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Analysis of Factors Influencing Insomnia and Construction of a Prediction Model:A Cross-sectional Survey on Rescuers 被引量:3
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作者 SAI Xiao Yong CHEN Qiao +3 位作者 LUO Ting Gang SUN Yuan Yuan SONG Yu Jian CHEN Juan 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2020年第7期502-509,共8页
Objective To determine the factors influencing insomnia and construct early insomnia warning tools for rescuers to informbest practices for early screening and intervention.Methods Cluster sampling was used to conduct... Objective To determine the factors influencing insomnia and construct early insomnia warning tools for rescuers to informbest practices for early screening and intervention.Methods Cluster sampling was used to conduct a cross-sectional survey of 1,133 rescuers from one unit in Beijing,China.Logistic regression modeling and R software were used to analyze insomniarelated factors and construct a PRISM model,respectively.Results The positive rate of insomnia among rescuers was 2.74%.Accounting for participants’age,education,systolic pressure,smoking,per capita family monthly income,psychological resilience,and cognitive emotion regulation,logistic regression analysis revealed that,compared with families with an average monthly income less than 3,000 yuan,the odds ratio(OR)values and the[95%confidence interval(CI)]for participants of the following categories were as follows:average monthly family income greater than 5,000 yuan:2.998(1.307–6.879),smoking:4.124(1.954–8.706),and psychological resilience:0.960(0.933–0.988).The ROC curve area of the PRISM model(AUC)=0.7650,specificity=0.7169,and sensitivity=0.7419.Conclusion Insomnia was related to the participants’per capita family monthly income,smoking habits,and psychological resilience on rescue workers.The PRISM model’s good diagnostic value advises its use to screen rescuer early sleep quality.Further,advisable interventions to optimize sleep quality and battle effectiveness include psychological resilience training and smoking cessation. 展开更多
关键词 Rescuers INSOMNIA Influencing factors Cross sectional survey Prediction model
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Is China’s Foreign Investment Policy to Blame for US-China “Forced Technology Transfer” and Trade Conf lict?——Comment on Trump Administration’s Section 301 Investigation of China
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作者 Li Liming Liu Haibo Zhang Yafeng 《China Economist》 2020年第2期42-63,共22页
The paper presents a new analytical framework to discuss the effect of Chinese foreign investment policy on the international technology transfer absorbed by enterprises of different ownership.The US Trade Representat... The paper presents a new analytical framework to discuss the effect of Chinese foreign investment policy on the international technology transfer absorbed by enterprises of different ownership.The US Trade Representative claims that the Chinese government’s requirements regarding joint ventures pressure US companies to transfer intellectual property to Chinese companies.However,we argue that:(1)Based on analysis of the technical fees of technology import contracts and the number of US patents transferred to enterprises registered in the Chinese mainland,China’s foreign investment policy does not pressure US companies to transfer unremunerated technology to Chinese companies.(2)The invention and utility model patents filed by Chinese joint-venture enterprises or Chinese partner companies do not show an abnormally rapid growth,which means China’s FDI policy does not force US companies to transfer intellectual property in exchange for China’s market.(3)After 2012,the US-China technology transfer absorbed by enterprises of different ownership showed a significantly positive effect in reducing China-US trade surplus. 展开更多
关键词 technology transfer intellectual property trade balance FDI policy section 301 survey
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