Based on the perspectives of social risk amplification and the knowledge-attitudes-practice model, this study aimed to test how the level of knowledge about COVID-19 and information sources can predict people's behavioral changes and to examine the effect mechanisms through the mediating roles of attitude, risk perception, and negative emotions in a survey of 498 older Chinese adults. The results showed that (1) older people had a lower level of factual knowledge regarding the variant strains and vaccines; (2) in the information sources-behavior, information sources had a critical influence on elderly individuals' coping behaviors; and (3) in the knowledge-behavior, factual knowledge had a significant effect on elderly individuals' coping behaviors. Specifically, for prevention behaviors, both risk perception and negative emotions played full mediating roles. The findings have significant implications for the development of an effective COVID-19 prevention program to older adults coping with pandemic conditions.
{"title":"Mediating Effects of Attitudes, Risk Perceptions, and Negative Emotions on Coping Behaviors","authors":"Wei Zhang, Luyao Li, Jian Mou, Mei Zhang, Xusen Cheng, Hongwei Xia","doi":"10.4018/joeuc.308818","DOIUrl":"https://doi.org/10.4018/joeuc.308818","url":null,"abstract":"Based on the perspectives of social risk amplification and the knowledge-attitudes-practice model, this study aimed to test how the level of knowledge about COVID-19 and information sources can predict people's behavioral changes and to examine the effect mechanisms through the mediating roles of attitude, risk perception, and negative emotions in a survey of 498 older Chinese adults. The results showed that (1) older people had a lower level of factual knowledge regarding the variant strains and vaccines; (2) in the information sources-behavior, information sources had a critical influence on elderly individuals' coping behaviors; and (3) in the knowledge-behavior, factual knowledge had a significant effect on elderly individuals' coping behaviors. Specifically, for prevention behaviors, both risk perception and negative emotions played full mediating roles. The findings have significant implications for the development of an effective COVID-19 prevention program to older adults coping with pandemic conditions.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44216196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the COVID-19 outbreak, the governments of different countries adopted, such as locking down cities and restricting travel and social contact. Online health communities (OHCs) with specialized physicians have become an important way for the elderly to access health information and social support, which has expanded their use since the outbreak. This paper examines the factors influencing elderly people’s behavior in terms of the continuous use of OHCs from a social support perspective, to understand the impact of public health emergencies. Research collected data from March to April 2019, February 2020, and August 2021, in China. A total of 189 samples were collected and analyzed by using SmartPLS. The results show that (1) social support to the elderly during different stages has different influences on their sense of community and (2) the influence of the sense of community on the intention to continuously use OHCs also seems to change over time. The results of this study provide important implications for research and practice related to both OHCs and COVID-19.
{"title":"How COVID-19 Affects the Willingness of the Elderly to Continue to Use the Online Health Community: A Longitudinal Survey","authors":"Yiming Ma, Yadi Gu, Wenjia Hong, Zhao Shu Ping, Changyong Liang, Dong-xiao Gu","doi":"10.4018/joeuc.308820","DOIUrl":"https://doi.org/10.4018/joeuc.308820","url":null,"abstract":"In response to the COVID-19 outbreak, the governments of different countries adopted, such as locking down cities and restricting travel and social contact. Online health communities (OHCs) with specialized physicians have become an important way for the elderly to access health information and social support, which has expanded their use since the outbreak. This paper examines the factors influencing elderly people’s behavior in terms of the continuous use of OHCs from a social support perspective, to understand the impact of public health emergencies. Research collected data from March to April 2019, February 2020, and August 2021, in China. A total of 189 samples were collected and analyzed by using SmartPLS. The results show that (1) social support to the elderly during different stages has different influences on their sense of community and (2) the influence of the sense of community on the intention to continuously use OHCs also seems to change over time. The results of this study provide important implications for research and practice related to both OHCs and COVID-19.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"111 1","pages":"1-17"},"PeriodicalIF":6.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89333227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, Artificial Intelligence assisted rule-based confidence metric (AI-CRBM) framework has been introduced for analyzing environmental governance expense prediction reform. A metric method is to assess a level of collective environmental governance representing general, government, and corporate aspects. The equilibrium approach is used to calculate improvements in the source of environmental management based on cost, and it is tailored to test the public sector-corporation for environmental shared governance. The overall concept of cost prediction or estimation of environmental governance is achieved by the rule-based confidence method. The framework compares the expected cost to the environment of governance to determine the efficiency of the cost prediction process.
