Evidence map›Paper›PMID 42245365›Full record

ArticleFrontiers in public health2026

From access to capability: digital literacy and health inequality in contemporary China.

Guangyu Chen, Jinyuan He

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

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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

2 authors.

Guangyu ChenChangzhou Administration Institute, Changzhou, China.
Jinyuan HeThe Second People's Hospital of Changzhou, the Third Affiliated Hospital of Nanjing Medical University, Changzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As digital environments become part of everyday life, digital inequality may be increasingly associated with health differences. Using data from the Chinese Social Survey 2023 (CSS2023), this study examines whether digital literacy is associated with self-rated physical health and self-rated psychological health, whether these associations persist beyond traditional socioeconomic status, and how digital literacy among internet users relates to health alongside the broader access divide in the full sample. Ordinary least squares (OLS) models indicate that digital literacy is positively associated with both outcomes after adjustment for age, gender, marriage, hukou (household registration), education, income, and subjective social status. The association is more pronounced for self-rated psychological health than for self-rated physical health. Supplementary models show that internet access is also associated with health in the full sample, while digital literacy captures capability differences among respondents already online. Because these analyses are based on different samples and differently measured variables, the access-literacy comparison is interpreted descriptively rather than as a formal test of relative importance. Heterogeneity analyses suggest that the association with self-rated psychological health is stronger among older respondents and those with lower educational attainment. These findings are consistent with the view that health-related digital inequality extends beyond access and also involves capability differences among internet users. Digital literacy may therefore be understood as a capability-based health-related resource and a dimension of contemporary health inequality, although the cross-sectional design precludes causal inference.

Indexed as

Computer LiteracyHealth LiteracyHealth Status DisparitiesInternet AccessSocioeconomic Disparities in HealthAdultChinaDigital MediaFemaleHumansInternetMental HealthMiddle AgedSocioeconomic FactorsChinadigital inequalitydigital literacyhealth inequalityinternet accessself-rated psychological health

Identifiers

PMID42245365
PMCPMC13230076

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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.