relative contribution of predictors. Findings: The results indicated that all three cultural variables significantly predicted decision-making quality, with norm compliance emerging as the strongest predictor, followed by risk perception and uncertainty avoidance. Ensemble models demonstrated superior predictive performance, with Gradient Boosting achieving the highest accuracy and classification efficiency compared to other models. Feature importance analysis confirmed the dominant role of norm compliance in influencing decision-making outcomes. Additionally, significant positive relationships were observed among all study variables, indicating that higher levels of cultural alignment correspond to improved decision-making quality. Conclusion: The findings highlight the critical role of cultural determinants in shaping decision-making processes and demonstrate the effectiveness of machine learning approaches in modeling complex behavioral patterns. Integrating cultural variables into predictive frameworks enhances both theoretical understanding and practical applications of decision-making research.
Copyright (c) 2025 Stefan Leitner (Author); Tiago Figueiredo (Corresponding Author); Søren Mikkelsen (Author)
Predicting Parenting Inconsistency Using Machine Learning: Executive Dysfunction, Stress Reactivity, and Role Overload
Objective: The present study aimed to predict parenting inconsistency using machine learning models based on executive dysfunction, stress reactivity, and role overload among parents. Methods and Materials: This study employed a descriptive–correlational design with a machine learning predictive approach. The statistical population consisted of parents residing in Canada with at least one child aged 6 to 16 years, from which 412 participants were selected using stratified random sampling. Data were collected through standardized self-report instruments, including the Parenting Consistency Scale, the Behavior Rating Inventory of Executive Function–Adult Version (BRIEF-A), the Perceived Stress Reactivity Scale (PSRS), and the Role Overload Scale. Data preprocessing involved normalization, outlier detection, and handling missing values using multiple imputation. Statistical analyses were initially conducted using SPSS-27 to compute descriptive statistics and correlations. Subsequently, machine learning models including Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Networks were implemented using Python-based libraries. Model performance was evaluated using accuracy, mean squared error (MSE), coefficient of determination (R²), and area under the receiver operating characteristic curve (AUC-ROC), with a 70/30 train–test split and cross-validation procedures applied. Findings: The results indicated that executive dysfunction, stress reactivity, and role overload were all significant positive predictors of parenting inconsistency. Among these variables, executive dysfunction demonstrated the strongest predictive contribution across all models. Machine learning analyses revealed that the Gradient Boosting model achieved the highest performance (Accuracy = 0.86, R² = 0.66, AUC = 0.90), followed by Random Forest (Accuracy = 0.84, R² = 0.62). Artificial Neural Networks showed moderate predictive performance, while Support Vector Machine exhibited comparatively lower explanatory power. Feature importance analyses confirmed that executive dysfunction was the most influential predictor, followed by role overload and stress reactivity. Conclusion: Integrating executive functioning, stress regulation, and environmental demands within predictive frameworks offers a comprehensive understanding of parenting variability and provides a foundation for developing targeted interventions to promote consistent parenting practices.
Copyright (c) 2026 Fiona MacLeod (Author); Jacob Sinclair (Corresponding Author); Sophie Krier (Author)
Identifying Cultural Drivers of Help-Seeking Behavior Using Machine Learning: Stigma Internalization, Norm Salience, and Self-Construal
Objective: The present study aimed to identify and model the cultural determinants of help-seeking behavior using machine learning techniques, focusing on stigma internalization, norm salience, and self-construal. Methods and Materials: This cross-sectional predictive study was conducted among 512 adults residing in Canada, selected through a stratified sampling approach to ensure demographic diversity. Data were collected using standardized instruments, including the General Help-Seeking Questionnaire (GHSQ), the Internalized Stigma of Mental Illness Scale (ISMI), a validated Social Norms Salience Scale, and the Self-Construal Scale (SCS). Data preprocessing involved normalization, handling missing values via multiple imputation, and encoding categorical variables. Statistical analyses were performed using SPSS-27 for descriptive and correlational assessments. Machine learning models, including Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting, were implemented in Python using Scikit-learn and XGBoost. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics, with k-fold cross-validation ensuring robustness. Feature importance was examined using SHAP (SHapley Additive Explanations) to determine the relative contribution of predictors. Findings: The results indicated that stigma internalization was the strongest negative predictor of help-seeking behavior, while norm salience and self-construal showed significant positive predictive effects. Gradient Boosting demonstrated the highest performance (AUC = 0.92), outperforming other models. Correlation analysis revealed significant negative associations between stigma internalization and help-seeking (p < .01), and positive associations for norm salience and self-construal (p < .01). SHAP analysis confirmed the hierarchical importance of predictors, with stigma internalization exerting the greatest influence, followed by norm salience and self-construal, and revealed nonlinear interaction effects among variables. Conclusion: The findings highlight the central role of culturally embedded psychological factors in shaping help-seeking behavior and demonstrate the effectiveness of machine learning approaches.
