Health interventions often do not reach blue-collar workers. Citizen science engages target groups in the design and execution of health interventions, but has not yet been applied in an occupational setting. This preliminary study determines barriers and facilitators and feasible elements for citizen science to improve the health of blue-collar workers. The study was conducted in a terminal and construction company by performing semi-structured interviews and focus groups with employees, company management and experts. Interviews and focus groups were analyzed using thematic content analysis and the elements were pilot tested. Workers considered work pressure, work location and several personal factors as barriers for citizen science at the worksite, and (lack of) social support and (negative) social culture both as barriers and facilitators. Citizen science to improve health at the worksite may include three elements: (1) knowledge and skills, (2) social support and social culture, and (3) awareness about lifestyle behaviors. Strategies to implement these elements may be company specific. This study provides relevant indications on feasible elements and strategies for citizen science to improve health at the worksite. Further studies on the feasibility of citizen science in other settings, including a larger and more heterogeneous sample of blue-collar workers, are necessary.
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http://dx.doi.org/10.3390/ijerph17144917 | DOI Listing |
Environ Manage
January 2025
North Carolina State University, Department of Parks, Recreation and Tourism Management, Raleigh, NC, USA.
Citizen science has been increasingly utilized for monitoring resource conditions and visitor use in protected areas. However, the quality of data provided by citizen scientists remains a major concern that hinders wider applications in protected area management. We evaluated a prototype, citizen science-based trail assessment and monitoring program in Hong Kong using an integrated evaluative approach with a specific focus on the congruence of data collected by trained volunteers and managers.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Architecture and Urban Planning, Shenzhen University, Guangdong, China. Electronic address:
Amid rapid urbanization, land use shifts in cities globally have profound effects on ecosystems and biodiversity. Birds, as a crucial component of urban biodiversity, are highly sensitive to environmental changes and often serve as indicator species for biodiversity. This study, using Shenzhen as a case study, integrates machine learning techniques with spatial statistical methods.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Senior Citizen Service Management, National Taichung University of Science and Technology, Taichung 40343, Taiwan.
A diabetic heart is characterized by fibrosis, autophagy, oxidative stress, and altered mitochondrial functions. For this review, three databases (PubMed, EMBASE, and Web of Science) were searched for articles written in English from September 2023 to April 2024. Studies that used exercise training for at least 3 weeks and which reported positive, negative, or no effects were included.
View Article and Find Full Text PDFAnimals (Basel)
January 2025
Institut de Systématique, Évolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE-PSL, Université des Antilles, 75005 Paris, France.
The common bottlenose dolphin () exhibits significant intraspecific diversity globally, with distinct ecotypes identified in various regions. In the Guadeloupe archipelago, the citizen science NGO OMMAG has been monitoring these dolphins for over a decade, documenting two distinct morphotypes. This study investigates whether these morphotypes represent coastal and oceanic ecotypes, which have not been previously identified in the region.
View Article and Find Full Text PDFSci Rep
January 2025
Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan.
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. Although genes causing familial cases have been identified, those of sporadic ALS, which occupies the majority of patients, are still elusive. In this study, we adopted machine learning to build binary classifiers based on the New York Genome Center (NYGC) ALS Consortium's RNA-seq data of the postmortem spinal cord of ALS and non-neurological disease control.
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