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Mapping reviews are valuable tools for synthesizing and visualizing research evidence, providing a comprehensive overview of studies within a specific field. Their visual approach enhances accessibility, enabling researchers, policymakers, and practitioners to efficiently identify key findings, trends, and knowledge gaps. These reviews are particularly significant in guiding future research, informing funding decisions, and shaping evidence-based policymaking. In environmental science-similar to health and social sciences-mapping reviews play a crucial role in identifying effective conservation strategies, tracking interventions, and supporting targeted programs.Unlike systematic reviews, which assess intervention effectiveness, mapping reviews focus on broad research questions, aiming to chart the existing evidence on a given topic. They use structured methodologies to identify patterns, gaps, and trends, often employing visual tools to enhance data accessibility. A well-defined scope, guided by inclusion and exclusion criteria, ensures a transparent study selection process. Comprehensive search strategies, often spanning multiple databases, maximize evidence capture. Effective screening, combining automated and manual processes, ensures relevance, while data extraction emphasizes high-level categories such as study design and population demographics. Advanced software tools, including EPPI-Reviewer and MindMeister, support data extraction and visualization, with evidence gap maps highlighting robust areas and research voids.Despite their advantages, mapping reviews present challenges. The categorization and coding of studies can introduce subjective biases, and the process demands substantial resources. Automation and artificial intelligence offer promising solutions, improving efficiency while addressing integration and multilingual limitations. As methodological advancements continue, interdisciplinary collaboration will be essential to fully realize the potential of mapping reviews across scientific disciplines.
Khalil et al. (Mon,) studied this question.