Machine Learning Identification of Cultural Value Systems Using Collectivism, Power Distance, and Uncertainty Avoidance
Objective: The present study aimed to identify and model latent cultural value system profiles using machine learning techniques based on collectivism, power distance, and uncertainty avoidance. Methods and Materials: This study employed a cross-sectional, descriptive–analytical design with a predictive modeling approach. The sample consisted of 512 adult participants from Canada selected through stratified random sampling to ensure demographic and cultural diversity. Data were collected using standardized instruments measuring collectivism, power distance, and uncertainty avoidance, all of which demonstrated established validity and reliability in prior research. After data preprocessing, including normalization and handling of missing values, both supervised and unsupervised machine learning techniques were applied. Classification models included support vector machines, random forest, gradient boosting, and logistic regression, while k-means clustering was used to identify latent cultural profiles. Model evaluation was conducted using stratified k-fold cross-validation, with performance metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Feature importance and interpretability were assessed using SHAP analysis. Findings: The results indicated significant positive associations among collectivism, power distance, and uncertainty avoidance (p < 0.01). Among classification models, gradient boosting demonstrated the highest predictive performance (AUC-ROC = 0.927), followed by random forest (AUC-ROC = 0.912), indicating strong model discrimination. Logistic regression showed comparatively lower performance, suggesting the presence of nonlinear relationships among variables. Clustering analysis identified three distinct cultural profiles characterized by low, moderate, and high levels of the examined dimensions. Feature importance analysis revealed that power distance was the strongest predictor of cultural profile classification, followed by uncertainty avoidance and collectivism. Conclusion: The study underscores the importance of power distance and uncertainty avoidance in shaping cultural profiles and supports the utility of advanced computational methods in cultural research.
Modeling Cultural Influences on Risk-Taking Using Machine Learning: Sensation Seeking, Norm Deviance, and Peer Influence
Objective: The present study aimed to model and predict risk-taking behavior by examining the interactive effects of sensation seeking, norm deviance, and peer influence within a cultural framework using machine learning techniques. Methods and Materials: This study employed a cross-sectional, predictive-correlational design with a sample of 462 young adults from Greece selected through stratified sampling. Data were collected using standardized self-report instruments assessing risk-taking behavior, sensation seeking, norm deviance, and peer influence. After data preprocessing, including normalization and missing data imputation, both statistical and machine learning analyses were conducted. Pearson correlations were used to examine associations among variables, followed by the implementation of multiple supervised machine learning models, including Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Networks. The dataset was divided into training and testing subsets using an 80/20 split, and 10-fold cross-validation was applied to enhance model generalizability. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics, while SHAP analysis was used to determine feature importance and interpret model predictions. Findings: Inferential analyses indicated significant positive relationships between risk-taking behavior and sensation seeking (r = 0.54, p < 0.01), peer influence (r = 0.52, p < 0.01), and norm deviance (r = 0.49, p < 0.01). Machine learning results revealed that the Gradient Boosting model demonstrated the highest predictive performance (accuracy = 0.88, AUC-ROC = 0.93), followed by Random Forest and Neural Network models. Feature importance analysis using SHAP values showed that sensation seeking was the strongest predictor (mean SHAP = 0.37), followed by peer influence (0.31) and norm deviance (0.28), indicating that both individual and social factors significantly contribute to the prediction of risk-taking behavior. Conclusion: The superior performance of ensemble machine learning models highlights the importance of capturing nonlinear and complex relationships among predictors.
Copyright (c) 2026 Eleni Christou (Author); Eleni Kouris (Corresponding Author); Daniel Farrugia (Author)
Machine Learning-Based Modeling of Family Decision Processes Using Shared Mental Models, Power Dynamics, and Negotiation Styles
Objective: The present study aimed to model and predict family decision-making effectiveness using machine learning techniques by examining the roles of shared mental models, power dynamics, and negotiation styles. Methods and Materials: This descriptive–correlational study with a predictive modeling approach was conducted on 412 married adults in Thailand selected through multistage cluster sampling. Data were collected using standardized instruments assessing shared mental models, family power dynamics, and negotiation styles, all of which demonstrated acceptable validity and reliability. Data analysis was performed using a hybrid approach combining statistical analysis in IBM SPSS Statistics (version 27) and machine learning modeling in Python with scikit-learn and TensorFlow. Predictive models including Random Forest, Support Vector Machine, Gradient Boosting, and Multilayer Perceptron were trained and evaluated using cross-validation, with performance assessed through accuracy, precision, recall, F1-score, and AUC-ROC metrics. Findings: The results indicated that shared mental models had a significant positive effect on family decision-making effectiveness, while power dynamics showed a significant negative effect. Integrative negotiation style significantly and positively predicted decision-making effectiveness, whereas dominating style had a significant negative association. Machine learning analysis revealed that the Gradient Boosting model achieved the highest predictive performance (accuracy = 0.89, AUC = 0.92), outperforming other models. Feature importance analysis demonstrated that shared mental models were the strongest predictor, followed by integrative negotiation style and power dynamics, confirming the relative contribution of cognitive, behavioral, and structural variables in predicting decision outcomes. Conclusion: The findings highlight the central role of cognitive alignment and collaborative negotiation in enhancing family decision-making effectiveness, while unequal power structures undermine optimal outcomes. The integration of machine learning approaches provides a robust and nuanced framework for modeling complex family processes, offering both theoretical advancement and practical implications for improving relational functioning.