{"title":"Analysis of Environmental Governance Expense Prediction Reform With the Background of Artificial Intelligence","authors":"Xiaohui Wu","doi":"10.4018/joeuc.287874","DOIUrl":"https://doi.org/10.4018/joeuc.287874","url":null,"abstract":"In this paper, Artificial Intelligence assisted rule-based confidence metric (AI-CRBM) framework has been introduced for analyzing environmental governance expense prediction reform. A metric method is to assess a level of collective environmental governance representing general, government, and corporate aspects. The equilibrium approach is used to calculate improvements in the source of environmental management based on cost, and it is tailored to test the public sector-corporation for environmental shared governance. The overall concept of cost prediction or estimation of environmental governance is achieved by the rule-based confidence method. The framework compares the expected cost to the environment of governance to determine the efficiency of the cost prediction process.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"44 1","pages":"1-19"},"PeriodicalIF":6.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90774933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and predicting their satisfaction can help patients narrow down their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, we integrated XGBoost and SMOTE algorithm to examine what factors and these factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithm can achieve better performance. We further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.
{"title":"Predicting Patients' Satisfaction With Doctors in Online Medical Communities: An Approach Based on XGBoost Algorithm","authors":"Yunhong Xu, Guangyu Wu, Yu Chen","doi":"10.4018/joeuc.287571","DOIUrl":"https://doi.org/10.4018/joeuc.287571","url":null,"abstract":"Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and predicting their satisfaction can help patients narrow down their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, we integrated XGBoost and SMOTE algorithm to examine what factors and these factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithm can achieve better performance. We further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"121 1","pages":"1-17"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79443928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.
{"title":"Theory-Based Problem Formulation and Ideation in mHealth: Analysis and Recommendations","authors":"Coquessa Jones, J. Venable","doi":"10.4018/joeuc.289434","DOIUrl":"https://doi.org/10.4018/joeuc.289434","url":null,"abstract":"This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"41 1","pages":"1-21"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85023210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Healthcare insurance fraud influences not only organizations by overburdening the already fragile healthcare systems, but also individuals in terms of increasing premiums in health insurance and even fatalities. Identifying the behavioral characteristics of fraudulent claims can help shed light on the development of artificial intelligence and machine learning technologies to detect fraud in health information system research. In this paper, a theoretical model of medical insurance fraud identification is proposed, which characterizes the judgment variables of fraud from the three dimensions of time, quantity, and expenses. The model is verified with large-scale, real-world medical records. Our study shows that, in comparison with claims made by normal people, fraudulent claims usually have a greater frequency of hospital visits, and more medical bills, accompanied by higher amounts of medical expenses. An interesting discovery is that the price per bill for fraudulent cases is not statistically different from the normal cases.
{"title":"A Study of Health Insurance Fraud in China and Recommendations for Fraud Detection and Prevention","authors":"Jie Li, Qiaoling Lan, Enya Zhu, Yong Xu, Dan Zhu","doi":"10.4018/joeuc.301271","DOIUrl":"https://doi.org/10.4018/joeuc.301271","url":null,"abstract":"Healthcare insurance fraud influences not only organizations by overburdening the already fragile healthcare systems, but also individuals in terms of increasing premiums in health insurance and even fatalities. Identifying the behavioral characteristics of fraudulent claims can help shed light on the development of artificial intelligence and machine learning technologies to detect fraud in health information system research. In this paper, a theoretical model of medical insurance fraud identification is proposed, which characterizes the judgment variables of fraud from the three dimensions of time, quantity, and expenses. The model is verified with large-scale, real-world medical records. Our study shows that, in comparison with claims made by normal people, fraudulent claims usually have a greater frequency of hospital visits, and more medical bills, accompanied by higher amounts of medical expenses. An interesting discovery is that the price per bill for fraudulent cases is not statistically different from the normal cases.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"1 1","pages":"1-19"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83707130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This purpose of this study is to develop a research model by extending the theory of planned behavior in a new application context, and applies it to investigate the extrinsic factors influencing people’s attitude towards donating to medical crowdfunding projects appearing on mobile social networking sites (MSNS) and their intention to donate. A survey of 356 Chinese users was conducted and structural equation modeling was used to validate the proposed model and hypotheses. The results indicate that project information, retweeter information and MSNS information all have the significant effect on the general attitude towards donating to medical crowdfunding projects, and general attitude positively affects people’s donation intention. In addition, perceived behavioral control also has positive effect on people’s donation intention, while experienced donating to medical crowdfunding projects has negative effect on people’s donation intention. The research findings provide important theoretical and practical implications.
