Most studies on workers' health are based on non-specific models of occupational stress, thereby limiting the understanding and research on efficient interventions. This qualitative approach aimed to explore the structure of resources and constraints in the working environment of nurses in a deliberately open approach. Semi-structured interviews were conducted with 37 nurses working in closed and open inpatient psychiatric wards in a French university hospital. The data were statistically analyzed using a hierarchical clustering method. Our model highlighted a systemic structure, describing the interactions, including patients, nurses, doctors, and managers in a specific material, communicational, and organizational environment. The results show a discursive structure organized around dimensions pertaining to "environment", "patients", "medical-care group", and "the individual". Our model showed interest in an interdisciplinary approach that encompasses occupational medicine and social psychology.
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http://dx.doi.org/10.3390/ijerph17010142 | DOI Listing |
Environ Monit Assess
January 2025
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
This study investigates the effectiveness and efficiency of two topological data analysis (TDA) techniques, the conventional Mapper (CM) and its variant version, the Ball Mapper (BM), in analyzing the behavior of six major air pollutants (NO, PM, PM, O, CO, and SO) across 60 air quality monitoring stations in Malaysia. Topological graphs produced by CM and BM reveal redundant monitoring stations and geographical relationships corresponding to air pollutant behavior, providing better visualization than traditional hierarchical clustering. Additionally, a comparative analysis of topological graph structures was conducted using node degree distribution, topological graph indices, and Dynamic Time Warping (DTW) to evaluate the sensitivity and performance of these TDA techniques.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Central Pollution Control Board, MoEF & CC, Government of India, New Delhi, India.
The swift industrial expansion has posed significant environmental challenges, particularly in the context of water pollution. Industrial effluents consist of substantial amounts of harmful pollutants that enter the main rivers via various tapped and untapped drains/local water streams, causing alterations in their physical and chemical properties. This study investigated 153 grossly polluting industries (GPIs) that were identified to release their effluents into the main rivers through different drains within multiple sectors in the industrial zone of four northern states of India in 2023.
View Article and Find Full Text PDFAssist Technol
January 2025
Shaanxi Key Laboratory of Behavior and Cognitive Neuroscience, School of Psychology, Shaanxi Normal University, Xi'an, China.
Socially assistive robots (SARs) are increasingly recognized for their potential in helping older adults age in place. Effectively meeting the diverse needs of older adults requires a proper classification of SARs' functions. However, existing function categories are primarily proposed from the perspective of researchers, rarely from older adults themselves.
View Article and Find Full Text PDFLancet Reg Health Eur
February 2025
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Background: The evidence on the link between cardiometabolic diseases (CMDs) and motor neuron diseases (MNDs) remains inconsistent. We aimed to determine whether there is an association of CMDs, namely, any cardiovascular disease, cardiac arrhythmia, heart failure, thromboembolic disease, hypertension, cerebrovascular disease, ischemic heart disease, diabetes mellitus type 2, and hypercholesterolemia with the risk and progression of MNDs.
Methods: We included 1463 MND patients (amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS), progressive spinal muscular atrophy (PSMA), and unspecified MND) diagnosed from January 1, 2015, to July 1, 2023, in Sweden according to the Swedish Motor Neuron Disease Quality Registry (i.
Heliyon
December 2024
Xinxiang Medical University, Xinxiang, 453000, China.
This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model's performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness.
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