Next Article in Journal
Impact of Respectfulness on Semantic Integration During Discourse Processing
Previous Article in Journal
Individual and Contextual Morality: How Educators in Oppositional and Permissive Communities Use Culturally Responsive Practices
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 15
Issue 4
10.3390/bs15040447
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Comparison of Pre- and Post-Clinical Simulation Anxiety Levels of Undergraduate Medical Students Before and During the COVID-19 Pandemic: A Prospective Cohort Study

by
Rafael Martín-Sánchez
1,2,
Ancor Sanz-García
3,4,5,
Samantha Diaz-Gonzalez
3,
Miguel Ángel Castro Villamor
6,*,
Silvia Sáez-Belloso
1,2,
Joseba Rabanales Sotos
7,8,
Leyre T. Pinilla-Arribas
9,
Pablo González-Izquierdo
9,
Sara de Santos Sánchez
6 and
Francisco Martín-Rodríguez
1,6
1
Advanced Life Support, Emergency Medical Services (SACYL), 47003 Valladolid, Spain
2
Faculty of Nursing, Universidad de Valladolid, 47003 Valladolid, Spain
3
Faculty of Health Sciences, University of Castilla la Mancha, Talavera de la Reina, 45600 Toledo, Spain
4
Technological Innovation Applied to Health Research Group (ITAS Group), Faculty of Health Sciences, University of de Castilla-La Mancha, Talavera de la Reina, 45600 Toledo, Spain
5
Evaluación de Cuidados de Salud (ECUSAL), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
6
Faculty of Medicine, University of Valladolid, Edificio de Ciencias de la Salud, Avda. Ramón y Cajal, 7, 47005 Valladolid, Spain
7
Faculty of Nursing, University of Castilla la Mancha, 02071 Albacete, Spain
8
Group of Preventive Activities in the University Health Sciences Setting, University of Castilla-La Mancha, 02071 Albacete, Spain
9
Emergency Department, Hospital Clínico Universitario, 47003 Valladolid, Spain
*
Author to whom correspondence should be addressed.
Behav. Sci. 2025, 15(4), 447; https://doi.org/10.3390/bs15040447
Submission received: 13 February 2025 / Revised: 10 March 2025 / Accepted: 25 March 2025 / Published: 1 April 2025
Browse Figures
Review Reports Versions Notes

Abstract

:
The aim of the present study was to compare the anxiety of undergraduate medical students who were conducting clinical simulation (CS) prepandemic, during the pandemic, and postvaccination. The participants carried out an emergency simulation in a high-fidelity clinical skills laboratory. A prospective, simulation-based clinical cohort study of sixth-year undergraduate medical students was performed over three time periods: from 1 January to 15 April 2019; from 28 September to 18 December 2020; and from 11 May to 18 May 2022. The primary outcome was anxiety level (pre- and postsimulation) measured with the STAI test. Data on student demographics and baseline vital signs (before CS) were collected. A total of 373 students were ultimately included. A total of 40.2% of the cases were prepandemic (150 cases), 20.4% were pandemic (76 cases), and 39.4% were postvaccination (147 cases). The study period had a statistically significant effect on anxiety. There was a statistically significant increase in the incidence of anxiety during the pandemic time period compared with that during the prepandemic and postvaccination periods; no difference was found between the prepandemic and postvaccination periods. Performing CS in biohazardous environments significantly increases anxiety levels, so establishing mitigating measures to minimize the undesired effects of anxiety and promote the simulation-based learning process is necessary. The study was carried out at a single university; in future studies, it is necessary to carry out multicenter investigations to confirm the results.

