The impact and return-on-investment of evidence-based practice in conservation and environmental management: a machine learning-assisted scoping review protocol
File(s)
Published version
Author(s)
Christie, Alec P
Martin, Philip A
Taylor, Nigel G
Type
Journal Article
Abstract
Evidence-based Practice (EBP) is a vital principle, with its origins in the 1970s, that has transformed the disciplines of medicine and healthcare. The use of best available evidence to inform decisions and best practice has since spread across other disciplines, including in the environmental sciences through evidence-based conservation and environmental management. However, ironically there only appears to be a single scoping review on the impacts and return-on-investment of EBP in healthcare and it is unclear whether any such evidence exists in the broad field of conservation and environmental management. In this scoping review, we aim to explore the extent to which evaluations of the impacts and return-on-investment of EBP and evidence use have been conducted in conservation and environmental management on both human and environmental outcomes. We will search at least ten different electronic bibliographic platforms, databases, and search engines for published and grey literature, from 1992 to 2025 – there will be no geographical or language restrictions on the documents included. A machine learning-assisted review process will be followed using open source tools (ASReview and SysRev) and following the comprehensive SYstematic review Methodology Blending Active Learning and Snowballing (SYMBALS). The findings from the scoping review will be useful to inform organisations and practitioners considering implementing EBP on its benefits and costs and will also highlight potential research gaps on the impact of EBP and evidence use.
Editor(s)
Gobin, Jenilee
Date Issued
2025-06-01
Date Acceptance
2025-05-30
Citation
PLoS One, 2025, 20 (6)
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS One
Volume
20
Issue
6
Copyright Statement
© 2025 Christie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
10.1371/journal.pone.0326521
Subjects
Humans
Conservation of Natural Resources
Evidence-Based Practice
Machine Learning
Scoping Review as Topic
Publication Status
Published
Article Number
e0326521
Date Publish Online
2025-06-25