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Digital divides in verification: investigating the heterogeneity of verification patterns for AI-generated health information

Identification

Clé publique

radar:r-dbbcd8f22053bdb60b86d9ad848c2898

Auteurs

Dechuan Liu · Yuhang Yuan

Année déclarée

2026

Langue déclarée

en

Type source

article

Première détection

2026-11-13T12:44:01.831947+00:00

Dernière observation des métadonnées

2026-11-13T12:44:01.831947+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Fudan University · Beijing Normal University

Pays d’affiliation déclarés

CN

Résumé documentaire — langue originale

With the widespread integration of generative artificial intelligence (GenAI) into health information seeking, the prevalence of AI hallucinations necessitates effective verification behaviors. Grounded in the digital divide framework, this study investigates heterogeneous verification patterns for AI-generated health information. We surveyed 597 Chinese adult residents and identified four verifier profiles through the latent profile analysis: verifiers preferring internal verification, verifiers preferring institutional sources, verifiers preferring interpersonal sources and verifiers emphasizing multiple sources. Further analyses suggested that demographic and socioeconomic factors (gender, age, education and income) significantly predicted profile membership. Additionally, the study revealed significant outcome divides associated with individual differences in verification patterns. Specifically, verifiers emphasizing multiple sources reported the highest levels of self-efficacy in identifying AI-generated health misinformation and superior health management outcomes. Notably, verifiers preferring interpersonal sources exhibited weaker self-efficacy and worse health management outcomes compared to verifiers preferring institutional sources. These findings highlight the structural barriers underlying verification divides, offering empirical implications for designing targeted interventions to promote public verification behaviors and narrow associated digital divides.

Identifiants

OPENALEX

https://openalex.org/W7133807062

DOI

10.1080/01292986.2026.2639664

Repères documentaires

Famille documentaire

article

Types déclarés par les sources

article

Éditeurs

Routledge

Les mentions d’entités ne constituent pas une preuve de reconnaissance juridique. La juridiction étudiée est distincte du pays d’affiliation des auteurs.

Sources

OpenAlex · Document 1

• Document · https://doi.org/10.1080/01292986.2026.2639664

Statut documentaire

Pertinence à examiner

Bibliothèque

Cette identité n’est pas encore intégrée à la Bibliothèque juridique.