Paths to social licence for tracking-data analytics in university research and services.
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ABSTRACT:
SUBMITTER: White JP
PROVIDER: S-EPMC8139460 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Health services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them. OBJECTIVE:This paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research. METHODS:The design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study. RESULTS:The application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access. CONCLUSIONS:To address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.
| S-EPMC7303827 | biostudies-literature
Project description:BackgroundThe rise of Big Data-driven health research challenges the assumed contribution of medical research to the public good, raising questions about whether the status of such research as a common good should be taken for granted, and how public trust can be preserved. Scandals arising out of sharing data during medical research have pointed out that going beyond the requirements of law may be necessary for sustaining trust in data-intensive health research. We propose building upon the use of a social licence for achieving such ethical governance.Main textWe performed a narrative review of the social licence as presented in the biomedical literature. We used a systematic search and selection process, followed by a critical conceptual analysis. The systematic search resulted in nine publications. Our conceptual analysis aims to clarify how societal permission can be granted to health research projects which rely upon the reuse and/or linkage of health data. These activities may be morally demanding. For these types of activities, a moral legitimation, beyond the limits of law, may need to be sought in order to preserve trust. Our analysis indicates that a social licence encourages us to recognise a broad range of stakeholder interests and perspectives in data-intensive health research. This is especially true for patients contributing data. Incorporating such a practice paves the way towards an ethical governance, based upon trust. Public engagement that involves patients from the start is called for to strengthen this social licence.ConclusionsThere are several merits to using the concept of social licence as a guideline for ethical governance. Firstly, it fits the novel scale of data-related risks; secondly, it focuses attention on trustworthiness; and finally, it offers co-creation as a way forward. Greater trust can be achieved in the governance of data-intensive health research by highlighting strategic dialogue with both patients contributing the data, and the public in general. This should ultimately contribute to a more ethical practice of governance.
| S-EPMC8353823 | biostudies-literature
Project description:BACKGROUND:Both the research literature and headline news stories indicate that the public cares about how their health data are used. The objective of this study was to learn more about the general public's attitudes toward users and uses of linked administrative health data held by ICES in Ontario, Canada. METHODS:Eight focus groups, with a total of 65 members of the general public, were conducted in urban and northern settings in Ontario, Canada, in 2015 and 2017 using qualitative market research panels established by a market research/public opinion research firm. RESULTS:Three major themes emerged: (a) the need for assurance about privacy and security, (b) general support for research based on linked administrative health data with some conditions and (c) mixed and more negative reaction when there is private sector involvement. Two minor themes were also derived from the data: (a) low knowledge and understanding of how linked administrative health data are used for research and (b) mixed views on the need to obtain consent when health data do not include identifying information. INTERPRETATION:The public generally supports research based on linked administrative health data, but there is no blanket approval. Researchers and organizations that hold health data should engage with members of the public to understand and address their concerns about privacy and security and to ensure that research is aligned with social licence, particularly where there is private sector involvement.
| S-EPMC6375226 | biostudies-literature
Project description:Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.
| S-EPMC5704676 | biostudies-literature
Project description:Collecting and managing data for clinical and translational research presents significant challenges for clinical and translational researchers, many of whom lack needed access to data management expertise, methods, and tools. At many institutions, funding constraints result in differential levels of research informatics support among investigators. In addition, the lack of widely shared models and ontologies for clinical research informatics and health information technology hampers the accurate assessment of investigators' needs and complicates the efficient allocation of crucial resources for research projects, ultimately affecting the quality and reliability of research. In this paper, we present a model for providing flexible, cost-efficient institutional support for clinical and translational research data management and informatics, the research management team, and describe our initial experiences with deploying this model at our institution.
| S-EPMC3531876 | biostudies-literature
Project description:The project’s primary purpose is to establish a network of Hospice Palliative Care settings using a common and consistent method of assessing and documenting bowel functioning in order to be able to carry out future collaborative studies of constipation treatments. The secondary goal is to gather normative data on current bowel care function and outcomes of current treatments which can be used to determine sample size calculations for future controlled trials of bowel management protocols. In order to complete this goal the project requires a thorough assessment of current nursing practice in regard to bowel care.
| 2059254 | ecrin-mdr-crc
Project description:BackgroundModifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.ObjectiveThe objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.MethodsWe performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies.ResultsThe initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.ConclusionsOur review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.
| S-EPMC7735906 | biostudies-literature
Project description:Household waste segregation and recycling is ranked at a high priority of the waste management hierarchy. Its management remains a great challenge due to the high dependency on social behaviours. The integration of Internet of Things (IoT) and subscription accounts on social media platforms related to household waste management could be an effective and environmentally friendly publicity approach than traditional publicity via posters and newspapers. However, there is a paucity of literature on measuring social media publicity in household waste management, which brings challenges for practitioners to characterise and improve this publicity pathway. In this study, under an integrated framework, data mining approaches are employed or extended for multidimensional publicity analytics using the data of online footprints of propagandist and users. A real-world case study based on a subscription account on the WeChat platform, Shanghai Green Account, is analysed to reveal useful insights for personalised improvements of household waste management. This study suggests that the current publicity related to household waste management leans towards propagandist-centred in both timing and topic dimensions. The identified timing, which has high user engagement, is 12:00-13:00 and 21:00-22:00 on Thursday. The overall relative publicity quality of historical posts is calculated as 0.95. Average user engagement under the macro policy in Shanghai was elevated by 138.5% from 2018 to 2019, during which the collections of biodegradable food waste and recyclable waste were elevated by 88.8% and 431.8%. Intelligent decision support by publicity analytics could enhance household waste management through effective communication.
| S-EPMC7462838 | biostudies-literature
Project description:This paper presents data on the academic performance of a particular set of accounting students from the year of inception into a Nigerian university to the year of graduation. Descriptive analysis was performed on the dataset and a regression model which is capable of making predictions was fitted to the dataset. From the dataset, 24 out of the students who started with a first class result (CGPA above 4.50) still maintained a first class result at graduation. 4 out of the students who started with a first class result dropped to second class upper division before graduation. 4 out of the students who started with a second class upper division result moved to first class result before graduation. 28 out of 35 students who started with a second class upper division maintained a second class upper division result at graduation.
| S-EPMC6141959 | biostudies-literature
Project description:The paper introduces Bayesian Mindsponge Framework (BMF) analytics, a new analytical tool for investigating socio, psychological, and behavioral phenomena. The strengths of this method derive from the combination of the mindsponge mechanism's conceptual formulation power and Bayesian analysis's inferential advantages. The BMF-based research procedure includes six main steps, in which the mindsponge-based conceptualization and model construction is the key step that makes the method unique. Therefore, we elaborate on the fundamental components and functions of the mindsponge mechanism and summarize them into five memorable principles so that other researchers can capitalize directly. An exemplary analysis was performed using a dataset of 3071 Vietnamese entrepreneurs' decisiveness and perceptions of the likelihood of success/continuity to validate the method.•The paper provides five strong points of BMF analytics, originating from the good match between the mindsponge mechanism and Bayesian inference.•The paper also provides a step-by-step procedure for conducting BMF-based research.•The mindsponge mechanism's basic components and functions are elaborated and summarized into five core principles that can be applied directly for research conceptualization and model construction.
| S-EPMC9400117 | biostudies-literature