Evidence map›Paper›PMID 36062111›Full record

ArticleFrontiers in public health2022

Research on the influence mechanism and governance mechanism of digital divide for the elderly on wisdom healthcare: The role of artificial intelligence and big data.

Jian Zhou, Zeyu Wang, Yang Liu, Jian Yang

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. 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

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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

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

4 authors.

Jian ZhouDepartment of Business Management, School of Business, Qingdao University, Qingdao, China.
Zeyu WangDepartment of Business Administration, Edinburgh Business School, Heriot-Watt University, Edinburgh, United Kingdom.
Yang LiuDepartment of Cultural Industry, Cultural Industry Research Institute, Qilu University of Technology, Jinan, China.
Jian YangDepartment of Computer Science and Technology, Ocean University of China, Qingdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of digital information technology, life has become more convenient for people; however, the digital divide for the elderly was even more serious, so they became a forgotten group in the internet age over time. Residents' demand for healthcare is rising, but the wisdom healthcare service supported by digital information technology is less acceptable to the elderly due to the digital divide. Based on the knowledge gap theory and combining the value perception and satisfaction model, this study explores the influence of the digital divide for the elderly on wisdom healthcare satisfaction and takes the perceived value of wisdom healthcare as a mediator, and artificial intelligence and big data as moderators into the research framework. Based on the data of 1,052 elderly people in China, the results show that the digital divide for the elderly has a negative influence on wisdom healthcare satisfaction and perceived value. Moreover, it is found that wisdom healthcare perception value mediated the relationship between the digital divide for the elderly and the wisdom healthcare satisfaction, which enhances the negative effect of the digital divide for the elderly on wisdom healthcare satisfaction. Furthermore, the moderating effect of artificial intelligence and big data on the relationship between the digital divide for the elderly and the perceived value of wisdom healthcare is opposite to that between the perceived value of wisdom healthcare and wisdom healthcare satisfaction. Therefore, this study has a reference value for the development and optimization of smart medical industry.

Indexed as

Big DataDigital DivideAgedArtificial IntelligenceDelivery of Health CareHumansIndustryartificial intelligencebig datadigital dividegovernance mechanismolder peoplewisdom health care

Identifiers

PMID36062111
PMCPMC9428348

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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.