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Topic search filters: a systematic scoping review
Author(s) -
Damarell Raechel A.,
May Nikki,
Hammond Sue,
Sladek Ruth M.,
Tieman Jennifer J.
Publication year - 2019
Publication title -
health information and libraries journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 38
eISSN - 1471-1842
pISSN - 1471-1834
DOI - 10.1111/hir.12244
Subject(s) - terminology , computer science , filter (signal processing) , information retrieval , grey literature , clarity , scope (computer science) , data science , field (mathematics) , world wide web , medline , philosophy , linguistics , biochemistry , chemistry , mathematics , political science , pure mathematics , law , computer vision , programming language
Abstract Background Searching for topics within large biomedical databases can be challenging, especially when topics are complex, diffuse, emerging or lack definitional clarity. Experimentally derived topic search filters offer a reliable solution to effective retrieval; however, their number and range of subject foci remain unknown. Objectives This systematic scoping review aims to identify and describe available experimentally developed topic search filters. Methods Reports on topic search filter development (1990‐) were sought using grey literature sources and 15 databases. Reports describing the conception and prospective development of a database‐specific topic search and including an objectively measured estimate of its performance (‘sensitivity’) were included. Results Fifty‐four reports met inclusion criteria. Data were extracted and thematically synthesised to describe the characteristics of 58 topic search filters. Discussion Topic search filters are proliferating and cover a wide range of subjects. Filter reports, however, often lack clear definitions of concepts and topic scope to guide users. Without standardised terminology, filters are challenging to find. Information specialists may benefit from a centralised topic filter repository and appraisal checklists to facilitate quality assessment. Conclusion Findings will help information specialists identify existing topic search filters and assist filter developers to build on current knowledge in the field.