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ARTICLE IN PRESS
JID: ECSN [mECSN; September 27, 2022;16:24 ]
Clinical Simulation in Nursing (2022) 000 , 1- 8
Simulation Anxiety and its Effect on Clinical
Judgment for Undergraduate Nursing Students
Janet M. Reed, PhD, RN, CMSRN∗
Kent State University, 6000 Frank Ave, North Canton, OH, 44720
KEYWORDS
Simulation ;
Anxiety ;
Simulation Anxiety ;
Clinical Judgment
Abstract
Background: High anxiety during simulation has been well documented with calls to reduce students’
anxiety. Simulation anxiety is often assumed to be harmful to students and a variety of anxiety-reducing
interventions have been suggested. The purpose of this study was to explore the effect of different types
of anxiety on the clinical judgment of undergraduate nursing students in simulation.
Methods: This research used a one-group repeated measures quantitative design using the conceptual
framework of Tanner’s (2006) model of clinical judgment.
Results: Anxiety did not have a signicant impact on clinical judgment, both overall and within each
of the four phases of Tanner’s (2006) model.
Conclusion: The ndings imply a changed focus to reframe anxiety and how we think about its effects.
Understanding that not all anxiety is debilitating but some is facilitative challenges the assumption
that faculty need to attempt to lower students’ anxiety in simulation. Rather than seeking to lower
anxiety for all students, nursing educators should help students function despite anxiety, in order to
prepare them for real world nursing practice.
Cite this article:
Reed, J.M. (2022, Month). Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate
Nursing Students. Clinical Simulation in Nursing , 000, 1-8. https://doi.org/10.1016/j.ecns.2022.08.
005 .
© 2022 International Nursing Association for Clinical Simulation and Learning. Published by
Elsevier Inc. All rights reserved.
Introduction/Background
Research has demonstrated the effectiveness of simulation
in nursing education to improve clinical judgment ( Klenke-
Borgmann, Cantrell, & Mariani, 2020 ; Lawrence, Mes-
sias, & Cason, 2018 ). Even before the COVID-19 pan-
demic, simulation was increasingly being used in nursing
education to replace clinical. Both face-to-face and vir-
tual simulations provide an adjunct to clinical learning in
which students can have standardized practice in devel-
oping clinical judgment (CJ) in a safe environment with-
∗Corresponding author: Jreed56@kent.edu .
out risk of patient harm ( Fogg, Kubin, Wilson, & Trinka,
2020 ). The value of simulation has been extensively re-
searched over the past two decades, showing its ability to
increase competency, teamwork, safety, caring, and self-
confidence ( Hayden, Smiley, Alexander, Edgren, & Jef-
fries, 2014 ; Li, Au, Tong, Ng, & Wang, 2022 ).
One potential barrier to simulation effectiveness is stu-
dent anxiety. In a survey of college students from 140
different schools, the American College Health Associ-
ation found that 26.5% of students reported anxiety as
a factor impacting their academic performance (ACHA,
2018 ). Since the COVID-19 pandemic, studies have con-
firmed even higher levels of reported anxiety and stress
among college students with increased screen time being a
1876-1399/© 2022 International Nursing Association for Clinical Simulation and Learning. Published by Elsevier Inc. All rights reserved.
https://doi.org/10.1016/j.ecns.2022.08.005
Please cite this article as: Reed, J.M., Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate Nursing Students, Clinical Simulation
in Nursing, https:// doi.org/ 10.1016/ j.ecns.2022.08.005
Clinical Simulation in Nursing 2
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JID: ECSN [mECSN; September 27, 2022;16:24 ]
risk factor ( Liyanage et al., 2021 ). Within the nursing ma-
jor, students experience significantly higher anxiety than
the general college student population, with disturbingly
high anxiety around the time they start clinical rotations
Key Points
•Simulation anxiety
has been assumed to
be problematic.
•Suggested anxiety-
reducing interventions
in the literature have
mixed effectiveness.
•In this study, anxiety
did not significantly
predict clinical judg-
ment scores.
