This research seeks to explore the experiences of social work educators and students working and learning from home. The findings, from an international survey sample of 166 educators and students, showed that the respondents faced issues with private and personal boundaries, felt the impact of working and learning from home on both physical and emotional levels, and experienced challenges to what was expected of them. The respondents primarily used two types of coping mechanisms to manage these challenges. These findings contribute to a broader discussion of the impact of working and learning from home and are relevant for education administrators responsible for their employees' and students' well-being.
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http://dx.doi.org/10.1177/00208728211051412 | DOI Listing |
Br J Hosp Med (Lond)
January 2025
Department of Surgery & Cancer, Imperial College London, London, UK.
Predictive algorithms have myriad potential clinical decision-making implications from prognostic counselling to improving clinical trial efficiency. Large observational (or "real world") cohorts are a common data source for the development and evaluation of such tools. There is significant optimism regarding the benefits and use cases for risk-based care, but there is a notable disparity between the volume of clinical prediction models published and implementation into healthcare systems that drive and realise patient benefit.
View Article and Find Full Text PDFJ Integr Neurosci
January 2025
Sports, Exercise and Brain Sciences Laboratory, Sports Coaching College, Beijing Sport University, 100084 Beijing, China.
Background: Sports fatigue in soccer athletes has been shown to decrease neural activity, impairing cognitive function and negatively affecting motor performance. Transcranial direct current stimulation (tDCS) can alter cortical excitability, augment synaptic plasticity, and enhance cognitive function. However, its potential to ameliorate cognitive impairment during sports fatigue remains largely unexplored.
View Article and Find Full Text PDFJ Insect Sci
January 2025
School of Biological Sciences, University of Aberdeen, King's College, Aberdeen, UK.
Radio frequency identification (RFID) technology and marker recognition algorithms can offer an efficient and non-intrusive means of tracking animal positions. As such, they have become important tools for invertebrate behavioral research. Both approaches require fixing a tag or marker to the study organism, and so it is useful to quantify the effects such procedures have on behavior before proceeding with further research.
View Article and Find Full Text PDFMult Scler
January 2025
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Testing for myelin oligodendrocyte glycoprotein immunoglobulin G antibodies (MOG-IgG) is essential to the diagnosis of MOG antibody-associated disease (MOGAD). Due to its central role in the evaluation of suspected inflammatory demyelinating disease, the last 5 years has been marked by an abundance of research into MOG-IgG testing ranging from appropriate patient selection, to assay performance, to utility of serum titers as well as cerebrospinal fluid (CSF) testing. In this review, we synthesize current knowledge pertaining to the "who, what, where, when, why, and how" of MOG-IgG testing, with the aim of facilitating accurate MOGAD diagnosis in clinical practice.
View Article and Find Full Text PDFViruses
January 2025
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico.
Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time- and resource-efficient manner, as wastewater samples are representative of all cases within the catchment area, whether they are clinically reported or not. However, analysis and interpretation of WBS datasets for decision-making during public health emergencies, such as the COVID-19 pandemic, remains an area of opportunity. In this article, a database obtained from wastewater sampling at wastewater treatment plants (WWTPs) and university campuses in Monterrey and Mexico City between 2021 and 2022 was used to train simple clustering- and regression-based risk assessment models to allow for informed prevention and control measures in high-affluence facilities, even if working with low-dimensionality datasets and a limited number of observations.
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