Evidence map›Paper›PMID 42703343›Full record

ArticleDigital health

How incidental exposure shapes continued use of AI chatbots in healthcare: A multigroup analysis.

Shuming Yang, Zikun Liu, Yangwen Jiang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shuming YangDepartment of Communication, George Mason University, Fairfax, VA, USA.ORCID https://orcid.org/0009-0006-9593-144X
Zikun LiuSchool of Journalism and Communication, Wuhan University, Wuhan, People's Republic of China.
Yangwen JiangSchool of Journalism and Communication, Wuhan University, Wuhan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to examine the linkage between incidental information exposure and individuals' continuous intention to use AI chatbots in healthcare, and whether this process varies by pre-existing attitudes toward human doctors. Method: Data were collected through a cross-sectional survey ( Result: Incidental exposure to information about AI chatbots in healthcare was positively linked to both subjective ( Conclusion: Incidental exposure to media information about AI chatbots in healthcare was positively linked to continuous usage intention through knowledge acquisition and attitude formation. Individuals with divergent levels of pre-existing attitudes toward human doctors performed differently within the mechanism.

Indexed as

AI chatbots in healthcareAI knowledgecontinuous usage intentionhierarchy-of-effects modelincidental exposure

Identifiers

PMID42703343
PMCPMC13546469

What OpenQuestion holds

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Registered trials

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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.