Evidence map›Paper›PMID 37304654›Full record

ArticleProceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting2022

Preparing for the lasting effects of COVID-19 on medical training: The design and pilot study of a low-fidelity virtual reality prototype for Central Venous Catheterization training.

Jessica Gonzalez-Vargas, Haroula Tzamaras, Dailen Brown, Jason Moore, Scarlett Miller

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting, 2022. 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, top 71% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

5 authors at 1 institution in 1 country.

Jessica Gonzalez-VargasPennsylvania State University Industrial Engineering.
Haroula TzamarasPennsylvania State University Industrial Engineering.
Dailen BrownPennsylvania State University Mechanical Engineering.
Jason MoorePennsylvania State University Mechanical Engineering.
Scarlett MillerPennsylvania State University Industrial Engineering.
Pennsylvania State University · US

Funding

Dynamic Haptic Robotic Training for Central Venous Catheter InsertionR01HL127316 · NHLBI · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · PI Scarlett Miller, Jason Zachary Moore · 2015 to 2026
$3.7M
NHLBI NIH HHS R01 HL127316
6 · The paper itself

Abstract

Over the last several decades we have seen a shift from in-person to online training that has been exasperated by the COVID-19 pandemic. Researchers believe that many of these effects will be lasting which makes it even more important that the Human Factors community seek to step back and understand how to best train complex skills in a virtual world. The current paper is focused on understanding the utility of Virtual Reality (VR) in medical education for a hands-on procedural heavy procedure - ultrasound-guided Internal Jugular Central Venous Catheterization. Specifically, the objective of this study is to identify the potential utility of VR in US-IJCVC training through the design of a low-fidelity prototype and user interviews with three subject-matter experts. Results showed that the VR prototype designed is useful and provides a depth of knowledge and educational value which can be used to design innovative VR training approaches.

Identifiers

PMID37304654
PMCPMC10249787
OpenAlexW4312310755

What OpenQuestion holds

Textmetadata
LicenceTDM
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.