Enhancing nursing competency through virtual reality simulation among nursing students: a systematic review and meta-analysis

Abstract

Aim
Studies on the effectiveness of virtual reality (VR) in nursing education have explored its impact on learning outcomes, emotional immersion and engagement, learner self-confidence, and satisfaction, generally showing positive aspects. However, there is a need for a systematic review to examine the specific influence of VR-based education on nursing students’ practical competency.

Method
According to the PRISMA 2020 guidelines, 22 studies were selected based on inclusion criteria from 579 articles, published from January 1, 2018, to March 31, 2024, across nine major databases including PubMed and EMbase. The target population comprised nursing students, and the intervention focused on VR-based simulations aimed at enhancing competency, compared to control groups receiving either no intervention or conventional non-virtual simulation. The primary outcome, nursing competency, was analyzed using MIX 2.0 Pro (Ver. 2.0.1.6, BiostatXL, 2017) to calculate pooled effect sizes.

Result
The pooled effect size for nursing competency was determined to be large, with Hedge’s g = 0.88 (95% CI, 0.47 to 1.29). Meta-regression analysis identified several factors associated with an increase in nursing competency. These included studies published after 2022, approval of an IRB, absence of funding, randomized controlled trials (RCTs), interventions reported as shorter than 4 weeks or not reported, sessions fewer than 4 or not reported, session duration under 1 h or not reported, and observational measurement methods. Additional factors enhancing nursing competency were the inclusion of a pre-briefing before simulations, the absence of a debriefing afterward, and the exclusion of other activities during the simulation.

Conclusion
By combining the results of the included studies, the systematic review and meta-analysis accounted for variations in sample size, study methodology, and independent intervention effects, providing an overall evaluation of the effectiveness of simulation-based education in improving nursing students’ competency.

Limitation
The selection criteria for the studies analyzed, which included only those published in English or Korean and reported precise means, standard deviations, and sample sizes, could lead to selection bias and limit the generalization of our study results.

Systematic review registration
PROSPERO International Prospective Register of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023446348.

Full text

Frontiers in Medicine 01 frontiersin.org
Enhancing nursing competency
through virtual reality simulation
among nursing students: a
systematic review and
meta-analysis
Mi-KyoungCho
1
and MiYoungKim
2*
1 Department of Nursing Science, Chungbuk National University, Cheongju, Republic of Korea,
2 College of Nursing, Hanyang University, Seoul, Republic of Korea
Aim: Studies on the eectiveness of virtual reality (VR) in nursing education
have explored its impact on learning outcomes, emotional immersion and
engagement, learner self-confidence, and satisfaction, generally showing
positive aspects. However, there is a need for a systematic review to examine
the specific influence of VR-based education on nursing students’ practical
competency.
Method: According to the PRISMA 2020 guidelines, 22 studies were selected
based on inclusion criteria from 579 articles, published from January 1, 2018,
to March 31, 2024, across nine major databases including PubMed and EMbase.
The target population comprised nursing students, and the intervention focused
on VR-based simulations aimed at enhancing competency, compared to control
groups receiving either no intervention or conventional non-virtual simulation.
The primary outcome, nursing competency, was analyzed using MIX 2.0 Pro
(Ver. 2.0.1.6, BiostatXL, 2017) to calculate pooled eect sizes.
Result: The pooled eect size for nursing competency was determined to
belarge, with Hedge’s g= 0.88 (95% CI, 0.47 to 1.29). Meta-regression analysis
identified several factors associated with an increase in nursing competency.
These included studies published after 2022, approval of an IRB, absence of
funding, randomized controlled trials (RCTs), interventions reported as shorter
than 4 weeks or not reported, sessions fewer than 4 or not reported, session
duration under 1 h or not reported, and observational measurement methods.
Additional factors enhancing nursing competency were the inclusion of a pre-
briefing before simulations, the absence of a debriefing afterward, and the
exclusion of other activities during the simulation.
Conclusion: By combining the results of the included studies, the systematic
review and meta-analysis accounted for variations in sample size, study
methodology, and independent intervention eects, providing an overall
evaluation of the eectiveness of simulation-based education in improving
nursing students’ competency.
Limitation: The selection criteria for the studies analyzed, which included
only those published in English or Korean and reported precise means,
standard deviations, and sample sizes, could lead to selection bias and limit the
generalization of our study results.
OPEN ACCESS
EDITED BY
Hani Salem Atwa,
Arabian Gulf University, Bahrain
REVIEWED BY
Youngho Lee,
Mokpo National University,
Republic of Korea
Hayam Hanafi Abdulsamea,
Ibn Sina National College for Medical Studies,
SaudiArabia
Azza Aly,
Ibn Sina National College for Medical Studies,
Saudi Arabia
*CORRESPONDENCE
Mi Young Kim
miyoung0@hanyang.ac.kr
RECEIVED 20 January 2024
ACCEPTED 17 April 2024
PUBLISHED 07 May 2024
CITATION
Cho M-K and Kim MY (2024) Enhancing
nursing competency through virtual reality
simulation among nursing students: a
systematic review and meta-analysis.
Front. Med. 11:1351300.
doi: 10.3389/fmed.2024.1351300
COPYRIGHT
© 2024 Cho and Kim. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication
in this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Systematic Review
PUBLISHED 07 May 2024
DOI 10.3389/fmed.2024.1351300

