Introduction: Learning is essential for sustainable employability. However, various factors make work-related learning more difficult for certain groups of workers, who are consequently at a disadvantage in the labour market. In the long term, that in turn can have adverse health implications and can make those groups vulnerable. With a view to encouraging workers to continue learning, the Netherlands has a policy on work-related learning, which forms part of the 'Vitality Package'.
Aim: A Health Impact Assessment with equity focus (HIAef) was undertaken to determine whether the policy on work-related learning affected certain groups of workers and their health in different ways, and whether the differences were avoidable.
Methods: The HIAef method involved the standard phases: screening, scoping, appraisal and recommendations. Equity was the core principle in this method. Data were collected by means of both literature searches (e.g., Scopus, Medline) and interviews with experts and stakeholders (e.g., expertise regarding work, training and/or health).
Results: The HIAef identified the following groups as potentially vulnerable in the field of work-related learning: the chronically sick, older people, less educated people, flexi-workers/the self-employed and lay carers (e.g., thresholds to learning). Published literature indicates that work-related learning may have a positive influence on health through (work-related) factors such as pay, employability, longer employment rate and training-participation. According to experts and stakeholders, work-related learning policy could be adapted to take more account of vulnerable groups through alignment with their particular needs, such as early support, informal learning and e-learning.
Conclusion: With a view to reducing avoidable inequalities in work-related learning, it is recommended that early, low-threshold, accessible opportunities are made available to identified vulnerable groups. Making such opportunities available may have a positive effect on (continued) participation in the labour market and thus on the health of the relevant groups.
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http://dx.doi.org/10.1016/j.healthpol.2015.03.011 | DOI Listing |
Comput Methods Programs Biomed
January 2025
School of Information Science and Technology, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging, Computing and Computer Assisted Intervention, Shanghai, 200433, China. Electronic address:
Background And Objective: Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and prognosis prediction is significant for treatment. Existing weakly-supervised learning frameworks based on multi-instance learning have potential in WSIs analysis, but the large number of patches from WSIs challenges the effective extraction of key local patch and neighboring patch microenvironment info. Therefore, this paper aims to develop an automatic neural network that effectively extracts tumor microenvironment information from WSIs to predict molecular typing and prognosis of glioma.
View Article and Find Full Text PDFBrain Spine
December 2024
Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
Diagnostics (Basel)
January 2025
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.
: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they are often challenging and complex to apply due to the absence of a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged as a promising approach to effectively monitor and mitigate biomechanical risks.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Gastric Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Background: The demand for frailty care is continuously increasing in hospitalized tumor patients with the aging of the population. Nurses are the primary care providers of hospitalized tumor patients with frailty but research on exploring their behavior and associated factors is limited. This study aims to describe the current situation of frailty care behaviors in oncology nurses and to explore the factors influencing frailty care behaviors.
View Article and Find Full Text PDFObjective: To identify lifting actions and count the number of lifts performed in videos based on robust class prediction and a streamlined process for reliable real-time monitoring of lifting tasks.
Background: Traditional methods for recognizing lifting actions often rely on deep learning classifiers applied to human motion data collected from wearable sensors. Despite their high performance, these methods can be difficult to implement on systems with limited hardware resources.
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