Background: Motivation for learning, as an important aspect pertaining to studying the phenomenon of elder learning, is not fully explored in Hong Kong.
Objective: This study was designed to create a measurement to investigate the possible diversity of motivations of elder learners, so as to harness the older people's potential in learning and thus capitalize on productive ageing.
Methods: 283 older learners participating in learning activities at elder centres were interviewed. Exploratory factor analysis was conducted to identify the latent factors in the learning motivation scale. Reliability of the scale was assessed. ANOVA testing was used to assess for differences in learning motivation by different socio-demographic variables.
Results: Four dimensions of older Chinese adults' motivations for engaging in learning have been found: 'keeping up with and contributing to society', 'fulfilment', 'social integration' and 'reemployment'. Elders with higher education levels were more likely to seek out opportunities for lifelong learning. Younger (aged 55 to 64) participants of learning activities were more likely than their older (aged 75 or above) counterparts to learn for fulfillment. Older adults who had volunteer experience were more motivated to engage in learning through keeping up with and contributing to society.
Conclusion: Older learners in Hong Kong participated in learning for self-fulfilment and development, contributing to society, maintaining social connection, and acquisition of knowledge and qualifications for possible (re)employment. Some of their socio-demographic features might influence their motivations. Learning programmes could be designed and improved based on older adults' motivations to meet their needs.
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http://dx.doi.org/10.2174/1874609809666160506122024 | DOI Listing |
J Cheminform
December 2024
Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Ensuring the safety of chemicals for environmental and human health involves assessing physicochemical (PC) and toxicokinetic (TK) properties, which are crucial for absorption, distribution, metabolism, excretion, and toxicity (ADMET). Computational methods play a vital role in predicting these properties, given the current trends in reducing experimental approaches, especially those that involve animal experimentation. In the present manuscript, twelve software tools implementing Quantitative Structure-Activity Relationship (QSAR) models were selected for the prediction of 17 relevant PC and TK properties.
View Article and Find Full Text PDFJ Neuroinflammation
December 2024
Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Central nervous system (CNS) resident memory CD8 T cells (T) that express IFN-γ contribute to neurodegenerative processes, including synapse loss, leading to memory impairment. Here, we show that CCR2 signaling in CD8 T that persist within the hippocampus after recovery from CNS infection with West Nile virus (WNV) significantly prevents the development of memory impairments. Using CCR2-deficient mice, we determined that CCR2 expression is not essential for CNS T cell recruitment or virologic control during acute WNV infection.
View Article and Find Full Text PDFBMC Med Educ
December 2024
Department of Oral Biology, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: Modern dental education necessitates dynamic methodologies to foster critical thinking and teamwork skills, which might include case-based learning (CBL) and role play (RP).
Objectives: To evaluate the impact of the combined CBL and RP (CBL-RP) approaches on critical thinking and teamwork skills among dental students by comparing pre- and post-RP evaluation scores.
Methods: This pre-post intervention study was conducted at the Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
BMC Bioinformatics
December 2024
College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao, 028000, China.
As a heterogeneous disease, prostate cancer (PCa) exhibits diverse clinical and biological features, which pose significant challenges for early diagnosis and treatment. Metabolomics offers promising new approaches for early diagnosis, treatment, and prognosis of PCa. However, metabolomics data are characterized by high dimensionality, noise, variability, and small sample sizes, presenting substantial challenges for classification.
View Article and Find Full Text PDFBMC Cancer
December 2024
Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Background: Accurate prediction of pathological complete response (pCR) and disease-free survival (DFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (NCRT) is essential for formulating effective treatment plans. This study aimed to construct and validate the machine learning (ML) models to predict pCR and DFS using pathomics.
Method: A retrospective analysis was conducted on 294 patients who received NCRT from two independent institutions.
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