Copyright (c) 2026 Noam Ben-Ari (Author); Nattaporn Chaisiri (Corresponding Author)
Predicting Adolescent Risk Behavior via Family Monitoring, Sensation Seeking, and Peer Deviance with Machine Learning Analysis
Objective: The present study aimed to predict adolescent risk behavior by examining the combined effects of family monitoring, sensation seeking, and peer deviance using machine learning analytical approaches. Methods and Materials: This cross-sectional predictive study was conducted among 512 adolescents aged 14 to 18 years recruited from secondary schools in Tunisia using multistage cluster random sampling. Data were collected using standardized self-report instruments, including the Youth Risk Behavior Surveillance Questionnaire, the Parental Monitoring Scale, the Brief Sensation Seeking Scale, and the Peer Delinquency Scale, all of which demonstrated acceptable reliability and validity. Statistical analyses were performed using SPSS-27 to compute descriptive indices and Pearson correlations. Machine learning models, including random forest, support vector machine, gradient boosting, and multilayer perceptron neural networks, were implemented in Python. Data preprocessing included normalization and feature scaling, and model performance was evaluated using k-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve. Findings: The results revealed that adolescent risk behavior was significantly negatively associated with family monitoring and positively associated with sensation seeking and peer deviance. Among the predictive models, the neural network demonstrated the highest performance, achieving the greatest accuracy and discriminative power. Feature importance analyses consistently identified peer deviance as the strongest predictor of risk behavior, followed by sensation seeking, while family monitoring showed a weaker but still significant contribution. The models indicated that nonlinear interactions among predictors significantly improved prediction accuracy compared to linear approaches. Conclusion: The findings highlight the critical role of peer deviance and sensation seeking in shaping adolescent risk behavior, while confirming the protective function of family monitoring. The superior performance of machine learning models underscores their utility in capturing complex behavioral patterns and enhancing predictive precision.
Copyright (c) 2026 Andre McKenzie (Author); Amira Chennoufi (Corresponding Author); Sophie Langlois (Author)
Ranking Family Role Models Shaping Adolescent Decision-Making
Objective: This study aimed to identify and rank the most influential family role models that shape adolescents’ decision-making processes through an integrated qualitative–quantitative research approach. Methods and Materials: The research employed a sequential mixed-method design. In the first phase, a qualitative literature-based analysis was conducted until theoretical saturation was achieved. Using NVivo 14, eight major themes were identified, representing the primary family role models influencing adolescents’ decision-making: parental modeling, sibling influence, grandparental wisdom, extended family role models, parental communication style, family values and beliefs, family decision climate, and socioeconomic context. In the second phase, a quantitative ranking survey was administered to 200 adolescents in Italy, who evaluated the importance of each theme using a 5-point Likert scale. Data were analyzed with SPSS 26 using descriptive statistics, Friedman’s test, and Kendall’s W to assess agreement among participants. Findings: The analysis revealed significant differences among the ranked family role model themes (p < 0.001). Parental modeling received the highest mean score (M = 4.72, SD = 0.41), followed by parental communication style (M = 4.58) and family values and beliefs (M = 4.39). Socioeconomic and environmental context ranked lowest (M = 3.48). Kendall’s W coefficient of 0.84 indicated a high level of consensus among participants regarding the hierarchy of family influences. Conclusion: The study concludes that family influence on adolescent decision-making is primarily shaped by direct behavioral and emotional modeling within supportive, communicative, and value-oriented households. Strengthening family dynamics through targeted mentoring and communication-focused interventions may enhance adolescents’ decision-making competence and moral reasoning.