•Faculty should help
students function
through anxiety rather
than eliminating it.
( Wedgeworth, 2016 ).
Moreover, students have
described nursing simu-
lation as terrifying, and
they are especially fearful
of both the unknown and
making mistakes in front
of others who are watch-
ing ( Mills, Carter, Rudd,
Claxton, & O’Brien,
2016 ).
The complex rela-
tionship between anxiety
and student performance
has been studied with
mixed results. While
moderate levels of anxiety
may improve perfor-
mance, excessive anxiety
can hinder learning and lead to low self-confidence and
performance issues ( Al-Ghareeb, Cooper, & Mckenna,
2017 ; Reed & Ferdig, 2021 ). Excessively high levels of
anxiety can also impede students from making appropri-
ate clinical decisions by creating cognitive interference,
which often includes negative self-talk which consumes
attention and drains working memory ( Sarason et al.,
1996 ). Higher cognitive load and resulting simulation
anxiety can be due to pretend realism, time pressure,
dual-tasking, interruptions, task complexity, and distrac-
tions during the simulation ( Rogers & Franklin, 2021 ).
In the nursing literature, there have been many interven-
tions suggested and tested for reducing student anxiety dur-
ing simulation. These anxiety-reducing interventions are
diverse and include such things as classical music sessions
or aromatherapy before simulation; research has demon-
strated mixed results as to their effectiveness ( Turner &
McCarthy, 2017 ). The use of anxiety-reducing interven-
tions is built on the assumption that simulation anxiety is
harmful and therefore, needs reduced. However, scant re-
search addresses how anxiety specifically affects clinical
judgment. Therefore, the purpose of this study was to ex-
amine what relationships, if any, exist between state and
trait anxiety and clinical judgment (CJ) within simulation.
Theoretical Framework
The theoretical framework for this study was Tanner’s
(2006) Clinical Judgment Model . Tanner’s (2006) model of
CJ includes four phases
–noticing, interpreting, respond-
ing and reflecting. Because of the variations and con-
fusion surrounding the concept of CJ, it has been pro-
posed that a standardized language and tool is needed
( Lasater, 2011 ; Lee, 2021 ). To help build this language,
it has been suggested to use Tanner’s (2006) model of CJ
as the emerging theory and the evaluation tool directly as-
sociated with it —the Lasater Clinical Judgment Rubric
(LCJR) ( Lasater, 2007 ; Lee, 2021 ).
Spielberger’s (1970) theories on anxiety provided the
basis for understanding anxiety in this study. Anxiety can
be defined as an emotional state of distress from an in-
dividual’s perceived feelings of tension, apprehension, and
nervousness which is accompanied by activation of the au-
tonomic nervous system ( Spielberger, Gorsuch, & Lushene,
1970 ). Two unique types of anxiety exist as constructs
according to Spielberger ( 1970 ): state and trait anxiety.
State anxiety results from a temporary state or stressor in
a current situation, whereas trait anxiety indicates a gen-
eral propensity or chronic personality trend towards high
levels of arousal ( Saviola et al., 2020 ).
Research Questions
1. What is the relationship, if any, between state and trait
anxiety and overall clinical judgment (CJ) among under-
graduate nursing students in simulation?
2: What is the relationship, if any, between state and trait
anxiety and the four phases of clinical judgment (noticing,
interpreting, responding, and reflecting) among undergrad-
uate nursing students in simulation?
