When the editorial to the first philosophy thematic edition of this journal was published in 2010, critical questioning of underlying assumptions, regarding such crucial issues as clinical decision making, practical reasoning, and the nature of evidence in health care, was still derided by some prominent contributors to the literature on medical practice. Things have changed dramatically. Far from being derided or dismissed as a distraction from practical concerns, the discussion of such fundamental questions, and their implications for matters of practical import, is currently the preoccupation of some of the most influential and insightful contributors to the on-going evidence-based medicine debate. Discussions focus on practical wisdom, evidence, and value and the relationship between rationality and context. In the debate about clinical practice, we are going to have to be more explicit and rigorous in future in developing and defending our views about what is valuable in human life.
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http://dx.doi.org/10.1111/jep.12831 | DOI Listing |
PLoS One
December 2024
Département de Psychologie, Université de Montréal, Montréal, Québec, Canada.
Cognitive biases have been studied in relation to schizophrenia and psychosis for over 50 years. Yet, the quality of the evidence linking cognitive biases and psychosis is not entirely clear. This umbrella-review examines the quality of the evidence and summarizes the effect sizes of the reasoning and interpretation cognitive biases studied in relation to psychotic characteristics (psychotic disorders, psychotic symptoms, psychotic-like experiences or psychosis risk).
View Article and Find Full Text PDFFront Artif Intell
December 2024
Department of Economics, University of Crete, Rethymnon, Greece.
The use of Financial Technology (Fintech) has been proposed as a promising way to bridge the gender gap, both financially and socially. However, there is evidence that Fintech is far from achieving this objective, and that women's perceptions of Fintech usages are not clear. Therefore, the main objective of the this study is to segment women's perceptions toward Fintech tools and interpret these segments using machine learning methods.
View Article and Find Full Text PDFInt J Nurs Knowl
December 2024
Department of Nursing, Federal University of Ceará, Fortaleza, Brazil.
Purpose: To evaluate the accuracy of clinical indicators and etiological factors associated with the nursing diagnosis of excessive sedentary behavior among university students.
Method: This study employed a cross-sectional diagnostic accuracy design. The sample comprised 108 students from a Brazilian public university.
Chin Med J (Engl)
December 2024
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, Shaanxi 710032, China.
Large language models (LLMs) such as ChatGPT, Claude, Llama, and Qwen are emerging as transformative technologies for the diagnosis and treatment of various diseases. With their exceptional long-context reasoning capabilities, LLMs are proficient in clinically relevant tasks, particularly in medical text analysis and interactive dialogue. They can enhance diagnostic accuracy by processing vast amounts of patient data and medical literature and have demonstrated their utility in diagnosing common diseases and facilitating the identification of rare diseases by recognizing subtle patterns in symptoms and test results.
View Article and Find Full Text PDFPatient Educ Couns
December 2024
Department of Pediatrics and Neonatology, OLVG, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Vrije Universiteit, Emma Children's Hospital, Amsterdam, the Netherlands.
Objective: To examine the effects of clinicians' provision of (un)reasonable arguments on parent-related outcomes in neonatal (intensive) care (NICU), starting from the NICU Communication Framework.
Methods: A video-vignette experiment, in which we systematically varied clinicians' use of (reasonable, unreasonable, no) argumentation across two non-acute and two acute decision-making scenarios (3×4 design). Reasonable arguments were medically appropriate and constructive reasons to support the treatment plan, as defined by an expert panel.
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