Faculty practice as partnership with a community coalition can be a dynamic strategy for retooling the future of nursing. The Escalante ElderCARE Coalition was formed in 1991, with the Community Health Division of the Arizona State College of Nursing taking a leadership role. Since that time, more than 50 aging network and community agencies have become involved. More than $300,000 in grant funding has been awarded for Healthy WAY services with low-income seniors as health care and program partners. The conceptual model includes health-promotion services, participation of community elders in program planning and evaluation, and education of health professionals. Participation theory is the basis for the conceptual model. A large number of undergraduate and graduate nursing students have been involved in the nontraditional delivery of services provided by the coalition. The Short Form 36 (SF-36) and the Lifestyle Directions Questionnaire are the health status outcome measures, and elder satisfaction, coalition effectiveness, and cost-savings measures are the process indicators. Elders reported healthier scores in six of the eight SF-36 dimensions, including general health, than the older US general population, but they also report that their amount of physical exercise and fiber intake is less than adequate. Overall, the elders express great satisfaction with the Healthy WAY programs but do not perceive as much ownership as do the coalition's agency professionals. Coalitions are emerging as a force for change and a public health strategy, and faculty members are encouraged to take seriously the opportunities afforded by them for proactive, advanced practice roles.
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http://dx.doi.org/10.1016/s8755-7223(98)80047-1 | DOI Listing |
BMC Public Health
January 2025
Centre for Prevention, Lifestyle and Health, National Institute for Public Health and The Environment, Bilthoven, The Netherlands.
Background: A new paradigm of hybrid working exists, with most office workers sharing their work between the office and home office environment. Working from home increases time spent or prolonged sitting, which is associated with an increased risk of chronic disease. Interventions to reduce sitting time, specifically designed for both the office and home-office environments, are required to address this growing public health issue.
View Article and Find Full Text PDFBMC Nephrol
January 2025
Department of Nephrology-Dialysis-Transplantation, University of Liège, CHU Sart Tilman, Liège, Belgium.
Background: Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine learning models to assess the trustworthiness (i.
View Article and Find Full Text PDFAnn Pharmacother
January 2025
Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.
Objective: There is limited knowledge about severe urinary tract infections associated with SGLT2i, despite this being the basis for the Food and Drug Administration (FDA) warning. We aim to provide real-world evidence to clarify this relationship further.
Data Source: A literature review was performed in PubMed and Embase for cohort studies published up to August 2024 using PICO-consistent terms.
Int J Obes (Lond)
January 2025
Department of Internal Medicine, Malatya Training and Research Hospital, Malatya, Turkey.
Objective: Obesity is known to be associated with inflammation and impaired sleep quality. In addition, the anti-inflammatory properties of the daily diet provide positive effects on health. The aim of this study was to investigate the relationship between the inflammatory index of the diet consumed by people with obesity and inflammatory biomarkers and sleep quality.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, Serbia.
The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting.
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