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Title [SEI] What evidence exists regarding the impact of biodiversity on human health and well-being? A systematic map protocol
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This protocol outlines a systematic approach to explore the link between biodiversity and human health and well-being, utilizing machine learning for efficient screening and aiming to provide a concise overview of evidence.



 



Global biodiversity is rapidly declining, yet we still do not fully understand the relationships between biodiversity and human health and well-being. As debated, the loss of biodiversity or reduced contact with natural biodiversity may lead to more public health problems, such as an increase in chronic disease. There is a growing body of research that investigates how multiple forms of biodiversity are associated with an increasingly diverse set of human health and well-being outcomes across scales. This protocol describes the intended method to systematically mapping the evidence on the associations between biodiversity from microscopic to planetary scales and human health and well-being from individual to global scales.



The authors will systematically map secondary studies on the topic by following the Collaborations for Environmental Evidence Guidelines and Standards for Evidence Synthesis in Environment Management. They developed the searching strings to target both well established and rarely studied forms of biodiversity and human health and well-being outcomes in the literature. A pairwise combination search of biodiversity and human health subtopics will be conducted in PubMed, Web of Science platform (across four databases) and Scopus with no time restrictions. To improve the screening efficiency in EPPI reviewer, supervised machine learning, such as a bespoke classification model, will be trained and applied at title and abstract screening stage. A consistency check between at least two independent reviewers will be conducted during screening (both title-abstract and full-text) and data extraction process. No critical appraisal will be undertaken in this map. The authors may use topic modelling (unsupervised machine learning) to cluster the topics as a basis for further statistical and narrative analysis.
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