Objective: to evaluate the work ability and health status of security guards at a public University.
Methods: a cross-sectional, descriptive, and analytical study was carried with 119 security guards. The following instruments were used: Work Ability Index (WAI), Patient Health Questionnaire (PHQ-9), International Physical Activity Questionnaire (IPAQ, short), Alcohol Use Disorders Identification Test (AUDIT), Medical Outcomes Study (MOS), and Demand-Control-Support (DCS). Descriptive statistics were used to describe the study samples and the Spearman's coefficient correlation was performed to assess the WAI. Significance level was set at 5%.
Results: samples were composed by men; the mean age was 54.9 years (SD=5.7); 80% had partners, and 75% had basic education. The majority (95%) had only one job, the average length of service was 24.8 years (SD=11), ranging from 3 to 43 years. 88.9% worked ≤40 hours and 75% did not work at night shift or rotating shifts. The average score given to work ability was good (40.7 points), with significant correlation to social support at work (p-value=0.002), health conditions (p-value=0.094), and depression symptoms (p-value=0.054).
Conclusion: this study showed that many characteristics might affect the work ability scores. Considering the results, we note that healthy life habits and a reorganization of work environments should be encouraged.
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http://dx.doi.org/10.1590/1518-8345.0616.2725 | DOI Listing |
Nature
January 2025
Machine Learning Lab, University of Freiburg, Freiburg, Germany.
Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory for Information System of Mountainous Area and Protection of Ecological Environment of Guizhou Province, Guizhou Normal University, Guian, 550025, China.
Removal of accumulated dyes from the environment water bodies is essential to prevent further harm to humans. The development and design of new alternative nanoadsorbents that can conveniently, quickly, and efficiently improve the adsorption and removal efficiency of dyes from wastewater remains a huge challenge. An amorphous TiO with a magnetic core-shell-shell structure (FeO@PDA@a-TiO, denoted as FPaT) was constructed through a series of steps.
View Article and Find Full Text PDFLearn Behav
January 2025
Department of Psychology, University of Cambridge, Cambridge, UK.
Professor Nicola Clayton is perhaps best known for her work on food-caching scrub jays. Her seminal 1998 paper, together with Anthony Dickinson, showed that scrub jays could remember what food they had cached, where and how long ago, suggesting memory ability that is 'episodic-like' in nature. Here, we present data from a previously unpublished study that sought to replicate and extend these findings.
View Article and Find Full Text PDFInt Microbiol
January 2025
Center for the Development of Functional Materials (CDMF), Universidade Federal de São Carlos (UFSCar), Rodovia Washington Luís, Km 235, São Carlos, SP, 13565-905, Brazil.
Among the vast array of functional nanoparticles (NPs) under development, nickel tungstate (NiWO) has gained prominence due to its potential applications as a catalyst, sensor, and in the development of supercapacitors. Consequently, new studies on the environmental impact of this material must be conducted to establish a regulatory framework for its management. This work aims to assess the effects of NiWO (NPs) on multiple endpoints (e.
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