Journal of evaluation in clinical practice
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The WHO Surgical Safety Checklist is a communication tool designed to improve surgical safety processes and enhance teamwork. It has been widely adopted since its introduction over ten years ago. As surgical safety needs evolve, organizations should periodically review and update their checklists. A holistic evaluation of the checklist in the context of an organization is the first step to making informed updates. In this article, we describe a comprehensive but feasible strategy for checklist evaluation which we developed and implemented as part of a surgical safety initiative in a high-performing center. ⋯ We developed and implemented a comprehensive, scalable approach to checklist evaluation which directly informed improvements to the checklist that were tailored to the organization's current context. Organizations can apply this framework to breathe new life into their checklist and transform their safety culture.
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Preventive health is a core part of primary care clinical practice and it is critical for both disease prevention and reducing the consequences of chronic disease. In primary care, the 5As framework is often used to guide behaviour change consultations for smoking, nutrition, alcohol use and physical activity. ⋯ The language and content of the guidelines contrast with known effective components of behaviour change consultations. Future revisions could reconsider emphasis of 5As terms to avoid paternalistic approaches, improve shared language across guidelines and incorporate behavioural science principles to enhance preventative care delivery.
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The fragility index (FI) and fragility quotient (FQ) are increasingly used measures for assessing the robustness of clinical studies with binary outcomes in terms of statistical significance. The FI is the minimum number of event status modifications that can alter a study result's statistical significance (or nonsignificance), and the FQ is calculated as the FI divided by the study's total sample size. The literature has no widely recognized criteria for interpreting the fragility measures' magnitudes. This article aims to provide an empirical assessment for the FI and FQ based on a large database of clinical studies in the Cochrane Library. ⋯ The statistical significance of clinical studies could be changed after modifying a few events' statuses. Many studies' findings are fairly fragile. The distributions of the FI and FQ provide insights for appraising the robustness of evidence in clinical decision-making.
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Low-value care in public health can be addressed via disinvestment with the support of disinvestment research generated evidence. Consumers' views of disinvestment have rarely been explored despite the potential effects of this process on the care they will receive and the importance of consumer participation in decision-making in public healthcare. ⋯ Consumers' main perception of disinvestment processes was that the removal of a clinical care activity depended on financial imperatives from hospital administration and political agendas. This tended to cause suspicion about reasons behind the removal of care, which overshadowed comprehension of the ineffective/inconclusive evidence that were key to disinvestment.
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Social determinants of health (SDOH) are being considered more frequently when providing orthopaedic care due to their impact on treatment outcomes. Simultaneously, prognostic machine learning (ML) models that facilitate clinical decision making have become popular tools in the field of orthopaedic surgery. When ML-driven tools are developed, it is important that the perpetuation of potential disparities is minimized. One approach is to consider SDOH during model development. To date, it remains unclear whether and how existing prognostic ML models for orthopaedic outcomes consider SDOH variables. ⋯ The current level of reporting and consideration of SDOH during the development of prognostic ML models for orthopaedic outcomes is limited. Healthcare providers should be critical of the models they consider using and knowledgeable regarding the quality of model development, such as adherence to recognized methodological standards. Future efforts should aim to avoid bias and disparities when developing ML-driven applications for orthopaedics.