title: 'Clustering' documents automatically to support scoping reviews of research: a case study.
abstract: BACKGROUND
  Scoping reviews of research help determine the feasibility and the resource requirements of conducting a systematic review, and the potential to generate a description of the literature quickly is attractive.
  
  
  OBJECTIVE
  To test the utility and applicability of an automated clustering tool to describe and group research studies to improve the efficiency of scoping reviews.
  
  
  METHODS
  A retrospective study of two completed scoping reviews was conducted. This compared the groups and descriptive categories obtained by automatically clustering titles and abstracts with those that had originally been derived using traditional researcher-driven techniques.
  
  
  RESULTS
  The clustering tool rapidly categorised research into themes, which were useful in some instances, but not in others. This provided a dynamic means to view each dataset. Interpretation was challenging where there were potentially multiple meanings of terms. Where relevant clusters were unambiguous, there was a high precision of relevant studies, although recall varied widely.
  
  
  CONCLUSIONS
  Policy-relevant scoping reviews are often undertaken rapidly, and this could potentially be enhanced by automation depending on the nature of the dataset and information sought. However, it is not a replacement for researcher-developed classification. The possibilities of further applications and potential for use in other types of review are discussed.
authors.affiliation: Evidence for Policy and Practice Information and Coordinating Centre (EPPI-Centre), Social Science Research Unit, Institute of Education, University of London, 18 Woburn Square London, London, WC1H 0NR, UK. c.stansfield@ioe.ac.uk
authors.forename: Claire
authors.initials: C
authors.lastname: Stansfield
authors.affiliation: Evidence for Policy and Practice Information and Coordinating Centre (EPPI-Centre), Social Science Research Unit, Institute of Education, University of London, 18 Woburn Square London, London, WC1H 0NR, UK.
authors.forename: James
authors.initials: J
authors.lastname: Thomas
authors.affiliation: Evidence for Policy and Practice Information and Coordinating Centre (EPPI-Centre), Social Science Research Unit, Institute of Education, University of London, 18 Woburn Square London, London, WC1H 0NR, UK.
authors.forename: Josephine
authors.initials: J
authors.lastname: Kavanagh
mesh_terms: D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D004282:Documentation / Q000145:classification*; D000069550:Machine Learning; D009323:Natural Language Processing; D010506:Periodicals as Topic / Q000145:classification; D058028:Research Report; D018875:Vocabulary, Controlled; D000098584:Scoping Reviews as Topic
keywords: automatic clustering; automation; information storage and retrieval; methods, mapping; scoping reviews; text mining