Open AccessLatent classes associated with the intention to use a symptom checker for self-triage
Author(s) -
Stéphanie Aboueid,
Samantha B. Meyer,
James R. Wallace,
Ashok Chaurasia
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0259547
Subject(s) - latent class model , odds , triage , odds ratio , structural equation modeling , confounding , population , psychology , clinical psychology , medicine , computer science , psychiatry , environmental health , machine learning , logistic regression
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.