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 02 frontiersin.org
Systematic review registration: PROSPERO International Prospective Register
of Systematic Reviews: http://www.crd.york.ac.uk/PROSPERO/, identifier
CRD42023446348.
KEYWORDS
virtual reality, simulation, nursing students, competency, meta-analysis
1 Introduction
Nursing education is an applied discipline in which theory and
practical education are combined; prospective nurses prepare to
become competent by applying the knowledge learned in theoretical
education to the practical education process. e need for nursing
education to train professionals who provide nursing and medical
services to humans utilizing digital-based, non-face-to-face media
such as articial intelligence (AI) and big data has recently become
more urgent (1). In nursing education, there has been an increasing
interest in virtual-reality simulation (VRS) education as an alternative
and complementary method to traditional simulation education,
providing students with new learning experiences in a reproduced
clinical environment and enhancing clinical adaptability (2). Virtual
reality (VR) is dened as “the use of partial immersion through a
digital learning environment (computer, tablet, phone, screen, etc.) to
foster a perceived lived experience for an intended outcome (e.g.,
learning and entertainment)” (3). is study denes VRS to include
VR and its derivatives, augmented reality (AR), and mixed reality
(MR), using the terminology consistently. In VRS, learners can
collaborate with other healthcare professionals to provide
interventions, such as solving patients’ problems or practicing simple
skills (4, 5). Improved clinical performance skills, knowledge, and
metacognition, as well as enhanced learning satisfaction,
communication, self-ecacy, condence, and teamwork have been
reported as eects of these VR programs (4, 6). In addition, studies on
the eectiveness of nursing education using VR have been conducted
on learning eectiveness, emotional engagement and immersion,
learner condence, and satisfaction (7, 8). Reportedly, VRS programs
for nursing skills are eective in improving skills (9) and have the
advantage of enabling safe and repetitive training without time and
space constraints (10). us, learning through VRS has demonstrated
improvement in various factors related to clinical nursing competency,
albeit oen assessed in a fragmented manner. As various forms of VRS
are being applied in nursing education, and diverse elements
contributing to nursing competency are considered, there is a need to
comprehend the holistic outcomes of these studies. Consequently, this
study aims to comprehensively review the results, considering nursing
competency in a broader sense that encompasses collaboration,
interpersonal relationships, communication, professional
development, and the nursing process, skills, and education (11).
Moreover, a systematic review and analysis of nursing students’
outcomes are essential for determining specic factors that are
deemed eective. Systematic reviews and meta-analyses can
amalgamate the results of included studies, accounting for dierences
in sample size, variations in research approaches, and intervention
eects among independent studies. Webelieve that the systematic
review and meta-analysis in this study will enable an assessment of the
overall eect of VRS-based education on nursing students’ nursing
competency. Consequently, this study aims to provide foundational
data on VRS by conducting a systematic literature review and meta-
analysis, investigating the improvement eect of VRS on nursing
students’ nursing competency as a primary outcome, and examining
knowledge, self-ecacy, problem-solving skills, condence, and
satisfaction as secondary outcomes.
is study aims to acquire and analyze evidence regarding the
enhancement of nursing students’ nursing competency through
VRS. e primary outcome focuses on nursing students’ self-reported
feelings and reactions, while the secondary outcome assesses nursing
students’ nursing competency following exposure to VRS.
2 Materials and methods
2.1 Search strategy and data sources
e search was jointly conducted by two researchers, Cho,
M.-K. and Kim, M.Y., across nine electronic databases or e-journals:
PubMed, Cochrane, EMBASE-OVID, CINAHL, World of Science,
SCOPUS, PQDT, APA PsycArticles, and Research Information
Sharing Service. e primary search, conducted from July 18, 2023, to
August 20, 2023, targeted articles published in English and Korean
from January 1, 2003, to April 30, 2023. A secondary search was
carried out from April 6, 2024, to April 9, 2024, focusing on articles
published from May 1, 2023, to March 31, 2024, also in English and
Korean. e search strategy and formula, following the PICO-SD
framework (population, intervention, comparison, outcome, study
design), are detailed in Table 1. e keywords employed in search
terms across the nine databases included combinations and variations
of “nursing students,” “virtual reality,” “augmented reality,” “extended
reality,” “metaverse,” “competency-based education,” “clinical
competence,” “competency,” and “controlled clinical trial.” ese
keywords were chosen to comprehensively capture studies relevant to
the impact of virtual reality simulation on nursing competency.
2.2 Inclusion and exclusion criteria
e reporting of the results adhered to the PRISMA 2020
checklist. Inclusion criteria comprised nursing students aged 19 years
or older (Population), interventions involving VRS (Intervention),
with conventional learning methods or no intervention as the control
(comparison). e primary outcome was nursing competency, and
secondary outcomes included knowledge, self-ecacy,

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 03 frontiersin.org
TABLE1 Search strategy according to PICO.
PICO Key terms MeSH PubMed Entry Terms EMTREE (EMBASE) Text words
P (Patient, Population, Participants,
Problems)
Nursing student(s) “Students, Nursing”[Mesh] Pupil Nurses
Student, Nursing
Nurses, Pupil
Nurse, Pupil
Pupil Nurse
Nursing Student
Nursing Students
Nursing student/ [(student* OR pupil*) AND nurs*]
I (Intervention or Exposure or Index
Test)
Virtual reality “Virtual Reality”[Mesh] Reality, Virtual
Virtual Reality, Educational
Educational Virtual Realities
Educational Virtual Reality
Reality, Educational Virtual
Virtual Realities, Educational
Virtual Reality, Instructional
Instructional Virtual Realities
Instructional Virtual Reality
Realities, Instructional Virtual
Reality, Instructional Virtual
Virtual Realities, Instructional
Virtual reality/ [(educational OR instructional)
AND virtual realit*]
Augmented reality “Augmented Reality”[Mesh] Augmented Realities
Realities, Augmented
Reality, Augmented
Mixed Reality
Mixed Realities
Realities, Mixed
Reality, Mixed
Augmented reality/ (augmented OR mixed) AND
realit*
Mixed reality
Extended reality – – Extended realit*
Metaverse – – Metaverse OR meta-verse
C (Comparators, Comparisons,
Controls)
None or usual
O (Outcomes, Eects) Competency “Competency-Based Education” [Mesh] Competency-based education
education, competency-based
competency-based educations
education, competency-based
educations, competency-based
–
(Continued)

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 04 frontiersin.org
problem-solving, condence, satisfaction, and other variables, which
were concurrently measured. If multiple measurements were
conducted post-intervention, the rst measurement was used to
calculate the eect size. Only studies presenting subject numbers,
means, and standard deviations in the results were selected for precise
eect-size calculation. e study designs included randomized
controlled trials (RCTs) and quasi-experimental studies. Exclusion
criteria included studies encompassing students from majors other
than nursing, interventions using conventional simulation-learning
methods instead of VRS, the absence of nursing competency as an
outcome variable, studies not reported in Korean or English, studies
with inaccessible original texts, and single-group studies lacking a
control group.
2.3 Data extraction
Two researchers, Cho, M.-K. and Kim, M.Y., independently
conducted searches and selected studies for analysis based on the
predened inclusion and exclusion criteria. e selected studies were
extracted, incorporating information such as author, year of
publication, country, publication language, number of schools,
institutional review board (IRB) approval, funding details, number of
participants, study design, intervention characteristics (type,
facilitator, duration, session, time/session, pre-brieng, debrieng,
other activities, outcome measurement time, and measurement
method), quality assessment score, and dependent variables. is
information was meticulously recorded in a coding book created
using the Microso Excel spreadsheet soware. Any disparities in
coding were addressed by revisiting the original text to ascertain and
input the nal coding values (Table2).
2.4 Quality assessment
e quality assessment of selected articles was independently
performed by Cho, M.-K. and Kim, M.Y. using the Joanna Briggs
Institute (JBI) Checklist for RCTs and the Checklist for Quasi-
Experimental Studies. Five RCTs were assessed using the 13-question
JBI Checklist; the average score was 8.40, and all ve studies lacked
clear reporting on “Q2. Was allocation to treatment groups
concealed?” and “Q4. Were participants blind to treatment
assignment?” Quasi-experimental studies comprised eight articles,
and on evaluation using the 9-item JBI Checklist for Quasi-
Experimental Studies (32), the average score was 8.50, with generally
well-reported items (Table3).
2.5 Statistical analyses
MIX 2.0 Pro (Ver. 2.0.1.6, BiostatXL, 2017) was used to calculate
and merge eect sizes for both the primary outcome of nursing
competency and secondary outcomes. e overall eect was
determined using a random-eects model, considering between-
subject variability and heterogeneity between studies. Hedge’s g was
employed for eect-size calculation, and signicance was assessed
using 95% condence intervals (CIs), Z tests, and p-values. e weight
of each eect size was determined using the inverse of variance (33).
PICO Key terms MeSH PubMed Entry Terms EMTREE (EMBASE) Text words
“Clinical Competence”[Mesh] competency, clinical
competence, clinical
clinical competency
clinical competencies
competencies, clinical
clinical skill
skill, clinical
skills, clinical
clinical skills
clinical competence/ Clinical compete*
Study Design RCT, Quasi-experimental “Controlled Clinical Trials as
Topic”[Mesh]
Clinical Trials, Controlled as Topic Controlled clinical trial (topic)/OR
Controlled Clinical Trials as Topic.
mp.
Restrictions English, Korean/Humans (Adult: 19+ years), (Young Adult 19–24 years) Male, Female/1900.01.01–2024.03.31
TABLE1 (Continued)