{"title":"Factors Influencing Donation Intention to Personal Medical Crowdfunding Projects Appearing on MSNS","authors":"Qihua Liu, Li Wang, Jingyi Zhou, Wei Wu, Yiran Li","doi":"10.4018/joeuc.287572","DOIUrl":"https://doi.org/10.4018/joeuc.287572","url":null,"abstract":"This purpose of this study is to develop a research model by extending the theory of planned behavior in a new application context, and applies it to investigate the extrinsic factors influencing people’s attitude towards donating to medical crowdfunding projects appearing on mobile social networking sites (MSNS) and their intention to donate. A survey of 356 Chinese users was conducted and structural equation modeling was used to validate the proposed model and hypotheses. The results indicate that project information, retweeter information and MSNS information all have the significant effect on the general attitude towards donating to medical crowdfunding projects, and general attitude positively affects people’s donation intention. In addition, perceived behavioral control also has positive effect on people’s donation intention, while experienced donating to medical crowdfunding projects has negative effect on people’s donation intention. The research findings provide important theoretical and practical implications.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"79 1","pages":"1-26"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84127370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinjin Song, Yan Li, Xitong Guo, K. Shen, Xiaofeng Ju
As M-Health apps become more popular, users can access more mobile health information (MHI) through these platforms. Yet one preeminent question among both researchers and practitioners is how to bridge the gap between simply providing MHI and persuading users to buy into the MHI for health self-management. To solve this challenge, this study extends the Elaboration Likelihood Model to explore how to make MHI advice persuasive by identifying the important central and peripheral cues of MHI under individual difference. The proposed research model was validated through a survey. The results confirm that (1) both information matching and platform credibility, as central and peripheral cues, respectively, have significant positive effects on attitudes toward MHI, but only information matching could directly affect health behavior changes; (2) health concern significantly moderates the link between information matching and cognitive attitude and only marginally moderates the link between platform credibility and attitudes. Theoretical and practical implications are also discussed.
{"title":"Making Mobile Health Information Advice Persuasive: An Elaboration Likelihood Model Perspective","authors":"Jinjin Song, Yan Li, Xitong Guo, K. Shen, Xiaofeng Ju","doi":"10.4018/joeuc.287573","DOIUrl":"https://doi.org/10.4018/joeuc.287573","url":null,"abstract":"As M-Health apps become more popular, users can access more mobile health information (MHI) through these platforms. Yet one preeminent question among both researchers and practitioners is how to bridge the gap between simply providing MHI and persuading users to buy into the MHI for health self-management. To solve this challenge, this study extends the Elaboration Likelihood Model to explore how to make MHI advice persuasive by identifying the important central and peripheral cues of MHI under individual difference. The proposed research model was validated through a survey. The results confirm that (1) both information matching and platform credibility, as central and peripheral cues, respectively, have significant positive effects on attitudes toward MHI, but only information matching could directly affect health behavior changes; (2) health concern significantly moderates the link between information matching and cognitive attitude and only marginally moderates the link between platform credibility and attitudes. Theoretical and practical implications are also discussed.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"248 1","pages":"1-22"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78424192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiuli Qin, Xing Yang, Runtong Zhang, Manlu Liu, Yu-Hua Ma
To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.
{"title":"An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk","authors":"Qiuli Qin, Xing Yang, Runtong Zhang, Manlu Liu, Yu-Hua Ma","doi":"10.4018/joeuc.287574","DOIUrl":"https://doi.org/10.4018/joeuc.287574","url":null,"abstract":"To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"129 1","pages":"1-14"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72837864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suli Zheng, Po-Ya Chang, Jiahe Chen, Yu-Wei Chang, H. Fan
eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant research has focused on the adoption of eHealth services, the decision-making process from online to offline health services still remains unclear. This study aims to examine patients’ decisions to use online and offline health services by integrating the extended valence framework and the halo effect. By analyzing 221 samples with online consultation experiences, the results show that trust significantly influences perceived benefits and perceived risks, while trust, perceived benefits, and perceived risks significantly influence the intention to consult. The intention to consult positively influences the intention to visit. Considering the moderating effects of payment types, the influence of perceived risks on the intention to consult is larger for the free group than for the paid group. The findings are useful to better understand patients’ decisions to use eHealth.
{"title":"An Investigation of Patient Decisions to Use eHealth: A View of Multichannel Services","authors":"Suli Zheng, Po-Ya Chang, Jiahe Chen, Yu-Wei Chang, H. Fan","doi":"10.4018/joeuc.289433","DOIUrl":"https://doi.org/10.4018/joeuc.289433","url":null,"abstract":"eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant research has focused on the adoption of eHealth services, the decision-making process from online to offline health services still remains unclear. This study aims to examine patients’ decisions to use online and offline health services by integrating the extended valence framework and the halo effect. By analyzing 221 samples with online consultation experiences, the results show that trust significantly influences perceived benefits and perceived risks, while trust, perceived benefits, and perceived risks significantly influence the intention to consult. The intention to consult positively influences the intention to visit. Considering the moderating effects of payment types, the influence of perceived risks on the intention to consult is larger for the free group than for the paid group. The findings are useful to better understand patients’ decisions to use eHealth.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"74 1","pages":"1-24"},"PeriodicalIF":6.5,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84015947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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