Copyright (c) 2026 Gisela Soares Vaneli (Corresponding Author); Micaela Daun Crudo , Paolo Del Croce (Author)
Ranking Spiritual and Religious Practices Strengthening Family Bonds
Objective: This study aimed to identify, categorize, and rank the most significant spiritual and religious practices that strengthen family bonds, integrating qualitative content analysis and quantitative prioritization to develop a multidimensional framework of family spirituality. Methods and Materials: The study adopted a sequential exploratory mixed-methods design. In the first phase, a qualitative content analysis was conducted through an extensive literature review until theoretical saturation was reached. Extracted data were coded and analyzed using NVivo 14, producing six overarching themes of family spirituality. In the second phase, these categories were incorporated into a structured questionnaire and administered to 200 adult participants from Italy. Respondents rated the perceived importance of each spiritual and religious practice on a five-point Likert scale. The quantitative data were analyzed using SPSS version 26, applying descriptive statistics and Friedman’s test to rank the practices by mean scores and determine relative importance. Findings: The results revealed six primary categories of family spirituality ranked as follows: shared religious rituals (M = 4.68), moral and ethical alignment (M = 4.51), emotional and relational spirituality (M = 4.32), community and social belonging (M = 4.09), meaning and purpose orientation (M = 3.88), and interfaith respect and inclusivity (M = 3.47). The Friedman test confirmed significant differences in perceived importance across categories (p < 0.001). The highest-ranked themes emphasized ritual participation and moral coherence as foundational mechanisms of family unity, while emotional, communal, and inclusive practices played complementary but secondary roles. Conclusion: Spirituality strengthens family bonds through an integrated system of shared rituals, moral alignment, emotional connection, and social belonging. Keywords: family spirituality; religious practices; family cohesion; moral alignment
Copyright (c) 2025 Tristan McKay-Tempest, Damián Cenci Pertile, Yirsa Meza Villanueva (Author)
Identifying Generational Gaps in Emotional Expression among Youth and Parents
Objective: This study aimed to explore the generational gaps in emotional expression between youth and their parents, focusing on differences in communication styles, regulation strategies, and cultural influences within Canadian families. Methods and Materials: A qualitative research design was employed using an interpretive phenomenological approach to capture participants’ lived experiences of emotional communication across generations. The study involved 20 participants from Canada, including 10 adolescents aged 15–20 years and 10 parents aged 40–55 years. Data were collected through semi-structured interviews and analyzed using thematic analysis in NVivo 14. Theoretical saturation guided the sampling process, and themes were identified through iterative coding and constant comparison. Findings: Five major themes emerged from the analysis: (1) emotional communication styles, highlighting contrasts between direct and indirect expression; (2) cultural and social influences, emphasizing the interplay of traditional and contemporary emotional norms; (3) emotional awareness and vocabulary, revealing differences in emotional literacy between generations; (4) family dynamics and relationship quality, identifying emotional distance and mismatched support expectations; and (5) coping and adaptation across generations, showing gradual mutual adjustment through empathy and communication. Conclusion: Generational differences in emotional expression reflect broader cultural, psychological, and developmental shifts within families. While these gaps may cause miscommunication and emotional tension, they also provide opportunities for intergenerational learning and resilience through dialogue, empathy, and shared emotional growth.
Copyright (c) 2025 Winfrida Lusignan, Nicole H. Garcia , David W. Harris (Author)
Identifying Cultural Pathways of Parent–Child Attachment Development
Objective: This study aimed to explore and identify the cultural pathways that shape the development of parent–child attachment among families from diverse cultural backgrounds living in Canada. Methods and Materials: The research adopted a qualitative, phenomenological design to capture parents’ lived experiences of attachment formation within multicultural contexts. Data were collected through semi-structured, in-depth interviews with 23 participants (13 mothers and 10 fathers) representing South Asian, East Asian, Middle Eastern, European, and Indigenous cultural groups. Participants were selected using purposive sampling, and interviews continued until theoretical saturation was reached. Each interview lasted between 60 and 90 minutes and focused on parental beliefs, emotional expression, cultural transmission, and adaptation to Canadian norms. All interviews were audio-recorded, transcribed verbatim, and analyzed thematically using NVivo 14 software following Braun and Clarke’s six-phase framework. Findings: Analysis revealed three main themes—Cultural Interpretations of Emotional Bonding, Migration and Cultural Adaptation in Parenting, and Pathways of Attachment Formation. These themes encompassed 17 subthemes that described how cultural values, intergenerational beliefs, and migration experiences influence emotional expression and attachment behaviors. The findings showed that while traditional collectivist cultures emphasized caregiving through protection, respect, and shared rituals, integration into Canadian society encouraged more open emotional communication and autonomy-promoting parenting. Conclusion: The study concludes that parent–child attachment development among Canadian families is a culturally dynamic process influenced by enculturation, migration, and social adaptation. Recognizing multiple cultural pathways of attachment can enhance culturally sensitive family interventions, parenting education, and cross-cultural counseling practices.
