Aim: This research aimed to explore the literature regarding the model of the Shared Decision Making (SDM) in the field of nursing.
Method: A scoping review was conducted. The electronic literature research was made on Medline, CINAHL, The Cochrane Library, Google Scholar, using a combination of key words: "Decision Making", "Shared Decision Making", "Nursing", "Nursing Patient relationship". The review was carried out following the Levac model.
Results: 29 studies were included, in a time range between 1972 and 2015. The analysis identifies the main characteristics of the SDM model, the tools for its implementation, the patients experience, the fields of application and the integration among SDM e evidence based practice.
Conclusion: the analysis showed that the Shared Decision Making model is not widespread, especially in the Italian context. This phenomenon could be explained by three fundamental aspects. The concept is not widely disseminated and full scientific maturity. His application also seems to be related to extensive knowledge of gold standard interventions and possible alternatives. Finally, there are cultural barriers to the implementation of the SDM.
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http://dx.doi.org/10.7429/pi.2016.693141 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125.
Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Institute of Data Science, National University of Singapore, 117602, Singapore.
Objectives: This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.
Materials And Methods: Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL.
Otol Neurotol
February 2025
Department of Radiology, Yale School of Medicine, New Haven, CT.
Background: Vestibular schwannoma (VS) is a common intracranial tumor that affects patients' quality of life. Reliable imaging techniques for tumor volume assessment are essential for guiding management decisions. The study aimed to compare the ABC/2 method to the gold standard planimetry method for volumetric assessment of VS.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan.
Background: Training opportunities, work satisfaction, and the factors that influence them according to gender and subspecialties are understudied among Japanese cardiologists.
Methods: We investigated the career development of Japanese cardiologists with an e-mail questionnaire. Feelings of inequality in training opportunities, work dissatisfaction, and reasons were assessed by examining the cardiologists' gender and invasiveness of subspecialties.
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