Evidence map›Paper›PMID 42289447›Full record

ArticleScientific reports2026

Digital readiness and family support are associated with later learning engagement through digital self-efficacy among rural Chinese children.

Xiang Yu, Ruixue Wu, Yidan Ma, Chunyan Jin

Abstract read
In one paragraph

Article in Scientific reports, 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

4 authors.

Xiang YuSchool of Education Science, Leshan Normal University, Leshan, China. 547067634@qq.com.
Ruixue WuSchool of Education Science, Leshan Normal University, Leshan, China.
Yidan MaSchool of Education Science, Leshan Normal University, Leshan, China.
Chunyan JinSchool of Education Science, Leshan Normal University, Leshan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital technologies are increasingly embedded in primary education, yet rural children's engagement may depend not only on access to technology but also on readiness to use digital resources for learning. This two-wave time-lagged study examined whether digital readiness was associated with later learning engagement among rural Chinese children through digital self-efficacy, and whether family support strengthened this indirect association. Participants were 768 Grade 5 and Grade 6 students from 15 rural primary schools in Sichuan Province, China. Digital readiness, family support, baseline learning engagement, and demographic variables were assessed at T1; digital self-efficacy and learning engagement were assessed three months later at T2. After controlling for baseline engagement and demographic covariates, T1 digital readiness was positively associated with T2 learning engagement. Digital self-efficacy partially accounted for this longitudinal association, and family support strengthened the association between digital readiness and digital self-efficacy. Measurement validation, attrition effect sizes, and school-clustered robustness checks supported the robustness of the findings. These results suggest that rural children's later learning engagement is linked to readiness-related conditions and family support, but the findings should be interpreted as longitudinal associations rather than causal effects.

Indexed as

LearningRural PopulationSelf EfficacyChildChinaDigital MediaEast Asian PeopleFamily SupportFemaleHumansMaleStudentsDigital readinessDigital self-efficacyFamily supportLearning engagementRural children

Identifiers

PMID42289447
PMCPMC13526775

What OpenQuestion holds

Textmetadata
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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.