Copyright (c) 2025 Curry Bordelon (Author); Leila Saeidtaleshi (Corresponding Author)
Family Conflict and Adolescent Self-Injury: The Mediating Role of Alexithymia
Objective: This study aimed to examine the relationship between family conflict and adolescent self-injury, with a focus on the mediating role of alexithymia. Methods and Materials: A descriptive correlational design was employed using a sample of 396 South African secondary school students selected based on the Morgan and Krejcie sampling table. Standardized instruments were used to assess family conflict, alexithymia, and self-injurious behavior. Data were analyzed using SPSS-27 for Pearson correlation and AMOS-21 for structural equation modeling (SEM). Assumptions for normality, linearity, and multicollinearity were confirmed prior to analysis. The structural model tested both direct and indirect effects to evaluate the mediating function of alexithymia in the relationship between family conflict and self-injury. Findings: Pearson correlations showed significant positive relationships among all variables: family conflict and alexithymia (r = .42, p < .01), family conflict and self-injury (r = .34, p < .01), and alexithymia and self-injury (r = .49, p < .01). The structural model demonstrated excellent fit indices (χ²/df = 2.43, CFI = 0.96, RMSEA = 0.059). Path analysis revealed significant direct effects of family conflict on alexithymia (B = 0.43, β = .42, p < .001), alexithymia on self-injury (B = 0.38, β = .46, p < .001), and family conflict on self-injury (B = 0.19, β = .24, p = .004). The indirect effect of family conflict on self-injury through alexithymia was also significant (B = 0.16, β = .19, p < .001), confirming partial mediation. Conclusion: Findings underscore alexithymia as a critical emotional mechanism linking family conflict to self-injurious behavior in adolescents. Interventions targeting emotional awareness and family communication may help mitigate the risks associated with adolescent self-harm, especially in culturally diverse contexts.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Cultural Orientation and Marital Quality: The Mediating Role of Gender Role Attitudes
Objective: This study aimed to investigate the relationship between cultural orientation and marital quality among Canadian married individuals, with a specific focus on the mediating role of gender role attitudes. Methods and Materials: The study employed a descriptive correlational design involving 385 married participants residing in Canada, selected using stratified random sampling based on Krejcie and Morgan's sample size guidelines. Standardized instruments were used to assess marital quality (Dyadic Adjustment Scale), cultural orientation (Cultural Orientation Scale), and gender role attitudes (Gender Role Beliefs Scale). Data were analyzed using SPSS-27 for descriptive and correlational statistics, and AMOS-21 was used to perform structural equation modeling (SEM). Model fit was evaluated using χ²/df, GFI, AGFI, CFI, TLI, and RMSEA indices. Mediation analysis included both direct and indirect path coefficients. Findings: Pearson correlation analysis showed that marital quality was significantly related to both cultural orientation (r = .42, p < .001) and gender role attitudes (r = .48, p < .001), with a significant correlation between cultural orientation and gender role attitudes as well (r = .45, p < .001). The SEM results indicated that the model had good fit (χ²/df = 2.11, GFI = 0.94, CFI = 0.96, RMSEA = 0.053). Cultural orientation significantly predicted marital quality directly (β = 0.28, p < .001) and indirectly through gender role attitudes (β = 0.22, p < .001), confirming a partial mediating effect. Gender role attitudes also had a strong direct effect on marital quality (β = 0.49, p < .001). Conclusion: The findings underscore the critical role of cultural orientation and internalized gender role attitudes in shaping marital quality. Gender role attitudes serve as a psychological pathway through which cultural values influence relational satisfaction, emphasizing the need for culturally sensitive approaches in marital counseling and intervention.
Family Expressiveness and Adolescent Empathy: The Mediating Role of Emotional Awareness
Objective: This study aimed to examine the mediating role of emotional awareness in the relationship between family expressiveness and adolescent empathy. Methods and Materials: A descriptive correlational research design was used, involving 400 Mexican adolescents selected based on the Morgan and Krejcie sampling table. Data were collected using three standardized self-report instruments: the Family Expressiveness Questionnaire (FEQ), the Emotional Awareness Questionnaire (EAQ), and the Interpersonal Reactivity Index (IRI) for empathy. Pearson correlation analysis was conducted using SPSS-27 to explore bivariate associations, and structural equation modeling (SEM) was performed using AMOS-21 to assess direct, indirect, and total effects among the variables and test the hypothesized mediation model. Findings: The results showed significant positive correlations between family expressiveness and emotional awareness (r = .46, p < .001), family expressiveness and adolescent empathy (r = .38, p < .001), and emotional awareness and adolescent empathy (r = .53, p < .001). The structural model demonstrated a good fit to the data (χ²/df = 1.66, RMSEA = 0.041, CFI = 0.99). Direct effects from family expressiveness to emotional awareness (B = 0.51, β = 0.46, p < .001), from family expressiveness to empathy (B = 0.24, β = 0.28, p < .001), and from emotional awareness to empathy (B = 0.44, β = 0.39, p < .001) were statistically significant. The indirect effect of family expressiveness on empathy via emotional awareness was also significant (B = 0.22, β = 0.18, p < .001), supporting partial mediation. Conclusion: These findings suggest that emotionally expressive family environments foster adolescents' empathy partly by enhancing their emotional awareness. Emotional awareness serves as a key developmental mechanism through which family dynamics shape prosocial emotional outcomes in adolescence.
