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Visualising Conversation Structure across Time: Insights into Effective Doctor-Patient Consultations

Overview of attention for article published in PLOS ONE, June 2012
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Title
Visualising Conversation Structure across Time: Insights into Effective Doctor-Patient Consultations
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038014
Pubmed ID
Authors

Daniel Angus, Bernadette Watson, Andrew Smith, Cindy Gallois, Janet Wiles

Abstract

Effective communication between healthcare professionals and patients is critical to patients' health outcomes. The doctor/patient dialogue has been extensively researched from different perspectives, with findings emphasising a range of behaviours that lead to effective communication. Much research involves self-reports, however, so that behavioural engagement cannot be disentangled from patients' ratings of effectiveness. In this study we used a highly efficient and time economic automated computer visualisation measurement technique called Discursis to analyse conversational behaviour in consultations. Discursis automatically builds an internal language model from a transcript, mines the transcript for its conceptual content, and generates an interactive visual account of the discourse. The resultant visual account of the whole consultation can be analysed for patterns of engagement between interactants. The findings from this study show that Discursis is effective at highlighting a range of consultation techniques, including communication accommodation, engagement and repetition.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 163 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Italy 2 1%
Bangladesh 1 <1%
Indonesia 1 <1%
Turkey 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
India 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 150 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 17%
Researcher 25 15%
Student > Master 22 13%
Student > Bachelor 16 10%
Professor 12 7%
Other 37 23%
Unknown 23 14%
Readers by discipline Count As %
Computer Science 24 15%
Psychology 23 14%
Medicine and Dentistry 23 14%
Social Sciences 15 9%
Linguistics 12 7%
Other 34 21%
Unknown 32 20%