A general model of conversational dynamics and an example application in serious illness communication.

Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a nov...

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Autores principales: Laurence A Clarfeld, Robert Gramling, Donna M Rizzo, Margaret J Eppstein
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/5edab7a1cc294a139cc05848499318bc
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spelling oai:doaj.org-article:5edab7a1cc294a139cc05848499318bc2021-12-02T20:05:14ZA general model of conversational dynamics and an example application in serious illness communication.1932-620310.1371/journal.pone.0253124https://doaj.org/article/5edab7a1cc294a139cc05848499318bc2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253124https://doaj.org/toc/1932-6203Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn't audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique "fingerprints" of information flow.Laurence A ClarfeldRobert GramlingDonna M RizzoMargaret J EppsteinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0253124 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Laurence A Clarfeld
Robert Gramling
Donna M Rizzo
Margaret J Eppstein
A general model of conversational dynamics and an example application in serious illness communication.
description Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn't audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique "fingerprints" of information flow.
format article
author Laurence A Clarfeld
Robert Gramling
Donna M Rizzo
Margaret J Eppstein
author_facet Laurence A Clarfeld
Robert Gramling
Donna M Rizzo
Margaret J Eppstein
author_sort Laurence A Clarfeld
title A general model of conversational dynamics and an example application in serious illness communication.
title_short A general model of conversational dynamics and an example application in serious illness communication.
title_full A general model of conversational dynamics and an example application in serious illness communication.
title_fullStr A general model of conversational dynamics and an example application in serious illness communication.
title_full_unstemmed A general model of conversational dynamics and an example application in serious illness communication.
title_sort general model of conversational dynamics and an example application in serious illness communication.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/5edab7a1cc294a139cc05848499318bc
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