Journal of evaluation in clinical practice
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Evidence-based medicine (EBM), the dominant approach to assessing the effectiveness of clinical and public health interventions, focuses on the results of association studies. EBM+ is a development of EBM that systematically considers mechanistic studies alongside association studies. ⋯ (a) Assessment of combination therapy for MERS highlights the need for systematic assessment of mechanistic evidence. (b) That hypertension is a risk factor for severe disease in the case of SARS-CoV-2 suggests that altering hypertension treatment might alleviate disease, but the mechanisms are complex, and it is essential to consider and evaluate multiple mechanistic hypotheses. (c) Confidence that public health interventions will be effective requires a detailed assessment of social and psychological components of the mechanisms of their action, in addition to mechanisms of disease. (d) In particular, if vaccination programmes are to be effective, they must be carefully tailored to the social context; again, mechanistic evidence is crucial. We conclude that coronavirus research is best situated within the EBM+ evaluation framework.
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Though strong evidence-based medicine is assertive in its claims, an insufficient theoretical basis and patchwork of arguments provide a good case that rather than introducing a new paradigm, EBM is resisting a shift to actually revolutionary complexity theory and other emergent approaches. This refusal to pass beyond discredited positivism is manifest in strong EBM's unsuccessful attempts to continually modify its already inadequate previous modifications, as did the defenders of the Ptolemaic astronomical model who increased the number of circular epicycles until the entire epicycle-deferent system proved untenable. ⋯ The analysis in Part 1 of this three part series showed epistemological confusion as strong EBM plays the discredited positivistic tradition out to the end, thus repeating in a medical sphere and vocabulary the major assumptions and inadequacies that have appeared in the trajectory of modern science. Paper 2 in this series examines application, attending to strong EBM's claim of direct transferability of EBM research findings to clinical settings and its assertion of epistemological normativity. EBM's contention that it provides the "only valid" approach to knowledge and action is questioned by analyzing the troubled story of proposed hierarchies of the quality of research findings (especially of RCTs, with other factors marginalized), which falsely identifies evaluating findings with operationally utilizing them in clinical recommendations and decision-making. Further, its claim of carrying over its normative guidelines to cover the ethical responsibilities of researchers and clinicians is questioned.
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Medical schools and residency programs have become very adept at teaching medical students and residents an enormous amount of information. However, it is much less clear whether they are effective at fostering virtuous qualities like empathy or professionalism in trainees. This would come as no surprise to Plato, who famously argued in the Meno that virtue cannot be taught. ⋯ As such, we address the question of the teachability of virtue in the realm of medicine, analysing Plato's contradictory analyses in the Meno and Protagoras, and drawing upon modern neuroscience to turn an empirical lens on the question. We explore the ways in which Noddings' Ethic of Care may offer a way forward for medical educators keen to foster virtue in trainees. We conclude by demonstrating how, by harnessing the power of caring relationships, the principles of Noddings' Ethic of Care have already been applied to medical education at a university in Israel.
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This paper aims to show how the focus on eradicating bias from Machine Learning decision-support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision-making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision-making. ⋯ We show that Machine Learning systems join doctors and patients in co-designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases.
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The onset of acute illness may be accompanied by a profound sense of disorientation for patients. Addressing this vulnerability is a key part of a physician's purview, yet well-intended efforts to do so may be impeded by myriad competing tasks in clinical practice. Resolving this dilemma goes beyond appealing to altruism, as its limitless demands may lead to physician burnout, disillusionment, and a narrowed focus on the biomedical aspects of care in the interest of self-preservation. The authors propose an ethic of hospitality that may better guide physicians in attending to the comprehensive needs of patients that have entered "the kingdom of the sick." ⋯ While it is unlikely that anything physicians do will make the hospital a place where patients and caregivers will desire to be, hospitality may focus their efforts upon making it less unwelcoming. Specifically, it offers an orientation that supports patients in navigating the disorienting and unfamiliar terrains of acute illness, the hospital setting in which help is sought, and engagement with the health care system writ large.