Paperfetcher: A tool to automate handsearch for systematic reviews
This tool addresses the time-consuming and costly manual handsearch problem for researchers conducting systematic reviews, though it is incremental as it automates an existing process rather than introducing a new paradigm.
The paper tackles the labor-intensive and error-prone process of manual handsearch in systematic reviews by introducing Paperfetcher, a free, open-source tool that automates article retrieval from journals and includes snowballing features, enabling researchers to download results as DOIs or RIS databases.
Handsearch is an important technique that contributes to thorough literature search in systematic reviews. Traditional handsearch requires reviewers to systematically browse through each issue of a curated list of field-specific journals and conference proceedings to find articles relevant to their review. This manual process is not only time-consuming, laborious, costly, and error-prone, but it also lacks replicability and cross-checking mechanisms. In an attempt to solve these problems, this paper presents a free and open-source Python package and an accompanying web-app, Paperfetcher, to automate handsearch for systematic reviews. With Paperfetcher's assistance, researchers can retrieve articles from designated journals within a specified time frame with just a few clicks. In addition to handsearch, this tool also incorporates snowballing in both directions. Paperfetcher allows researchers to download retrieved studies as a list of DOIs or as an RIS database to facilitate seamless import into citation management and systematic review screening software. To our knowledge, Paperfetcher is the first tool that automates handsearch with high usability and a multi-disciplinary focus.