Evidence map›Paper›PMID 37550684›Full record

ArticleBMC medical education2023

Exploring factors that influence the behavioural intention of medical students to use 3D gastroscopic model to learn how to operate gastroscope using UTAUT Model.

Shuting Wei, Pu Ge, Jinzi Zhang, Shuxian Xu, Yujia Wang, Qiyu Li, Bojunhao Feng, Wenli Yu, Baojun Suo, Yueyang Zhang and 4 more

Abstract read
In one paragraph

Article in BMC medical education, 2023. 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
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

14 authors.

Shuting Wei *Translational Medicine Research Center, Medical Innovation Research Division, Fourth Medical Center of the Chinese PLA General Hospital, Beijing, China.
Pu Ge *School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Jinzi ZhangCollege of Humanities and Social Sciences, Harbin Medical University, Heilongjiang, China.
Shuxian XuChina Pharmaceutical University, Nanjing, China.
Yujia WangCollege of Humanities and Social Sciences, Harbin Medical University, Heilongjiang, China.
Qiyu LiSchool of Humanities and health management, Jinzhou Medical University, Jinzhou, China.
Bojunhao FengSchool of Medicine, Macau University of Science and Technology, Macao, China.
Wenli YuSchool for Sports Humanities and Social Science, Jilin Sport University, Changchun, China.
Baojun SuoDepartment of Gastroenterology, Peking University Third Hospital, Beijing, China.
Yueyang ZhangDepartment of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Tsinghua University, Beijing, China.
Mingxing WangThe Third Clinical Medical College, Harbin Medical University, Heilongjiang, China.
Xinying SunSchool of Public Health, Peking University, Beijing, China.
Zhiqiang SongDepartment of Gastroenterology, Peking University Third Hospital, Beijing, China. szqinputh@163.com.
Yibo WuSchool of Public Health, Peking University, Beijing, China.

Funding

Beijing Natural Science Foundation No. 7232199Capital's funds for health improvement and research No. 2022-2-4093National Natural Science Foundation of China No. 82170562Youth Incubation Fund of Peking University Third Hospital No. BYSYFY2021003
6 · The paper itself

Abstract

backgroundThe application of virtual reality (VR) in gastroscopic operation teaching can be safe and effective, but the advantages can be realized only when students accept and use it. This study aims to identify the factors influencing Chinese clinical medical postgraduates on their intention to use the 3D gastroscopic model constructed based on VR technology using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Students' demographic factors are also taken into consideration.

methodsAll methods were carried out in accordance with relevant guidelines. Data were collected from clinical medical postgraduates students in China using stratified sampling. A total of 292 questionnaires including valid responses were used in this study. Data were processed using Amos 24.0 and SPSS 26.0 software and the statistical analysis technique was based on structural equation modeling (SEM).

resultsThe results showed that different from the mediator of home location and year of clinical learning, mediator of gender, university kind and graduate degree did not affect the behavioral intention. In addition, performance expectancy, facilitating condition, and social influence directly and indirectly have effect on behavioral intention. Also, the significance between social influence and performance expectancy, social influence and effort expectancy were verified.

conclusionsThis study manifested that the proposed framework based on the UTAUT had explanatory power to identify the factors influencing the students' behavioral intention to use the 3D gastroscopic model constructed based on VR technology. Whereas, an important variable of effort expectancy in the frame of the SEM were not certified, thereby indicating that particular attention should be paid to this variable by universities and teachers before applying 3D gastroscopic model constructed based on VR technology in teaching. Added preparatory work is required such as explaining the basic knowledge of the operating steps of VR model and make students adequately understand its accessibility, which can probably improve the intentions of them to use it. The positive effects of social influence on performance expectancy and effort expectancy we proposed was also verified in this study, which provided a direction for future research.

Indexed as

IntentionStudents, MedicalGastroscopesHumansLearningSoftwareBehavioural intentionGastroscopyStructural equation modelingUnified theory of Acceptance and Use of TechnologyVirtual reality

Identifiers

PMID37550684
PMCPMC10408095

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LicenceCC BY
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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.