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 05 frontiersin.org
TABLE2 Descriptive summary of the included studies.
Study
ID
Author
(Year) Country Center IRB Fund Research
design Participants Intervention
type
Program
facilitator
Intervention
duration
Intervention
session
Intervention
time/
session
Outcome
measurement
time
outcome
variable
Pre-
briefing Debriefing
Non-
simulation
activities
Quality
score
1 Lee (12)Korea 1Ye s Yes Quasi 40
Senior nursing
students from
a nursing
college
(E: 20, C:20)
Virtual reality
simulation
(VRS)
Researcher None
reported
None
reported
80 min Delayed (3 days
aer
interventions for
each team)
-Knowledge
-Performance
condence
-Clinical
practice
competency
Yes Yes None 8
2 Ahn and
Lee (13)
Korea 2Ye s No Quasi 84
Nursing
students
(E: 44, C: 40)
Virtual reality
simulation
(VRS)
Nursing
faculty
1 day 1 session 35–50 min Immediately -Knowledge
-Condence
-Self-ecacy
-Clinical
competency
Yes Yes None 8
3 Rossler etal.
(14)
USA 1 Yes Ye s RCT 20
Prelicensure
baccalaureate
nursing
students
(E: 5, C: 15)
Virtual reality
simulation
(VRS)
Investigator None
reported
None
reported
None
reported
Delayed (1 week) -Knowledge of
OR re safety
-Transfer of
knowledge of
OR re safety
skills
Yes None None 4
4 Aebersold
etal. (15)
USA 1 Yes Ye s RCT 69
Sophomore
and junior
nursing
students
(E: 35, C: 34)
Virtual reality
simulation
(VRS)
None
reported
Over 4 weeks None
reported
None
reported
Immediately -Skill
competency
evaluation
Yes Yes None 8
5 An etal.
(16)
Korea 2Ye s No RCT 62
First- and
second-year
nursing
students
(E: 31, C:31)
Virtual reality
simulation
(VRS)
Researcher 4 weeks None
reported
None
reported
Immediately - Self-regulated
learning
competency
- Perceived
learning
competency
- Knowledge
- Learning ow
- Academic
stress
Yes None None 11
(Continued)

Cho and Kim 10.3389/fmed.2024.1351300
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TABLE2 (Continued)
Study
ID
Author
(Year) Country Center IRB Fund Research
design Participants Intervention
type
Program
facilitator
Intervention
duration
Intervention
session
Intervention
time/
session
Outcome
measurement
time
outcome
variable
Pre-
briefing Debriefing
Non-
simulation
activities
Quality
score
6 Chang etal.
(17)
Taiwan 1No Yes Quasi 42
Two classes at
a nursing
university
(E: 21, C:21)
Virtual reality
simulation
(VRS)
Nursing
faculty
3 weeks Non e
reported
None
reported
Immediately - OSCE
competency
- Problem-
solving skills
- Learning
engagement
- Learning
satisfaction
None None None 6
7 Ahn (11)Korea 1Ye s No Quasi 72
second-year
nursing
students
(E: 34, C:38)
Metaverse
based
simulation
Nursing
faculty
None
reported
1 session each 25–35 min Immediately - Knowledge of
core nursing
skills
- Condence in
core nursing
skill
performance
-Clinical
competency
Yes Yes None 8
8 Kim and
Jung (18)
Korea 1Ye s No RCT 73
First- and
second-year
nursing
students
(E: 37, C:36)
Virtual reality
simulation
(VRS)
Researcher None
reported
None
reported
30 min Immediately -Clinical
competency
-Self-ecacy
-Satisfaction
Yes None None 9
9 Ha etal.
(19)
Korea 1No No RCT 70
ird-year
nursing
students
(E: 35, C: 35)
Virtual reality
simulation
(VRS)
Researcher None
reported
None
reported
2 h Immediately -Clinical
competency
-Self-ecacy
-Nursing skill
competency
-Satisfaction
None None None 10
10 Yoo and
Yan g ( 20)
Korea 1Ye s No Quasi 48
Second-year
nursing
students
(E: 24, C: 24)
Virtual reality
simulation
(VRS)
Researcher 5 weeks 5 sessions 20–30 min/
once
Immediately - Clinical
competency
- Problem-
solving skills
- Condence in
core nursing
skill
performance
Yes Yes None 7
(Continued)

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 07 frontiersin.org
Study
ID
Author
(Year) Country Center IRB Fund Research
design Participants Intervention
type
Program
facilitator
Intervention
duration
Intervention
session
Intervention
time/
session
Outcome
measurement
time
outcome
variable
Pre-
briefing Debriefing
Non-
simulation
activities
Quality
score
11 Bae and
Shin (21)
Korea 1Ye s No Quasi 45
Fourth-year
nursing
student
(E: 24, C: 21)
Virtual reality
simulation
(VRS)
Researcher None
reported
None
reported
35 min Immediately - Clinical
performance
competency
- Problem-
solving skill
- Condence in
performance
Yes Yes None 8
12 Song
(22)
Korea 1No No Quasi 117
ird-year
nursing
student
(E: 58, C: 59)
Virtual reality
simulation
(VRS)
Nursing
faculty
10 days 10 sessions 8 h/day Immediately -Competencies
of socio-
emotion
- Psychiatric
nursing
competency
- Learning
self-ecacy
- Transition
synchronization
-Social distance
None None Yes 7
13 Raman etal.
(23)
Oman 1Ye s Yes Quasi 74
Fourth-year
nursing
student
(E: 34, C: 40)
Virtual reality
simulation
(VRS)
Nursing
faculty
34 h of
HFS + 101 h of
TCT
None
reported
None
reported
Immediately -Clinical
competencies
-Knowledge
levels among
nursing students
Yes Yes None 8
14 Cho etal.
(24)
Korea 1Ye s Yes Q uasi 69
Senior nursing
students
(E: 36, C: 33)
Metaverse-
based
simulation
Researcher 1 day None
reported
1 h Immediately -Competency
-Self-ecacy
-Learning
realism
-Learning
satisfaction
Yes Yes Yes 9
15 Lee and
Baek (25)
Korea 1Ye s No Quasi 44
ird-year
nursing
students
(E: 22, C: 22)
Virtual reality
simulation
(VRS)
Researcher 2 weeks None
reported
2 h of
VRS + 4 h of
HFS
Immediately -Performance
condence
-Clinical
decision-
making ability
Yes Yes None 9
(Continued)
TABLE2 (Continued)

