目的探讨案例分析教学法(case-basedlearning,CBL)联合教师标准化病人(teacher-standardized patient,TSP)在卒中后神经源性膀胱医患沟通教学中的应用效果。方法纳入首都医科大学附属北京天坛医院接受规范化培训的60名住院医师,随机分...目的探讨案例分析教学法(case-basedlearning,CBL)联合教师标准化病人(teacher-standardized patient,TSP)在卒中后神经源性膀胱医患沟通教学中的应用效果。方法纳入首都医科大学附属北京天坛医院接受规范化培训的60名住院医师,随机分为对照组和试验组。对照组采用传统以授课为导向的教学法,试验组采用CBL联合TSP的教学方法。教学内容为卒中后神经源性膀胱的医患沟通,共计12学时。教学结束后使用TSP和医患沟通技能评价量表(set the stage,elicit information,give information,understand the patient’s perspective,and end the encounter;SEGUE)评估住院医师的医患沟通能力。结果对照组和试验组在年龄、性别、入组前理论考试分数及操作考试分数方面差异无统计学意义。教学结束后,试验组在SEGUE的问诊准备[(4.6±0.6)分vs.(3.7±0.8)分,P<0.0001]、信息采集[(8.6±1.1)分vs.(7.3±0.9)分,P<0.0001]、信息提供[(3.7±0.5)分vs.(3.3±0.6)分,P=0.0099]、患者理解[(3.5±0.4)分vs.(2.4±0.7)分,P<0.0001]及总分[(22.1±1.5)分vs.(18.5±2.0)分,P<0.0001]方面均显著高于对照组,在问诊结束[(1.7±0.6)分vs.(1.7±0.5)分,P=0.6305]方面与对照组差异无统计学意义。结论采用CBL联合TSP的教学方法能够显著提高住院医师在卒中后神经源性膀胱医患沟通方面的能力。这一教学策略有望成为医学教育中提高医患沟通技能的有效手段,从而提高医疗服务质量。展开更多
近年来,公司风险投资迅速发展,投资绩效问题越来越重要。本文基于公司风险投资的数据,利用Log i s t ic模型和社会网络分析方法,从行业相互投资网络的视角研究了公司风险投资行为对投资绩效的影响。研究结果表明:(1)公司风险投资绩效受...近年来,公司风险投资迅速发展,投资绩效问题越来越重要。本文基于公司风险投资的数据,利用Log i s t ic模型和社会网络分析方法,从行业相互投资网络的视角研究了公司风险投资行为对投资绩效的影响。研究结果表明:(1)公司风险投资绩效受领投跟投地位、投资轮次、被投企业所属区域、被投企业成立时长、联合投资网络地位等因素的影响。(2)同行业投资与异行业投资具有不同的战略协同和竞争关系,投资方与被投方所属行业的网络关系显著影响被投企业的成长绩效,强联系行业相互投资事件的投资绩效显著好于弱联系行业,多行业的分散投资会降低投资绩效。(3)公司风险投资中投资方投资项目数量越多,被投企业成长和存续情况越差,过度投资会导致投资绩效降低,且投资绩效的降低不是对强关联行业投资导致的。基于本文研究得出的管理启示为:公司风险投资应该警惕投资行业分散和过度投资行为,应在坚持主业主体地位的情况下布局产业协同性较强的行业。展开更多
The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technic...The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and empirical studies. The main finding of this paper is that both lower and upper shadows in Japanese Candlestick Granger contribute to the DVAR model explanation power, and thus, providing useful information for improving the DVAR forecasts. This finding makes sense as it means that the infor- mation contained in the lower and upper shadows should be used when modeling the stock returns with DVAR. Empirical studies performed on China SSEC stock index demonstrate that DVAR model with upper and lower shadows as exogenous variables does have informative and valuable out-of-sample forecasts.展开更多
The authors aim to interpret human and AI interactions from the decision perspective.The authors decompose the interaction analysis into the following main components in the context of interactions:Individual behavior...The authors aim to interpret human and AI interactions from the decision perspective.The authors decompose the interaction analysis into the following main components in the context of interactions:Individual behavior patterns,interaction relationships,and comprehensive analysis.The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study.About the individual behavior patterns,the authors find that human players prefer short-term periods to AI in decision-making.The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run.The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy.The main behavior pattern of players in the game of Go is switching between careful and daring behaviors.The differences in the decision hierarchies imply a discrepancy of patience between humans and AI.展开更多
