International Journal Scientific and Professional

Exploring Technology Needs to Improve Mental Health Service Coordination

2025-12-28T09:56:14+00:00

Mental health services in Indonesia face significant challenges in coordination between agencies, including mental hospitals, community health centers (Puskesmas), and private institutions. Lack of data integration, limited communication, and low technology utilization hinder service effectiveness. This study aims to explore the need for technology to improve mental health service coordination and identify solutions that can address these challenges. Using a qualitative descriptive approach, the study involved healthcare workers from mental hospitals, community health centers, and private institutions in the provinces of South Sumatra, Lampung, and Jakarta. The results indicate that the implementation of a technology-based integrated information system, telemedicine applications, and intensive training for healthcare workers are key desired solutions. Furthermore, improving inter-agency communication and providing adequate technological infrastructure are also considered important. These findings align with health information systems theory and technology accessibility theory, which suggest that technology can improve coordination and access to services. Despite limitations in terms of sample size and perspective, this study provides important insights into the application of technology in mental health services in Indonesia and suggests solutions to improve the effectiveness of coordination within the mental health service system.

The Influence of Peer Counselors on Adolescent Mental Health Using the Spiritual Emotional Freedom Technique (SEFT) Method

2025-11-08T17:04:31+00:00

Adolescence is a transitional phase of self-discovery and lifestyle adjustments that can cause stress. Mentally healthy adolescents are those who are able to cope with life's pressures, be productive, and contribute to their communities. According to the 2024 I-NAMHS survey, 15.5-2.45 million adolescents have mental health problems, with the highest prevalence being 14 years old (75%). Mental health problems in adolescents, such as stress and anxiety, can hinder physical activity and quality. Mental health is crucial for adolescents, with psychoeducation to build a strong mental character, one of which is through peer counselors. The Spiritual Emotional Freedom Technique (SEFT) method as an educational medium has proven to be effective in addressing adolescent mental health issues. The purpose of this study was to determine the influence of peers using the SEFT method approach on adolescent mental health. The method used was a Quasi-Experimental Design with a Pretest Posttest Control Group design. The results showed a significant influence of the SEFT method by peers on improving the emotional mental state of adolescents in the depression category with a t-test p value of 0.015, while in the anxiety and stress problem categories there was no influence of the SEFT method given by peers. The approach of those closest to them with continuous education through beneficial spiritual methods has an impact on the continuation of a generation of mentally and physically healthy adolescents.

Enhancing Student Anxiety Detection: A Multimodal Transformer Approach to Video-Based Screening

2025-12-20T03:35:20+00:00

This study developed and applied a multimodal Transformer model for student anxiety screening through video analysis of short interviews that included facial expressions, speech, and numerical data. Student anxiety is a problem that often affects mental health and academic performance, so early detection is important. The model combines three main data sources: facial expression features, speech analysis (including speech speed, intonation, and negative word count), and demographic information. The data used came from 500 students who participated in interviews lasting 20-40 seconds. The multimodal Transformer model was trained to classify anxiety levels into low, medium, and high categories, with evaluation using accuracy, precision, and recall metrics. The results showed that this model had a prediction accuracy of 88%, with a significant correlation between facial expressions and negative word counts on anxiety levels. Compared to the linear regression model used for comparison, the multimodal Transformer model shows better performance in detecting anxiety. These findings indicate that a multimodal approach using AI technology can improve accuracy and efficiency in student anxiety screening. This research opens up opportunities for the development of a more objective, non-invasive, and efficient video-based automated screening system, with potential applications in the field of mental health in higher education.