Background: Nursing postgraduate supervisors serve as educators, mentors, and research facilitators, ensuring the holistic development of postgraduate students to meet the evolving demands of nursing care.
Purpose: This study explored factors influencing academic supervision relationships (playing a critical role in the academic and professional development of students). It innovatively applied 2 large language models (LLMs) to analyze qualitative interviews with postgraduate nursing students.
Methods: Data were collected through semi-structured interviews with 14 nursing graduate students, and 2 LLMs widely used in China were used for interview transcript analysis.
Results: The themes extracted by 2 LLM models were highly consistent and can be grouped into 4 main categories: (1) academic supervisor-related factors: supervisory style, personal traits, leadership style, and research capabilities/resources; (2) student-related factors: independence, initiative, and career expectations; (3) academic supervisor-student interaction: communication frequency and quality and shared goals; and (4) environmental factors: academic environment and team culture.
Conclusion: Active communication, clear role expectations, and cooperation optimize supervisory relationships, enhancing nursing training and research.
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http://dx.doi.org/10.1097/NNE.0000000000001826 | DOI Listing |
JMIR Med Inform
March 2025
Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University, No.119 Nansihuanxi Road, Fengtai District, Beijing, 100070, China, 86 17611757717.
Background: Publicly accessible critical care-related databases contain enormous clinical data, but their utilization often requires advanced programming skills. The growing complexity of large databases and unstructured data presents challenges for clinicians who need programming or data analysis expertise to utilize these systems directly.
Objective: This study aims to simplify critical care-related database deployment and extraction via large language models.
Int J Popul Data Sci
March 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Accurate data linkage across large administrative databases is crucial for addressing complex research and policy questions, yet linkage errors-stemming from inconsistent name representations-can introduce biases, predominantly for names not given in English. This data note examines the impact of romanisation on linkage accuracy, focusing on Chinese names and comparing standardised systems (Jyutping and Pinyin) with the non-standardised Hong Kong Government Cantonese Romanisation (HKG-romanisation). We identify three primary issues: language-specific variations in romanisation, the loss of tonal information inherent to tonal languages, and discrepancies in name order conventions.
View Article and Find Full Text PDFCrohns Colitis 360
January 2025
Department of Medicine, Karsh Division of Digestive and Liver Diseases, Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Generative pre-trained transformer-4 (GPT-4) is a large language model (LLM) trained on a vast corpus of data, including the medical literature. Nutrition plays an important role in managing inflammatory bowel disease (IBD), with an unmet need for nutrition-related patient education resources. This study examines the accuracy, comprehensiveness, and reproducibility of responses by GPT-4 to patient nutrition questions related to IBD.
View Article and Find Full Text PDFImaging Neurosci (Camb)
March 2025
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, United States.
The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in high-level vision. One intriguing aspect of this region is its functional lateralization, with neural responses to words being stronger in the left hemisphere, and neural responses to faces being stronger in the right hemisphere; such patterns can be summarized with a signed laterality index (LI), positive for leftward laterality. Converging evidence has suggested that word laterality emerges to couple efficiently with left-lateralized frontotemporal language regions, but evidence is more mixed regarding the sources of the right lateralization for face perception.
View Article and Find Full Text PDFNatl Sci Rev
April 2025
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities.
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