ArticlePloS one2021
Latent classes associated with the intention to use a symptom checker for self-triage.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Exploring Laypersons' Experiences With a Mobile Symptom Checker App as an Interface Between eHealth Literacy, Health Literacy, and Health-Related Behavior: Qualitative Interview Study.JMIR formative research · 2025Article
- The RepVig framework for designing use-case specific representative vignettes and evaluating triage accuracy of laypeople and symptom assessment applications.Scientific reports · 2024Article
- Unveiling Usage Patterns and Explaining Usage of Symptom Checker Apps: Explorative Longitudinal Mixed Methods Study.Journal of medical Internet research · 2024Article
- The digitalization of emergency department triage: the perspectives of health professionals and patients.BMC health services research · 2024Article
- Only the anxious ones? Identifying characteristics of symptom checker app users: a cross-sectional survey.BMC medical informatics and decision making · 2024Article
- Bridging evidence-to-care gaps with mHealth: Designing a symptom checker for parents accessing knowledge translation resources on acute children's illnesses in a smartphone application.PEC innovation · 2023Article
- Patients' self-triage for unscheduled urgent care: a preliminary study on the accuracy and factors affecting the performance of a Belgian self-triage platform.BMC health services research · 2022Article
- Ethical, Legal, and Social Implications of Symptom Checker Apps in Primary Health Care (CHECK.APP): Protocol for an Interdisciplinary Mixed Methods Study.JMIR research protocols · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.