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All Journal Jurnal Keterapian Fisik JKG (Jurnal Keperawatan Global) JSN : Jurnal Sains Natural Journal of Indonesia Laboratory Students (JILTS)
Anggit Jiwantoro, Yudha
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Nursing Physics Public Health

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Journal : JKG (Jurnal Keperawatan Global)

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Assessing the Role of Nursing Professionals in the Delivery of Integrated Primary Health Care Ardesa, Yopi Harwinanda; Setyorini, Yuyun; Anggit Jiwantoro, Yudha; Sulistya Affarah, Wahyu; Firdausi Paramaiswari, Nurul; Darawati, Made
JKG (JURNAL KEPERAWATAN GLOBAL) (JKG) Jurnal Keperawatan Global Volume 9 Number 1 Year 2024
Publisher : Poltekkes Kemenkes Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37341/jkg.v9i1.948

Abstract

Background: The integration of Primary Health Care (PHC) services is crucial for improving health outcomes and efficiency in health systems globally. Nursing professionals play a critical role in delivering these integrated services. Understanding their contribution and the impact of their involvement is essential for optimising PHC delivery. The objective of this systematic review is to evaluate the roles and contributions of nursing professionals in the delivery of integrated primary healthcare services. Methods: A comprehensive search strategy will be implemented across electronic databases, including PubMed, CINAHL, and Cochrane Library, to identify relevant studies. Studies published from [start date] to [end date] will be considered. Two independent reviewers will screen titles and abstracts for eligibility, followed by a full-text review of potentially relevant articles. Data extraction and quality appraisal will be conducted using standardised forms. A narrative synthesis will be performed to analyse and summarise the findings. Conclusion: This systematic review will provide evidence on the contributions of nursing professionals to integrated primary health care delivery. The findings will recommend policy and practice in primary health care services.
Effectiveness of Non-Invasive Sensor-Based Tools for Blood Glucose Detection Anggit Jiwantoro, Yudha; Dian Ayu Anggraeni, Ni Putu; Nurhidayah, Ninik; Wijayanti, I Gusti Ayu Sri Puja Warnis; Cembun, Cembun
JKG (JURNAL KEPERAWATAN GLOBAL) (JKG) Jurnal Keperawatan Global Volume 9 Number 2 Year 2024
Publisher : Poltekkes Kemenkes Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37341/jkg.v9i2.1095

Abstract

Background: Monitoring blood glucose levels is one of the main pillars of diabetes management to prevent complications and reduce the risk of morbidity and mortality. Today's blood glucose monitoring is a non-invasive method that offers speed, accuracy, and painless convenience. Referring to this need, this study aims to demonstrate the effectiveness of non-invasive sensor-based detection devices in checking blood glucose levels in order to provide a more comfortable and efficient alternative for diabetes patients. Methods: This study developed a non-invasive glucometer using the latest and smaller version of Arduino Uno and tested it on 20 samples, consisting of 10 diabetes mellitus patients and 10 with normal blood glucose. The test was carried out by comparing the measurement results from the non-invasive device and the standard GCU Easy Touch 3-in-1 device to determine the accuracy of the device. The tool-testing method uses sensitivity, specificity, and accuracy. Results: This non-invasive measuring tool is more effective when used to measure patients with diabetes mellitus. This device shows an error rate of 9.21%, a sensitivity of 80%, and a specificity of 50%. Meanwhile, the overall measurement accuracy, calculated at 83.3%, reinforces the tool's effectiveness in providing reliable results. Conclusion: This device has the potential to be a convenient and painless method of blood glucose monitoring for diabetic patients. However, further development is needed to improve the development of machine learning-based algorithms to process sensor data so that tools can identify unique patterns from each individual and provide more accurate results.
Co-Authors Aniek Puspitosari Cembun, Cembun Dian Ayu Anggraeni, Ni Putu Firdausi Paramaiswari, Nurul Getas, I Wayan Isnaeni, Robiatul Kristinawati, Erna Made Darawati Mandayani , Baiq Dinda Annas Tasya Ninik Nurhidayah, Ninik Nurul Inayati Puput Dinda Kurniawan, Ganesa Sulistya Affarah, Wahyu Wijayanti, I Gusti Ayu Sri Puja Warnis Yopi Harwinanda Ardesa Yuyun Setyorini