1. Introduction

The ongoing pandemic caused by coronavirus disease 2019 (COVID-19) has led to a shock to health systems worldwide. The most severely ill patients who developed novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection required hospitalization, with a previously unknown rate of intensive care unit admission and highly significantly associated mortality (Grasselli et al., 2020). This disruption has been carried out in all sectors of society, including unsurprisingly, education, especially advanced education in healthcare sciences (Rose, 2020).
On the one hand, during the crisis, in the toughest phases of the pandemic, the quarantine phase, face-to-face education, was not possible (Al-Balas et al., 2020). Even though distance learning (online) is an invaluable aid in studies such as those in the health sciences (Hernández-Martínez et al., 2021), it cannot replace clinical or laboratory practice (Dost et al., 2020) (Sindiani et al., 2020). With research-derived knowledge about the mode of transmission, pathophysiology, treatment, and mass vaccination (ElBagoury et al., 2021), the incidence of adverse outcomes from this disease is declining, leading to acceptable biosafety conditions for continuing face-to-face education, at least fundamental practices (Althwanay et al., 2020). On the other hand, certain specialties (e.g., medicine, nursing, midwifery, physiotherapists, and podiatrists) necessarily require face-to-face internships, which are not possible in hospitals for obvious reasons; however, conditions must be provided so that at least students in the final courses can conclude their programs (Rajab et al., 2020). In this special scenario, high-fidelity clinical simulation (CS) has become an incalculable resource that can decisively aid in training in the decision-making process at critical moments but can be performed under controlled conditions and with very rigorous biosafety protocols (Chu et al., 2020).
Undergraduate medical students, as part of the final curricular program, must compulsorily rotate in an advanced clinical simulation center. As an educational method, CSs have manifold benefits and are becoming a standard and well-proven teaching strategy in health sciences studies (McCoy et al., 2018). In this sense, during a regular simulation training session, undergraduate medical students are faced with the autonomous decision-making process, which includes, among other things, giving orders, managing multidisciplinary teams, evaluating the simulated patient, etc., causing considerable anxiety, just by using the pedagogical method of clinical simulation (Weimer et al., 2025; Chow et al., 2024; Ju et al., 2024).
Clinical scenarios (however, simulations) can trigger powerful reactions in healthcare students, generating mixed feelings. As part of this special context, undergraduates must address complex conditions, play an essential role in the decision-making process, and even accept that, despite following all the standard procedures, the simulated patient may not experience a favorable outcome. Clinical simulation provides undergraduate medical students with an opportunity to face this anxiety before they finish regular training in a safe and secure environment (Métais et al., 2025; Anderson et al., 2025).
CS is a well-known teaching skill associated with a remarkable load of anxiety and unpredictability. Students must operate way outside of their comfort zone, be observed by classmates and facilitators, have unfamiliar conditions and equipment, and be generally in critical scenarios (Martín-Rodríguez et al., 2021).
Therefore, the aim of the present study was to analyze three cohorts taken at different chronological time periods (all undergraduate medical students who were conducting the same simulation scenarios), namely, the prepandemic cluster (2019), pandemic cluster (September to December 2020), and last postvaccination cluster (2022), to test baseline and postsimulation anxiety levels and to determine the impact of COVID-19.

2. Materials and Methods

2.1. Study Design

A prospective, simulation-based clinical cohort study was carried out with volunteer sixth-year undergraduate medical students at the Advanced Clinical Simulation Center, Faculty of Medicine, Valladolid University (Spain), over the following three chronological time periods: from 1 January to 15 April 2019; from 28 September to 18 December 2020; and from 11 May to 18 May 2022.
The study complied with the Declaration of Helsinki, was approved by the Institutional Review Board of the Public Health Service (reference: PI-033/18), and was registered with the WHO International Clinical Trials Registry Platform (doi.org/10.1186/ISRCTN32132176). This study conforms to the broad EQUATOR guidelines and is aligned with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative (Supplementary Data p. 3). Written informed consent was obtained from all the participants. For compulsory clinical simulation practices for final-year medical students, a short prebriefing was held to explain the purpose of the study and its implications. Students who volunteered (and with no compensation in return) were registered and then read and signed the informed consent form.

2.2. Biosafety Protocol

In the pandemic cluster, to safeguard the health of everyone, a stringent and meticulous procedure was applied.
The simulation lab extends over 68 m2, and the debriefing suite covers 32 m2 with a capacity for 16 students. Upon access to the simulation center, an antigen test (nasal swab) was performed via the SARS-CoV-2 Rapid Antigen Test (Roche Diagnostics, Mannheim, Germany), and tympanic temperature and hand washing with antiseptic gel were mandatory for every check-in or check-out at the center; moreover, the use of the FFP2 facemask was mandatory at all times.
In addition, frequent surface cleaning, social distancing (a maximum of 8 students per group and two teachers), forced air ventilation, and air quality monitoring were performed. Finally, in the simulation laboratory, the use of an FFP2 mask, gloves, and face shield was compulsory (Nembhard et al., 2020).
The normal prepandemic and postvaccination protocols were identical but without biosecurity precautions.

