Literature DB >> 30561081

Usage of automation tools in systematic reviews.

A J van Altena1, R Spijker2,3, S D Olabarriaga1.   

Abstract

Systematic reviews are a cornerstone of today's evidence-informed decision making. With the rapid expansion of questions to be addressed and scientific information produced, there is a growing workload on reviewers, making the current practice unsustainable without the aid of automation tools. While many automation tools have been developed and are available, uptake seems to be lagging. For this reason, we set out to investigate the current level of uptake and what the potential barriers and facilitators are for the adoption of automation tools in systematic reviews. We deployed surveys among systematic reviewers that gathered information on tool uptake, demographics, systematic review characteristics, and barriers and facilitators for uptake. Systematic reviewers from multiple domains were targeted during recruitment; however, responders were predominantly from the biomedical sciences. We found that automation tools are currently not widely used among the participants. When tools are used, participants mostly learn about them from their environment, for example, through colleagues, peers, or organization. Tools are often chosen on the basis of user experience, either by own experience or from colleagues or peers. Lastly, licensing, steep learning curve, lack of support, and mismatch to workflow are often reported by participants as relevant barriers. While conclusions can only be drawn for the biomedical field, our work provides evidence and confirms the conclusions and recommendations of previous work, which was based on expert opinions. Furthermore, our study highlights the importance that organizations and best practices in a field can have for the uptake of automation tools for systematic reviews.
© 2018 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2019        PMID: 30561081     DOI: 10.1002/jrsm.1335

Source DB:  PubMed          Journal:  Res Synth Methods        ISSN: 1759-2879            Impact factor:   5.273


  5 in total

1.  Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.

Authors:  Allison Gates; Samantha Guitard; Jennifer Pillay; Sarah A Elliott; Michele P Dyson; Amanda S Newton; Lisa Hartling
Journal:  Syst Rev       Date:  2019-11-15

2.  The views of health guideline developers on the use of automation in health evidence synthesis.

Authors:  Anneliese Arno; Julian Elliott; Byron Wallace; Tari Turner; James Thomas
Journal:  Syst Rev       Date:  2021-01-08

3.  MeSH and text-word search strategies: precision, recall, and their implications for library instruction.

Authors:  Michelle M DeMars; Carol Perruso
Journal:  J Med Libr Assoc       Date:  2022-01-01

4.  Towards a new model for producing evidence-based guidelines: a qualitative study of current approaches and opportunities for innovation among Australian guideline developers.

Authors:  Steve McDonald; Julian H Elliott; Sally Green; Tari Turner
Journal:  F1000Res       Date:  2019-06-24

5.  The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research.

Authors:  Ian J Saldanha; Bryant T Smith; Evangelia Ntzani; Jens Jap; Ethan M Balk; Joseph Lau
Journal:  Syst Rev       Date:  2019-12-20
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.