Cho and Kim 10.3389/fmed.2024.1351300
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TABLE2 (Continued)
Study
ID
Author
(Year) Country Center IRB Fund Research
design Participants Intervention
type
Program
facilitator
Intervention
duration
Intervention
session
Intervention
time/
session
Outcome
measurement
time
outcome
variable
Pre-
briefing Debriefing
Non-
simulation
activities
Quality
score
16 Kim and
Heo (26)
Korea 2Ye s Ye s Quasi 63
Sophomore
nursing
students
(E: 33, C:
30)
Augmented
reality
Researcher 2 weeks 2 sessions 2 h Immediately -Learning
satisfaction
-Skill
competency
-Condence
in medication
safety
Ye s None None 8
17 Park and
Yo on (27)
Korea 1Ye s No Quasi 44
Second-year
students
(E: 22, C:
22)
Virtual
reality
simulation
(VRS)
Researcher 3 weeks 3 sessions 30 min Immediately -Nursing
skills
-Performance
condence
-Learning
satisfaction
Ye s None None 9
18 Sahin
Karaduman
and Basak
(28)
Tur k e y 1Ye s No RCT 126
ird-year
nursing
students
(E1: 42, E2:
42, C: 42)
Virtual
patient
simulations
Researcher None
reported
2 sessions 15 min Immediately -Nursing
anxiety
-Self-
condence
-Learning
evaluation
-Performance
Ye s Ye s None 10
19 Moon (29)Korea 1Ye s Ye s Quasi 72
ird-year
nursing
students
(E: 34, C:
38)
Metaverse
based
program
Nursing
faculty
1 day 1 session 3 h Immediately -Clinical
competency
-Problem
solving
ecacy
-Learning
satisfaction
Ye s No Ye s 8
20 Lee (30)Korea 1Ye s No Quasi 48
Senior
nursing
students
(E: 24, C:
24)
Virtual
reality
simulation
(VRS)
Nursing
faculty
None
reported
None
reported
3 h Immediately -Critical
thinking
disposition
-Clinical
competency
-Self-ecacy
Ye s Ye s No 8
(Continued)

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 09 frontiersin.org
Heterogeneity was evaluated using Higgin’s I
2
(34), with an I
2
of >50%
indicating heterogeneity (35). Subgroup analysis, meta-regression,
and exclusion-sensitivity analysis were conducted for nursing
competency to identify factors contributing to heterogeneity.
Publication bias was examined using funnel plots, trim-and-ll plots,
Begg’s test, Egger’s regression, and the trim-and-ll method to correct
for the overall eect (36).
3 Results
3.1 Characteristics of the included studies
A total of 579 articles were initially identied from 9 databases
following the search strategy. Aer excluding duplicates, 373 articles
were extracted. Following the application of the inclusion and
exclusion criteria, 21 research articles were ultimately selected. e
research by Sahin Karaduman and Basak (28) was designed using two
experimental groups and was analyzed as two separate studies,
resulting in 22 studies being analyzed (Figure1). Of these, six studies
were published before 2022; three were conducted in the UnitedStates
(USA), twelve studies were published in English; nineteen were
conducted at a single university; nineteen and nine studies had IRB
approval and funding, respectively. e study designs included seven
RCTs, een quasi-experimental studies, and eight studies with fewer
than 60 participants. Interventions comprised 18 VR/AR simulations
and four metaverse. Eight studies had a professor as a facilitator, four
had an intervention duration of more than 4 weeks, two had four or
more intervention sessions, eight had an intervention time of more
than 1 h per session, 19 had a pre-brieng, and nine had a debrieng.
Dependent-variable measurements were taken immediately aer the
intervention in 20 studies, the measurement method was observational
measurement in 12 studies, 19 studies had no additional activities,
such as reection, besides the simulation, and 14 studies had an
above-average quality assessment score (Table2).
3.2 Eect of VRS-based intervention on
nursing competency
e overall eect of nursing competency, as the primary
outcome for the 22 VRSs, was found to beHedge’s g = 0.88 (95% CI:
0.47 to 1.29). is was interpreted as a large eect based on the
criteria provided by Brydges (37) for interpreting eect sizes
(Figure2). e high degree of heterogeneity among the studies,
indicated by Higgins’s I
2
of 91.8% in the heterogeneity test, prompted
subgroup and meta-regression analyses to explore factors
contributing to this heterogeneity.
In subgroup analyses, the characteristics of studies signicantly
associated with improvements in nursing competency IRB-approved
studies (Hedge’s g= 1.02, 95% CI: 0.57, 1.48); interventions with a
duration not reported or those with a duration of less than 4 weeks
(Hedge’s g= 1.05, 95% CI: 0.56, 1.53); interventions with sessions not
reported or those with less than 4 sessions (Hedge’s g= 0.95, 95% CI:
0.50, 1.39); those with outcome measurement immediately aer the
intervention (Hedge’s g = 0.93, 95% CI: 0.50, 1.37); those with
pre-brieng before the simulation (Hedge’s g= 0.71, 95% CI: 0.23,
1.20); those with debrieng aer the simulation (Hedge’s g= 1.02, 95%
Study
ID
Author
(Year) Country Center IRB Fund Research
design Participants Intervention
type
Program
facilitator
Intervention
duration
Intervention
session
Intervention
time/
session
Outcome
measurement
time
outcome
variable
Pre-
briefing Debriefing
Non-
simulation
activities
Quality
score
21 Ahn (31)Korea 1Ye s No Quasi 70
Nursing
students
(E: 34, C:
36)
Metaverse
based
training
Nursing
faculty
1 day 1 session 10–15 min Immediately -Performance
condence
-Performance
ability
Ye s Ye s No 7
IRB, Institutional Review Board; USA, UnitedStates of America; E, experimental group; C, control group; RCT, randomized controlled trial; Quasi, quasi-experimental study; HFS, high delity stimulation. TCT, traditional clinical training; OSCE, objective structured
clinical examination; OR, operating room.
TABLE2 (Continued)