2.3. Participants and Randomization

The participants were selected from sixth-year undergraduate medical students interested in collaborating in the study, without receiving any compensation in exchange. Students receiving anticonvulsant or anxiolytic treatment and participants completing the State-Trait Anxiety Inventory (STAI) during the last year were excluded.
The participants were randomly assigned to one of eight simulated clinical scenarios (hip fracture, asthmatic exacerbation, anaphylactic shock, polytraumatized, acute heart failure, sepsis, thromboembolism in pregnant women, and acute myocardial infarction). Nonreplacement randomization with a 1:8 ratio was performed via R, version 4.1.2 (package dply). The participants remained unfamiliar with the scenario up to the time of prebriefing and entry into the simulation lab.

2.4. Outcome

The primary outcome was the delta of anxiety level (pre- or postsimulation) on the STAI state and trait subscales according to the timeline cohorts.
The validated Spanish STAI version (Buela-Casal et al., 2011, s.f.) is a self-reported scale composed of two subscales (20 items per subscale), namely, state (how the person feels at that specific moment) and trait (how the person usually feels), and it was used for measuring anxiety levels; these two scales exhibited internal consistency (Cronbach’s = 0.9–0.93) (Gustafson et al., 2020). Anxiety is understood as an emotional reaction or as a personality feature. Anxiety refers to an individual’s tendency to react anxiously, while anxiety is described as a transitory and fluctuating emotional state over time.

2.5. Study Protocol

All staff received specific training in the use of the data collection notebook (using paper questionnaires), the standardized acquisition of vital signs, and the correct completion of the STAI. No masking of the intervention was provided. To minimize cross-contamination, the investigators were unaware of the initial assignment, and only the PI and data manager were familiar with the coding and correction items of the STAI. To guarantee the blinding of the results and the anonymization of the confidential data, only the identification data of the volunteer appeared in the informed consent document. In this document, each volunteer was assigned a four-digit code, which was used successively to identify them. Only the PI had access to the informed consent and the code of each participant.
The participants were randomly assigned to one of the eight simulated clinical scenarios by using a high-fidelity manikin SimMan® ALS (Laerdal Medical AS, Stavanger, Norway).
High-fidelity clinical simulation sessions last approximately ten minutes. Two undergraduate medical students entered the simulation laboratory accompanied by a registered nurse. During the case, one student played the role of the leader and the other played the role of the assistant, performing the case history, clinical examination, and initial treatment of the proposed cases.
A survey containing epidemiological details and baseline restricted-time (STAI) scores was completed on the day of the test. Prerandomization, a registered nurse collected a set of baseline vital signs (oxygen saturation, perfusion index, blood pressure, heart rate, and temperature).
A standardized 20-min presentation, fully videotaped, on the features of the simulation manikin and the correct way to operate in the laboratory, was broadcast before the simulation cases. The simulation was conducted by two-person teams (leader and assistant) along with a registered nurse (with direct radio feedback to facilitators) for a maximum of 10 min. Following every scenario, both participants completed the STAI (unrestricted time), and their final vital signs were collected immediately. Pulse oximetry saturation, systolic and diastolic pressure, heart rate, and temperature were measured via the Connex® Vital Signs Monitor (Welch Allyn, Inc., Skaneateles Falls, NY, USA).

2.6. Statistical Analysis

Details regarding the data collection, missing values, and sample size calculations can be found in Supplementary Data p. 3; briefly, sample size calculations were performed via balanced one-way analysis of variance tests. Absolute values and percentages were used for categorical variables, and median interquartile ranges (IQRs) were used for continuous variables. The associations between the study period (prepandemic, pandemic, or postvaccination) were assessed by univariate analysis, ANOVA, the Kruskal–Wallis test, or the chi-square test, when appropriate. To determine the pairwise difference between each of the three groups of time periods, a post hoc analysis (Tukey) was performed for those continuous variables that presented a statistically significant main effect in the univariate analysis. Additionally, a multinomial logistic regression was performed for the study period (including the three groups), and all the variables that presented a p value < 0.001 in the univariate analysis were used to determine the variables associated with the study period. All calculations and analyses were performed by using our own codes, R packages, and base functions in R, version 4.2.2 (http://www.R-project.org; the R Foundation for Statistical Computing, Vienna, Austria).

