Journal of clinical epidemiology
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To assess the feasibility of a modified workflow that uses machine learning and crowdsourcing to identify studies for potential inclusion in a systematic review. ⋯ Using a crowd to screen search results for systematic reviews can be an accurate method as long as the agreement algorithm in place is robust.
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Health guidelines are a key knowledge translation tool produced and used by numerous stakeholders worldwide. Effective participation in guideline development groups or development groups is crucial for guideline success, yet little guidance exists for members of these groups. In this study, we present the Guideline Participant Tool (GPT) to support effective participation in guideline groups, in particular those using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. ⋯ The GPT contains helpful guidance for all guideline participants, particularly those without previous guideline experience. Future research should further explore the need for additional tools to support guideline participants and identify and develop strategies for improving guideline members' participation in guideline groups. This work will be incorporated into INGUIDE.org guideline training and credentialing efforts by the Guidelines International Network and McMaster University.
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The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). ⋯ This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).
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Coronavirus disease 2019 (COVID-19) is a global pandemic. Governments have implemented combinations of "lockdown" measures of various stringencies, including school and workplace closures, cancellations of public events, and restrictions on internal and external movements. These policy interventions are an attempt to shield high-risk individuals and to prevent overwhelming countries' healthcare systems, or, colloquially, "flatten the curve." However, these policy interventions may come with physical and psychological health harms, group and social harms, and opportunity costs. These policies may particularly affect vulnerable populations and not only exacerbate pre-existing inequities but also generate new ones. ⋯ Our conceptual framework highlights the fact that COVID-19 policy interventions can generate or exacerbate interactive and multiplicative equity harms. Applying this framework can help in three ways: (1) identifying the areas where a policy intervention may generate inequitable adverse effects; (2) mitigating the policy and practice interventions by facilitating the systematic examination of relevant evidence; and (3) planning for lifting COVID-19 lockdowns and policy interventions around the world.