Evidence map›Paper›PMID 42774038›Full record

ArticleFrontiers in public health2026

Promotion or suppression? Does internet use by the Chinese public affect physician trust?

Haifeng Ding, Xinbin Xia, Jiashan Teng

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. 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.

Haifeng DingSchool of Humanities and Management, Hunan University of Chinese Medicine, Changsha, China.
Xinbin XiaSchool of Humanities and Management, Hunan University of Chinese Medicine, Changsha, China.
Jiashan TengSchool of Public Administration and Law, Hunan Agricultural University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to explore the causal relationship between internet use and the level of physician trust among Chinese residents, providing rigorous empirical evidence for understanding the changes in doctor-patient relationships in the digital era. Methods: Using four waves of panel data from the China Family Panel Studies (CFPS) spanning 2016 to 2022, we construct a two-way fixed effects model to control for individual and time heterogeneity. We further employ propensity score matching (PSM) to alleviate sample selection bias, thereby robustly examining the relationship between internet use and residents' physician trust. Results: Internet use is significantly associated with a lower level of residents' trust in physicians, and this conclusion remains consistent across multiple robustness tests. Heterogeneity analysis reveals that physician trust among individuals with poorer health, lower social status, and no medical insurance is more susceptible to the impact of internet information. Conclusion: When understanding the relationship between the internet and physician trust, one cannot generalize; instead, refined policy design should be implemented by considering individuals' health status, social position, and institutional participation. Particularly for groups with high information sensitivity and vulnerable trust, efforts should be made to strengthen online information guidance and health education to prevent them from falling into information cocoons and cognitive polarization.

Indexed as

InternetInternet UsePhysician-Patient RelationsPhysiciansTrustAdultChinaDigital MediaEast Asian PeopleFemaleHumansMaleMiddle AgedChinadigital mediainternet usemedical servicesphysician trust

Identifiers

PMID42774038
PMCPMC13593445

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.