3. Results

A total of 373 students fulfilled the inclusion criteria, with no missing data and three dropouts (Figure 1). The median age was 23 years (IQR: 23–24; range: 22–41), and 68.6% of the students were females (256 participants). The population distribution in the respective cohorts was 40.2% prepandemic (150 cases), 20.4% pandemic (76 cases), and 39.4% postvaccination (147 cases). No significant differences were reported for age, sex, leader or assistant, or clinical scenario. Table 1 displays the epidemiological variables and vital signs and the baseline and postsimulation evaluation results. A comparison of baseline characteristics according to the scenario is shown in Supplementary Table S1.

3.1. Primary Outcome

The STAI state subscale revealed notable differences. The pandemic cluster presented a median of 57 points vs. 41 points in the other clusters; i.e., the students analyzed during the pandemic period presented 28% more anxiety as a baseline. Following the simulation, similar disparities were maintained, with a 32% increase in the level of anxiety in the pandemic cluster vs. the prepandemic and postvaccination clusters (p < 0.001 in all cases) (Table 2). The STAI trial results presented fewer differences in the means than did the State trial results but were still significantly different. The level of baseline anxiety as a trait during the pandemic phase was significant, with a median of 41 points vs. 31 or 32 points in the other clusters. This difference was maintained at the end of the simulated scenario (Table 2). Finally, the difference between the postsimulation and baseline data, not surprisingly, yielded the same variation.

3.2. Logistic Regression

To evaluate the associations of the parameters analyzed during the study period, logistic regression was performed (Table 3), and the results included previous experience, initial systolic blood pressure, initial mean blood pressure, differences pre- and posttraining for the trait STAI and state STAI, and the study period. Only differences pre- and postintervention for the trait STAI were statistically significant (p < 0.001 for both the prepandemic and postvaccination groups). The difference in initial systolic blood pressure was significant only for the prepandemic group (p = 0.040).
Figure 2 shows the differences before and after training for the trait STAI and the state STAI. According to the post hoc analysis for the trait STAI, there was a statistically significant increase in the incidence of anxiety between the pandemic and prepandemic (p < 0.001) and postvaccination (p = 0.001) periods, but no difference was found between the prepandemic and postvaccination periods (p = 0.974). Similarly, for the state STAI post hoc analysis, there was a statistically significant increase in the incidence of anxiety in the pandemic group compared with the prepandemic (p < 0.001) and postvaccination (p < 0.001) groups, but no difference was found between the prepandemic and postvaccination groups (p = 0.512). Notably, the pandemic group was the only group with no outliers with elevated values (Figure 2). A post hoc comparison of those variables with a statistically significant main effect can be found in Supplementary Table S3. The corresponding figure of each statistical significance can be found in Supplementary Figure S2.

