Evidence map›Paper›PMID 41204278›Full record

ArticleBMC public health2025

The relationship between children's digital literacy and parents' mental health: evidence from China.

Yun Ye

Abstract read
In one paragraph

Article in BMC public health, 2025. 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

1 author.

Yun YeSchool of Management, Hainan Medical University, Haikou, Hainan Province, China. hy0205048@muhn.edu.cn.

Funding

the Research Fund of Hainan Medical University RZ2300006014
6 · The paper itself

Abstract

backgroundUnderstanding how to improve the mental health of middle-aged and elderly people is an important issue that needs to be addressed urgently to promote healthy ageing. Moreover, children's digital literacy has become critical for families to adapt to the digital age. However, few studies have investigated the relationship between children's digital literacy and parents' mental health.

methodsBased on data from the China Family Panel Studies (CFPS) dataset, this study investigates the relationship between children's digital literacy and parents' mental health and its mediating mechanism. Empirical analyses are conducted using the double/debiased machine learning (DML) model and causal mediation analysis (CMA) model.

results(1) Increased digital literacy among children improves parents' mental health, and the effect is more pronounced for mothers, while there is no significant effect for fathers. These results have been verified by endogeneity and robustness tests, further confirming the reliability of the above findings; (2) these effects are heterogeneous at the regional, household, and individual levels and are more pronounced in the eastern and central regions, in urban areas, in areas with high household socioeconomic status, and among parents with poorer health outcomes; and (3) household income and emotional support are key mediating pathways through which children's digital literacy improves parents' mental health.

conclusionThis study highlights the importance of intergenerational digital spillover in addressing the mental health of middle-aged and older adults in China's rapidly digitizing society. It provides novel insights for strengthening intergenerational digital support to promote healthy ageing.

Indexed as

Computer LiteracyMental HealthParentsAdultAgedChildChinaFemaleHumansMaleMiddle AgedParent-Child RelationsCausal mediation analysisDigital literacyDouble/Debiased machine learningMental health

Identifiers

PMID41204278
PMCPMC12595821

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.