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Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan
Published by Universitas Harapan Bangsa Purwokerto
ISSN : 19791034     EISSN : 26561034     DOI : -
Core Subject : Health,
Health Professions Medicine & Pharmacology Nursing Public Health
Viva Medika Is a journal that publishes articles or research results relating to health, nursing and midwifery issues. Viva Medika is published by Harapan Bangsa University twice a year (September and February). The mission of the Journal of Viva Medika is to disseminate and discuss scientific writings on midwifery, nursing, and various issues within the scope of health problems. This journal is intended as a medium of communication for lecturers and people who have attention to health, obstetrics, nursing.
Arjuna Subject : -
Articles 593 Documents
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Association Between Maternal Myopia and Myopia in Children Aged 10-12 Years in Karanggondang, Indonesia: A Cross-Sectional Study Nabila Dwi Desi Puspita; Susanto, Herry; Indra Tri Astuti; Loan Thi Dang
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2197

Abstract

Myopia is the most common refractive error among school-aged children and a growing public health concern, particularly in developing countries. Although parental myopia is widely recognized as a genetic risk factor, evidence from Indonesia, especially among children aged 10 to 12 years, remains limited and inconsistent. By focusing on maternal myopia as a single and practical familial indicator in school-based settings, this study also considers a null association as a meaningful context-specific finding. This observational analytic study employed a cross-sectional design and was conducted from August to October 2025 in three public elementary schools in Karanggondang, Indonesia. A total of 88 child-mother pairs were recruited using purposive sampling. Visual acuity in both children and mothers was assessed using a Snellen chart without cycloplegic refraction. Refractive status was classified dichotomously as myopia or non-myopia. Data were analyzed using descriptive statistics and Chi-square tests with Yates’ continuity correction, and associations were expressed as odds ratios with 95 percent confidence intervals. The prevalence of myopia was 20.5 percent among children and 59.1 percent among mothers. Childhood myopia was slightly more frequent among children of myopic mothers at 12.5 percent compared with 8.0 percent among those of non-myopic mothers. However, no statistically significant association was found between maternal myopia and childhood myopia, with a p value of 1.000. The estimated odds ratio was 1.12 with a 95 percent confidence interval of 0.385 to 3.209. Maternal myopia was not a significant independent predictor of myopia in children aged 10 to 12 years in this population. This context-specific null finding suggests that maternal myopia alone may have limited explanatory value. Preventive strategies should prioritize modifiable lifestyle factors alongside routine vision screening, and future studies should incorporate cycloplegic refraction and broader familial and environmental measures
Digital Burnout as a Psychological Risk Factor for Loneliness in Generation Z: The Mediating Function of Self-Esteem Kusumawati, Mira Wahyu; Carsita , Wenny Nugrahati; Basir , Muhammad Ichsan; Faradisa, Elok; Budiman, Amin Aji
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2203

Abstract

Background: Adolescents aged 15–17 years, often classified as Generation Z, are considered vulnerable to mental health problems because they are in a developmental transition from childhood to adulthood. Gen Z typically has high social media use, which may reduce opportunities for direct face-to-face interaction. Objective: This study aimed to examine the association between digital burnout and loneliness, with self-esteem as a potential mediator. Methods: This study employed a path analysis design. The study was conducted at SMAN 1 Sliyeg, Indramayu Regency, Indonesia. Participants were selected using purposive sampling with the following inclusion criteria: students aged 15–18 years, not currently using psychotropic medication, willing to participate, and able to read and write. Data were collected using a demographic questionnaire, the UCLA Loneliness Scale, and the Rosenberg Self-Esteem Scale (RSES). Results: Path analysis indicated that digital burnout significantly contributed to higher loneliness, while self-esteem showed a significant protective association with lower loneliness among Gen Z adolescents (p < 0.05). Conclusion: Digital burnout may influence loneliness directly; however, the mediating role of self-esteem requires further confirmation based on the full mediation pathway.
Artificial Intelligence in Biomedical Psychology: A Systematic Review of Clinical and Cognitive Applications Wulandari, Annastasya Nabila Elsa; Agung Budi Prasetio; Baballe, Muhammad Ahmad; Taraknath Paul
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2214

