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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Laurence A Clarfeld, Robert Gramling, Donna M Rizzo, Margaret J Eppstein
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5edab7a1cc294a139cc05848499318bc
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5edab7a1cc294a139cc05848499318bc
record_format	dspace
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
work_keys_str_mv	AT laurenceaclarfeld ageneralmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT robertgramling ageneralmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT donnamrizzo ageneralmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT margaretjeppstein ageneralmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT laurenceaclarfeld generalmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT robertgramling generalmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT donnamrizzo generalmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication AT margaretjeppstein generalmodelofconversationaldynamicsandanexampleapplicationinseriousillnesscommunication
_version_	1718375484759212032

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

Ejemplares similares