Over the past two decades, research in a variety of countries and settings has revealed the extent to which psychological morbidity and social problems constitute an important proportion of primary health care contacts, either in their own right or in association with physical ill-health. The development of appropriate responses, medical and non-medical, to such problems is, in part, hindered by the relatively low level of awareness concerning the significance of such problems and by currently inappropriate methods of describing and classifying health care problems in the primary care setting. There is a pressing need for an appropriate, relatively simple and flexible classification or list of problems presenting in primary care. This study describes the development, under WHO auspices and in an international setting, of a triaxial classification of health problems and the testing and modification of the classification by means of an international case vignette rating exercise. A psychological problem list and a social problem list, together with detailed glossaries, suitably modified in the light of experience derived from the study, are described, and proposals enumerated of possible ways in which these lists might be further developed and used in research and clinical undertakings in primary care.
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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 PDFAnnu Rev Clin Psychol
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3Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
Most people with mental health needs cannot access treatment; among those who do, many access services only once. Accordingly, single-session interventions (SSIs) may help bridge the treatment gap. We conducted the first umbrella review synthesizing research on SSIs for mental health problems and service engagement in youth and adults.
View Article and Find Full Text PDFACS Sens
January 2025
Department of Physics and Astronomy, Franklin College of Arts and Sciences, The University of Georgia, Athens, Georgia 30602, United States.
Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.
View Article and Find Full Text PDFJMIR 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 Cancer
January 2025
Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.
Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
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