Purpose Non-technical skills have gained attention, since enhancement of these skills is presumed to improve the process of trauma resuscitation. However, the reliability of assessing non-technical skills is underexposed, especially when using video analysis. Therefore, our primary aim was to assess the reliability of the Trauma Non-Technical Skills (T-NOTECHS) tool by video analysis. Secondarily, we investigated to what extent reliability increased when the T-NOTECHS was assessed by three assessors [average intra-class correlation (ICC)] instead of one (individual ICC). Methods As calculated by a pre-study power analysis, 18 videos were reviewed by three research assistants using the T-NOTECHS tool. Average and individual degree of agreement of the assessors was calculated using a two-way mixed model ICC. Results Average ICC was ‘excellent’ for the overall score and all five domains. Individual ICC was classified as ‘excellent’ for the overall score. Of the five domains, only one was classified as ‘excellent’, two as ‘good’ and two were even only ‘fair’. Conclusions Assessment of non-technical skills using the T-NOTECHS is reliable using video analysis and has an excellent reliability for the overall T-NOTECHS score. Assessment by three raters further improve the reliability, resulting in an excellent reliability for all individual domains.
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We assess the incidence of numeracy skills mismatch in five countries: Belgium, Chile, Italy, Netherlands, and the United States of America. To do this, we make use of a new approach (Brun-Schamme & Rey, 2021), namely by identifying someone as being mismatched if the score for numeracy skills is outside the interval [median – SD , median + SD]. We make use of the PIAAC dataset, collected by the OECD, a survey that measures adults’ proficiency in numeracy among other type of skills. We find that 14% of the workers are over-skilled, whereas 16% are under-skilled. Being over-skilled is more likely for men, younger age-groups, having a high level of education, using numeracy skills often at work, and having studied science, mathematics, and engineering.
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This study provides a comprehensive analysis of the AI-related skills and roles needed to bridge the AI skills gap in Europe. Using a mixed-method research approach, this study investigated the most in-demand AI expertise areas and roles by surveying 409 organizations in Europe, analyzing 2,563 AI-related job advertisements, and conducting 24 focus group sessions with 145 industry and policy experts. The findings underscore the importance of both general technical skills in AI related to big data, machine learning and deep learning, cyber and data security, large language models as well as AI soft skills such as problemsolving and effective communication. This study sets the foundation for future research directions, emphasizing the importance of upskilling initiatives and the evolving nature of AI skills demand, contributing to an EU-wide strategy for future AI skills development.
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