Methods
This quantitative study used a one-group repeated measures
research design to examine the relationship between state
and trait anxiety and clinical judgment in simulation for a
group of sophomore undergraduate nursing students. Af-
ter Institutional Review Board (IRB) approval, participants
were recruited from one undergraduate nursing course at
a large Midwestern university. Using G
∗power with an a
priori power analysis for a linear multiple regression, a
power of .80, and significance set at .05 (medium effect
size 0.15), an estimated sample size was set at 55 par-
ticipants. Inclusion criteria was any student enrolled in
sophomore-level Foundations of Nursing at the public uni-
versity. Exclusion criteria was any student who currently or
previously held a license to practice as any type of nurse
(ex. LPN). Fifty-one students consented to participate in
the research at the beginning of the semester; however, six
were lost due to attrition by the time the simulation was
completed later in the semester. Ages ranged from 19 to 35
years with the mean age at 20.7 years. There were 45 fe-
males (88.2%) and 6 males (11.8%). Ethnicities identified
were Caucasian (90%), Hispanic (4%), Black (2%), Asian
(2%), & American Indian (2%). None of the participants
had ever been licensed as an LPN, but 41% of them had
pp 1–8 •Clinical Simulation in Nursing •Volume 000
Please cite this article as: Reed, J.M., Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate Nursing Students, Clinical Simulation
in Nursing, https:// doi.org/ 10.1016/ j.ecns.2022.08.005
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experience as a State-Tested Nursing Aide (STNA) with a
range of experience from 4 to 36 months.
Two tools were used with the authors’ permission to
measure each of the constructs: the Lasater Clinical Judg-
ment Rubric for clinical judgment (LCJR) ( Lasater, 2007 )
and Zsido et al.’s (2020) 5-item short forms of Spiel-
berger’s (1970) State-Trait Anxiety Inventory (STAI) which
has two subscales- one for state anxiety ( STAIS-5 ) and one
for trait anxiety ( STAIT-5 ). The psychometric properties of
STAIS-5 and STAIT-5 were determined by Zsido, Teleki,
Csokasi, Rozsa, and Bandi (2020) to show good reliability
and internal consistency (Cronbach’s alpha 0.86-0.91), as
well as high correlations with the full STAI (0.88 for trait;
0.86 for state).
The LCJR breaks down each of Tanner’s ( 2006 ) four
phases of clinical judgment into 11 dimensions that were
developed based on qualitative and quantitative research
( Lasater, 2007 ; Lasater, 2011 ). The rubric provides stan-
dardized language that can be used to evaluate performance
by assigning a score for each dimension as either 1 (be-
ginning), 2 (developing), 3 (accomplished), or 4 (exem-
plary). The total possible scores on the LCJR range from
11 to 44 and also provides a sub score within each of
Tanner’s ( 2006 ) four phases. LCJR has well-documented
validity and reliability for use in undergraduate nurs-
ing students in simulation ( Victor-Chmil & Larew, 2013 ).
Inter-rater reliability has been reported at 0.89 and in-
ternal consistency (Cronbach’s alpha) at 0.974 ( Adamson
& Kardong-Edgren, 2012 ; Adamson, Gubrud, Sideras, &
Lasater, 2012 ). In order to best answer the research ques-
tions, individual simulations were conducted for this study.
LCJR was scored with direct observation of students in
simulation which was the way it was originally intended
to be used ( Lee, 2021 ).
State and trait anxiety surveys were administered at
the beginning of the semester to determine baseline anxi-
ety and demographics were also collected at this time. A
sophomore level introductory simulation experience was
planned using Jeffries (2016) simulation framework; the
simulation scenario used a high-fidelity manikin to mimic
an elderly hospitalized patient with respiratory alterations.
Students were expected to receive a verbal and written
report, complete an assessment, notice and document im-
portant findings, communicate effectively, take at least one
action step, and reflect on the simulation. The simulation
was checked for content validity by two expert nursing fac-
ulty who were not involved in the research prior to use. In
accordance with other simulation research using individual
simulations ( Shinnick & Cabrera-Mino, 2021 ), each indi-
vidual simulation took about 10-12 minutes with an addi-
tional 10 minutes for reflection immediately following the
scenario.