Abstract

Biomedical psychology emphasises psychological and neurocognitive assessment through the integration of biological, neurophysiological, and quantitative behavioural data to support clinical decision-making. However, conventional assessment approaches remain limited by issues of objectivity, scalability, and longitudinal monitoring, prompting the utilisation of artificial intelligence (AI) as a computational tool in clinical and cognitive contexts. This systematic review synthesises the application of AI in biomedical psychology with an explicit focus on assessment functions, rather than intervention or therapy, following the PRISMA 2020 guidelines through a systematic search of four major databases. The included studies cover a variety of clinical and cognitive applications with variations in psychological constructs, data modalities, and AI methods. The synthesis results show that AI is most often used for diagnostic classification, risk screening, and continuous estimation of cognitive functions and dimensional constructs. Differences in assessment objectives between clinical and cognitive domains reveal consistent methodological trade-offs related to model selection, validation strategies, and overfitting risks. As a key contribution, this review presents an assessment-oriented cross-domain synthesis and proposes fit-forpurpose design principles as a conceptual framework for developing robust, interpretable, and clinically relevant AI-based assessment systems
Understanding the Role of Artificial Intelligence in Community and Home Nursing Care: A Systematic Literature Review Sony Kartika Wibisono; Oktavia Putri Handayani; Burhanuddin bin Mohd Aboobaider
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2225

Abstract

Community and home nursing care are increasingly central to health systems in response to population ageing, rising chronic disease burden, and the need to reduce avoidable hospital utilization. Artificial intelligence (AI) has emerged as a technological innovation with potential to support nursing practice in non-hospital settings. However, the role and implications of AI within community and home nursing care have not been systematically synthesized. This systematic literature review aimed to examine how AI supports community and home nursing practice, identify the types of AI technologies applied, and analyze their reported outcomes and implications for nursing care. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 237 records were identified through electronic database searches. After duplicate removal and screening, 38 full-text articles were assessed for eligibility, and 15 studies were included in the final qualitative synthesis. The included studies, published between 2024 and 2026, encompassed diverse methodological designs and were conducted in community-based, home health, telemonitoring, and mobile nursing contexts. The findings indicate that AI technologies primarily include machine learning–based predictive models, clinical decision support systems, telemonitoring platforms, digital wound assessment tools, and large language model–supported analytics are used to enhance risk prediction, remote monitoring, chronic disease management, and care coordination. Across studies, AI was associated with improved early detection of clinical deterioration, enhanced workflow efficiency, and potential reductions in hospital admissions. Nevertheless, effective implementation depended on nurse engagement, system usability, digital literacy, and organizational support. AI demonstrates substantial potential to strengthen community and home nursing care when integrated within a human-centered and ethically grounded framework that preserves professional nursing judgment.
A Narrative Review of Privacy Preserving Artificial Intelligence in Nursing Practice Through Federated Learning Iis Setiawan Mangkunegara; Purwono, Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2226

Abstract

The rapid integration of artificial intelligence in nursing practice has enhanced predictive analytics, clinical decision support, and workforce management. However, concerns regarding data privacy, data silo fragmentation, and limited model generalizability remain significant challenges. Federated learning has emerged as a privacy preserving distributed machine learning approach that enables collaborative model development without transferring raw patient data across institutions. This narrative review aims to examine the conceptual foundation of federated learning and analyze its relevance for nursing practice and research. A literature search was conducted using Scopus and ScienceDirect databases covering publications from 2015 to 2025. Articles were analyzed through thematic synthesis focusing on technical architecture, clinical applications, ethical implications, and implementation challenges. The review indicates that federated learning has substantial potential to support predictive risk modeling, multicenter nursing outcome research, and integration within clinical decision support systems while maintaining patient confidentiality. Nevertheless, challenges related to non identical data distribution, governance accountability, interoperability, and digital literacy among nurses must be addressed to ensure safe and equitable implementation. Federated learning represents a strategic pathway for developing collaborative and privacy conscious artificial intelligence in nursing, provided that ethical safeguards, standardized data frameworks, and institutional readiness are systematically strengthened.
The Use of a Multi-Drug Therapy (MDT) Monitoring Calendar as a Medium to Support Medication Adherence in Leprosy Patients Marianti Agustina Gudipun; Yoany Maria Vianney Bita Aty; Maria Selestina Sekunda; Aries Wawomeo; Florentianus Tat; Aben B.Y. Romana
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2117

