OBJECTIVES: This study aims to evaluate the effectiveness of Psychoeducational and Supportive Therapy (PeSo) on the resilience of families with members suffering from mental disorders. METHODS: This study employed a quasi-experimental pre-test and post-test control group design. A total of 120 families were recruited and divided into two groups: the intervention group (n=60) and the control group (n=60). The intervention group received the Psychoeducational and Supportive Therapy (PeSo) module, which was conducted in 6 sessions. Data were collected using a Family Resilience Questionnaire and analyzed using dependent and independent t-tests. RESULTS: In the intervention group, the mean family resilience score before therapy was 1.87 (SD=0.676), increasing to 2.57 (SD=0.500) after therapy, with a mean difference of 0.700 (SD=0.591), a 95% confidence interval ranging from 0.853 to 0.547, and a p-value<0.001. In the control group, the mean score before therapy was 1.85 (SD=0.685) and 1.97 (SD=0.610) after therapy, with a mean difference of 0.117 (SD=0.415), a 95% confidence interval of 0.224 to 0.009, and a p-value=0.034, indicating a statistically significant improvement in both groups. However, the intervention group showed a much more significant improvement compared to the control group (p<0.001). CONCLUSION: Psychoeducational and Supportive Therapy (PeSo) significantly improves the resilience of families with mental disorders. This therapy is recommended as an effective nursing intervention to be integrated into community mental health services to support family caregivers.
This study explores predictive risk management in green technology investments by leveraging Artificial Intelligence (AI) to address uncertainties associated with sustainable projects. As global financial institutions and governments increasingly allocate capital toward renewable energy, smart infrastructure, and low-carbon innovation, investors face multidimensional risks, including market volatility, technological failure, and regulatory change. Therefore, this research aims to develop an AI-driven predictive framework capable of identifying, analyzing, and forecasting potential investment risks in green technology portfolios to support informed decision-making. The study employs a quantitative approach using machine learning algorithms, including Random Forest, Gradient Boosting, and Neural Networks, trained on historical financial indicators, environmental performance metrics, and policy datasets. Each algorithm is selected based on its strengths: Random Forest for robustness, Gradient Boosting for predictive accuracy, and Neural Networks for capturing complex nonlinear relationships. A comparative perspective is used to highlight their tradeoffs, followed by feature importance analysis and predictive validation through cross-validation and evaluation metrics such as accuracy, precision, and RMSE. The findings show that the proposed model improves early risk detection compared to conventional statistical models, highlighting the effectiveness of machine learning in handling complex sustainability data. Furthermore, it identifies key risk determinants and enhances predictive reliability. Consequently, integrating AI-based predictive analytics into green investment strategies can strengthen risk mitigation, improve investor confidence, and support sustainable financial decision-making.
This narrative case study explores parents' perspectives on character education programs in Islamic boarding schools (pesantren). Semi-structured interviews were conducted with fifteen parents to capture their perceptions, expectations, and lived experiences regarding their children's moral and character development. Using interpretative phenomenological analysis (IPA), four key themes emerged: (1) the influence of parents' social and cultural backgrounds on their choice of pesantren; (2) the character values parents prioritize for their children; (3) parents' evaluations of the pesantren's effectiveness in fostering these values; and (4) the role of parents in reinforcing character development at home. Findings indicate that parents generally view pesantren positively, highlighting both observable behavioral improvements in their children and their own active participation in supporting character education. These insights underscore the importance of fostering strong parent-school partnerships and provide valuable guidance for educators, practitioners, and policymakers aiming to enhance holistic character development in pesantren settings.
Collaboration is a strategic pillar in the development of higher education institutions as it directly supports the implementation of the Tridharma of Higher Education. However, collaboration management in many universities is still conducted manually or semi-digitally, resulting in fragmented data, low document traceability, and difficulties in monitoring and evaluation. This study focuses on the design and development of a web-based University Partnership Monitoring Information System with workflow automation, implemented as the SIMONIKA application at Muhammadiyah University of Jambi. The system aims to integrate collaboration data, standardize partnership processes, and provide real-time monitoring information for decision-makers.Purpose: This research aims to design and develop a web-based collaboration monitoring information system with workflow automation to improve document traceability, process consistency, and strategic decision-making in higher education partnership governance.Methods/Study design/approach: The study adopts a Research and Development (R&D) approach using an SDLC prototyping model. Data were collected through observation, stakeholder interviews, and analysis of collaboration documents (MoU, MoA, and IA). The system was developed iteratively through requirement analysis, system design, prototyping, testing, and evaluation stages.Result/Findings: The results indicate that SIMONIKA successfully integrates partner data, organizational units, and collaboration documents into a centralized web-based platform. The implemented workflow automation supports structured processes ranging from submission and hierarchical validation to real-time monitoring of partnership status. System testing shows that all core functionalities operate as expected, enhancing document traceability, operational consistency, and the availability of strategic information for university leadership.Novelty/Originality/Value: This study contributes a novel system model that combines higher education collaboration management with workflow automation, going beyond conventional data storage systems. The proposed approach strengthens digital governance by ensuring transparent, standardized, and traceable partnership processes, and can serve as a reference model for other higher education institutions.
Digital technologies have transformed higher education learning environments, providing unprecedented access to information, communication tools, and online platforms. However, this transformation has introduced digital distractions, including off-task device use, habitual multitasking, and social media engagement, which can fragment attention and reduce learning effectiveness. This study critically analyzes the phenomenon of digital distraction among university students and its implications for instructional design. Through a synthesis of empirical and conceptual studies, digital distraction is shown to be multidimensional, arising from the interaction of learner characteristics, technology features, and learning environment factors. Evidence indicates that distraction can undermine engagement, perceived learning, and satisfaction, while self-regulation and attention management strategies can mitigate these effects. Instructional design that anticipates distraction, fosters structured interaction, and ensures equitable access to learning tools is essential for optimizing student focus and outcomes. Beyond immediate classroom strategies, the findings highlight the importance of ongoing reflection on how digital environments shape attention, engagement, and educational quality. Future research should explore adaptive pedagogical approaches, technology-supported scaffolds, and systemic interventions to sustain attention and enhance learning experiences in increasingly digital and hybrid higher education contexts.
