In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operati...In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.展开更多
Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and h...Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and human clinicians.Methods:From September to October 2024,two AI-VAs(Apple’s Siri and Huawei’s Xiaoyi)were tested on 15 KOA-related questions in Chinese and English.The assessment focused on the accuracy of voice recognition,response capabilities,and medical advice.Siri was further tested in four international regions(USA,UK,Germany,Hong Kong)using localized languages.Results:In Chinese-language tests,Siri and Xiaoyi showed comparable voice recognition(recognition accuracy:95.6%vs.93.3%)and response ability(speech response:88.9%vs.85.7%).However,Siri provided significantly more accurate medical advice(medical advice:95.6%vs.53.3%;Z=2.762,P<0.001).External validation via Global Quality Score further confirmed Siri’s superiority(mean Global Quality Score=4.0 vs.Xiaoai=0.9).Siri outperformed Xiaoyi in English-language tests(53.3%vs.0%).While Siri’s medical advice accuracy(95.6%)surpassed non-specialist clinicians(Z=2.685,P=0.007),it primarily reflects filtered search results(Baidu/Google)rather than clinical synthesis.Claims of equivalence to junior surgeons(98.2%)must be interpreted cautiously,as AI-VAs lack diagnostic reasoning capabilities.This distinction is critical to avoid overstating their role in clinical decision-making.Conclusion:Current AI-VAs offer limited value in providing precise medical advice for KOA,primarily serving as intermediaries for web search results.Their performance varies across languages,regions,and search engines.展开更多
This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed ...This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed the profile of a female teaching assistant on Twitter and Facebook. While there was little difference between the two social media, the use of self-disclosure on Twitter seemed slightly more inappropriate for sharing personal information.展开更多
Background:Work engagement(WE)is critical to quality primary healthcare delivery.However,limited research has explored its levels and determinants among healthcare professionals in low-and middle-income countries.This...Background:Work engagement(WE)is critical to quality primary healthcare delivery.However,limited research has explored its levels and determinants among healthcare professionals in low-and middle-income countries.This study assessed the levels and correlates of work engagement among physician assistants(PAs)in Ghana.Methods:A cross-sectional study was conducted among 439 PAs from October to December 2024.Participants were recruited via emails,social media platforms,and posters featuring study links and scannable questionnaire codes.WE was measured using the validated Utrecht Work Engagement Scale questionnaire.Results:Overall,WE levels were average,with similar trends across the three subdomains.In the bootstrapped multivariate linear regression model,anxiety was negatively associated with WE(β=-0.49,95%confidence interval[CI]:-0.77 to-0.21).Conversely,working in an urban area(β=0.36,95%CI:0.05 to 0.67),holding the rank of PA/Senior PA(β=0.27,95%CI:0.03 to 0.52),reporting good self-rated health(β=0.54,95%CI:0.19 to 0.88),and working at health centers(β=0.86,95%CI:0.22 to 1.50)were positively associated with WElevels.Conclusion:WE levels are average in the study sample,highlighting the need for strategic interventions to improve and sustain the healthcare workforce's motivation and performance.Addressing workplace stressors,enhancing professional development opportunities,and fostering supportive work environments could improve engagement among PAs and healthcare professionals in general.Strengthening WE is essential for ensuring resilient quality primary healthcare systems and achieving the goals of universal health coverage.展开更多
基金the Talent Fund of Beijing Jiaotong University(Grant No.2024XKRC055).
文摘In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.
文摘Background:This study evaluates the ability of mobile AI voice assistants(AI-VAs)to provide accurate medical advice for early knee osteoarthritis(KOA)and compares their performance with conventional web searches and human clinicians.Methods:From September to October 2024,two AI-VAs(Apple’s Siri and Huawei’s Xiaoyi)were tested on 15 KOA-related questions in Chinese and English.The assessment focused on the accuracy of voice recognition,response capabilities,and medical advice.Siri was further tested in four international regions(USA,UK,Germany,Hong Kong)using localized languages.Results:In Chinese-language tests,Siri and Xiaoyi showed comparable voice recognition(recognition accuracy:95.6%vs.93.3%)and response ability(speech response:88.9%vs.85.7%).However,Siri provided significantly more accurate medical advice(medical advice:95.6%vs.53.3%;Z=2.762,P<0.001).External validation via Global Quality Score further confirmed Siri’s superiority(mean Global Quality Score=4.0 vs.Xiaoai=0.9).Siri outperformed Xiaoyi in English-language tests(53.3%vs.0%).While Siri’s medical advice accuracy(95.6%)surpassed non-specialist clinicians(Z=2.685,P=0.007),it primarily reflects filtered search results(Baidu/Google)rather than clinical synthesis.Claims of equivalence to junior surgeons(98.2%)must be interpreted cautiously,as AI-VAs lack diagnostic reasoning capabilities.This distinction is critical to avoid overstating their role in clinical decision-making.Conclusion:Current AI-VAs offer limited value in providing precise medical advice for KOA,primarily serving as intermediaries for web search results.Their performance varies across languages,regions,and search engines.
文摘This study analyzed how impressions are formed online depending on the type of social media and the implications that may come from the over-disclosure of information. Using a Qualtrics survey, 97 participants viewed the profile of a female teaching assistant on Twitter and Facebook. While there was little difference between the two social media, the use of self-disclosure on Twitter seemed slightly more inappropriate for sharing personal information.
文摘Background:Work engagement(WE)is critical to quality primary healthcare delivery.However,limited research has explored its levels and determinants among healthcare professionals in low-and middle-income countries.This study assessed the levels and correlates of work engagement among physician assistants(PAs)in Ghana.Methods:A cross-sectional study was conducted among 439 PAs from October to December 2024.Participants were recruited via emails,social media platforms,and posters featuring study links and scannable questionnaire codes.WE was measured using the validated Utrecht Work Engagement Scale questionnaire.Results:Overall,WE levels were average,with similar trends across the three subdomains.In the bootstrapped multivariate linear regression model,anxiety was negatively associated with WE(β=-0.49,95%confidence interval[CI]:-0.77 to-0.21).Conversely,working in an urban area(β=0.36,95%CI:0.05 to 0.67),holding the rank of PA/Senior PA(β=0.27,95%CI:0.03 to 0.52),reporting good self-rated health(β=0.54,95%CI:0.19 to 0.88),and working at health centers(β=0.86,95%CI:0.22 to 1.50)were positively associated with WElevels.Conclusion:WE levels are average in the study sample,highlighting the need for strategic interventions to improve and sustain the healthcare workforce's motivation and performance.Addressing workplace stressors,enhancing professional development opportunities,and fostering supportive work environments could improve engagement among PAs and healthcare professionals in general.Strengthening WE is essential for ensuring resilient quality primary healthcare systems and achieving the goals of universal health coverage.
