Metadata about: Searching literature for a systematic review begins with a manually constructed search strategy by an expert information specialist. The typical process of constructing search strategies is often undocumented, ad-hoc, and subject to individual expertise, which may introduce bias in the systematic review. A new method for objectively deriving search strategies has arisen from information specialists attempting to address these shortcomings. However, this proposed method still presents a number of manual, ad-hoc interventions, and trial-and-error processes, potentially still introducing bias into systematic reviews. Moreover, this method has not been rigorously evaluated on a large set of systematic review cases, thus its generalisability is unknown. In this work, we present a computational adaptation of this proposed objective method. Our adaptation removes the human-in-the-loop processes involved in the initial steps of creating a search strategy for a systematic review; reducing bias due to human factors and increasing the objectivity of the originally proposed method. Our proposed computational adaptation further enables a formal and rigorous evaluation over a large set of systematic reviews. We find that our computational adaptation of the original objective method provides an effective starting point for information specialists to continue refining. We also identify a number of avenues for extending and improving our adaptation to further promote supporting information specialists.

About: Searching literature for a systematic review begins with a manually constructed search strategy by an expert information specialist. The typical process of constructing search strategies is often undocumented, ad-hoc, and subject to individual expertise, which may introduce bias in the systematic review. A new method for objectively deriving search strategies has arisen from information specialists attempting to address these shortcomings. However, this proposed method still presents a number of manual, ad-hoc interventions, and trial-and-error processes, potentially still introducing bias into systematic reviews. Moreover, this method has not been rigorously evaluated on a large set of systematic review cases, thus its generalisability is unknown. In this work, we present a computational adaptation of this proposed objective method. Our adaptation removes the human-in-the-loop processes involved in the initial steps of creating a search strategy for a systematic review; reducing bias due to human factors and increasing the objectivity of the originally proposed method. Our proposed computational adaptation further enables a formal and rigorous evaluation over a large set of systematic reviews. We find that our computational adaptation of the original objective method provides an effective starting point for information specialists to continue refining. We also identify a number of avenues for extending and improving our adaptation to further promote supporting information specialists.

an Entity references as follows:

Graph IRI	Count
http://ns.inria.fr/covid19/graph/entityfishing	10
http://ns.inria.fr/covid19/graph/articles	3

Faceted Search & Find service v1.13.91

This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License.
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Copyright © 2009-2025 OpenLink Software