Evidence map›Paper›PMID 39517869›Full record

ArticleSensors (Basel, Switzerland)2024

Optimizing DG Handling: Designing an Immersive MRsafe Training Program.

Chi Ho Li, Elle Wing Ho Chow, Manviel Tam, Pak Ho Tong

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Chi Ho LiDepartment of Construction and Quality Management, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China.ORCID 0000-0003-2765-2589
Elle Wing Ho ChowDepartment of Supply Chain and Information Management, School of Decision Science, Hang Seng University of Hong Kong, Hong Kong, China.ORCID 0009-0007-5209-5901
Manviel TamDepartment of Supply Chain and Information Management, School of Decision Science, Hang Seng University of Hong Kong, Hong Kong, China.ORCID 0009-0004-6805-6067
Pak Ho TongDepartment of Supply Chain and Information Management, School of Decision Science, Hang Seng University of Hong Kong, Hong Kong, China.ORCID 0000-0001-7369-4814

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid growth of e-commerce has significantly increased demands on logistics providers, particularly in the areas of product handling and shipment. One of the most challenging and critical aspects of this process is the handling of dangerous goods (DG). This is a complex and time-intensive operation that requires safety measures and proper packaging and labelling, because mishandling DG can lead to severe injuries, property loss, and legal liability. This puts additional pressure on logistics providers to efficiently manage DG while maintaining speed and safety in the context of e-commerce. To meet this challenge, there is an urgent need to advance training programs and enhance the operational efficiency for DG handling. The use of mixed reality (MR) technology offers a promising solution. By seamlessly integrating virtual elements with real-world environments, MR has the potential to greatly improve the effectiveness and efficiency of the training of DG handling. Earlier research has examined MR in various fields, while there is still a research gap in applying MR specifically to the training of DG handling. This paper seeks to address the current research gap by presenting a novel MR model, named "MRSafe," for a training program on the safe handling of DG. The model offers users virtual experiences and comprehensive guidance to provide operational decision support.

Indexed as

DG handlingimmersive systemsmixed realitytraining program

Identifiers

PMID39517869
PMCPMC11548140

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.