4. Discussion

This prospective, simulation-based clinical cohort study was carried out with sixth-year undergraduate medical students during three different time periods (prepandemic, pandemic, and postvaccination) to compare anxiety levels before and after the randomized sham clinical simulation. The STAI revealed substantial dissimilarities among the pandemic clusters compared with the other two analyzed clusters concerning traits as well as state subscales, confirming the former assumption that the COVID-19 pandemic was a major disruption across multiple dimensions, including naturally, undergraduate medical education. These findings will help make simulation sessions more effective and identify external stressors that have not been assessed until now and that may influence students.
CS is a well-known teaching skill associated with a remarkable load of anxiety and unpredictability. Students must operate way outside of their comfort zone, be observed by classmates and facilitators, have unfamiliar conditions and equipment, and be generally in critical scenarios (Martín-Rodríguez et al., 2021). Moreover, stressors such as these can contribute to a breeding ground that increases baseline anxiety levels and, in particular, to the results of simulations performed during the prevaccination phase of COVID-19, with the added guesswork generated by the pandemic response (Lim et al., 2020). Fredericks, S. et al. reported how high-fidelity scenarios of critically ill patients can induce emotional arousal in students and impact their cognitive load (Fredericks et al., 2021). Bommer, C. et al. revealed a significant anxiety burden linked to CS and revealed how prescenario briefing can alleviate anxiety, increase confidence, and improve performance (Bommer et al., 2018). Barbadoro, P. et al. demonstrated that stress was increased more by simply being in a simulation scenario than by the intrinsic complexity of the required duty (Barbadoro et al., 2023).
Therefore, CS, as a pedagogical technique, is well-established and educationally effective, with an intrinsic burden of associated anxiety from which we cannot disengage. CS provides a learning tool that uses simulators and anatomical models to recreate clinical scenarios with great accuracy (Shin et al., 2015), from critical situations that are difficult to reproduce to simpler scenarios where the evaluation of the situation is the fundamental skill to be developed. In this way, CS promotes quick decision-making, teamwork, self-learning, and self-criticism (Liaw et al., 2014).
From a pedagogical point of view, CS represents a minor revolution in the traditional educational model. The student is going to have training experience in the simulation laboratory; it is not just a question of automatically performing techniques and procedures; rather, the student-instructor tandem must understand CS cases as reflective-experiential practices. The most reliable possible recreation of real situations (environment, furniture, high-performance simulator based on a physiological model, apparatus, real consumables, communications, complementary tests, etc.) makes it possible to experience these situations, which would not be possible otherwise, and in safe conditions, both for the patient and for the students (Liaw et al., 2011).
In this sense, during the pandemic phase, in addition to anxiety related to CS, the uncertainty of COVID-19 evolution and the mandatory adoption of biosafety protocols to minimize risks were supplementary stressors (Nembhard et al., 2020). As demonstrated by health care providers, the use of personal protective equipment (PPE), such as FFP2 facemasks, gloves, protective suits, aprons, and face shields, is a standardized workflow procedure for achieving appropriate biosafety conditions to manage infectious patients (Martín Rodríguez et al., 2018) and is known to trigger an overactive physiological and stress-related response (Martín-Rodríguez et al., 2020; Tabah et al., 2020).
Anxiety levels (both as a state and as a trait) were significantly greater in the pandemic cohort. These disparities could not be attributed solely to isolated, individual, or interpersonal factors but rather to the simultaneous combination of time and space of a range of diverse factors with complex backgrounds, e.g., perceived fear of contracting disease, COVID-19 exposure during clinical fellowship training, limitations and/or social distancing, changes in education and curricular programs with the discontinuation of hospital practicums, information overload and worrisome news (Rose, 2020; Alsoufi et al., 2020; Son et al., 2020). Simulation experience, in turn, may smooth anxiety levels and improve performance in clinical situations (Bommer et al., 2018; Segura Azuara et al., 2020). However, in our study, the influence of COVID-19 overrode previous experience. In the prepandemic cohort, no students reported previous simulation experience, in contrast to the 67.1% reported in the pandemic cohort and the 69.4% reported in the postvaccination cohort; however, the pandemic cohort scored significantly above anxiety rates, supporting the hypothesis concerning the relevance and disruption of COVID-19 in medical education. Similarly, no significant differences were found concerning the simulation case conducted (eight different scenarios), sex at birth, or role in the simulation (leader or assistant); thus, the independent significance associated with COVID-19 seems to be established (Koca et al., 2023).
