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Identifying evidence for five realist reviews in primary health care: A comparison of search methods
Author(s) -
Duddy Claire,
Roberts Nia
Publication year - 2022
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1523
Subject(s) - inclusion (mineral) , systematic review , variety (cybernetics) , citation , data science , grey literature , computer science , data collection , health care , medline , management science , knowledge management , sociology , world wide web , social science , political science , artificial intelligence , law , economics
The approach to identifying evidence for inclusion in realist reviews differs from that used in ‘traditional’ systematic reviews. Guidance suggests that realist reviews should be inclusive of diverse data from a range of sources, gathered in iterative searching cycles. Saturation is prioritised over exhaustiveness. Supplementary techniques such as citation snowballing are emphasised as potentially important sources of evidence. This paper describes the processes used to identify evidence in a selection of realist reviews focused on primary health care settings and examines the origin and type of evidence selected for inclusion. Data from five realist reviews were extracted from (a) reviewers' reference management libraries and (b) records kept by review teams. Although all reviews focused on primary health care, they used data from a wide range of document types and research designs, drawing on learning from multiple perspectives and settings, and sourced the documents containing this data in a variety of ways. Systematic searching of academic databases played an important role, supplementary search techniques such as snowballing were used to identify a significant proportion of documents included in the reviews. Our analysis demonstrates the diverse data sources used within realist reviews and the need for flexible, responsive efforts to identify relevant documents. Reviewers and information specialists should devise approaches to data gathering that reflect the individual needs of realist review projects and report these transparently.