Parental Overprotection and Young Adults’ Autonomy: The Mediating Role of Self-Efficacy
Objective: This study aimed to examine the mediating role of self-efficacy in the relationship between perceived parental overprotection and autonomy in Malaysian young adults. Methods and Materials: A descriptive correlational research design was employed involving a sample of 423 university students in Malaysia, selected based on Krejcie and Morgan’s sampling table. Participants completed standardized measures including the Parental Bonding Instrument (Overprotection subscale), General Self-Efficacy Scale, and the Autonomous Functioning Scale. Data were analyzed using SPSS-27 for descriptive statistics and Pearson correlations, and AMOS-21 for structural equation modeling (SEM). Assumptions such as normality, multicollinearity, and linearity were checked and confirmed before inferential analysis. Findings: The results indicated a significant negative correlation between parental overprotection and autonomy (r = –.47, p < .001), and between parental overprotection and self-efficacy (r = –.41, p < .001). Self-efficacy was positively correlated with autonomy (r = .53, p < .001). SEM results showed that parental overprotection significantly predicted lower self-efficacy (β = –0.41, p < .001) and autonomy both directly (β = –0.28, p < .001) and indirectly via self-efficacy (β = –0.22, p < .001). The total effect of parental overprotection on autonomy was substantial (β = –0.50, p < .001). Model fit indices supported the adequacy of the hypothesized model (χ²/df = 1.74; CFI = 0.96; RMSEA = 0.041). Conclusion: These findings suggest that parental overprotection has a significant negative impact on young adults’ autonomy, both directly and through its detrimental effect on self-efficacy. Promoting self-efficacy may help mitigate the negative developmental consequences of overprotective parenting, especially in collectivist cultures where parental control is often normative.
Identifying Family Support Mechanisms in Postpartum Psychological Adjustment
Objective: This study aimed to explore the mechanisms through which family support contributes to the psychological adjustment of postpartum women. Methods and Materials: A qualitative research design was employed using semi-structured interviews with 23 postpartum women residing in Bangladesh. Participants were recruited through purposive sampling and represented a range of ages, educational backgrounds, and parity statuses. Data collection continued until theoretical saturation was reached. All interviews were transcribed verbatim and analyzed using thematic analysis, following Braun and Clarke’s six-step framework. NVivo 14 software facilitated systematic coding and theme development. Ethical approval was obtained, and all participants gave informed consent. Findings: Three main themes emerged: (1) Emotional Support from Family (including subthemes such as active emotional presence, verbal encouragement, and emotional understanding from the spouse), (2) Practical and Instrumental Support (including help with infant care, household tasks, and medical visits), and (3) Relational and Social Buffering (including protection from criticism, mediation of social relations, and promotion of couple intimacy). Across all themes, the perceived quality and timing of support were central to their psychological impact. Participants who experienced emotionally attuned, respectful, and reliable support reported greater psychological stability, reduced distress, and enhanced postpartum coping. Conclusion: The findings underscore that family support in the postpartum period is not solely about the presence of assistance but is critically shaped by its relational quality, emotional sensitivity, and alignment with maternal needs. Emotional reassurance, practical help, and protection from social stressors—when delivered with empathy and respect—can significantly buffer against psychological distress and promote adjustment. These insights suggest a need for culturally sensitive, family-inclusive mental health interventions tailored to postpartum women.
Exploring Communication Breakdowns in Marriages Affected by Emotional Neglect
Objective: This study aimed to explore the lived experiences of communication breakdowns in marital relationships affected by emotional neglect. Methods and Materials: A qualitative research design was employed using semi-structured, in-depth interviews with 21 adult participants (12 women and 9 men) residing in Iranian. Participants were purposively selected based on self-reported experiences of emotional neglect in current or past marriages of at least five years. Data collection continued until theoretical saturation was achieved. All interviews were audio-recorded, transcribed verbatim, and analyzed thematically using NVivo 14 software. Thematic analysis followed Braun and Clarke’s six-step framework to identify recurring patterns related to emotional neglect and communication dynamics. Findings: Four major themes emerged from the data: emotional unavailability, ineffective communication patterns, psychosocial impact of neglect, and barriers to reconnection. Participants consistently reported emotional detachment, disengaged listening, and suppression of emotional expression as core aspects of emotional neglect. Communication breakdowns included verbal withdrawal, misinterpretation of intent, and conflict escalation. Emotional neglect was linked to psychological consequences such as self-blame, isolation, diminished self-esteem, and hopelessness about the relationship. Fear of vulnerability, lack of emotional skills, and cultural norms emerged as key barriers to emotional repair. The results highlight emotional neglect as a relational process that undermines emotional safety and communicative trust. Conclusion: Emotional neglect in marriage is a pervasive and underrecognized dynamic that contributes significantly to communication breakdowns and psychological distress. This study underscores the importance of identifying emotional neglect in therapeutic settings and developing interventions that focus on emotional literacy, attunement, and trust-building.
