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      Health research system resilience: lesson learned from the COVID-19 crisis

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          Abstract

          Producing evidence in epidemics is crucial to control the current epidemic and prevent its recurrence in the future. Data must be collected and analyzed rapidly to recognize the most efficient and feasible methods with proper timelines. However, there are many challenges a research system may encounter during a crisis. This article has presented lessons learned from the COVID-19 pandemic for health research system (HRS) to deal with current and future crises. Therefore, a HRS needs to produce and use evidence in such a situation. The components Knowledge Translation Self-Assessment Tool for Research Institutes (SATORI) framework was used to review the actions required and respond to the COVID-19 pandemic in a national HRS. This framework consists of four categories of defining the research question, conducting research, translating the research results, and promoting the use of evidence. The work is proposed actions in response to the COVID-19 crisis and improving a HRS's resilience. While COVID-19 has serious harm to the health and broader socio-economic consequences, this threat should be accounted for as an opportunity to make research systems more accountable and responsible in the timely production and utilization of knowledge. It is time to seriously think about how HRS can build a better back to be resilient to potential shock and prepare for unforeseen emerging conditions.

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          Fair Allocation of Scarce Medical Resources in the Time of Covid-19

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            Waste in covid-19 research.

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              How long does biomedical research take? Studying the time taken between biomedical and health research and its translation into products, policy, and practice

              Background The time taken, or ‘time lags’, between biomedical/health research and its translation into health improvements is receiving growing attention. Reducing time lags should increase rates of return to such research. However, ways to measure time lags are under-developed, with little attention on where time lags arise within overall timelines. The process marker model has been proposed as a better way forward than the current focus on an increasingly complex series of translation ‘gaps’. Starting from that model, we aimed to develop better methods to measure and understand time lags and develop ways to identify policy options and produce recommendations for future studies. Methods Following reviews of the literature on time lags and of relevant policy documents, we developed a new approach to conduct case studies of time lags. We built on the process marker model, including developing a matrix with a series of overlapping tracks to allow us to present and measure elements within any overall time lag. We identified a reduced number of key markers or calibration points and tested our new approach in seven case studies of research leading to interventions in cardiovascular disease and mental health. Finally, we analysed the data to address our study’s key aims. Results The literature review illustrated the lack of agreement on starting points for measuring time lags. We mapped points from policy documents onto our matrix and thus highlighted key areas of concern, for example around delays before new therapies become widely available. Our seven completed case studies demonstrate we have made considerable progress in developing methods to measure and understand time lags. The matrix of overlapping tracks of activity in the research and implementation processes facilitated analysis of time lags along each track, and at the cross-over points where the next track started. We identified some factors that speed up translation through the actions of companies, researchers, funders, policymakers, and regulators. Recommendations for further work are built on progress made, limitations identified and revised terminology. Conclusions Our advances identify complexities, provide a firm basis for further methodological work along and between tracks, and begin to indicate potential ways of reducing lags. Electronic supplementary material The online version of this article (doi:10.1186/1478-4505-13-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                rezamajd@tums.ac.ir
                Journal
                Health Res Policy Syst
                Health Res Policy Syst
                Health Research Policy and Systems
                BioMed Central (London )
                1478-4505
                18 December 2020
                18 December 2020
                2020
                : 18
                : 136
                Affiliations
                [1 ]GRID grid.411705.6, ISNI 0000 0001 0166 0922, Knowledge Utilization Research Center, , Tehran University of Medical Sciences, ; Tehran, Iran
                [2 ]GRID grid.411705.6, ISNI 0000 0001 0166 0922, Community-based Participatory Research Center, Knowledge Utilization Research Center, School of Public Health, , Tehran University of Medical Sciences, ; Tehran, Iran
                [3 ]National Institute for Medical Research Development (NIMAD), Tehran, Iran
                Author information
                http://orcid.org/0000-0001-8429-5261
                Article
                667
                10.1186/s12961-020-00667-w
                7747187
                33339524
                175c73ba-b83e-4bf5-a298-0409f00b4d16
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 5 April 2020
                : 6 December 2020
                Categories
                Commentary
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                © The Author(s) 2020

                Health & Social care
                coronavirus infections/prevention and control,health policy,pandemics/prevention and control,policy making,translational medical research

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