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      Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs

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          Abstract

          This perspective provides a rationale for redesigning and a framework for expanding the graduate health science analytics and biomedical doctoral program curricula. It responds to digital revolution pressures, ubiquitous proliferation of big biomedical data, substantial recent advances in scientific technologies, and rapid progress in health analytics. Specifically, the paper presents a set of common prerequisites, a proposal for core computational and data analytic curriculum, and a list of expected outcome competencies for graduates of doctoral health science and biomedical programs. The manuscript emphasizes the necessity for coordinated efforts of all stakeholders, including trainees, educators, academic institutions, funding agencies, and policy makers. Concrete recommendations are presented of how to ensure graduates with terminal health science analytics and biomedical degrees are trained and able to continuously self-learn, effectively communicate across disciplines, and promote adaptation and change to counteract the relentless pace of automation and the law of diminishing returns.

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          Mapping the backbone of science

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            Data-driven discovery of partial differential equations

            Researchers propose sparse regression for identifying governing partial differential equations for spatiotemporal systems.
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              Implementation research: new imperatives and opportunities in global health

              Implementation research is important in global health because it addresses the challenges of the know-do gap in real-world settings and the practicalities of achieving national and global health goals. Implementation research is an integrated concept that links research and practice to accelerate the development and delivery of public health approaches. Implementation research involves the creation and application of knowledge to improve the implementation of health policies, programmes, and practices. This type of research uses multiple disciplines and methods and emphasises partnerships between community members, implementers, researchers, and policy makers. Implementation research focuses on practical approaches to improve implementation and to enhance equity, efficiency, scale-up, and sustainability, and ultimately to improve people's health. There is growing interest in the principles of implementation research and a range of perspectives on its purposes and appropriate methods. However, limited efforts have been made to systematically document and review learning from the practice of implementation research across different countries and technical areas. Drawing on an expert review process, this Health Policy paper presents purposively selected case studies to illustrate the essential characteristics of implementation research and its application in low-income and middle-income countries. The case studies are organised into four categories related to the purposes of using implementation research, including improving people's health, informing policy design and implementation, strengthening health service delivery, and empowering communities and beneficiaries. Each of the case studies addresses implementation problems, involves partnerships to co-create solutions, uses tacit knowledge and research, and is based on a shared commitment towards improving health outcomes. The case studies reveal the complex adaptive nature of health systems, emphasise the importance of understanding context, and highlight the role of multidisciplinary, rigorous, and adaptive processes that allow for course correction to ensure interventions have an impact. This Health Policy paper is part of a call to action to increase the use of implementation research in global health, build the field of implementation research inclusive of research utilisation efforts, and accelerate efforts to bridge the gap between research, policy, and practice to improve health outcomes.
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                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                13 February 2020
                2020
                : 8
                : 22
                Affiliations
                [1] 1Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan , Ann Arbor, MI, United States
                [2] 2Neuroscience Graduate Program, University of Michigan , Ann Arbor, MI, United States
                [3] 3Michigan Institute for Data Science, University of Michigan , Ann Arbor, MI, United States
                [4] 4Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, United States
                Author notes

                Edited by: Harshad Thakur, Tata Institute of Social Sciences, India

                Reviewed by: Jagmeet S. Kanwal, Georgetown University, United States; Donna Jeanne Petersen, University of South Florida, United States

                *Correspondence: Ivo D. Dinov statistics@ 123456umich.edu

                This article was submitted to Public Health Education and Promotion, a section of the journal Frontiers in Public Health

                Article
                10.3389/fpubh.2020.00022
                7031195
                8f0cd044-34c3-46fa-865e-3a8cde5ace46
                Copyright © 2020 Dinov.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 17 July 2019
                : 23 January 2020
                Page count
                Figures: 0, Tables: 4, Equations: 0, References: 66, Pages: 10, Words: 7449
                Categories
                Public Health
                Curriculum, Instruction, and Pedagogy

                doctoral training,health science,graduate curricula,methods,analytics,data science,quantitative education

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