Evidence map›Paper›PMID 40481505›Full record

ArticleBMC public health2025

Impact of digital literacy on aging attitudes of the elderly from the perspective of social cognitive theory.

Xiaodong Di, Lijian Wang

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. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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.

Xiaodong DiSchool of Public Policy and Administration, Xi'an Jiaotong University, No 28 Xianning West Road, Xi'an, 710049, Shaanxi, China. dixiaodong@xjtu.edu.cn.
Lijian WangSchool of Public Policy and Administration, Xi'an Jiaotong University, No 28 Xianning West Road, Xi'an, 710049, Shaanxi, China.

Funding

China Postdoctoral Science Foundation 2024M752602the Postdoctoral Program of Shaan xi province 2023BSHEDZZ90the Postdoctoral Researcher Program of China GZC20232080the Project of National Natural Science Foundation of China 72404219
6 · The paper itself

Abstract

backgroundUnder the background of digital economy, increasing awareness of active ageing through digital literacy is a realistic requirement to deal with deep aging. However, the lack of digital literacy and skills of the elderly seriously affects the ways and means of their participation in the digital society, and then produces negative aging attitudes such as loneliness, isolation and uselessness.

methodsWe adopt stratified sampling to conduct a comprehensive social survey on the elderly care status of the elderly in six provinces including Henan, Hubei, Shaanxi, Gansu, Jiangsu and Shandong in July 2023, and use Ordinal Logistic Regression method to analysis the impact of digital literacy on aging attitudes.

resultsThe study finds that the improvement of digital literacy can significantly inhibit the aging attitude of the elderly. Further, mechanism analysis finds that the improvement of digital literacy has empowerment effect and energizing effect. In addition, compared with the urban, high-income and low-age elderly groups, the aging attitudes of rural, low-income and high-age elderly groups are relatively less dependent on digital literacy.

conclusionsThe improvement of digital literacy can significantly inhibit the aging attitude of the elderly, so the government should optimize the balance and accessibility of digital resources and eliminate digital inequality among the elderly.

Indexed as

AgingAttitudeComputer LiteracySocial TheoryAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedSurveys and QuestionnairesAging attitudeDigital literacyInfluence mechanismSelf-efficacySocial cognitive theory

Identifiers

PMID40481505
PMCPMC12142949

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.