Abstract

Background: Treatment of leprosy with Multi-Drug Therapy (MDT) requires a relatively long duration, depending on the type of leprosy. The extended treatment period often leads to patient fatigue, which may reduce medication adherence and increase the risk of treatment discontinuation. This situation can hinder therapeutic success and elevate the risk of disease transmission and disability. Therefore, simple and practical strategies are needed to support patients in maintaining adherence throughout the treatment period. One potential strategy is the use of an MDT monitoring calendar as a reminder and monitoring tool. This study aimed to determine the effect of the MDT monitoring calendar on medication adherence among leprosy patients in Ende Regency. Methods: This quantitative study employed a pre-experimental one-group pre-test–post-test design. Total sampling was used, involving 16 respondents. Data were analyzed using the Wilcoxon Signed-Rank Test. Results: All respondents demonstrated an increase in medication adherence after the implementation of the Multi-Drug Therapy (MDT) monitoring calendar, with no observed decrease in adherence. The Wilcoxon Signed-Rank Test indicated statistically significant results (Z = −3.532; p < 0.05), confirming the effect of the MDT monitoring calendar on medication adherence among leprosy patients in Ende Regency. Conclusion: The implementation of the Multi-Drug Therapy (MDT) monitoring calendar significantly improved medication adherence among leprosy patients. The MDT monitoring calendar can serve as an effective and easily implementable health promotion tool to support treatment success. Health service facilities are encouraged to integrate the MDT monitoring calendar into the leprosy control program in Ende Regency
Parental Knowledge and Motivation in HPV Vaccination Decision-Making for Cervical Cancer Prevention Among Children Elin Ermi Eliani; Riska Hediya Putri; Surmiasih; Rini Palupi
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2121

Abstract

Cervical cancer remains one of the leading causes of mortality among women in Indonesia, with persistent challenges in preventive efforts. Vaccination against the Human papillomavirus (HPV) is recognized as an effective primary prevention strategy to reduce the incidence of cervical cancer. However, HPV vaccination coverage among school-aged children in Indonesia remains suboptimal, partly due to limited parental awareness and varying levels of motivation. This study aimed to examine the relationship between parental knowledge of cervical cancer prevention and parental motivation toward HPV vaccination in children. A quantitative analytical study with a cross-sectional design was conducted among parents of female students in grades five and six at SD Negeri 5 Kresnomulyo, Pringsewu. A total of 67 respondents were recruited using a total sampling technique. Data were collected through validated questionnaires assessing knowledge and motivation levels and were analyzed using the Chi-square test. The findings demonstrated a statistically significant association between parental knowledge and motivation toward HPV vaccination (p < 0.001). These results suggest that improving parental knowledge through structured and targeted health education programs may strengthen motivation and increase acceptance of HPV vaccination as an essential preventive measure against cervical cancer
A Scoping Review of Machine Learning Applications in Nursing Practice: Clinical Decision Support, Risk Prediction, and Workflow Optimization Anton Suhendro; Wahyu Caesarendra; Purwono, Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2222

