Background: Academic integrity is an important component of nursing education, bridging academic ethics with professional practice. This study evaluated the effectiveness of a co-designed Academic Integrity digital serious game in improving nursing students' self-efficacy related to academic integrity, academic offenses, professionalism, and artificial intelligence use.
Methods: A pre-test/post-test design was employed, using a bespoke questionnaire to assess 303 first-year nursing students' self-efficacy before and after playing the game. The questionnaire covered five subscales: academic integrity standards, academic offenses, professional values, feedback processes, and AI use in academic work.
Results: Statistically significant improvements were observed across all subscales following the intervention, indicating enhanced self-efficacy in understanding and applying academic integrity principles, recognizing academic offenses, demonstrating professional behaviors, utilizing feedback, and appropriately using AI in academic contexts.
Conclusions: The Academic Integrity digital serious game has the potential to be an effective tool for enhancing nursing students' self-efficacy in the areas of academic and professional ethics. This approach shows promise for integrating academic integrity-based education in nursing curricula and preparing students for the ethical challenges of modern healthcare practice. This study was not registered.
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http://dx.doi.org/10.3390/nursrep15020045 | DOI Listing |
J Adv Nurs
March 2025
School of Nursing and Midwifery, Western Sydney University, Penrith, New South Wales, Australia.
Aim: To provide insights into the challenges and benefits of being an insider nurse researcher in case study research. To describe strategies used to maintain rigour and trustworthiness when conducting insider research.
Design: Case study with insider research methodology was used to evaluate a nurse-led supportive care model for patients with Chronic Obstructive Pulmonary Disease and their caregivers.
BMC Nurs
March 2025
School of Nursing and Midwifery, Medical Biology Centre, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, Northern Ireland, UK.
Background: Cardiovascular disease (CVD) is a prevalent cause of morbidity and mortality globally. Nurses and nursing students are in an optimum role to assess, manage and promote lifestyle changes associated with CVD risk. Patients and service users are more likely to adopt these changes if the person delivering the information embodies this lifestyle themselves.
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March 2025
Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
Large Language Models (LLMs), such as ChatGPT (OpenAI, CA, US), have revolutionized scientific writing and research processes across academic disciplines, providing comprehensive support throughout the entire research lifecycle. Generative artificial intelligence (GAI) tools enhance every aspect of scientific writing, from hypothesis generation and methodology design to data analysis and manuscript preparation. This review examines the applications of LLMs in hematological research, with particular emphasis on advanced techniques, including prompt engineering and retrieval augmented generation (RAG) frameworks.
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March 2025
Cancer Genome and Epigenetics Program; Sanford Burnham Prebys MDI, La Jolla, CA, USA.
Genomic instability and inflammation are distinct hallmarks of aging, but the connection between them is poorly understood. Here we report a mechanism directly linking genomic instability and inflammation in senescent cells through a mitochondria-regulated molecular circuit involving p53 and cytoplasmic chromatin fragments (CCF) that are enriched for DNA damage signaling marker γH2A.X.
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March 2025
Department of Chemical Engineering, Yildiz Technical University, Istanbul, Türkiye.
This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal).
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