Following INACSL best practice guidelines, students
were provided with pre-simulation activities and a pre-
briefing orientation. Immediately following pre-briefing
and prior to the simulation, students were asked to com-
plete the STAIT-5 and STAIS-5 to assess pre-simulation
anxiety levels. Then, students were brought into the sim-
ulation room individually and given a verbal and written
patient report with physician orders. Each participant’s per-
formance was viewed live from behind a one-way mirror
and scored based on observations made during the simu-
lation. Simulations were recorded to allow the researcher
to review when scoring the LCJR. Using only one person
as rater for the LCJR reduced threats to internal validity
by ensuring consistency in student evaluation. This rater
also completed training from Dr. Lasater on how to score
LCJR. Intra-rater reliability shows the level of agreement
or consistency that a singular judge or evaluator has mea-
suring the variable of interest repeatedly free from sys-
tematic error ( Koo & Li, 2016 ). The intra-class correlation
coefficient (ICC) was used to calculate intra-rater reliabil-
ity because in this study, one rater’s ratings were compared
at one point in time to the same rater’s ratings at another
point in time ( Koo & Li, 2016 ). The ICC for this study
was 0.977 showing consistent rating on LCJR.
Once the simulation ended, students were taken to a
quiet reflection room and asked to write a written self-
reflection. These written reflections were used to score the
reflecting phase of LCJR. At the end of this 10-minute
reflection time, students’ state and trait anxiety measure-
ments were again assessed using STAIT-5 and STAIS-5
to measure post-simulation anxiety. Debriefing happened
once all students had finished individual simulations in
order to prevent students from sharing exactly what they
were supposed to do with other students, which could have
skewed results. The researcher conducted debriefing with
all students, sharing formative observations from LCJR to
improve their practice as was suggested by Lasater (2011) .
An expert modeling video demonstrating an exemplar per-
formance of the desired actions during the simulation was
provided to students in the debriefing period.
All data were analyzed for statistics using IBM SPSS
Ve r s i o n 28 . Multiple linear regression was used to explore
the relationships between the dependent variable (LCJR
scores) and independent variables (state and trait anxiety).
Significance was set at p < .05 to minimize the proba-
bility of a Type 1 error. The data was assessed for out-
liers and assumptions to minimize probability of a Type
1 or Type 2 error. Assumptions checked for multiple lin-
ear regression included linearity, independence, normality,
homoscedasticity, and collinearity that could affect results
( Hahs-Vaughn & Lomax, 2020 ).
Results
In Zsido et al.’s (2020) STAIS-5 and STAIT-5 measures,
there are five questions for self-reported anxiety, which
are scored on a Likert scale ranging from 1 to 4 with
1 being ‘Not at all’ and 4 being ‘Very much.’ Table 1
displays the means and standard deviations for each of
pp 1–8 •Clinical Simulation in Nursing •Volume 000
Please cite this article as: Reed, J.M., Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate Nursing Students, Clinical Simulation
in Nursing, https:// doi.org/ 10.1016/ j.ecns.2022.08.005
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Table 1 Means & St. Deviations for STAIS-5 & STAIT-5 questions
STAIS-5 (State anxiety) Baseline Pre-simulation Post-Simulation
1. I feel upset. 1.1373 / 0.40 1.5333/0.75 1.6667/0.82
2. I feel frightened. 1.1176 / 0.32 2.2444/1.02 1.5778/0.75
3. I feel nervous. 1.8235 / 0.79 3.1111/0.91 2.1556/1.08
4. I am jittery. 1.4314 / 0.70 2.5556/0.94 2.2444/1.04
5. I feel confused. 1.1569 / 0.36 2.1111/0.83 1.9111/0.84
STAIT-5 (Trait Anxiety) Baseline Pre-simulation Post-Simulation
1. I feel that difculti es ar e pilin g up so that I cann ot over com e th em. 1.8431/0.75 1.9778/0.78 1.9333/0.72
2. I worry too much over something that really doesn’t matter. 2.5686/0.92 2.4667/0.86 2.3778/0.80
3. Some unimportant thoughts run through my mind and bother me. 2.1373/0.80 2.2444/0.90 2.0889/0.82
4. I take disappointments so keenly that I can’t put them out of my mind. 1.8824/0.81 2.1333/0.94 2.0000/0.85
5. I get in a state of tension or turmoil as I think over my recent concerns and interests. 1.8824/0.81 2.0222/0.86 2.0667/0.91
Table 2 Means & St. Deviations for Total Anxiety Scores
Baseline Pre-Simulation Post-Simulation
State Anxiety (STAIS-5) 6.67/ 1.77 11.47/ 3.06 9.56/ 3.18
Trait Anxiety (STAIT-5) 10.31/ 3.23 10.82/ 3.58 10.47/ 3.31
the five questions on state and trait anxiety for the three
time points. Table 2 shows the total means and standard
deviations for state and trait anxiety scores for each of the
three time points.