An additional determinant of anxiety levels may be pandemic fatigue (Pandemic Fatigue, 2020, s.f.). The prepandemic cohort and postvaccination cohort ratings on the STAI questionnaire (both trait and state) were comparable. Notably, the incidence of outliers in the pandemic cohort was significantly lower than that in the remaining cohort. These data may suggest that undergraduate medical students maintain a uniform anxiety level during the first pandemic peak and support the assumption that herd immunity resulting from mass vaccination decreases anxiety levels by increasing safety (Saddik et al., 2020). In fact, the results revealed similar anxiety values in the prepandemic and postvaccination periods, once again highlighting the crucial importance of COVID-19.
This study has several limitations. First, the data extractors were not blinded. To avoid cross-contamination, the associated partners were mandatorily trained face-to-face on handling the data collection notebook, randomization, and vital sign devices but were unaware that the STAI questionnaire was being used and that the score was derived. Only the principal investigator and data manager had full access to the complete database. Second, the STAI questionnaire was used as a tool for assessing anxiety levels, and the investigators were aware that there were other instruments or tests available for consideration. The STAI was chosen because it is a self-administered questionnaire validated in the Spanish version that has been widely used in a variety of contexts and has excellent internal consistency (Buela-Casal et al., 2011, s.f.; Gustafson et al., 2020). In future studies, different methods for determining anxiety levels may be of great interest, e.g., the 7-item Generalized Anxiety Disorder Scale (GAD-7) (Wang et al., 2023) or the 9-item Patient Health Questionnaire (PHQ-9) (Halperin et al., 2021). Third, vital signs (oxygen saturation; perfusion index; systolic, diastolic, and mean blood pressure; and heart rate and temperature) were measured as noninvasive, quick, and inexpensive methods to prevent unobtrusive interference during clinical simulation. Future studies could be very valuable for exploring the relationship between cortisol (or other biomarkers) and anxiety. In the present study, especially with respect to the pandemic cohort, invasive methods or techniques involving fluids (blood or saliva) to determine specific biomarkers were particularly inappropriate. Fourth, critical information such as the level of exposure to COVID-19 or the previous mental health of the participants was not collected; therefore, the lack of these confounding factors should be considered when interpreting the results. In this sense, comparisons to explore differences between subgroups, such as gender or previous simulation experience, that could influence anxiety levels were not performed since, for the first case, no previous statistically significant effects were found, and for the other variables, the differences were inherent to the nature of the study. Finally, the sample selection may be overly standardized. The participants were randomly recruited from among all the undergraduate medical students in their final year of medical school. The participants were students who had previous hands-on clinical experience in hospital residencies, primary care, or emergency medical services. Therefore, all the participants had fundamental knowledge about the initial management of critical patients and were familiar with the specific equipment and tools available in the laboratory simulation. This voluntarily imposed bias was designed to generate a homogeneous cohort to be assessed fairly accurately and where the educational background was comparable so that the influence on anxiety values would be consistent. In line with this last limitation, it is necessary to mention that the sample was obtained by a criterion of opportunity from a single university. However, in future studies, we expect to compare anxiety levels among different healthcare students (nurses, midwives, general practitioners, etc.) and different universities. Few studies have been conducted on the level of anxiety in undergraduate medical students who are exposed to the effects of COVID-19. Future studies analyzing this stressor or similar stressors that may arise and become a reality in students’ lives are essential for adequately planning simulation sessions.

