Students' attitudes toward interprofessional teamwork can be linked to successful interprofessional education. This points to the importance of identifying a scale that may be useful in keeping track of the change in students' attitudes over time. In response to this, using a combination of within- and between-network approaches to construct validity, we examined the psychometric acceptability of the Interprofessional Attitude Scale (IPAS) involving 274 Chinese healthcare and social care pre-licensure students in Hong Kong. Overall results indicated that IPAS had good internal consistency. Results of the confirmatory factor analysis provided support to the overall five-factor solution although one negatively worded item obtained non-significant factor loading. Results of the between-network analysis suggest that various subscales of IPAS correlated systematically with other theoretically relevant variables: teamwork attitudes, communication, and team effectiveness. The IPAS is a valid measure to examine predominantly Chinese healthcare and social care students' interprofessional attitudes in online interprofessional education.
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http://dx.doi.org/10.1080/13561820.2020.1869705 | DOI Listing |
Background: There is an urgent need for new therapeutic and diagnostic targets for Alzheimer's disease (AD). Dementia afflicts roughly 55 million individuals worldwide, and the prevalence is increasing with longer lifespans and the absence of preventive therapies. Given the demonstrated heterogeneity of Alzheimer's disease in biological and genetic components, it is critical to identify new therapeutic approaches.
View Article and Find Full Text PDFBackground: Genetic studies have established that loss of function SORL1 gene variants are associated with Alzheimer's disease (AD). SORL1 encodes an endosomal trafficking receptor, SORLA, which regulates endosomal protein recycling through its interaction with the retromer core complex (consisting of VPS26, VPS35 and VPS29). Deficits in the levels and function of the SORLA-retromer complex are thought to underlie AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Camden and Islington NHS Foundation Trust, London, United Kingdom; University College London, London, United Kingdom.
Background: Long-term care (LTC) home residents may be isolated or lonely. Social connection is important for their physical, mental and cognitive health, quality of life and care. However, measuring social connection in LTC residents is challenging and there are no existing measures with adequately established psychometric properties.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Central South University, Changsha, Hunan, China.
Background: This prediction model quantifies the risk of cognitive impairment. This aim of this study was to develop and validate a prediction model to calculate the 6-year risk of cognitive impairment.
Methods: Participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008-2014 and 2011-2018 surveys were included for developing the cognitive impairment prediction model.
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