Parents, Children, and (Adoptive?) Bonds: Theoretical Notes for Understanding Parenthood through Adoption
This article explores the complexities of adoptive parenthood through a theoretical lens, examining the social, emotional, and identity-shaping processes that distinguish it from biological parenthood. Grounded in psychological and sociological research, the study highlights the unique challenges faced by adoptive families, including bureaucratic hurdles, social judgment, and integration issues, while emphasizing the role of pre- and post-adoption support in fostering positive outcomes. The analysis reveals how adoptive parenthood is constructed through legal, cultural, and interpersonal dynamics, with particular attention to the selective preferences documented in Brazil’s National Adoption Registry (CNA), which reflect underlying racial and age-based biases. The article also addresses the historical evolution of motherhood and fatherhood, contextualizing adoptive roles within broader gender and family structures. By comparing adoptive and biological parenthood, the study underscores the significance of affective bonds, legal frameworks, and societal perceptions in shaping family identity. Key findings suggest that adoptive parenthood, while distinct, involves similar processes of identity formation and relational adjustment, challenging traditional notions of kinship. The article concludes with recommendations for tailored interventions and policies to better support adoptive families.
Copyright (c) 2025 Pablo Mateus dos Santos Jacinto (Corresponding Author); Maria Virgínia Machado Dazzani (Author)
Global Islam: A Sociological Study of Muslim, Integration, and the Challenges of Multiculturalism in the 21st Century
In the 21st century, globalization, migration, and multiculturalism have shaped the experiences of Muslim minorities in Western societies. This study explores the sociological aspects of Muslim integration, focusing on the challenges and opportunities faced by Muslim communities in multicultural settings. Using qualitative research methods, data were collected through in-depth interviews, focus group discussions (FGDs), case studies, and key-informant interviews (KII) with 15 Muslim immigrants from the United States, the United Kingdom, Switzerland, and Scotland. The findings reveal key barriers to integration, including employment discrimination, restrictions on religious clothing, difficulties in securing housing, limited access to halal food, and workplace discrimination. Furthermore, Islamophobia and negative media portrayals exacerbate societal tensions, leading to identity crises among young Muslims. Despite these challenges, Muslim communities employ various coping mechanisms, such as interfaith dialogues, advocacy initiatives, and community-driven support systems.
Copyright (c) 2025 Hasanuz Zaman (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
Narrative Therapy as an Intervention for Post-Divorce Adjustment and Grief: Examining Psychological Outcomes
Objective: This study aimed to evaluate the effectiveness of narrative therapy in improving post-divorce adjustment and reducing grief symptoms among individuals experiencing marital dissolution. Methods and Materials: A quasi-experimental design was used with a treatment group (n = 36) receiving ten structured narrative therapy sessions and a control group (n = 36) receiving no intervention. Participants were assessed at three time points: pre-test, post-test, and follow-up. Standardized measures, including the Post-Divorce Adjustment Scale (PDAS) and the Grief Experience Questionnaire (GEQ), were administered to evaluate changes over time. Data were analyzed using repeated measures ANOVA and Bonferroni post-hoc tests to determine within-group and between-group differences in post-divorce adjustment and grief levels. Findings: Results showed a significant improvement in post-divorce adjustment in the treatment group compared to the control group (F = 57.90, p = 0.0001). Grief levels significantly decreased in the treatment group over time (F = 71.92, p = 0.00001), with Bonferroni post-hoc comparisons indicating that post-divorce adjustment increased significantly from pre-test to post-test (p = 0.001) and was maintained at follow-up (p = 0.0003), while grief decreased significantly from pre-test to post-test (p = 0.0001) and continued to decline at follow-up (p = 0.00001). Conclusion: The findings suggest that narrative therapy is an effective intervention for enhancing post-divorce adjustment and reducing grief. By enabling individuals to reconstruct their divorce narratives, the therapy promotes emotional healing, resilience, and future-oriented self-perceptions. Narrative therapy should be considered a valuable therapeutic approach for individuals struggling with the emotional consequences of divorce.