Abstract

Machine learning (ML) is rapidly transforming nursing practice by enabling advancements in clinical decision support, risk prediction, and workflow optimization. This scoping review synthesizes evidence from empirical studies, reviews, and implementation reports published between 2018 and 2025, identified through Scopus and ScienceDirect. The findings indicate that supervised learning algorithms, deep learning, and natural language processing are widely utilized for risk assessment, early detection of patient deterioration, and enhancement of administrative efficiency. Natural language processing (NLP) also supports automation of nursing documentation and improved data quality. Despite favorable performance metrics, including AUROC values above 0.85 in many applications, most studies are limited by single-institution data, insufficient external validation, and heterogeneous reporting standards. Major barriers include ethical and legal concerns, data quality issues, algorithmic bias, infrastructural limitations, and limited nurse involvement in model development. Enhancing AI literacy and fostering nurse engagement in system design are highlighted as critical for successful clinical integration. Future research priorities include multicenter validation, development of explainable AI, adoption of standardized reporting guidelines, and interdisciplinary collaboration to address ethical, technical, and regulatory challenges. Overall, this scoping review demonstrates that machine learning offers substantial potential to improve patient outcomes and nursing operations, but responsible adoption requires rigorous validation, transparent governance, and active participation of nursing professionals throughout the technology lifecycle
Effectiveness of Incenerator-Based Practice in Improving Student’s Environmental Health Care Behavior Hisyam, Anwaruddin; Nurul Aini Binti Ismail; Rokhayati; Safirina Aulia Rahmi; Arif Rahman
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2224

Abstract

The waste emergency in Yogyakarta, following the collapse of the centralized waste management system and the closure of the Final Disposal Site (FDS), has necessitated the implementation of decentralized and self-managed waste solutions within institutional settings. In this study, the effectiveness of integrating incinerator technology as a practice-based intervention to enhance Environmental Health Care Behavior (EHCB) among university students was investigated. A quantitative pre-experimental study employing a one-group pretest–posttest design was conducted using total sampling (n = 46). The intervention consisted of structured demonstrations and supervised hands-on operation of a campus mini-incinerator embedded within environmental health learning activities. EHCB scores were analyzed using a paired-sample t-test. A statistically significant increase was identified between the pre-intervention mean score (Mean = 45.60) and the post-intervention mean score (Mean = 52.80), with p < 0.001. An improvement of 15.79%, accompanied by a large effect size, was observed. These findings indicate that experiential engagement with environmental technology enhances self-efficacy and facilitates the transformation of knowledge into responsible waste management behavior. The results underscore the importance of technology-enhanced experiential learning as an institutional strategy for strengthening sustainable environmental health practices within decentralized waste management systems
Energy Sustainability in Artificial Intelligence for Nursing Practice: Addressing the Hidden Cost Yen-Ching Chang; Agung Budi Prasetio
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2249

Abstract

The adoption of artificial intelligence in nursing practice has accelerated rapidly and offers substantial benefits in terms of efficiency, predictive accuracy, and clinical workflow optimization. Applications such as automated documentation, natural language processing of clinical notes, and decision support systems are increasingly embedded in routine nursing activities. While these technologies enhance performance and productivity, growing evidence indicates that artificial intelligence systems are associated with significant energy consumption during model training, data storage, and operational deployment. The healthcare sector already contributes a measurable proportion of global greenhouse gas emissions, and energy intensive digital infrastructures further amplify this burden. Training advanced artificial intelligence models may generate substantial carbon emissions, and repeated inference processes in daily clinical use accumulate additional energy demand.Despite these concerns, current evaluation frameworks for artificial intelligence in nursing remain primarily centered on clinical effectiveness, usability, safety, and organizational readiness. Energy consumption, carbon footprint, and broader ecological implications are rarely incorporated into technology assessment processes. This omission creates a critical gap between digital innovation and environmental responsibility within nursing informatics. This short communication synthesizes available evidence on the hidden energy costs of artificial intelligence in healthcare and nursing contexts, identifies structural gaps in prevailing evaluation paradigms, and proposes the integration of standardized sustainability metrics. The proposed framework emphasizes explicit reporting of energy consumption, carbon emissions, and life cycle environmental impacts alongside traditional clinical and operational indicators. By reframing artificial intelligence evaluation through a sustainability lens, nursing can contribute to advancing digital transformation that is not only safe and effective but also environmentally responsible

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