Background: Nursing education consists of theory and practice, and student nurses' perception of the learning environment, both educational and clinical, is one of the elements that determines the success or failure of their university study path. This study aimed to identify the currently available tools for measuring the clinical and educational learning environments of student nurses and to evaluate their measurement properties in order to provide solid evidence for researchers, educators, and clinical tutors to use in the selection of tools.
Methods: We conducted a systematic review to evaluate the psychometric properties of self-reported learning environment tools in accordance with the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) Guidelines of 2018. The research was conducted on the following databases: PubMed, CINAHL, APA PsycInfo, and ERIC.
Results: In the literature, 14 instruments were found that evaluate both the traditional and simulated clinical learning environments and the educational learning environments of student nurses. These tools can be ideally divided into first-generation tools developed from different learning theories and second-generation tools developed by mixing, reviewing, and integrating different already-validated tools.
Conclusion: Not all the relevant psychometric properties of the instruments were evaluated, and the methodological approaches used were often doubtful or inadequate, thus threatening the instruments' external validity. Further research is needed to complete the validation processes undertaken for both new and already developed instruments, using higher-quality methods and evaluating all psychometric properties.
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http://dx.doi.org/10.3390/healthcare11071043 | DOI Listing |
J Biochem Mol Toxicol
January 2025
Medical Experiment Center, Shaanxi University of Chinese Medicine, Xianyang, China.
Bisphenol A (BPA), an environmental endocrine disrupting chemical, is one of the most widely used chemicals in the world and is widely distributed in the external environment, specifically in food, water, dust, and soil. BPA exposure is associated with abnormal cognitive behaviors. However, the underlying mechanism remains unclear.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFormerB0 network is trained to provide an initial segmentation. In the second phase, the network is further refined using the Pix2Pix structure, which enhances attention to details and corrects any erroneous or additional segments in the output.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India.
Sci Rep
January 2025
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran. Electronic address:
Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks.
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