Journal of evaluation in clinical practice
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ChatGPT, a large-scale language model, is a notable example of AI's potential in health care. However, its effectiveness in clinical settings, especially when compared to human physicians, is not fully understood. This study evaluates ChatGPT's capabilities and limitations in answering questions for Japanese internal medicine specialists, aiming to clarify its accuracy and tendencies in both correct and incorrect responses. ⋯ This study underscores ChatGPT's potential utility and limitations in internal medicine. While effective in some aspects, its dependence on question type and context suggests that it should supplement, not replace, professional medical judgment. Further research is needed to integrate Artificial Intelligence tools like ChatGPT more effectively into specialized medical practices.
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Previous evidence underscores the need to assess potential clinical outcomes resulting from pharmaceutical care interventions and to monitor patient's progress to evaluate their clinical evolution, which is crucial for bolstering the relevance of implementing pharmaceutical care in healthcare services. ⋯ This study facilitated the consolidation of pharmaceutical care implementation in a geriatric ward. We conducted identification, evaluation, and proposed evidence-based solutions, as well as monitored cases for outcome analysis. It is anticipated that this methodology will inspire future research and the implementation of pharmaceutical care-related services.
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The retention of resuscitation skills is a widespread concern, with a rapid decay in competence frequently following training. Meanwhile, training programmes continue to be disconnected with real-world expectations and assessment designs remain in conflict with the evidence for sustainable learning. This study aimed to evaluate a programmatic assessment pedagogy which employed entrustment decision and the principles of authentic and sustainable assessment (SA). ⋯ In addition to confirming local sustainability issues, this study has highlighted the validity concerns that exist with conventional resuscitation training designs. We have successfully demonstrated an alternative pedagogy which responds to these concerns, and which embodies the principles of SA, quality in assessment practice, and the real-world expectations of professionals.
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Machine learning techniques (MLT) build models to detect complex patterns and solve new problems using big data. ⋯ Using created prediction model may allow early identification of mothers with a risk of not breastfeeding their babies exclusively for the first 6 months. In this way, mothers in the risk group can be closely monitored in the early period.
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Feedback-based learning (FBL) focuses on guiding the learning process according to educational objectives and the student's needs. This study aimed to investigate surgical nursing students' perceptions and explore their experiences of FBL. ⋯ In this study, the positive and negative aspects of FBL were explained. FBL is perceived as a motivational support mechanism to improve students' capabilities during their academic courses and also prepare them for future careers. Conversely, FBL may experience unpleasant learning due to negative feedback and negative emotions.