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 10 frontiersin.org
CI: 0.57, 1.48); and those with no other activities besides the
simulation, such as keeping a reective journal (Hedge’s g= 1.03, 95%
CI: 0.55, 1.50). Publication year, Country, publication language,
number of schools, funding status, research design, number of
participants, intervention type, facilitator, intervention time per
session, measurement method, debrieng, and quality assessment
score also showed statistically signicant eect sizes (Table4).
Univariate meta-regression identied factors inuencing the
overall eect. Publication year aer 2022 (Z= 2.68, p= 0.007); having
an IRB (Z = 5.17, p < 0.001); having an fund (Z= −2.61, p = 0.009);
RCT (Z= 2.02, p = 0.044); intervention duration over than 4 weeks
(Z = −3.33, p < 0.001); intervention session over than 4 sessions
(Z = −3.01, p < 0.001); intervention time/session over than 1 h
(Z = −5.20, p < 0.001); observational measurement rather than self-
reporting (Z = 3.21, p = 0.001); having a pre-brieng before the
simulation (Z = 3.76, p < 0.001); having a debrieng aer the
simulation (Z=−4.41, p< 0.001); and having other activities besides
the simulation (Z=−4.41, p< 0.001) had statistically signicant eects
on nursing competency (Table5).
e exclusion-sensitivity test (38), excluding one study at a time,
showed Hedge’s g ranging from 0.67 to 0.94, indicating a moderate to
large eect size. e 95% CI (0.36 ~ 0.53, 0.98 ~ 1.36) did not include
0, signifying statistical signicance. e eect sizes from the exclusion-
sensitivity test were not signicantly dierent from Hedge’s g = 0.88,
which included all 22 studies (Table6).
3.3 Eect of intervention program on
secondary outcomes
e secondary outcomes in this study included knowledge, self-
ecacy, problem-solving, condence, and satisfaction. Among these,
knowledge, self-ecacy, condence, and satisfaction exhibited
statistically signicant changes. Aer VRS, knowledge and self-
ecacy showed signicant increases, with moderate eect sizes of
Hedge’s g = 0.60 (95% CI: 0.07, 1.14) and Hedge’s g= 0.53 (95% CI:
0.09, 0.97), respectively. Condence and satisfaction exhibited
substantial increases, with large eect sizes of Hedge’s g = 1.02 (95%
TABLE3 Quality assessment of the included studies.
Study
ID
Joanna Briggs Institute of Critical Appraisal Tools Checklist for checklist for randomized controlled
trials Total
score
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13
3 0 0 0 0 0 1 0 1 0 1 1 0 0 4
4 0 0 1 0 1 1 1 1 1 1 1 0 0 8
5 1 0 1 0 1 1 1 1 1 1 1 1 1 11
8 1 0 1 0 0 1 0 1 1 1 1 1 1 9
9 1 0 1 0 0 1 1 1 1 1 1 1 1 10
18 1 0 1 0 1 0 1 1 1 1 1 1 1 10
Tot a l 4 0 5 0 3 5 4 6 5 6 6 4 4 8.67
Study
ID
Joanna Briggs Institute of Critical Appraisal Tools Checklist for quasi-experimental study Total
score
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9
1 1111111119
2 1111111119
6 1111101107
7 1111111119
10 1 1 1 1 1 0 1 1 1 8
11 1 1 1 1 1 1 1 1 1 9
12 1 1 1 0 1 1 1 1 1 8
13 1 1 1 1 1 1 1 1 1 9
14 1 1 1 1 1 1 1 1 1 9
15 1 1 1 1 1 1 1 1 1 9
16 1 1 1 1 1 0 1 1 1 8
17 1 1 1 1 1 1 1 1 1 9
19 1 1 1 1 0 1 1 1 1 8
20 1 1 1 1 0 1 1 1 1 8
21 1 0 1 1 0 1 1 1 1 7
Tot a l 15 14 15 14 12 12 15 15 14 8.40

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CI: 0.48, 1.57) and Hedge’s g= 1.35 (95% CI: 0.43, 2.28), respectively
(Table7).
3.4 Publication bias
To evaluate publication bias, funnel-plot and trim-and-ll plot
analyses were conducted. Represented by the black circle, the
individual eect sizes of the 22 studies included in the study were
asymmetrical— slightly skewed to the right—indicating some
potential publication bias (Figure 3). e trim-and-ll plot
suggested the addition of one study, represented by the white circle,
skewing to the le (Figure4). Further analysis, e coecient of the
bias was 8.58, indicating the initial value (intercept) and the p-value
was 0.001. us, the null hypothesis was rejected, and the existence
of a publication bias could beconrmed. Unlike Egger’s regression
test result, Begg’s test for rank correlation (Tau b = 0.27, ties = 0;
Z = 1.75, p = 0.080) conrmed the absence of publication bias.
Moreover, the trim-and-ll method suggested adding one article;
the eect size of the 23 corrected articles was 0.60 (95% CI: 0.49,
0.72). Although the eect size of nursing competency was somewhat
smaller aer correction than before, it remained statistically
signicant. In conclusion, this study was deemed free of publication
bias (Table8).
4 Discussion
In this study, the impact of simulation-based programs on nursing
competency demonstrated a signicant eect size of 0.88. It’s notable
that this simulation-based program yielded encouraging results by
positively enhancing nursing competency. is is consistent with
similar improvements observed in self-ecacy, a factor linked to
nursing competency (19), enhanced knowledge, educational
Articles found (n=579)
PubMed (n=132)
Cochrane (n=27)
EMBASE-OVID (n=73)
CINAHL (n=146)
WoS (n=40)
SCOPUS (n=76)
PQDT (n=4)
APA PsycArticles (n=0)
RISS (n=81)
Duplication of titles (n=206)
Potentially relevant
articles
(n=373)
Non-eligible articles (n=326)
completely irrelevant (n=10)
inappropriate research design (n=162)
unsuitable target population (n=41)
competency has not measured (n=5)
competency is not dependent variable (n=89)
unsuitable intervention (n=5)
single group intervention (n= 5)
Others (n=9)
Full articles reviewed for
more information
(n=47)
Identification
Screening
Eligibility
Included
Articles included in
meta-analysis
(n=21)
Studies included in
meta-analysis
(n=22)
Non-eligible articles (n=26)
inappropriate research design (n=4)
unsuitable target population (n=2)
competency has not measured (n=6)
unsuitable intervention (n=3)
single group intervention (n=5)
lack of statistical data (n=2)
unavailable full articles (n=3)
Others (n=1)
FIGURE1
PRISMA flow diagram. An article by Sahin Karaduman and Basak (28), designed using two experimental groups, was divided into two studies.

Cho and Kim 10.3389/fmed.2024.1351300
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satisfaction, and academic achievement through VR in a hospital
environment (39); and improved nursing-process performance (40),
heightened critical thinking, clinical performance, and practice
satisfaction through vSim for Nursing (41). Additionally, these results
partially correlate with those in a study indicating that hands-on
training utilizing scenario-based admission management in VR
increased learning immersion, learner condence, and learning
satisfaction (7).
In the meta-regression analysis evaluating nursing competency,
several factors emerged as inuential. First, in cases where the
publication year was 2022 or later, nursing competency was found to
be signicantly improved compared to studies that received IRB
approval, compared to studies published before then. In the evolving
landscape of clinical practice, recent emphasis on patient safety and
rights has shied the focus toward observing nursing behavior rather
than direct patient care (42). is shi underscores the active
implementation of simulation-based education, suggesting a more
systematic adaptation of teaching methods and educational systems
to enhance nursing competency compared to previous approaches.
Moreover, studies with an intervention duration not reported or one
of less than 4 weeks demonstrated a signicant eect on nursing
competency compared to those lasting more than 4 weeks. In cases of
intervention with fewer than four sessions, competency was
signicantly improved compared to intervention sessions with four or
more sessions. Similarly, interventions with time per session not
reported or those lasting less than 1 h were associated with a signicant
improvement in nursing competency compared to those lasting more
than 1 h. ese ndings suggest that shorter, more intensive
Id NES
95% CI
Zpw
Hedge’s g
Lower
limit
Upper
limit
Random effect model, 95%CI
140 0.48 -0.15 1.11 1.49 0.1364.6%
284 0.90 0.45 1.35 3.91 <0.001 4.9%
320 0.29 -0.73 1.30 0.55 0.5823.9%
469 0.64 0.16 1.13 2.60 0.0094.8%
562 -0.44-0.95 0.06 -1.72 0.0864.8%
642 0.72 0.10 1.35 2.26 0.0244.6%
772 0.79 0.31 1.27 3.22 0.0014.8%
873 2.79 2.15 3.44 8.43 <0.001 4.6%
970 -0.28-0.75 0.19 -1.16 0.2484.8%
10 48 0.86 0.27 1.45 2.85 0.0044.7%
11 45 0.76 0.15 1.37 2.46 0.0144.6%
12 117-0.07-0.44 0.29 -0.40 0.6925.0%
13 74 0.08 -0.38 0.54 0.34 0.7374.9%
14 69 0.18 -0.29 0.65 0.75 0.4544.8%
15 44 0.15 -0.44 0.74 0.49 0.6264.7%
16 63 19.35 15.88 22.82 10.93 <0.001 1.1%
17 44 0.47 -0.13 1.07 1.55 0.1224.7%
18a84 2.02 1.49 2.55 7.50 <0.001 4.8%
18b84 1.16 0.70 1.63 4.93 <0.001 4.8%
19 72 0.43 -0.04 0.90 1.80 0.0724.8%
20 48 0.99 0.39 1.59 3.23 0.0014.7%
21 70 1.36 0.84 1.88 5.10 <0.001 4.8%
Total 1352*0.88 0.47 1.29 4.23 <0.001 100% Heterogeneity: Q = 257.11, Q-df = 234.11 (p< 0.001);
I2=91.8% (95% CI: 89.0~94.0%)
Overall effect: Z = 4.23 (p<0.001)
-100 10 20 30
Hedge's g
FIGURE2
The eect of virtual reality simulation-based intervention on nursing competency. ES, eect size; CI, confidence interval. An article by Sahin
Karaduman and Basak (29), designed using two experimental groups, was divided into two studies (18a and 18b). *Removal of the number of duplicate
subjects in the 18th study.

