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Best practice guidelines for abstract screening large‐evidence systematic reviews and meta‐analyses
Author(s) -
Polanin Joshua R.,
Pigott Terri D.,
Espelage Dorothy L.,
Grotpeter Jennifer K.
Publication year - 2019
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.1354
Subject(s) - systematic review , best practice , extant taxon , meta analysis , set (abstract data type) , management science , computer science , process (computing) , quality (philosophy) , medline , data science , psychology , medicine , management , political science , epistemology , engineering , pathology , philosophy , evolutionary biology , law , economics , biology , programming language , operating system
screening is one important aspect of conducting a high‐quality and comprehensive systematic review and meta‐analysis. Abstract screening allows the review team to conduct the tedious but vital first step to synthesize the extant literature: winnowing down the overwhelming amalgamation of citations discovered through research databases to the citations that should be “full‐text” screened and eventually included in the review. Although it is a critical process, few guidelines have been put forth since the publications of seminal systematic review textbooks. The purpose of this paper, therefore, is to provide a practical set of best practice guidelines to help future review teams and managers. Each of the 10 proposed guidelines is explained using real‐world examples or illustrations from applications. We also delineate recent experiences where a team of abstract screeners double‐screened 14 923 abstracts in 89 days.