Journal of evaluation in clinical practice
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Schizophrenia is a complex mental health disorder that not only affects the individual diagnosed but also has profound implications for their families and caregivers. This paper aims to shed light on the emotional, social and practical challenges faced by caregivers, as well as the coping mechanisms they employ to navigate the complexities of caregiving. ⋯ Caregivers often experience a wide range of emotions, from love and empathy to frustration and helplessness, as they navigate the challenges of supporting their loved ones with schizophrenia. Additionally, caregivers may face stigma, social isolation and financial strain, further complicating their caregiving journey.
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The development of clinical practice guidelines (CPG) has evolved into a rigorous and complex process. There is a need for training of CPG developers including methodologists, panel members and patient representatives. This study explored the educational needs and experiences of CPG developers, with specific attention to the patient perspective and economic considerations. ⋯ This study underscores the importance of tailored CPG development training programmes addressing the specific competencies required for the different roles in CPG development. Thereby, recognising a holistic approach encompassing both content- and process-related aspects. Addressing economic considerations and the patient perspective in training will contribute to CPGs that support a patient-centred and sustainable healthcare system.
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This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. ⋯ This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID-19, as well as contribute to the development of innovative methods in medical practice.
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This commentary on Sturmberg and Mercuri's paper 'Every Problem is Embedded in a Greater Whole' [1] argues that those authors have approached complexity from a largely mathematical perspective, drawing on the work of Sumpter. Whilst such an approach allows us to challenge the simple linear causality assumed in randomised controlled trials, it is itself limited. ⋯ It overlooks, for example, how science itself is historically and culturally shaped and how values-driven misunderstandings and conflicts are inevitable when people with different world views come together to try to solve a problem. This paper argues that the mathematical version of complexity thinking is necessary but not sufficient in medical research, and that we need to enhance such thinking further with attention to human values.
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The potential applications of large language models (LLMs)-a form of generative artificial intelligence (AI)-in medicine and health care are being increasingly explored by medical practitioners and health care researchers. ⋯ Accordingly, this paper finds a strong case for the incorporation of LLMs into clinical practice and, if their risk of patient harm is sufficiently mitigated, this incorporation might be ethically required, at least according to principlism.