Background: The aim of this study was to examine whether work capabilities differ between workers with Multiple Sclerosis (MS) and workers from the general population. The second aim was to investigate whether the capability set was related to work and health outcomes.
Methods: A total of 163 workers with MS from the MS@Work study and 163 workers from the general population were matched for gender, age, educational level and working hours. All participants completed online questionnaires on demographics, health and work functioning. The Capability Set for Work Questionnaire was used to explore whether a set of seven work values is considered valuable (A), is enabled in the work context (B), and can be achieved by the individual (C). When all three criteria are met a work value can be considered part of the individual's 'capability set'.
Results: Group differences and relationships with work and health outcomes were examined. Despite lower physical work functioning (U = 4250, p = 0.001), lower work ability (U = 10591, p = 0.006) and worse self-reported health (U = 9091, p ≤ 0.001) workers with MS had a larger capability set (U = 9649, p ≤ 0.001) than the general population. In workers with MS, a larger capability set was associated with better flexible work functioning (r = 0.30), work ability (r = 0.25), self-rated health (r = 0.25); and with less absenteeism (r = - 0.26), presenteeism (r = - 0.31), cognitive/neuropsychiatric impairment (r = - 0.35), depression (r = - 0.43), anxiety (r = - 0.31) and fatigue (r = - 0.34).
Conclusions: Workers with MS have a larger capability set than workers from the general population. In workers with MS a larger capability set was associated with better work and health outcomes.
Trial Registration: This observational study is registered under NL43098.008.12: 'Voorspellers van arbeidsparticipatie bij mensen met relapsing-remitting Multiple Sclerose'. The study is registered at the Dutch CCMO register ( https://www.toetsingonline.nl ). This study is approved by the METC Brabant, 12 February 2014. First participants are enrolled 1 of March 2014.
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http://dx.doi.org/10.1186/s12955-018-0942-7 | DOI Listing |
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View Article and Find Full Text PDFACS Nano
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South China Advanced Institute for Soft Matter Science and Technology, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
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View Article and Find Full Text PDFSci Rep
January 2025
College of Electrical and Information Engineering, Hunan Institute of Traffic Engineering, Hunan, Hengyang, 421001, China.
This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models.
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