Evidence map›Paper›PMID 35911583›Full record

ArticleJournal of healthcare engineering2022

Technology Acceptance in Socially Assistive Robots: Scoping Review of Models, Measurement, and Influencing Factors.

Ying He, Qiu He, Qian Liu

Abstract readScoping Review
In one paragraph

Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Heterogeneity of Attitudes toward Robots in Healthcare among the Chinese Public: A Latent Profile Analysis.International journal of environmental research and public health · 2022
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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

3 authors.

Ying HeSchool of Medicine, Hunan Normal University School of Medicine, Changsha, Hunan, China.ORCID 0000-0003-4328-9708
Qiu HeSchool of Medicine, Hunan Normal University School of Medicine, Changsha, Hunan, China.
Qian LiuSchool of Medicine, Hunan Normal University School of Medicine, Changsha, Hunan, China.ORCID 0000-0003-4907-3350

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: We summarized technology acceptance and the influencing factors of elderly people toward socially assistive robots (SARs). Methods: A scoping review whereby a literature search was conducted in Embase, Cochrane, Scopus, PubMed, and Web of Science databases (2006-2021) to retrieve studies. No restrictions on study methodology were imposed. Results: Out of the 1187 retrieved papers, 35 studies were finally included in the study. The articles covered various aspects, including general attitudes towards using SARs, technology acceptance theory models, and factors associated with technology acceptance. Twelve studies reported a positive attitude towards SARs. Three explicit theoretical frameworks were reported. Studies involving the elderly reported three themes that influence attitudes towards SARs: individual characteristics, concerns/problems regarding robots, and social factors. Conclusions: This review elucidates on the suitability of theory-based framework as applied to acceptance of SARs. We found that research on technology acceptance with regard to SARs is still in the developmental stages, and further studies of assessment tools for SARs are required. It is also essential to consider the factors that influence the acceptance of SARs by older people to ensure that they meet the end goal requirements of the user.

Indexed as

RoboticsAgedHumans

Identifiers

PMID35911583
PMCPMC9337973

What OpenQuestion holds

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LicenceCC BY
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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.