Evidence map›Paper›PMID 37693368›Full record

ArticleJAMIA open2023

Predicting pediatric healthcare provider use of virtual reality using a technology acceptance model.

Ellen Y Wang, Kristin M Kennedy, Lijin Zhang, Daniel Qian, Ty Forbes, Michelle Zuniga-Hernandez, Brian S-K Li, Benjamin Domingue, Thomas J Caruso

Open access · goldAbstract read
In one paragraph

Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.6field-weighted citation impact, top 7% 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

8 citing papers in PubMed, 21 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

9 authors at 5 institutions in 1 country.

Ellen Y WangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.ORCID https://orcid.org/0000-0002-9151-938X
Kristin M KennedyStanford University School of Medicine, Stanford, CA, United States.
Lijin ZhangStanford University Graduate School of Education, Stanford, CA, United States.
Daniel QianIcahn School of Medicine at Mount Sinai, New York, NY, United States.
Ty ForbesMcGovern Medical School at University of Texas Health, Houston, TX, United States.
Michelle Zuniga-HernandezStanford Chariot Program, Lucile Packard Children's Hospital Stanford, Palo Alto, CA, United States.
Brian S-K LiPrinceton University, Princeton, NJ, United States.
Benjamin DomingueStanford University Graduate School of Education, Stanford, CA, United States.ORCID https://orcid.org/0000-0002-3894-9049
Thomas J CarusoDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, United States.
Stanford University · USIcahn School of Medicine at Mount Sinai · USLucile Packard Children's Hospital · USPrinceton University · USThe University of Texas Health Science Center at Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The primary aim of this study was to apply a novel technology acceptance model (TAM) for virtual reality (VR) in healthcare. The secondary aim was to assess reliability of this model to evaluate factors that predict the intentions of pediatric health providers' use of VR as an anxiolytic for hospitalized pediatric patients. Materials and Methods: Healthcare providers that interacted with pediatric patients participated in a VR experience available as anxiolysis for minor procedures and then completed a survey evaluating attitudes, behaviors, and technology factors that influence adoption of new technologies. Results: Reliability for all domain measurements were good, and all confirmatory factor analysis models demonstrated good fit. Usefulness, ease of use, curiosity, and enjoyment of the VR experience all strongly predict intention to use and purchase VR technologies. Age of providers, past use, and cost of technology did not influence future purchase or use, suggesting that VR technologies may be broadly adopted in the pediatric healthcare setting. Discussion: Previous VR-TAM models in non-healthcare consumers formulated that age, past use, price willing to pay, and curiosity impacted perceived ease of use. This study established that age, past use, and cost may not influence use in healthcare. Future studies should be directed at evaluating the social influences and facilitating conditions within healthcare that play a larger influence on technology adoption. Conclusion: The VR-TAM model demonstrated validity and reliability for predicting intent to use VR in a pediatric hospital.

Indexed as

healthcare systemspediatricstechnology assessmentvirtual reality

Identifiers

PMID37693368
PMCPMC10483581
OpenAlexW4386499444

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.