Evidence map›Paper›PMID 39649373›Full record

SynthesisFrontiers in rehabilitation sciences2024

Building trust in long-term care settings using assistive technology: a systematic review.

Kangjie Zheng, Fred Han, Siyu Yang, Nanxin Li

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in rehabilitation sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Kangjie ZhengSchool of Systems Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China.
Fred HanSchool of Systems Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China.
Siyu YangSchool of Systems Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China.
Nanxin LiSchool of Systems Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This review investigates the dynamics of trust between caregivers and care receivers in long-term care settings, where the implementation of assistive technology also becomes chronically crucial. Trust is essential in the care receiver-caregiver relationship as it impacts the effectiveness of care and the care receiver's participation in treatment. Moreover, integrating assistive technology significantly affects the quality of care by increasing care receivers' autonomy and reducing caregivers' workload. Despite its significance, the mechanisms of trust involving assistive technology in long-term care have not been clarified. Methods: To address this gap, this review systematically analyzed 32 articles published in English since 2,000, sourced from Web of Science, PubMed, Scopus, and Science Direct databases. Results: The review identified the dynamics of trust in long-term care settings involving assistive technology. Based on this trust dynamics, three critical factors were analyzed: care receiver-related, caregiver-related, and assistive technology-related. Discussion: The findings provide a detailed understanding of the factors affecting trust in long-term care settings involving assistive technology. These insights contribute to long-term care facility operators making informed decisions regarding technology adoption in care practice and care service strategies, ultimately enhancing trust and the quality of care in long-term care settings.

Indexed as

assistive technologylong-term carerelationshipreviewtrust

Identifiers

PMID39649373
PMCPMC11621047

What OpenQuestion holds

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LicenceCC BY
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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.