5. Conclusions

In summary, the COVID-19 pandemic opened a novel and unprecedented paradigm, disrupting healthcare and health sciences education. Under these unique circumstances, medical students had to complete studies under very challenging biosafety conditions and with noticeable uncertainty about the progression of the pandemic. Anxiety levels in the pandemic cohort were substantially elevated, regardless of previous experience in simulation, the role adopted (leader or assistant), or the randomized scenario was undertaken. In addition, these results may contribute to understanding the phenomenon encountered during the hard phase of the COVID-19 pandemic and could serve as training for similar situations in the future. Performing CS in biohazardous environments significantly increases anxiety levels, so establishing mitigating measures to minimize the undesired effects of anxiety and promote the simulation-based learning process is necessary.
Understanding the factors that directly affect anxiety levels, and therefore the efficiency of the proposed simulation session can be of great interest to education managers and teachers, helping them design curricular practices on the basis of clinical simulation in a more proportionate way.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/bs15040447/s1, Table S1: STROBE Statement—checklist of items that should be included in reports of observational studies; Figure S1: Sample size; Table S2: Comparison of baseline characteristics according to scenarios; Table S3: Comparison of those variables with a statistically significant main effect; Figure S2. Boxplot of the statistically significant variables.

Author Contributions

R.M.-S. and F.M.-R. conceptualized the project, managed and coordinated the project, assisted with the design of the methodology, analyzed the data, and prepared the initial and final drafts of the manuscript. A.S.-G. and M.Á.C.V. take responsibility for the data and their analysis. S.D.-G., P.G.-I., S.S.-B., J.R.S., L.T.P.-A. and S.d.S.S. assisted with the management and coordination of the project, assisted with the design of the methodology, and helped review the manuscript. All the authors performed a critical review and approved the final manuscript for interpretation of the data and important intellectual input. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Convocatoria de Proyectos de Innovación Docente de la. Conferencia Nacional de Decanas y Decanos de Enfermería (PID_03CNDE2022). Sponsor role: None.

Institutional Review Board Statement

This study was approved by the Ethics Committee for Drug Research of the Health Areas of Valladolid (PI-033/18) on 8 May 2018. The review protocol was registered as ISRCTN32132176 (doi.org/10.1186/ISRCTN32132176). Details of the study design, statistical analysis plan and raw data are available online.

Informed Consent Statement

Written informed consent was obtained from the participants to publish this paper.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
COVID-19Coronavirus Disease
CSClinical Simulation
GAD-7Generalized Anxiety Disorder Scale
IQRsInterquartile Ranges
PHQ-99-Item Patient Health Questionnaire
PPEPersonal Protective Equipment
SARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus
STAIState-Trait Anxiety Inventory
STROBEStrengthening The Reporting Of Observational Studies In Epidemiology

