Evidence map›Paper›PMID 39744356›Full record

ArticleFrontiers in public health2024

Exploring the acceptance of virtual reality training systems among construction workers: a combined structural equation modeling and artificial neural network approach.

Hui Liu, Xinyue Miao, Chunyan Shi, Tongyu Xu

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. A Novel Macro-Level Model in Evaluating Health and Safety Training Based on Virtual Reality.International journal of environmental research and public health · 2025
    Article
  3. 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.

Hui LiuSchool of Emergency Science and Engineering, Jilin Jianzhu University, Changchun, China.
Xinyue MiaoSchool of Emergency Science and Engineering, Jilin Jianzhu University, Changchun, China.
Chunyan ShiSchool of Emergency Science and Engineering, Jilin Jianzhu University, Changchun, China.
Tongyu XuThe University of Kitakyushu, Fukuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual Reality Training System (VRTS) has been verified effective in safety training in the construction field. However, in China, it is not widely used as a regular training tool. Among all the reasons, the acceptance level of construction workers (CWs) has the decisive impact on the promotion of VRTS. In view of this, this study is devoted to constructing a training model of CWs' acceptance level of VRTS training that integrates the technology acceptance level model with the theory of planned behavior. What's more, this paper innovatively introduces three crucial elements of external influences, namely, risk perception (RP), safety climate (SC) and self-efficacy (SE). In order to more accurately figure out the linear and nonlinear relationship between every structure and the factors of CWs' acceptance level, 528 participating CWs in this study filled in structured questionnaires, through the data of which the analyzing process uses structural equation model and artificial neural network two-stage analysis method. Based on the analyzing results of the study, this paper put forward a series of specific strategies and suggestions to significantly promote the acceptance level of CWs to VRTS training considering the designment, the enterprises and the government.

Indexed as

Construction IndustryNeural Networks, ComputerVirtual RealityAdultChinaFemaleHumansLatent Class AnalysisMaleMiddle AgedOccupational HealthSelf EfficacySurveys and Questionnairesartificial neural networkconstruction workerstechnology acceptance modeltheory of planned behaviorvirtual reality training system

Identifiers

PMID39744356
PMCPMC11688313

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.