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Purpose: Employers of small and medium-sized enterprises (SMEs) face challenges in supporting employees on long-term sick-leave, due to limited resources and expertise available. This study aimed to develop an intervention assisting employers of SMEs in supporting long-term sick-listed employees during sick-leave and return to work (RTW).
Methods: Intervention mapping (IM) steps 1-4 were employed to develop the intervention. For the needs assessment, 20 employers, 8 employees, 8 occupational physicians, and 9 other stakeholders were interviewed (step 1). A logic model of change was developed (step 2), followed by the identification of theoretical methods for achieving the changes required (step 3). The intervention was composed (step 4), incorporating the results of a pilot test with 4 employers, 4 employees, 4 occupational physicians, and 3 other stakeholders.
Results: Identified needs (step 1) span knowledge on legislation, communication skills, stakeholder engagement, practical support, actions regarding RTW, relapse prevention, and organizational policy. Using the self-determination theory as the theoretical basis for improving employer intention and ability to support sick-listed employees (steps 2 and 3), a web-based intervention was developed (step 4) (hereafter: SME tool). The SME tool includes succinct tips, communication videos, and practical checklists. Minor adjustments were made following the pilot test, such as adding supplementary information on privacy regulations and preventive strategies.
Conclusion: By focusing on enhancing SME employers' intention and ability to support their long-term sick-listed employee(s), the developed SME tool has the potential to improve the satisfaction of employees with the sick-leave and RTW support of their employer during long-term sick-leave.
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http://dx.doi.org/10.1007/s10926-025-10281-8 | DOI Listing |
J Mol Diagn
August 2025
Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington. Electronic address:
Structural variants are critical to genetic diversity and disease, yet their detection remains challenging with conventional cytogenetic techniques, including karyotyping, fluorescence in situ hybridization, and chromosome microarray analysis. These methods often lack the resolution and sensitivity needed for comprehensive characterization of chromosomal aberrations. To address these limitations, we implemented genomic proximity mapping (GPM), a genome-wide chromosome conformation capture technology, in a clinical setting.
View Article and Find Full Text PDFScand J Trauma Resusc Emerg Med
August 2025
University Hospitals Coventry & Warwickshire NHS Trust, Coventry, UK.
Background: Motor vehicle collisions (MVCs) are a leading cause of injury and death worldwide. Up to 40% of casualties may become trapped and entrapment is associated with delayed care and worse outcomes. There is little national or international consensus guiding the care of physically trapped patients who cannot self-extricate.
View Article and Find Full Text PDFVasc Health Risk Manag
August 2025
Department of Cardio-Thoracic Technology, Faculty of Allied Health Sciences, Naresuan University, Phitsanulok, Thailand.
Purpose: Blood pressure (BP) response observed during exercise stress tests has been recognized as a predictor of the onset of hypertension and arterial stiffness. However, access to such testing is often limited to specialized clinical settings. The purpose of this study was to assess the utility of a simple, equipment-free exercise, self-paced spot marching exercise (SME), along with the subsequent recovery BP in evaluating vascular functions.
View Article and Find Full Text PDFArtif Intell Med
October 2025
Department of Health Research Methods, Evidence, and Impact, Department of Medicine, McMaster University, Canada; MAGIC Evidence Ecosystem Foundation, Norway.
The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as Chatbot Health Advice (CHA) studies. CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist.
View Article and Find Full Text PDFBMC Med
August 2025
Department of Health Research Methods, Evidence, and Impact; Department of Medicine, McMaster University, Hamilton, Canada.
Background: The Chatbot Assessment Reporting Tool (CHART) is a reporting guideline developed to provide reporting recommendations for studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots when summarizing clinical evidence and providing health advice, referred to as Chatbot Health Advice (CHA) studies.
Methods: CHART was developed in several phases after performing a comprehensive systematic review to identify variation in the conduct, reporting, and methodology in CHA studies. Findings from the review were used to develop a draft checklist that was revised through an international, multidisciplinary modified asynchronous Delphi consensus process of 531 stakeholders, three synchronous panel consensus meetings of 48 stakeholders, and subsequent pilot testing of the checklist.