文摘目的探讨案例分析教学法(case-basedlearning,CBL)联合教师标准化病人(teacher-standardized patient,TSP)在卒中后神经源性膀胱医患沟通教学中的应用效果。方法纳入首都医科大学附属北京天坛医院接受规范化培训的60名住院医师,随机分为对照组和试验组。对照组采用传统以授课为导向的教学法,试验组采用CBL联合TSP的教学方法。教学内容为卒中后神经源性膀胱的医患沟通,共计12学时。教学结束后使用TSP和医患沟通技能评价量表(set the stage,elicit information,give information,understand the patient’s perspective,and end the encounter;SEGUE)评估住院医师的医患沟通能力。结果对照组和试验组在年龄、性别、入组前理论考试分数及操作考试分数方面差异无统计学意义。教学结束后,试验组在SEGUE的问诊准备[(4.6±0.6)分vs.(3.7±0.8)分,P<0.0001]、信息采集[(8.6±1.1)分vs.(7.3±0.9)分,P<0.0001]、信息提供[(3.7±0.5)分vs.(3.3±0.6)分,P=0.0099]、患者理解[(3.5±0.4)分vs.(2.4±0.7)分,P<0.0001]及总分[(22.1±1.5)分vs.(18.5±2.0)分,P<0.0001]方面均显著高于对照组,在问诊结束[(1.7±0.6)分vs.(1.7±0.5)分,P=0.6305]方面与对照组差异无统计学意义。结论采用CBL联合TSP的教学方法能够显著提高住院医师在卒中后神经源性膀胱医患沟通方面的能力。这一教学策略有望成为医学教育中提高医患沟通技能的有效手段,从而提高医疗服务质量。
文摘近年来,公司风险投资迅速发展,投资绩效问题越来越重要。本文基于公司风险投资的数据,利用Log i s t ic模型和社会网络分析方法,从行业相互投资网络的视角研究了公司风险投资行为对投资绩效的影响。研究结果表明:(1)公司风险投资绩效受领投跟投地位、投资轮次、被投企业所属区域、被投企业成立时长、联合投资网络地位等因素的影响。(2)同行业投资与异行业投资具有不同的战略协同和竞争关系,投资方与被投方所属行业的网络关系显著影响被投企业的成长绩效,强联系行业相互投资事件的投资绩效显著好于弱联系行业,多行业的分散投资会降低投资绩效。(3)公司风险投资中投资方投资项目数量越多,被投企业成长和存续情况越差,过度投资会导致投资绩效降低,且投资绩效的降低不是对强关联行业投资导致的。基于本文研究得出的管理启示为:公司风险投资应该警惕投资行业分散和过度投资行为,应在坚持主业主体地位的情况下布局产业协同性较强的行业。
基金supported by the National Natural Science Foundation of China under Grant No.71401033
文摘The decomposition-based vector autoregressive model （DVAR） provides a new framework for scrutinizing the efficiency of technical analysis in forecasting stock returns. However, its relation- ships with other technical indicators still remain unknown. This paper investigates the relationships of DVAR model with the Japanese Candlestick indicators using simulations, theoretical explanations and empirical studies. The main finding of this paper is that both lower and upper shadows in Japanese Candlestick Granger contribute to the DVAR model explanation power, and thus, providing useful information for improving the DVAR forecasts. This finding makes sense as it means that the infor- mation contained in the lower and upper shadows should be used when modeling the stock returns with DVAR. Empirical studies performed on China SSEC stock index demonstrate that DVAR model with upper and lower shadows as exogenous variables does have informative and valuable out-of-sample forecasts.
基金supported by the National Natural Science Foundation of China under Grant No.71988101.
文摘The authors aim to interpret human and AI interactions from the decision perspective.The authors decompose the interaction analysis into the following main components in the context of interactions:Individual behavior patterns,interaction relationships,and comprehensive analysis.The authors interpret intertemporal decisions from a physical perspective and employ cross-discipline concepts and methodologies to extract the behavior characteristics of players in the empirical case study.About the individual behavior patterns,the authors find that human players prefer short-term periods to AI in decision-making.The interaction relationship analysis reveals a dynamic relationship between possible short-term co-movement and nearly counter-movement in the long run.The authors apply principal component analysis to descriptive indicators and discover a regular decision hierarchy.The main behavior pattern of players in the game of Go is switching between careful and daring behaviors.The differences in the decision hierarchies imply a discrepancy of patience between humans and AI.