Zsido et al. (2020) suggested that someone scoring ≥10
on STAIS-5 (state anxiety) or ≥13.5 on the STAIT-5 (trait
anxiety) should be considered potentially clinically anx-
ious. State anxiety did go above Zsido et. al.’s (2020) cut-
off for clinically significant anxiety at the pre-simulation
and post-simulation measurement times. The state anxiety
levels at baseline for nursing students in this study were
6.61 for females and 6.16 for males; these were notice-
ably lower than the average state anxiety levels reported
by Zsido et al. (2020) : 8.31 for females and 7.09 for males.
So, at baseline, state anxiety was lower than average in this
study. The mean trait anxiety for nursing students in this
study was between 10.31 and 10.82 for all three times.
Zsido et al. (2020) reported that the mean trait anxiety
scores were 11.7 for females and 9.9 for men, so this sam-
ple of nursing students was not particularly trait anxious
at baseline since their mean trait anxiety scores were 10.5
for females and 7.66 for males. At baseline, 9 students
(17.6%) out of 51 were found to have high trait anxiety
(above 13). The mean LCJR score for all students in sim-
ulation was ¯x = 23.53 with a standard deviation of 5.83.
LCJR scores in this study ranged from 12 to 33 (possible
scores on LCJR could range from 11 to 44). Table 3 shows
the means and standard deviations for LCJR, both overall
and within each of Tanner’s ( 2006 ) four phases.
Table 3 Means and St. Deviations for LCJR
Measurement Range Mean SD
Total LCJR 11-44 23.53 5.837
Noticing 3-12 6.62 1.13
Interpreting 2-8 4.31 1.29
Responding 4-16 8.37 2.39
Reecting 2-8 4.22 1.13
To answer the first research question, baseline state and
trait anxiety were used as control variables within the mul-
tiple linear regression model; pre-simulation state and trait
anxiety scores were used as independent variables (n = 45)
with LCJR scores as the dependent variable. There were
no outliers removed from the model, and all assumptions
were met except for collinearity. Due to this possible viola-
tion of collinearity, separate regressions were run since the
independent variables (state and trait anxiety) were mod-
erately correlated. Data analysis from the multiple linear
regression showed that anxiety did not significantly predict
LCJR scores: ( F [4,44] = .770, p = .551). The R
2, or co-
efficient of multiple determination for the model was .072
indicating a small effect size.
To answer the second research question, multiple linear
regressions were run for each of the four phases within
LCJR to test if pre-simulation state or trait anxiety signifi-
cantly affected any of the individual phases. Data analysis
pp 1–8 •Clinical Simulation in Nursing •Volume 000
Please cite this article as: Reed, J.M., Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate Nursing Students, Clinical Simulation
in Nursing, https:// doi.org/ 10.1016/ j.ecns.2022.08.005
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Table 4 Pearson’s Correlations of Anxiety with Tanner’s Four Phases
Noticing Interpreting Responding Reecting
Baseline State -.125 ( p = .206) .033 ( p = .415) -.219 ( p = .075) - .136 ( p = .186)
Baseline Trait -.087 ( p = .285) -.126 ( p = .204) -.198 ( p = .097) - .195 ( p = .100)
Pre-Sim State -.133 ( p = .192) -.095 ( p = .268) -.238 ( p = .058) - .202 ( p = .092)
Pre-Sim Trait -.199 ( p = .095) -.101 ( p = .255) -.196 ( p = .097) - .266 ( p = .039)
showed that anxiety did not significantly predict any of
the scores of the four phases of Tanner’s ( 2006 ) model:
Noticing ( F [4,44] = .523, p = .719); Interpreting ( F
[4,44] = .366, p = .831); Responding ( F [4,44] = .1.01,
p = .412); Reflecting ( F [4,44] = .913, p = .466). Cor-
relations for state and trait anxiety on the four specific
phases are listed in Table 4 . Only pre-simulation trait anx-
iety had a significant small correlation with performance
scores during the reflecting phase. So, students with higher
trait anxiety at pre-simulation scored significantly lower for
reflection.
