Latent classes associated with the intention to use a symptom checker for self-triage.

It is currently unknown which attitude-based profiles are associated with symptom checker use for self-triage. We sought to identify, among university students, attitude-based latent classes (population profiles) and the association between latent classes with the future use of symptom checkers for...

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Autores principales:	Stephanie Aboueid, Samantha B Meyer, James Wallace, Ashok Chaurasia
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/00743bc0ae3c4795b38c742f1a7c84df
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spelling	oai:doaj.org-article:00743bc0ae3c4795b38c742f1a7c84df2021-12-02T20:04:27ZLatent classes associated with the intention to use a symptom checker for self-triage.1932-620310.1371/journal.pone.0259547https://doaj.org/article/00743bc0ae3c4795b38c742f1a7c84df2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259547https://doaj.org/toc/1932-6203It is currently unknown which attitude-based profiles are associated with symptom checker use for self-triage. We sought to identify, among university students, attitude-based latent classes (population profiles) and the association between latent classes with the future use of symptom checkers for self-triage. Informed by the Technology Acceptance Model and a larger mixed methods study, a cross-sectional survey was developed and administered to students (aged between 18 and 34 years of age) at a University in Ontario. Latent class analysis (LCA) was used to identify attitude-based profiles that exist among the sample while general linear modeling was applied to identify the association between latent classes and future symptom checker use for self-triage. Of the 1,547 students who opened the survey link, 1,365 did not use a symptom checker in the past year and were thus identified as "non-users". After removing missing data (remaining sample = n = 1,305), LCA revealed five attitude-based profiles: tech acceptors, tech rejectors, skeptics, tech seekers, and unsure acceptors. Tech acceptors and tech rejectors were the most and least prevalent classes, respectively. As compared to tech rejectors, tech seekers and unsure acceptors were the latent classes with the highest and lowest odds of future symptom checker use, respectively. After controlling for confounders, the effect of latent classes on symptom checker use remains significant (p-value < .0001) with the odds of future use in tech acceptors being 5.6 times higher than the odds of future symptom checker use in tech rejectors [CI: (3.458, 9.078); p-value < .0001]. Attitudes towards AI and symptom checker functionality result in different population profiles that have different odds of using symptom checkers for self-triage. Identifying a person's or group's membership to a population profile could help in developing and delivering tailored interventions aimed at maximizing use of validated symptom checkers.Stephanie AboueidSamantha B MeyerJames WallaceAshok ChaurasiaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259547 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Stephanie Aboueid Samantha B Meyer James Wallace Ashok Chaurasia Latent classes associated with the intention to use a symptom checker for self-triage.
description	It is currently unknown which attitude-based profiles are associated with symptom checker use for self-triage. We sought to identify, among university students, attitude-based latent classes (population profiles) and the association between latent classes with the future use of symptom checkers for self-triage. Informed by the Technology Acceptance Model and a larger mixed methods study, a cross-sectional survey was developed and administered to students (aged between 18 and 34 years of age) at a University in Ontario. Latent class analysis (LCA) was used to identify attitude-based profiles that exist among the sample while general linear modeling was applied to identify the association between latent classes and future symptom checker use for self-triage. Of the 1,547 students who opened the survey link, 1,365 did not use a symptom checker in the past year and were thus identified as "non-users". After removing missing data (remaining sample = n = 1,305), LCA revealed five attitude-based profiles: tech acceptors, tech rejectors, skeptics, tech seekers, and unsure acceptors. Tech acceptors and tech rejectors were the most and least prevalent classes, respectively. As compared to tech rejectors, tech seekers and unsure acceptors were the latent classes with the highest and lowest odds of future symptom checker use, respectively. After controlling for confounders, the effect of latent classes on symptom checker use remains significant (p-value < .0001) with the odds of future use in tech acceptors being 5.6 times higher than the odds of future symptom checker use in tech rejectors [CI: (3.458, 9.078); p-value < .0001]. Attitudes towards AI and symptom checker functionality result in different population profiles that have different odds of using symptom checkers for self-triage. Identifying a person's or group's membership to a population profile could help in developing and delivering tailored interventions aimed at maximizing use of validated symptom checkers.
format	article
author	Stephanie Aboueid Samantha B Meyer James Wallace Ashok Chaurasia
author_facet	Stephanie Aboueid Samantha B Meyer James Wallace Ashok Chaurasia
author_sort	Stephanie Aboueid
title	Latent classes associated with the intention to use a symptom checker for self-triage.
title_short	Latent classes associated with the intention to use a symptom checker for self-triage.
title_full	Latent classes associated with the intention to use a symptom checker for self-triage.
title_fullStr	Latent classes associated with the intention to use a symptom checker for self-triage.
title_full_unstemmed	Latent classes associated with the intention to use a symptom checker for self-triage.
title_sort	latent classes associated with the intention to use a symptom checker for self-triage.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/00743bc0ae3c4795b38c742f1a7c84df
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Latent classes associated with the intention to use a symptom checker for self-triage.

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