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TABLE4 Subgroup analysis of nursing competency according to study characteristics.
Variables Category KStudy ID NES
95% CI
Z p-value
Lower
limit
Upper
limit
Ye a r 2018 ~ 2021 6 2, 3, 4, 10, 12, 13 412 0.44 0.06 0.82 2.29 0.022
≥2022 16 1, 5, 6, 7, 8, 9, 11, 14, 15, 16, 17, 18a, 18b, 19, 20, 21 940 1.12 0.56 1.68 3.93 <0.001
Countr y Beyond the USA 19 1, 2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18a,18b, 19, 20, 21 1,219 0.96 0.50 1.43 4.08 <0.001
USA 3 3, 4, 17 133 0.54 0.19 0.89 3.00 0.003
Language Korean 10 2, 7, 8, 9, 10, 11, 12, 16, 19, 21 714 1.49 0.70 2.28 3.68 <0.001
English 12 1, 3, 4, 5, 6, 13, 14, 15, 17, 18a, 18b, 20 638 0.57 0.18 0.95 2.91 0.004
School 1 19 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18a, 18b, 19, 20, 21 1,143 0.72 0.40 1.05 4.36 <0.001
2 3 2, 5, 16 209 5.23 1.98 8.48 3.15 0.002
IRB No 3 6, 9, 12 229 0.08 −0.42 0.58 0.30 0.764
Ye s 19 1, 2, 3, 4, 5, 7, 8, 10, 11, 13, 14, 15, 16, 17, 18a, 18b, 19, 20, 21 1,123 1.02 0.57 1.48 4.39 <0.001
Fund No 13 2, 5, 7, 8, 9, 10, 11, 12, 17, 18a, 18b, 20, 21 859 0.86 0.39 1.32 3.61 <0.001
Ye s 9 1, 3, 4, 6, 13, 14, 15, 16, 19 493 1.07 0.28 1.87 2.66 0.008
Research design Quasi-E 15 1, 2, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21 932 0.85 0.38 1.31 3.59 <0.001
RCT 7 3, 4, 5, 8, 9, 18a,18b420 0.89 0.03 1.75 2.02 0.044
Participants < 60 8 1, 3, 6, 10, 11, 15, 17, 20 331 0.62 0.39 0.84 5.40 <0.001
≥ 60 14 2, 4, 5, 7, 8, 9, 12, 13, 14, 16, 18a, 18b, 19, 21 1,021 1.13 0.53 1.72 3.71 <0.001
Intervention type Metaverse 4 7, 14 19, 21 283 0.68 0.20 1.17 2.75 0.006
AR/VR 18 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18a, 18b, 20 1,069 0.97 0.47 1.47 3.78 <0.001
Facilitator Researcher 14 1, 3, 4, 5, 8, 9, 10, 11, 14, 15, 16, 17, 18a, 18b773 1.16 0.50 1.82 3.46 0.001
Nursing faculty 8 2, 6, 7, 12, 13, 19, 20, 21 579 0.63 0.27 0.98 3.45 0.001
Intervention duration Not reported or < 4 weeks 18 1, 2, 3, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18a,18b, 19, 20, 21 1,099 1.05 0.56 1.53 4.24 <0.001
≥ 4 weeks 4 4, 5, 10, 13 253 0.27 −0.28 0.83 0.97 0.333
Intervention session Not reported or < 4sessions 20 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18a,18b, 19, 20, 21 1,187 0.95 0.50 1.39 4.17 <0.001
≥ 4sessions 2 10, 12 165 0.36 −0.55 1.28 0.78 0.436
Intervention time/session Not reported or < 1 h 14 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 17, 18a, 18b, 21 829 0.89 0.49 1.29 4.34 <0.001
≥ 1 h 8 1, 9, 12, 14, 15, 16, 19, 20 523 1.08 0.22 1.93 2.47 0.013
Outcome measurement time Immediately 20 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18a, 18b, 19, 20, 21 1,292 0.93 0.50 1.37 4.19 <0.001
Delayed 2 1, 3 60 0.43 −0.11 0.96 1.56 0.119
Measurement method Self-report 10 2, 5, 7, 10, 12, 14, 15, 19, 20, 21 686 0.50 0.16 0.85 2.84 0.005
(Continued)