Discussion
There were four potential categories for anxiety’s effect
on LCJR that students in this study could fall into based
on their scores (see Figure 1 ). For some students, high
anxiety resulted in them performing very well in sim-
ulation, whereas for others it correlated with poor per-
formance. For students with low levels of anxiety, the
same was also true. In other words, low anxiety may
have helped them perform better or may have resulted
in them performing worse. This reinforces the idea from
Yerkes-Dodson’s law that a moderate amount of anxiety
is desirable for peak performance ( Ormrod, 2020 ). These
distinctions may help explain the mixed and inconsis-
tent results of applying anxiety-reducing interventions in
simulation ( Turner & McCarthy, 2017 ). Future research
should examine differences in coping mechanisms (e.g.,
cognitive appraisals and self-talk about students’ perceived
anxiety) between those experiencing positive or good ef-
fects of anxiety and those experiencing negative or bad
effects.
This finding is consistent with the findings of
Shinnick and Cabrera-Mino (2021) who examined pre-
dictors of CJ and concluded that only years of nurs-
ing experience (not stress) was a significant predictor of
CJ. Burbach, Struwe, Young, and Cohen (2019) used the
Creighton Competency Evaluation Instrument (CCEI) and
STAI to measure anxiety and found that pre-simulation
anxiety had no significant correlation with performance
during simulation. These studies—and this present study—
all reinforce the idea that some anxiety is good and does
not impair performance. Moreover, some anxiety may actu-
ally improve simulation performance up to a certain point
( Al-Ghareeb et al., 2017 ).
This is important for several reasons. There has been
discussion in the literature on the need to reduce simula-
tion anxiety for students, which assumes that all anxiety is
bad and will harm student performance—an assertion not
confirmed by this study. Educators and researchers can of-
ten get caught up in this assumption that anxiety is harmful
and must be reduced. Alpert and Haber (1960) differenti-
ated anxiety into two types: facilitating and debilitating
anxiety. Facilitating anxiety helps motivate learners to step
into challenges and assists them in making the extra effort
to overcome their anxiety. In contrast, debilitating anxiety
can cause learners to flee the task or shut down in order to
avoid the source of anxiety. Unfortunately, the State-Trait
Figure 1 Anxiety’s Effects on LCJR.
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Please cite this article as: Reed, J.M., Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate Nursing Students, Clinical Simulation
in Nursing, https:// doi.org/ 10.1016/ j.ecns.2022.08.005
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Anxiety Framework by Spielberger et al. (1970) does not
provide this distinction between helpful and harmful anx-
iety. As such, nursing educators should not automatically
assume that a student’s perceived anxiety in simulation is
debilitating. Rather, they should help students to reframe
their thinking about their own anxiety, knowing that some
anxiety can be a good thing which motivates them to per-
form better. Future research should seek to identify both
key distinguishing factors between facilitating and debil-
itating simulation anxiety and ways to measure or sepa-
rate the two. If debilitating anxiety can be identified and
separated from facilitating anxiety, then educators should
consider ways or techniques to assist those students who
struggle with the debilitating type.
Instead of working to decrease anxiety for all nurs-
ing students, educators should rather focus on helping stu-
dents learn how to function safely and make appropriate
patient decisions despite their anxiety levels. There are
several techniques discussed in the literature which may
help educators to do this. Helping students become mind-
ful and self-aware of their own emotions is a start to
building emotional intelligence—a process that may as-
sist them in being successful later in their nursing ca-
reer ( Sun et al., 2021 ). Techniques such as mental re-
hearsal strategies and autogenic training (a relaxation tech-
nique) may also be useful for students who are learning
to function through anxiety ( Holland, Gosselin, & Mulc-
ahy, 2017 ; Ignatio et al., 2016 , Ignatio et al., 2017 ). Some
of the anxiety-reducing interventions that have been sug-
gested in the literature should be reevaluated considering
the findings of this study. For example, reducing the num-
ber of observers in the simulation room ( Mills et al., 2016 )
is not realistic of real-life practice when multiple family
members and interdisciplinary team members are often in
the patient’s room while the nurse is interacting with the
patient.
