Evidence map›Paper›PMID 41775727›Full record

ArticleScientific reports2026

Virtual reality to enhance risk management and safety in electrical substations.

Jose Maria Gonzalez Del Pozo, Eduardo Roig Segovia

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Jose Maria Gonzalez Del PozoDepartamento de Ideación Gráfica. Doctorado en Comunicación Arquitectónica, Universidad Politécnica de Madrid, Madrid, Spain. jose.gdelpozo@alumnos.upm.es.
Eduardo Roig SegoviaDepartamento de Ideación Gráfica. Doctorado en Comunicación Arquitectónica, Universidad Politécnica de Madrid, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In high-risk environments, such as electrical substations, operation and maintenance are complex and dangerous tasks, which is reflected in the high occupational accident rate in the energy sector. The need to improve worker training to reduce accidents is critical, specially where human error or equipment failure can result in injury to workers or the public. his case study evaluates the effectiveness of immersive virtual reality (VR) versus traditional training methods used for electrical substation workers that must work in high electrical risk environments. The trial is developed in three phases within the context of the operational framework for an infrastructure and energy company in Spain. In the first phase, both training approaches are compared by means of theoretical tests complemented by immersive simulations, which are tested in a real project environment. In the second, the knowledge obtained in the real project environment is evaluated, and in the third, participants are subjected to an emergency simulation in a controlled environment to measure their response capacity. The results suggest that the incorporation of VR training significantly improves knowledge retention, risk identification and decision making under pressure. Therefore, this research confirms the value of VR simulation as an effective training tool in high-risk environments, providing a safe and practical experience that reduces the incidence of occupational accidents in electrical substations.

Indexed as

Accidents, OccupationalElectricityRisk ManagementVirtual RealityHumansSpainElectrical substationPedagogical applicationRisk mitigationSimulationVirtual reality

Identifiers

PMID41775727
PMCPMC12957506

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.