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interventions may bemore eective in enhancing nursing competency
through VRS. Establishing short-term intensive courses could thus
bea meaningful approach. Even in the case of pre-briengs, which are
recognized for their utility, the introduction and assignment of roles
and expectations during pre-briengs may not be optimal. is is
because simulation anxiety is linked to higher levels of extraneous
cognitive load (43). Further investigation into the timing and temporal
aspects of these activities is warranted to optimize their eectiveness.
erefore, further research specically focusing on the temporal
aspect is deemed necessary to comprehensively understand
its implications.
Furthermore, pre-brieng before simulation emerged as a
signicant factor contributing to the improvement of nursing
competency compared to that in the control group. is is consistent
with the recognized importance of pre-brieng in face-to-face
simulations, in which it inuences simulation readiness (44). Given
that most included studies conducted virtual pre-brieng activities
individually, such as pre-brieng lessons and quizzes, the ndings
imply that virtual pre-brieng can beactively utilized with comparable
eectiveness in face-to-face simulations. Various pre-brieng
methods, including role rubrics, are currently under development
(45). Further research will benecessary to ascertain the eectiveness
of these diverse pre-brieng approaches.
Moreover, this study identied that post-simulation debrieng
had a more signicant eect of improving nursing competency
compared to non-simulation debrieng. is could beattributed to
the characteristic of VRS that enables repeated and reective learning
through debrieng with immediate feedback, thus providing learner-
customized learning (46). e ability to facilitate individual
improvement in nursing competency through immediate feedback is
consistent with previous studies emphasizing the eectiveness and
importance of debrieng in simulation (47). While debrieng in a
virtual setting may dier from team interaction, reection, and
discussion in a face-to-face simulation, the results underscore the
crucial role of debrieng in VRS situations.
Competency improved signicantly when observation was
measured rather than self-report. Role assignment in nursing
simulation oen elicits signicant anxiety stemming from uncertainty,
performing in front of faculty and peers, and social evaluation (45).
Moreover, many individuals perceive themselves as lacking
prociency, particularly in terms of nursing competency.
Consequently, self-reported improvements in nursing competency
may underestimate actual progress observed through objective
evaluation. Hence, eective communication and encouragement
regarding the signicance of simulation are vital when implementing
simulation programs.
Nursing competency was statistically signicantly improved when
compared to those who did not engage in any other activities other
than simulation. Other activities take as much time, which suggests
that core simulation activities are important for improving nursing
competency. Non-simulation activities, denoting the absence of
activities other than simulation, exhibited a signicant eect on
nursing competency. While non-simulation activities may improve
competencies such as team cooperation, communication, or empathy,
they were not associated with improvements in nursing competency.
is suggests that clear simulation content, along with pre-brieng
and debrieng activities tailored to enhance nursing competency,
directly inuence this competency.
TABLE4 (Continued)
Variables Category KStudy ID NES
95% CI
Z p-value
Lower
limit
Upper
limit
Observation 12 1, 3, 4, 6, 8, 9, 11, 13, 16, 17, 18a, 18b666 1.41 0.66 2.15 3.69 <0.001
Pre-brieng No 3 6, 9, 12 229 0.08 −0.42 0.58 0.30 0.764
Ye s 19 1, 2, 3, 4, 5, 7, 8, 10, 11, 13, 14, 15, 16, 17, 18a, 18b, 19, 20, 21 1,123 1.02 0.57 1.48 4.39 <0.001
Debrieng No 9 3, 5, 6, 8, 9, 12, 16, 17, 19 563 1.39 0.42 2.35 2.81 0.005
Ye s 13 1, 2, 4, 7, 10, 11, 13, 14, 15, 18a, 18b, 20, 21 789 0.80 0.50 1.09 5.29 <0.001
Other activities No 19 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18a, 18b, 20, 21 1,094 1.03 0.55 1.50 4.25 <0.001
Ye s 3 12, 14, 19 258 0.15 −0.15 0.44 0.98 0.326
Quality score < Mean 8 3, 6, 10, 12, 16, 19, 20, 21 480 1.58 0.63 2.54 3.25 0.001
≥ Mean 14 1, 2, 4, 5, 7, 8, 9, 11, 13, 14, 15, 17, 18a, 18b872 0.68 0.26 1.11 3.19 0.001
K, number of analysis sets; N, number of participants; ES, eect size; CI, condence inter val; IRB, institutional review board; Quasi-E, quasi-experimental study; RCT, randomized controlled trial; AR/VR, augmented reality/virtual reality.

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TABLE5 Meta-regression analysis to evaluate competency.
Covariates (Ref.) Estimate SE 95% CI Z p-value
Lower limit Upper limit
Ye ar (Ref.: 2018 ~ 2021) 0.10 0.04 0.03 0.18 2.68 0.007
Country (Ref.: Beyond USA) −0.09 0.19 −0.46 0.28 −0.48 0.631
Language (Ref.: Korean) −0.12 0.12 −0.35 0.11 −1.03 0.303
School (Ref.: 1) −0.16 0.18 −0.52 0.19 −0.89 0.376
IRB (Ref.: No) 0.76 0.15 0.47 1.05 5.17 < 0.001
Fund (Ref.: No) −0.32 0.12 −0.56 −0.08 −2.61 0.009
Research design (Ref.: Quasi-E) 0.25 0.12 0.01 0. 50 2.02 0.044
Participants (Ref.: < 60) 0.01 0.13 −0.25 0.27 0.07 0.945
Intervention type (Ref.: Metaverse) −0.04 0.14 −0.32 0.23 −0.31 0.756
Facilitator (Ref.: Researcher) −0.14 0.12 −0.37 0.09 −1.21 0.225
Intervention duration (Ref.: Not reported or <4 weeks) −0.48 0.14 −0.76 −0.20 −3.33 < 0.001
Intervention session (Ref.: Not reported or <4sessions) −0.51 0.17 −0.84 −0.18 −3.01 < 0.001
Intervention time/session (Ref.: Not reported or <1 h) −0.62 0.12 −0.85 −0.39 −5.20 < 0.001
Outcome measurement time (Ref.: Immediately) −0.21 0.28 −0.75 0.34 −0.74 0.460
Measurement method (Ref.: Self-report) 0.37 0.12 0.15 0.60 3.21 0.001
Pre-brieng (Ref.: No) 0.76 0.15 0.47 1.05 5.17 < 0.001
Debrieng (Ref.: No) 0.45 0.12 0.21 0.68 3.76 < 0.001
Other activities (Ref.: No) −0.62 0.14 −0.90 −0.35 −4.41 < 0.001
Quality score (Ref.: < Mean) 0.02 0.12 −0.23 0.26 0.14 0.890
Ref, reference; SE, standard error; CI, condence interval; IRB: institutional review board; Quasi-E, quasi-experimental study.
TABLE6 Exclusion-sensitivity test of the virtual-reality simulation-based intervention.
Study ID KES 95% CI Z p-value
Lower limit Upper limit
1 21 0.91 0.48 1.33 4.18 <0.001
2 21 0.89 0.46 1.32 4.05 <0.001
3 21 0.91 0.49 1.32 4.25 <0.001
4 21 0.90 0.47 1.33 4.11 <0.001
5 21 0.94 0.53 1.36 4.47 <0.001
6 21 0.89 0.47 1.32 4.13 <0.001
7 21 0.89 0.46 1.32 4.08 <0.001
8 21 0.76 0.38 1.15 3.88 <0.001
9 21 0.94 0.52 1.36 4.40 <0.001
10 21 0.89 0.46 1.31 4.09 <0.001
11 21 0.89 0.47 1.32 4.11 <0.001
12 21 0.94 0.51 1.36 4.31 <0.001
13 21 0.93 0.50 1.35 4.27 <0.001
14 21 0.92 0.50 1.35 4.23 <0.001
15 21 0.92 0.50 1.34 4.26 <0.001
16 21 0.67 0.36 0.98 4.27 <0.001
17 21 0.91 0.48 1.33 4.18 <0.001
18 21 0.81 0.41 1.22 3.94 <0.001
19 21 0.87 0.45 1.30 4.01 <0.001
20 21 0.91 0.48 1.34 4.16 <0.001
21 21 0.88 0.46 1.30 4.07 <0.001
22 21 0.86 0.44 1.28 4.00 <0.001
K, number of analysis sets; ES, eect size; CI, condence interval.

