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 study examined the interrelationships among loneliness, smartphone addiction, sleep quality, and positive mental health among university students in Indonesia and Malaysia. Using a cross-sectional design, data were collected from 317 students (216 from Indonesia and 101 from Malaysia) through an online survey. Structural equation modeling (SEM) was applied to test both direct and indirect pathways within a serial mediation framework. The results revealed a significant direct negative effect of loneliness on positive mental health (β = −0.386, p < .001), indicating that loneliness is associated with lower levels of psychological well-being. While most hypothesized mediation pathways through smartphone addiction and sleep quality were not supported, loneliness was indirectly related to poorer sleep quality via higher smartphone addiction (β = 0.104, p < .001). These findings suggest that loneliness primarily associated with mental health directly but also contributes to behavioral and sleep-related difficulties. The study refines existing theoretical models by highlighting the limited mediating role of smartphone addiction and sleep quality, emphasizing instead the dominant influence of loneliness. Practical implications include promoting social connectedness and healthy digital habits as strategies to enhance sleep and mental well-being among students in Southeast Asia.
Background:Beta thalassemia major is the most common monogenic mutation disorder in Indonesia, with steadily increasing frequency. However, there are limited studies regarding genetic distribution and its relationship with the patient's clinical manifestation. This study aimed to identify the genetic mutation frequency and its association with the clinical phenotype pattern among β-thalassemia major patients in East Java, Indonesia. Methods:In this observational study, we include subjects who have diagnosed with β-thalassemia previously through Hb electrophoresis. Demographic distribution with several ethnicities of Javanese, Sundanese, Chinese, Maduranese, and Batak was recorded. From each subject, a total of 6 mL of blood sample was collected and divided into two ethylene diamine tetraacetic acid (EDTA) tubes for CBC and DNA extraction. DNA samples were analyzed by PCR and followed by Sanger sequencing. Results:A total of 91 subjects were included in this study, with a median age of 22.25 ± 7.56 years old; consisting of 52 females and 39 males, with Javanese as the most common ethnicity. There are 22 types of mutation were identified through Sanger sequencing. The most common mutation was IVS-1-5/CD 26 and the CD 35/CD 26 observed in 36 (39.5%) and 19 (20.8%), respectively. While 9 subjects (9.8%) had no mutation detected. Several clinical phenotypes, including iron overload, short stature, severe anemia, and splenomegaly, were most prevalent among the two most common genetic mutations. Conclusion:There is variability in clinical phenotype in β-thalassemia observed in several types of genotype mutations. Among all the mutations found in East Java, the genotypes IVS-1-5/CD 26 and CD 35/CD 26 were the two most frequent genotypes. Those genotypes are linear with the severity of the phenotype in β-thalassemia, such as severe anemia, iron overload, short stature, and splenomegaly.
Kaliwates Hospital as a hospital faces the challenge of digital transformation to increase patient satisfaction amid a surge in polyclinic visits and the demand for fast services. This study examines the effect of digitalization of information system management and modernization of information technology on patient satisfaction with the quality of digital services as an intervening variable, as well as leadership development on employee performance through the same mediation. Using an explanatory design quantitative approach with a sample of 160 non-probability engineering patients and a saturated sample of employees, data were collected through a valid questionnaire (Cronbach α>0.8) and analyzed by SmartPLS-based Structural Equation Modeling (SEM). The results showed that the digitization of SIMRS had a positive effect on patient satisfaction through data integration and process automation; IT modernization is influential through WiFi-RME-telemedicine infrastructure; digital service quality (efficiency-privacy-promptness) mediates the relationship; Leadership Development improves employee performance through application efficiency and promptness of digital staff. The effect of partial mediation was confirmed to be moderate. The findings enrich the TAM-SDL theory in the context of East Java hospitals, recommending an integrated Digital Service Excellence Center Satu Sehat to optimize patient experience
