Introduction: Self-management leads to blood glucose control and reduced morbidity and mortality in adolescents with type 1 diabetes. Different factors affect the self-management whose role and effect are still unknown. Among the influential factors whose effect is vague are spiritual intelligence, and this study aims to investigate the predictive role of spiritual intelligence in diabetes management.
Materials And Methods: In this descriptive-correlation study, 200 adolescents with type 1 diabetes were enrolled. To measure spiritual intelligence, the 24-question SISRI questionnaire and to measure self-management of diabetes, the SMOD-A questionnaire (48 questions) were used. Data were analyzed using SPSS software version 18 using linear regression analysis tests. Data collection was conducted by simple sampling.
Results: Mean score of self-management of diabetes and spirituality was 86.1 ± 15.1 and 60.42 ± 12.9, respectively. Linear regression test (ANOVA: 0.002, = 9.839) showed effect on diabetes self-management (β: 0.218).
Conclusion: This study showed that spiritual intelligence can predict diabetes self-management, though poorly predicted, and by strengthening it, has a decisive role in improving the health of adolescents with diabetes. Considering the findings of this study, a new window of nurses' performance in managing diabetes based on the promotion of spiritual intelligence in the educational, care, counseling, and support roles of nursing science can be opened.
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http://dx.doi.org/10.4103/jehp.jehp_182_17 | DOI Listing |
JMIR Form Res
January 2025
Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
Background: Advancements in medical science have focused largely on patient care, often overlooking the well-being of health care professionals (HCPs). This oversight has consequences; not only are HCPs prone to mental and physical health challenges, but the quality of patient care may also endure as a result. Such concerns are also exacerbated by unprecedented crises like the COVID-19 pandemic.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Pediatrics and Child Health, Makerere University, College of Health Sciences, Kampala, Uganda.
Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, it has introduced new challenges and threats to medical education. We assessed the use of ChatGPT and other AI tools among medical students in Uganda.
View Article and Find Full Text PDFTranscult Psychiatry
January 2025
Department of Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Mental health service use by individuals of South Asian origin living outside of South Asia is influenced by cultural factors such as endorsing psycho-social-spiritual over biological explanations, somatisation, and stigma. The aim of this review is to synthesise the evidence about (a) explanatory models of common mental disorders (CMDs) among people of South Asian origin residing in high-income countries, and (b) their help-seeking for CMDs, including formal and informal care. The systematic review protocol was registered a priori on Prospero (registration number CRD42021287583).
View Article and Find Full Text PDFIran J Nurs Midwifery Res
November 2024
Department of Nursing, Nursing and Midwifery School, Iranian Research Center on Healthy Aging, Sabzevar University of Medical Sciences, Sabzevar, Iran.
Curr Oncol
December 2024
Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), University of Salento and ASL (Local Health Authority), 73100 Lecce, Italy.
Introduction: This qualitative literature review explored the intersection of art, creativity, and the nurse-patient relationship in the context of oncology nursing. It delved into the perceptions and reflections of nurses as captured by Generative Artificial Intelligence (GAI) analysis from two specialized nursing databases.
Methods: The protocol was registered on the Open Science Framework (OSF) Platform.
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