Nursing is a highly stressful and anxiety-provoking pro-
fession, and educators should be more focused on prepar-
ing students for real world practice than on preventing anx-
iety during nursing school. With the crisis of new gradu-
ates lacking CJ as they enter practice, more focus needs
to be put on increasing CJ regardless of anxiety levels. An
extremely high turnover of employment for new nurses
should alert educators and students alike to the fact that
the real world is often more stressful than nursing school
( Shaffer & Curtin, 2020 ). Students need to recognize the
realities of the anxieties they will face in the healthcare set-
ting, especially since the COVID-19 pandemic and with a
worsening nursing shortage ( Zheng et al., 2021 ). Resilience
training for undergraduate nursing students has been pro-
posed as a technique to decrease the attrition and burnout
of nurses by teaching positive coping mechanisms for how
to deal with stressful situations ( Lopez, Yobas, Chow, &
Shorey, 2018 ).
Future research should be completed on whether in-
dividual simulations can increase CJ and competency. It
is challenging to ensure individual competency in group
simulations, but even more challenging in the clinical set-
ting where faculty cannot be with every student at ev-
ery moment. There have been several calls in the liter-
ature for more research on using LCJR to examine CJ
during individual simulations ( Chmil, Turk , Adamson, &
Larew, 2015 ; Lee, 2021 ). Virtual simulation and its ef-
fect on CJ development is another recommended area for
future research since virtual simulation allows for individ-
ual simulation to be completed in a cost-effective setting
( Haerling, 2018 ).
There are several factors in this research study which
may have impacted students’ anxiety levels. First is the
occurrence of social evaluation anxiety. Students in this
research study performed their simulations individually.
Therefore, they would not have had social evaluation anx-
iety from peers watching, although they knew their perfor-
mance was being evaluated by the researcher. Conducting
individual simulations may have resulted in more anxiety
since they were solely responsible for the patient and could
not fall back on others in a group effort. Students might
also have experienced higher technostress since it was their
first time using the high-fidelity manikins in a simulation.
Future research should examine anxiety and outcomes be-
tween group and individual simulations for nursing stu-
dents, as well as different levels of nursing students since
this study only examined sophomores.
Conclusion
In summary, the findings of this study highlight that neither
state nor trait anxiety significantly affected CJ scores on
LCJR during simulation. This may be attributed to the fact
that individuals react to anxiety very differently. Moreover,
research has provided evidence that some anxiety is good
and can facilitate improved performance. Educators should
not assume that anxiety is something negative that needs
eliminated; such a move may not help prepare students for
real-life nursing practice which is needed more than ever
in today’s healthcare environment. Educators need to be
able to focus on creating challenging learning experiences
which will develop clinical judgment, rather than trying to
implement anxiety-reducing measures to lessen anxiety for
all students. Future research could focus on the differentia-
tion between helpful and harmful anxiety so that educators
can better help students who experiencing harmful anxiety.
Ethical Approval
IRB approved Kent State University 21-259
Conict of Interest
None declared.
pp 1–8 •Clinical Simulation in Nursing •Volume 000
Please cite this article as: Reed, J.M., Simulation Anxiety and its Effect on Clinical Judgment for Undergraduate Nursing Students, Clinical Simulation
in Nursing, https:// doi.org/ 10.1016/ j.ecns.2022.08.005
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Acknowledgments
Thanks to my mentor Dr. Richard Ferdig, as well as Dr.
Debra Shelestak and Dr. Aryn Karpinski, and my many
nursing colleagues who helped make this research happen.
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