This Viewpoint brings together insights from health system experts working in a range of settings. Our focus is on examining the state of the resilience field, including current thinking on definitions, conceptualisation, critiques, measurement, and capabilities. We highlight the analytical value of resilience, but also its risks, which include neglect of equity and of who is bearing the costs of resilience strategies. Resilience depends crucially on relationships between system actors and components, and-as amply shown during the COVID-19 pandemic-relationships with wider systems (eg, economic, political, and global governance structures). Resilience is therefore connected to power imbalances, which need to be addressed to enact the transformative strategies that are important in dealing with more persistent shocks and stressors, such as climate change. We discourage the framing of resilience as an outcome that can be measured; instead, we see it emerge from systemic resources and interactions, which have effects that can be measured. We propose a more complex categorisation of shocks than the common binary one of acute versus chronic, and outline some of the implications of this for resilience strategies. We encourage a shift in thinking from capacities towards capabilities-what actors could do in future with the necessary transformative strategies, which will need to encompass global, national, and local change. Finally, we highlight lessons emerging in relation to preparing for the next crisis, particularly in clarifying roles and avoiding fragmented governance.
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http://dx.doi.org/10.1016/S2214-109X(23)00279-6 | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFMenopause
January 2025
National Institute of Health, Cheongju, Republic of Korea.
Objectives: We examined the health-related quality of life (HRQoL) during menopause transition (MT) among middle-aged Korean women.
Methods: This cross-sectional study comprised 2,290 middle-aged women who completed web-based questionnaires between 2020 and 2022. Based on self-reported menstrual cycle patterns, menopause status was classified as premenopausal, early or late transition, or postmenopausal.
JMIR Res Protoc
January 2025
School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Burwood, Australia.
Background: Heart failure (HF) is a chronic, progressive condition where the heart cannot pump enough blood to meet the body's needs. In addition to the daily challenges that HF poses, acute exacerbations can lead to costly hospitalizations and increased mortality. High health care costs and the burden of HF have led to the emerging application of new technologies to support people living with HF to stay well while living in the community.
View Article and Find Full Text PDFJMIR Form Res
January 2025
CIRCLE - Complex Intervention Research in Health and Care, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
Background: Parents of children treated for cancer may experience psychological difficulties including depression, anxiety, and posttraumatic stress. Digital interventions, such as internet-administered cognitive behavioral therapy, offer an accessible and flexible means to support parents. However, engagement with and adherence to digital interventions remain a significant challenge, potentially limiting efficacy.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!