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 16 frontiersin.org
Meanwhile, several variables did not demonstrate a statistically
signicant eect of improving nursing competency. e country,
number of centers, funding status, research design, and all the
variables related to the operation of the intervention program
(participants, intervention type, facilitator, intervention session, and
outcome-measurement time), as well as the quality score, did not
show signicant dierences in improving nursing competency. e
inconsistency in trends observed across these variables can
beattributed to the diverse denitions and measurements of nursing
competency utilized in the included studies. is variability in
research outcomes underscores the absence of a standardized
measurement tool for nursing competency, potentially leading to
increased heterogeneity in results.
Furthermore, the secondary outcomes measured alongside
nursing competency in this study included knowledge, self-ecacy,
problem-solving, condence, and satisfaction. Among these,
knowledge and condence demonstrated statistically signicant
improvement. ese variables, particularly knowledge and condence,
are closely related to nursing competency and can concurrently
improve with it in VRS. Conversely, self-ecacy, problem-solving, and
satisfaction did not show signicant improvement. is is consistent
with previous research indicating that VR nursing education improves
knowledge (48) and increases learning satisfaction (49) but does not
enhance technical skills (48) or signicantly impact self-ecacy (49).
is suggests that while VRS is eective in improving knowledge-
related competencies, consistent improvements in self-ecacy,
problem-solving, and satisfaction may depend on its design
and utilization.
Given that learning immersion through simulation has been
demonstrated to impact the development of clinical-nursing
competence (50), and VR-based programs have been eective in
improving cognitive performance, such as theoretical knowledge,
through realism (51), VRS holds promise in nursing education.
However, the results of this study underscore the need to carefully
consider elements that are more challenging to implement in virtual
situations than in face-to-face scenarios. erefore, further research,
TABLE7 Eects of virtual reality simulation-based intervention on other variables.
Variables KStudy ID NES
95% CI
Z p-value
Lower
limit
Upper
limit
Knowledge 6 1, 2, 5, 6, 7, 13 374 0.60 0.07 1.14 2.22 0.027
Self-ecacy 7 2, 8, 9, 12, 14,19, 20 533 0.53 0.09 0.97 2.34 0.019
Problem-solving 3 6, 10, 11 135 0.99 0.00 1.98 1.95 0.051
Condence 13 1, 2, 5, 7, 9, 10, 11, 15, 16, 17, 18a, 18b, 21 768 1.02 0.48 1.57 3.66 <0.001
Satisfaction 7 6, 8, 9, 14, 16, 17, 19 433 1.35 0.43 2.28 2.86 0.004
K, number of analysis sets; N, number of participants; ES, eect size; CI, condence inter val.
0
1
2
3
4
5
6
-10 -5 0510 15 20
25
precision
Hedge's g
0.05 limit 2
0.1 limit
0.1 limit 2
Synthesis estimate
FIGURE3
Funnel plot of virtual reality simulation-based intervention on nursing competency. Precision = 1/standard error; 0.05; limit line = 95% confidence limit.

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 17 frontiersin.org
such as systematic reviews and meta-analyses exploring other
variables in VRS, is recommended for a more comprehensive
understanding of its impact on nursing education.
VR-based nursing education represents an innovative eld that
has not been previously explored. ese simulators oer a range of
environments that transcend physical constraints, enabling
participants to immerse themselves within the virtual space (52). It’s
crucial for educators responsible for program development to grasp
the distinctions between virtual reality and reality to facilitate
eective education.
is study underscores the signicance of pre-brieng and
debrieng elements in VR-based simulation, highlighting the
importance of their organization. Rather than focusing solely on
operational time or the duration of the simulation itself, the key lies
in how these elements are implemented for optimal educational
outcomes. Additionally, when assessing eectiveness, weadvocate for
a combined approach utilizing both self-reported evaluations and
objective evaluations through observation or assessment.
4.1 Limitations of the study
is study acknowledges several limitations. First, there is
variability in reporting randomization methods among the included
studies, with some providing comprehensive discussions on the
topic while others lack detailed information on the methods
employed. Second, the diverse interpretations and denitions of
nursing competency across the included studies may introduce
variability in the study outcomes. ird, the absence of a
standardized measurement tool for nursing competency could
contribute to increased heterogeneity. Fourth, the selection criteria
for the studies analyzed, which included only those published in
English or Korean and reported precise means, standard deviations,
and sample sizes, could lead to selection bias and limit the
generalization of our study results. Additionally, the studies
encompass sample sizes from dierent countries, further
contributing to overall heterogeneity. To enhance the robustness of
future research and validate the eectiveness of interventions for
nursing students, larger sample sizes and higher-quality studies
are recommended.
5 Conclusion
e meta-analysis of nursing competency in VRS revealed the
latter’s eectiveness in enhancing nursing competency. Notably, the
0
1
2
3
4
5
6
-25 -20 -15 -10 -5 0510 15 20
25
precision
Hedge's g
0.05 limit 2
0.1 limit
0.1 limit 2
Filled synthesis estimate
FIGURE4
Trim and fill plot of virtual reality simulation-based intervention on nursing competency. Precision = 1/standard error; 0.05; limit line = 95% confidence
limit.
TABLE8 Publication bias test of virtual reality simulation-based
intervention on competency.
Begg’s
test Tau b K
S
(P-
Q)
Ties Zp-
value
Standard 0.27 22 63 0 1.78 0.076
Corrected 0.27 22 63 0 1.75 0.080
Egger’s
regression
test
Coecient SE
95% CI
Z P-value
Lower
limit
Upper
limit
Intercept 8.58 2.30 4.08 13.08 3.74 0.001
Slope −1.63 0.62 −2.85 −0.41 −2.62 0.009
Trim and
ll method KES
95% CI
Z P-value
Lower
limit
Upper
limit
Original 22 0.88 0.47 1.29 4.23 <0.001
Corrected 23 0.60 0.49 0.72 10.28 <0.001
Begg’s test for rank correlation; Egger’s regression test for zero intercepts; SE, standard error;
CI, condence interval; K, number of analysis sets; ES, eect size.

Cho and Kim 10.3389/fmed.2024.1351300
Frontiers in Medicine 18 frontiersin.org
incorporation of key elements from face-to-face simulation, such as
pre-brieng and debrieng, signicantly improved nursing
competency compared to scenarios in which these elements were
absent. is study suggests the importance of reecting core
simulation elements in virtual simulations and underscores the need
to enhance the quality of pre-brieng and debrieng in virtual
contexts. Moreover, the ndings suggest that intensively operating
VRS over a short period could bemore eective in improving nursing
competency. is implies the signicance of considering the
eectiveness of short-term intensive courses for nursing-competency
improvement within virtual spaces. e study ndings provide
valuable insights for the design of VRS aimed at enhancing
nursing competency.
Data availability statement
e original contributions presented in the study are included in
the article/supplementary material, further inquiries can bedirected
to the corresponding authors.
Author contributions
M-KC: Conceptualization, Data curation, Formal analysis,
Methodology, Visualization, Writing – original dra, Writing – review
& editing. MK: Conceptualization, Data curation, Funding acquisition,
Project administration, Resources, Supervision, Validation, Writing
– original dra, Writing – review & editing.
Funding
e author(s) declare nancial support was received for the
research, authorship, and/or publication of this article. is study was
supported by the National Research Foundation of Korea grant
funded by the South Korea Government (MSIT; no.
2022R1F1A1076248).
Acknowledgments
e authors sincerely thank those who participated in this study.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed
by the publisher.
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