A structured and systematic care process for preventive work, aimed to reduce falls, pressure ulcers and malnutrition among older people, has been developed in Sweden. The process involves risk assessment, team-based interventions and evaluation of results. Since development, this structured work process has become web-based and has been implemented in a national quality registry called 'Senior Alert' and used countrywide. The aim of this study was to describe nursing staff's experience of preventive work by using the structured preventive care process as outlined by Senior Alert. Eight focus group interviews were conducted during 2015 including staff from nursing homes and home-based nursing care in three municipalities. The interview material was subjected to qualitative content analysis. In this study, both positive and negative opinions were expressed about the process. The systematic and structured work flow seemed to only partly facilitate care providers to improve care quality by making better clinical assessments, performing team-based planned interventions and learning from results. Participants described lack of reliability in the assessments and varying opinions about the structure. Furthermore, organisational structures limited the preventive work.
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http://dx.doi.org/10.1111/hsc.12400 | DOI Listing |
Sci Rep
December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFNat Commun
December 2024
Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Record breaking atmospheric methane growth rates were observed in 2020 and 2021 (15.2±0.5 and 17.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Introduction: Alzheimer's disease (AD), primary age-related tauopathy (PART), and chronic traumatic encephalopathy (CTE) all feature hyperphosphorylated tau (p-tau)-immunoreactive neurofibrillary degeneration, but differ in neuroanatomical distribution and progression of neurofibrillary degeneration and amyloid beta (Aβ) deposition.
Methods: We used Nanostring GeoMx Digital Spatial Profiling to compare the expression of 70 proteins in neurofibrillary tangle (NFT)-bearing and non-NFT-bearing neurons in hippocampal CA1, CA2, and CA4 subregions and entorhinal cortex of cases with autopsy-confirmed AD (n = 8), PART (n = 7), and CTE (n = 5).
Results: There were numerous subregion-specific differences related to Aβ processing, autophagy/proteostasis, inflammation, gliosis, oxidative stress, neuronal/synaptic integrity, and p-tau epitopes among these different disorders.
JMIR Form Res
December 2024
Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States.
Background: Anxiety disorders are common in alcohol use disorder (AUD) treatment patients. Such co-occurring conditions ("comorbidity") have negative prognostic implications for AUD treatment outcomes, yet they commonly go unaddressed in standard AUD care. Over a decade ago, we developed and validated a cognitive behavioral therapy intervention to supplement standard AUD care that, when delivered by trained therapists, improves outcomes in comorbid patients.
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