PurposeCurrently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.Design/methodology/approachBased on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.FindingsThe results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.Originality/valueUnlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.
The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data-driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human-centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.
PurposeThis paper aims to identify consumers' health information consultation patterns by analyzing information sources to better understand consumers' health information needs and behavior in the context of multisource health information.Design/methodology/approachHaodaifu Online, an online health consultation (OHC) website in China, was used as a research data source, and 20,000 consultation cases were collected from the website with Python. After screening and cleaning, 1,601 consultation cases were included in this study. A content analysis-based mixed-methods research approach was applied to analyze these cases.FindingsThe results indicate that with the participation of OHC, there are 15 patterns of consumer health information consultation. Besides OHC, health information sources reported by consumers included medical institutions family/friends and the Internet. Consumers consult on a wide range of health issues including surgical conditions obstetrical and gynecological conditions and other 20 subjects. Consumers have multiple information needs when using OHC: getting prescriptions, diagnosing diseases, making appointments, understanding illnesses, confirming diagnoses and reviewing costs. Through further analysis it was found that consumers’ health information consultation patterns were also significantly different in health issues and health information needs.Originality/valueThis study broadens one’s understanding of consumer health information behavior, which contributes to the field of health information behavior, and also provides insight for OHC stakeholders to improve their services.
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