清晰
分类
利益相关方参与
批判性评价
系统回顾
数据科学
循证实践
利益相关者
科学证据
管理科学
基础(证据)
计算机科学
心理学
循证医学
经验证据
术语
形成性评价
严厉
工程伦理学
科学文献
编码(社会科学)
多学科方法
知识管理
梅德林
检查表
叙述的
行为改变
作者
Hanan Khalil,Vivian Welsh,Matthew Grainger,Fiona Campbell
标识
DOI:10.1016/j.envint.2025.109827
摘要
Systematic Evidence Maps (SEMs) are a form of evidence synthesis offering structured approaches to categorizing and organizing scientific evidence by identifying trends and gaps. SEMs support researchers and policymakers in navigating complex evidence landscapes. By synthesizing evidence, they lay the foundation for targeted systematic reviews and primary research, supporting evidence-informed decision-making. These outputs can be hosted on websites, providing an interactive tool. In environmental health, SEMs are systematically used to categorize evidence on topics such as pollution control measures, climate change impacts, and health disparities. The methodological framework for conducting SEMs involves defining the research scope, employing a systematic search strategy, screening studies systematically, optionally conducting critical appraisal (risk of bias assessment) when studies are categorized by effect direction or intended to inform subsequent syntheses, and coding data for synthesis and visualization. Narrative synthesis, heatmaps and network diagrams enhance SEMs usability. However, challenges remain, including methodological inconsistencies and the need for standardization. Advances in automation, machine learning, and stakeholder engagement can further refine SEMs methodologies. This commentary situates SEMs within the broader family of evidence synthesis, emphasizing their role in environmental health science. By enhancing methodological clarity and leveraging innovative tools, SEMs can support researchers and decision-makers in navigating complex evidence ecosystems and implementing evidence-based solutions for environmental scientists.
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