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Background: Implicit rationing of nursing care can adversely affect patient safety and the quality of care, and increase nurses' burnout and turnover tendency. Implicit rationing care occurs at the nurse-to-patient level (micro-level), and nurses are direct participants. Therefore, the strategies based on experience of nurses to reduce implicit rationing care have more reference value and promotion significance. The aim of the study is to explore the experience of nurses to reduce implicit rationing care, thereby to provide references for conducting randomized controlled trials to reduce implicit rationing care.
Methods: This is a descriptive phenomenological study. Purpose sampling was conducted nationwide. There are 17 nurses were selected and semi-structured in-depth interviews were conducted. The interviews were recorded, transcribed verbatim and analyzed via thematic analysis.
Results: Our study found that nurses' reported experience of coping with implicit rationing of nursing care contained three aspects: personal, resource, and managerial. Three themes were extracted from the results of the study: (1) improving personal literacy; (2) supplying and optimizing resources and (3) standardizing management mode. The improvement of nurses' own qualities are the prerequisites, the supply and optimization of resources is an effective strategy, and clear scope of work has attracted the attention of nurses.
Conclusion: The experience of dealing with implicit nursing rationing includes many aspects. Nursing managers should be grounded in nurses' perspectives when developing strategies to reduce implicit rationing of nursing care. Promoting the improvement of nurses' skills, improving staffing level and optimizing scheduling mode are promising measures to reduce hidden nursing rationing.
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http://dx.doi.org/10.1186/s12912-023-01334-5 | DOI Listing |
NEJM AI
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston.
Over the past two decades, network medicine (NM) has evolved to help define disease mechanisms, identify drug targets, and guide increasingly precise therapies. In recent years, the integration of NM with artificial intelligence (AI), particularly deep learning techniques, has evolved with increasing applications. AI techniques help elucidate complex disease mechanisms and define precise therapies.
View Article and Find Full Text PDFJ Pharm Policy Pract
August 2025
Discipline of Social and Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Penang, Malaysia.
Over the past few decades, the emergence of irrational medicine use has become a significant global health challenge. It has contributed to medication errors, adverse drug reactions, higher treatment costs, increased morbidity, and mortality. Problems with irrational prescribing are a matter of concern in low and middle-income countries, where regulatory control is underdeveloped, healthcare systems are constrained by economic pressures, and there is a shortage of trained personnel.
View Article and Find Full Text PDFJ Chem Inf Model
August 2025
Department of Chemistry, University of Southern California, Los Angeles, California 90089-1062, United States.
G-protein-coupled receptors (GPCRs) constitute the largest superfamily of integral membrane proteins in the human genome that mediate most transmembrane signaling processes. Malfunction of these signaling processes is related to many human pathologies (Parkinson's, heart diseases, etc.), causing GPCRs to be the largest family of druggable proteins.
View Article and Find Full Text PDFSci Rep
July 2025
School of Mechatronic Engineering, Changchun University of Technology, Yan'an St., Changchun, 130012, Jilin Province, China.
Since the practical constraints of unknown pedestrian goal information, research on inverse reinforcement learning (IRL) applied to social robots has focused on trajectory planning based on current motion direction, other pedestrians, and obstacles. However, social robots typically have clear navigation goals, and the practicality of the aforementioned methods is debatable. Moreover, trajectory prediction at longer distances also poses significant challenges for such trajectory planning methods.
View Article and Find Full Text PDFComput Methods Programs Biomed
October 2025
Department of Pathology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan 030032, China. Electronic address:
Background And Objective: Graph-based methods are widely applied in whole-slide histopathology images (WSI) analysis since they can effectively capture spatial relationship between nodes. However, existing methods focus on promoting positive nodes to have similar representations while ignoring the expression of negative samples of each node, failing to fully utilize various diagnostic information for comprehensive analysis.
Methods: In this paper, we propose a Dual Collaboration Heterogeneous Graph Convolutional Network (DCH-GCN) framework that considers both positive and negative samples implicit in whole-slide images (WSIs).