As people age the home environment becomes increasingly important. Retirement commonly leads to spending more time in one's home, and relocating from your own home in older age could be associated with reduced health or wellbeing. The relationship between home and person is complex and perceived aspects of one's housing such as social, emotional and cognitive ties are considered important factors for health and wellbeing. However, little is known about how perceived aspects of the home change in relation to retirement and relocation. This paper used Situational Analysis to explore, via situational mapping, how community dwelling older adults (aged 60-75) perceived their housing situation in relation to retirement and relocation. The results suggest complex relations between relocation/retirement and perceived housing, and between different aspects of perceived housing. Furthermore, the results suggest that the relationship between life transitions and perceived housing can be seen as bi-directional, where different life transitions affect aspects of perceived housing, and that perceived housing affects (decisions for) relocation. The results suggest complex relations between retirement and relocation, as well as other life transitions, and perceived aspects of one's housing. It is important to consider these interactions to understand factors that affect health and wellbeing in older adults.
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http://dx.doi.org/10.3390/ijerph192013314 | DOI Listing |
J Community Psychol
January 2025
Department of Social Psychology, Universidad de Alcalá, Alcalá de Henares, Spain.
Women experiencing homelessness constitute a group with idiosyncratic characteristics and needs that have largely remained invisible. Their discriminatory situation has been studied very little which may limit the design of specific intervention strategies. Buenos Aires (Argentina) is one of the main megalopolises in Latin America, where information on women experiencing homelessness is scarcely available.
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Academic Women's Health Unit, Bristol Medical School, University of Bristol, 5 Tyndall Avenue, Bristol, BS8 1UD, UK.
Background: Expectations of birth, and whether they are met, influence postnatal psychological wellbeing. Intrapartum interventions, for example induction of labour, are increasing due to a changing pregnant population and evolving evidence, which may contribute to a mismatch between expectations and birth experience. NICE recommends antenatal education (ANE) to prepare women for labour and birth, but there is no mandated UK National Health Service (NHS) ANE curriculum.
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Department of Community Health, Obafemi Awolowo University, Ile-Ife, Nigeria.
Introduction: The importance of community-based studies is not in doubt, however only few exist because of the complexity and challenges associated with them. Little data exists on these complexities and challenges in West Africa. This study aimed to describe the experiences, challenges and lessons learnt from a community-based Nutritional survey carried out in Nigeria.
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December 2024
Nivel, Netherlands Institute for Health Services Research, Otterstraat 118, Utrecht, 3513 CR, The Netherlands.
Background: At the beginning of the COVID-19 pandemic in 2020, little was known about the spread of COVID-19 in Dutch nursing homes while older people were particularly at risk of severe symptoms. Therefore, attempts were made to develop a nationwide COVID-19 repository based on routinely recorded data in the electronic health records (EHRs) of nursing home residents. This study aims to describe the facilitators and barriers encountered during the development of the repository and the lessons learned regarding the reuse of EHR data for surveillance and research purposes.
View Article and Find Full Text PDFSci Rep
December 2024
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China.
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.
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