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Direct linkJames Edward Hill; Catherine Harris; Andrew Clegg – Research Synthesis Methods, 2024
Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this…
Descriptors: Artificial Intelligence, Search Engines, Data Collection, Natural Language Processing
Hong, Quan Nha; Bangpan, Mukdarut; Stansfield, Claire; Kneale, Dylan; O'Mara-Eves, Alison; Grootel, Leonie; Thomas, James – Research Synthesis Methods, 2022
Reviewing complex interventions is challenging because they include many elements that can interact dynamically in a nonlinear manner. A systems perspective offers a way of thinking to help understand complex issues, but its application in evidence synthesis is not established. The aim of this project was to understand how and why systems…
Descriptors: Intervention, Systems Approach, Evidence, Synthesis
Gusenbauer, Michael; Haddaway, Neal R. – Research Synthesis Methods, 2020
Rigorous evidence identification is essential for systematic reviews and meta-analyses (evidence syntheses) because the sample selection of relevant studies determines a review's outcome, validity, and explanatory power. Yet, the search systems allowing access to this evidence provide varying levels of precision, recall, and reproducibility and…
Descriptors: Evidence, Databases, Meta Analysis, Validity
Briscoe, Simon; Abbott, Rebeca; Lawal, Hassanat; Shaw, Liz; Coon, Jo Thompson – Research Synthesis Methods, 2023
A commonly reported challenge of using Google Search to identify studies for a systematic review is the high number of results retrieved. Thus, 'stopping rules' are applied when screening, such as screening only the first 100 results. However, recent evidence shows that Google Search estimates a much higher number of results than the viewable…
Descriptors: Feasibility Studies, Search Engines, Information Retrieval, Literature Reviews
Cooper, Chris; Court, Rachel; Kotas, Eleanor; Schauberger, Ute – Research Synthesis Methods, 2021
Clinical trials registers form an important part of the search for studies in systematic reviews of intervention effectiveness but the search interfaces and functionality of registers can be challenging to search systematically and resource intensive to search well. We report a technical review of the search interfaces of three leading trials…
Descriptors: Databases, Medical Research, Search Engines, Computer Interfaces
Klopfenstein, D. V.; Dampier, Will – Research Synthesis Methods, 2021
We read with considerable interest the study by Gusenbauer and Haddaway (Gusenbauer and Haddaway, 2020, Research Synthesis Methods, doi:10.1002/jrsm.1378) comparing the systematic search qualities of 28 search systems, including Google Scholar (GS) and PubMed. Google Scholar and PubMed are the two most popular free academic search tools in biology…
Descriptors: Search Engines, Search Strategies, Databases, Information Retrieval
