Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.
BackgroundHealth literacy enables the patients in understanding the basic healthcare information and taking informed health decisions; thus, it is a desirable goal of any healthcare system. It increases patients’ adherence to treatment, improves the quality of care and eases the overall burden on the healthcare system. In recent years, technological solutions are being increasingly used in educating patients and achieving better health literacy. Augmented reality (AR) provides powerful, contextual and situated learning experiences and supplements the real world with virtual objects. AR could potentially be an effective learning methodology for the patients, thus, warranting a comprehensive overview of the current state of AR in patient education and health literacy.MethodsThe proposed scoping review will be based on the framework developed by Arksey and O’Malley, including the refinements suggested by Levac et al. A systematic search for references in the published literature will be conducted in nine research databases—Institute of Electrical and Electronics Engineers (IEEE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, PsycInfo, Embase, Web of Science, Scopus, Association for Computing Machinery (ACM) and Association for Information Systems eLibrary (AISeL). The unpublished studies from ProQuest Dissertations and Theses, Conference Proceedings Citation Index and grey literature references obtained from a web search will also be included. Databases will be searched from inception to 14 January 2020. Two independent reviewers will screen the studies from the search results in two successive stages of title/abstract screening followed by full-text screening. Data variables will be extracted from the selected studies to characterise study design, type of AR technology employed and the relational factors affecting patient education. Lastly, key stakeholders will be consulted to gather their insights about the study findings.Ethics and disseminationThe results will be disseminated through stakeholder meetings and conference presentations. The data used are from publicly available secondary sources, so this study does not require ethical review.
Responses were analyzed using descriptive statistics. RESULTS:Of 43 students who completed our initial survey, 32 indicated interest in general surgery and were paired with surgical mentors. Twenty-six paired students completed follow-up surveys (81% response rate). Of these 26 students, 39% reported increased interest in surgery since joining the mentorship program and 54% reported having a surgery mentor, increased from 21% of initial survey respondents. Topics most commonly discussed with mentors included career guidance (23%), research opportunities (23%), and work-life balance (27%). Barriers to mentorship included time constraints of mentees and mentors (46%), COVID-19 (8%), and lack of mentor-mentee communication (8%). Mentorship program impact on perceptions of a surgical career varied (Figure).CONCLUSION: A student-led mentorship program can improve medical students' access to surgical mentors, which might increase interest in surgery. However, more work is needed to address common concerns about a surgical career.
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