Evidence map›Paper›PMID 33364034›Full record

ReviewAcute medicine & surgery

Use of high-fidelity simulation technology in disasters: an integrative literature review.

Masataka Gunshin, Kent Doi, Naoto Morimura

Open access · goldAbstract readReview
In one paragraph

Review in Acute medicine & surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 34% of its field
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

6 citing papers in PubMed, 18 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Masataka GunshinDepartment of Emergency Medicine Toranomon Hospital Tokyo Japan.ORCID https://orcid.org/0000-0001-5310-3116
Kent DoiDepartment of Acute Medicine The University of Tokyo Hospital Tokyo Japan.
Naoto MorimuraDepartment of Acute Medicine The University of Tokyo Hospital Tokyo Japan.
University of Tokyo Hospital · JPToranomon Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New innovative high-fidelity simulation (HFS) technologies, including augmented reality and virtual reality, have begun being used for disaster response and preparedness. However, few studies have assessed the merit of these technologies in disaster simulation. This integrative literature review of 21 studies assesses the role of HFS technology in disaster. Most studies used a quantitative methodology (71.4%), followed by mixed (19%) or qualitative methods (9.6%). Nearly 60% covered only disaster preparedness phase, whereas 10% addressed disasters in middle-income countries without including low-income nations. The four most frequently mentioned technologies were immersive virtual reality simulation, computerized virtual reality simulation, full-scale simulation, and augmented reality wearable smart glasses simulation. Nearly 50% of the studies used technology for purposes other than disaster simulation education, including telemedicine (14.3%), risk planning (14.3%), high-risk map generation for preparedness purposes (9.5%), or rehabilitation medicine (4.8%). HFS technologies must be further evaluated outside of high-income countries and in different disaster phases to better understand their full potential in disaster simulation. Future research should consider different health professions and more robust protocols to assist disaster response professionals and agencies in the adoption of HFS technologies.

Indexed as

Augmented realitydisaster medicinehigh‐fidelity simulation trainingpublic healthvirtual reality

Identifiers

PMID33364034
PMCPMC7750648
OpenAlexW3113685236

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.