A growing amount of research shows a positive association between urban nature and urban residents' psychological and physical well-being and quality of life. With its focus on specific dimensions of well-being and reliance either on large-scale objective data sets or experimental and comparative designs, prior research does not explicitly address the relationships between the various types of urban nature as experienced by citizens and their overall well-being. The present research proposes a model for the potential influence of the perceptions of the type and characteristics of nature close to urban residences and citizens' well-being via their frequency of exposure to, and their activities in, nature. Using WHOQOL-26 as a measure of well-being and constructing or adapting measurements for the other variables, a questionnaire was designed and administered among a sample of 2500 French urban residents. Psychometric tests, structural equation modeling, and mediation analyses were conducted on the collected data. The results show that: (1) The perceived characteristics of nature near urban residences have both direct and indirect influences on the psychological, physical, environmental, and resources-related dimensions of citizens' well-being; (2) Less domesticated nature-forests, fields, and scrubland-contributes to psychological well-being by favoring light or calm activities in nature, and to physical well-being by supporting exposure to nature. Such wilder nature is also positively associated with the perceived quality of urban residences; (3) Small spots of nature, located close to one's residence-a balcony, a patio, or a roof garden-make a similar but smaller contribution to well-being; (4) Domesticated nature-city gardens and parks-contribute marginally to the physical and resources-related dimensions of well-being. These results complement past research on the perceived characteristics of nature by showing how nature, as experienced by urban citizens, refers to different vegetation spaces. They also invite urban planners and policymakers to acknowledge the positive association between "wilder" natural spaces and well-being.
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http://dx.doi.org/10.1016/j.healthplace.2022.102759 | DOI Listing |
Eur J Dent Educ
January 2025
QU Health College of Dental Medicine, Qatar University, Doha, Qatar.
Aims: This study aimed to evaluate the impact of community-based dental education (CBDE) on the learning experiences of undergraduate dental students and recent dental graduates from two diverse geographical regions.
Methods: The study followed a cross-sectional design, conducted online using Google Forms, with ethical approval from Qatar University. A non-probability purposive sampling method was used to recruit dental students and recent graduates from three institutions in India and one in Qatar.
Clin Transl Med
February 2025
Division of Infectious Diseases, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
J Agric Food Chem
January 2025
College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China.
T-2 toxin is a highly toxic fungal toxin that threatens humans and animals' health. As a major detoxifying and metabolic organ, the kidney is also a target of T-2 toxin. This article reviews T-2 toxin nephrotoxicity research progress, covering renal structure and function damage, nephrotoxicity mechanisms, and detoxification methods to future research directions.
View Article and Find Full Text PDFPhysiol Plant
January 2025
School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Legume leaves exhibit diverse compound forms, with various regulatory mechanisms underlying the development. The transcription factor-encoding KNOXI genes are required to promote leaflet initiation in most compound-leafed angiosperms. In non-IRLC (inverted repeat-lacking clade) legumes, KNOXI are expressed in compound leaf primordia but not in others (IRLC).
View Article and Find Full Text PDFMed Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
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