Copyright (c) 2025 Karina Batthyany , Sabine Kraus, Erwin A William (Author); Yaliu Yang (Corresponding Author) https://creativecommons.org/licenses/by-nc/4.0
The Role of Jealousy and Family Coping Strategies in Predicting Sexual Satisfaction in Romantic Relationships
Objective: This study examines the predictive role of jealousy in romantic relationships and family coping strategies in determining sexual satisfaction among individuals in committed relationships. Methods and Materials: A correlational descriptive design was used, with 376 participants selected based on Morgan and Krejcie’s sample size table. Standardized self-report measures, including the Sexual Satisfaction Scale (SSS), Romantic Jealousy Scale (RJS), and Family Crisis-Oriented Personal Evaluation Scales (F-COPES), were administered. Data were analyzed using Pearson’s correlation coefficient to assess relationships between variables and linear regression analysis to determine predictive effects. Assumptions of normality, linearity, and multicollinearity were checked and met, and statistical analyses were conducted using SPSS-27. Findings: Correlation analysis revealed that jealousy in romantic relationships was negatively associated with sexual satisfaction (r = -0.54, p < 0.01), while family coping strategies were positively associated with sexual satisfaction (r = 0.48, p < 0.01). The regression model demonstrated that jealousy negatively predicted sexual satisfaction (B = -0.46, β = -0.51, t = -6.57, p < 0.01), while family coping strategies positively predicted sexual satisfaction (B = 0.38, β = 0.45, t = 6.33, p < 0.01). The model explained 40% of the variance in sexual satisfaction (R² = 0.40, F = 69.91, p < 0.01). Conclusion: The findings suggest that higher jealousy in romantic relationships is associated with lower sexual satisfaction, whereas stronger family coping strategies enhance sexual satisfaction. These results highlight the importance of reducing jealousy and promoting adaptive coping mechanisms in fostering healthy and satisfying romantic relationships.
Copyright (c) 2025 Nayelli Muñoz (Corresponding Author); Bridget Abalorio , Klein Verena , Courtney Hébert (Author) https://creativecommons.org/licenses/by-nc/4.0
Reducing Parental Psychological Control: The Influence of Marital Support and Psychological Capital
Objective: This study aimed to investigate the predictive role of emotional support in marriage and psychological capital in parental psychological control, exploring how these factors contribute to variations in parenting behaviors. Methods and Materials: A correlational descriptive design was employed, with a sample of 320 married individuals with children selected based on the Morgan and Krejcie table. Participants completed standardized measures assessing parental psychological control, emotional support in marriage, and psychological capital. Data analysis was conducted using Pearson correlation to examine the relationships between variables and multiple linear regression to determine the predictive power of emotional support in marriage and psychological capital on parental psychological control. SPSS version 27 was used for statistical analysis, with significance set at p < 0.01. Findings: Results indicated significant negative correlations between parental psychological control and both emotional support in marriage (r = -0.52, p < 0.01) and psychological capital (r = -0.46, p < 0.01). Multiple regression analysis showed that emotional support in marriage (B = -0.41, p < 0.01) and psychological capital (B = -0.36, p < 0.01) significantly predicted lower parental psychological control, accounting for 34% of the variance (R² = 0.34). These findings suggest that parents with higher marital support and stronger psychological resources engage in less psychologically controlling parenting behaviors. Conclusion: This study highlights the crucial role of emotional support in marriage and psychological capital in reducing parental psychological control. Strengthening spousal support and enhancing psychological capital may serve as effective strategies for promoting healthier parenting practices and reducing manipulative parental behaviors. Future research should explore longitudinal effects and intervention strategies aimed at fostering supportive marital relationships and psychological resilience among parents.
Copyright (c) 2025 Badhon Ahmmed (Corresponding Author); Aman Ullah Chaudhary (Author) https://creativecommons.org/licenses/by-nc/4.0
Identifying the Strategies Used by Grandparents in Child-Rearing Across Different Cultural Contexts
Objective: This study aims to explore the strategies used by grandparents in child-rearing across different cultural contexts. Methods and Materials: This qualitative study utilized semi-structured interviews with 37 grandparents recruited through online platforms. The study employed a purposive sampling method to ensure diversity in cultural backgrounds. The interviews were conducted online, recorded, and transcribed verbatim. Data analysis was performed using thematic analysis with the assistance of NVivo software, following an inductive coding process to identify emerging themes. Theoretical saturation was achieved when no new themes emerged. Findings: The results indicated that grandparents play a crucial role in their grandchildren’s upbringing through four main strategies: emotional and psychological support, transmission of cultural values, disciplinary strategies, and intergenerational learning and adaptation. Grandparents provided emotional security by offering reassurance and fostering resilience, contributing to their grandchildren’s psychological well-being. They actively engaged in cultural transmission through storytelling, language preservation, and reinforcing moral values, ensuring continuity of traditions. Disciplinary strategies varied, with some grandparents employing non-punitive methods such as positive reinforcement and mediation, while others adhered to stricter rule-setting. Grandparents also demonstrated adaptability by incorporating modern parenting techniques and learning from their grandchildren, particularly in digital literacy and evolving social norms. Conclusion: The study highlights the evolving role of grandparents in child-rearing, demonstrating a balance between traditional caregiving practices and contemporary parenting adaptations. Their involvement remains essential in fostering emotional security, cultural continuity, and behavioral development in children. Given the generational shifts in parenting ideologies, intergenerational dialogue and mutual learning are crucial for enhancing the effectiveness of grandparental caregiving. Future research should further examine the long-term impact of grandparental caregiving on child development.
Copyright (c) 2025 Valerie Karstensen (Corresponding Author); Geoffrey Olsen, Mathias Bastholm (Author) https://creativecommons.org/licenses/by-nc/4.0