References

  1. Al-Balas, M., Al-Balas, H. I., Jaber, H. M., Obeidat, K., Al-Balas, H., Aborajooh, E. A., Al-Taher, R., & Al-Balas, B. (2020). Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: Current situation, challenges, and perspectives. BMC Medical Education, 20(1), 341. [Google Scholar]
  2. Alsoufi, A., Alsuyihili, A., Msherghi, A., Elhadi, A., Atiyah, H., Ashini, A., Ashwieb, A., Ghula, M., Hasan, H. B., Abudabuos, S., Alameen, H., Abokhdhir, T., Anaiba, M., Nagib, T., Shuwayyah, A., Benothman, R., Arrefae, G., Alkhwayildi, A., Alhadi, A., … Elhadi, M. (2020). Impact of the COVID-19 pandemic on medical education: Medical students’ knowledge, attitudes, and practices regarding electronic learning. PLoS ONE, 15(11), e0242905. [Google Scholar]
  3. Althwanay, A., Ahsan, F., Oliveri, F., Goud, H. K., Mehkari, Z., Mohammed, L., Javed, M., & Rutkofsky, I. H. (2020). Medical education, pre- and post-pandemic era: A review article. Cureus, 12(10), e10775. [Google Scholar] [CrossRef]
  4. Anderson, R. J., Bauer, P. R., Arghami, A., Haney, R. M., Reisdorf, E. M., & Baalson, K. (2025). Virtual Reality Simulation to Improve Postoperative Cardiothoracic Surgical Patient Outcomes. American Journal of Critical Care, 34(2), 111–118. [Google Scholar] [CrossRef]
  5. Barbadoro, P., Brunzini, A., Dolcini, J., Formenti, L., Luciani, A., Messi, D., Papetti, A., Ponzio, E., Germani, M., Starlab Working Collaborative Group, Arsego, D., Bianchi, E., De Angelis, R., Del Bene, L., Landi, R., Mandorli, F., Marcone, M. R., Micheletti, R., Paolucci, G., … Adrario, E. (2023). Stress responses in high-fidelity simulation and standard simulation training among medical students. BMC Medical Education, 23(1), 116. [Google Scholar] [CrossRef]
  6. Bommer, C., Sullivan, S., Campbell, K., Ahola, Z., Agarwal, S., O’Rourke, A., Jung, H. S., Gibson, A., Leverson, G., & Liepert, A. E. (2018). Pre-simulation orientation for medical trainees: An approach to decrease anxiety and improve confidence and performance. The American Journal of Surgery, 215(2), 266–271. [Google Scholar] [CrossRef]
  7. Buela-Casal, G., Guillén-Riquelme, A., & Seisdedos-Cubero, N. (2011). Cuestionario de ansiedad estado-rasgo: Adaptación Española (8th ed.). TEA Ediciones. [Google Scholar]
  8. Chow, J., Al-Duaij, L., Last, N., Sheth, U., Rehman, M., Azim, A., Khalid, F., Blissett, S., & Sibbald, M. (2024). Transformational learning and professional identity formation in postgraduate competency-based medical education. Medical Education, 59(4), 409–417. [Google Scholar] [CrossRef]
  9. Chu, D. K., Akl, E. A., Duda, S., Solo, K., Yaacoub, S., Schünemann, H. J., El-Harakeh, A., Bognanni, A., Lotfi, T., Loeb, M., Hajizadeh, A., Bak, A., Izcovich, A., Cuello-Garcia, C. A., Chen, C., Harris, D. J., Borowiack, E., Chamseddine, F., Schünemann, F., … Reinap, M. (2020). Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis. The Lancet, 395(10242), 1973–1987. [Google Scholar] [CrossRef]
  10. Dost, S., Hossain, A., Shehab, M., Abdelwahed, A., & Al-Nusair, L. (2020). Perceptions of medical students towards online teaching during the COVID-19 pandemic: A national cross-sectional survey of 2721 UK medical students. BMJ Open, 10(11), e042378. [Google Scholar] [CrossRef]
  11. ElBagoury, M., Tolba, M. M., Nasser, H. A., Jabbar, A., Elagouz, A. M., Aktham, Y., & Hutchinson, A. (2021). The find of COVID-19 vaccine: Challenges and opportunities. Journal of Infection and Public Health, 14(3), 389–416. [Google Scholar] [CrossRef] [PubMed]
  12. Fredericks, S., ElSayed, M., Hammad, M., Abumiddain, O., Istwani, L., Rabeea, A., Rashid-Doubell, F., & Bella, A. M. (2021). Anxiety is associated with extraneous cognitive load during teaching using high-fidelity clinical simulation. Medical Education Online, 26(1), 1994691. [Google Scholar] [CrossRef]
  13. Grasselli, G., Greco, M., Zanella, A., Albano, G., Antonelli, M., Bellani, G., Bonanomi, E., Cabrini, L., Carlesso, E., Castelli, G., Cattaneo, S., Cereda, D., Colombo, S., Coluccello, A., Crescini, G., Molinari, A. F., Foti, G., Fumagalli, R., Iotti, G. A., … Cecconi, M. (2020). Risk factors associated with mortality among patients with COVID-19 in intensive care units in Lombardy, Italy. JAMA Internal Medicine, 180(10), 1345–1355. [Google Scholar] [CrossRef]
  14. Gustafson, L. W., Gabel, P., Hammer, A., Lauridsen, H. H., Petersen, L. K., Andersen, B., Bor, P., & Larsen, M. B. (2020). Validity and reliability of State-Trait Anxiety Inventory in Danish women aged 45 years and older with abnormal cervical screening results. BMC Medical Research Methodology, 20(1), 89. [Google Scholar] [CrossRef]
  15. Halperin, S. J., Henderson, M. N., Prenner, S., & Grauer, J. N. (2021). Prevalence of anxiety and depression among medical students during the COVID-19 pandemic: A cross-sectional study. Journal of Medical Education and Curricular Development, 8, 2382120521991150. [Google Scholar] [CrossRef]
  16. Hernández-Martínez, A., Rodríguez-Almagro, J., Martínez-Arce, A., Romero-Blanco, C., García-Iglesias, J. J., & Gómez-Salgado, J. (2021). Nursing students’ experience and training in healthcare aid during the COVID-19 pandemic in Spain. Journal of Clinical Nursing, Online ahead of print. [Google Scholar] [CrossRef]
  17. Ju, M., Bochatay, N., Werne, A., Essakow, J., Tsang, L., Nottingham, M., Franzon, D., Lyndon, A., & van Schaik, S. (2024). Changing the conversation: Impact of guidelines designed to optimize interprofessional facilitation of simulation-based team training. Advances in Simulation, 9(1), 43. [Google Scholar] [CrossRef]
  18. Koca, A., Schlatter, S., Delas, Q., Denoyel, L., Lehot, J., Lilot, M., & Rimmelé, T. (2023). Influence of the embedded participant on learners’ performance during high-fidelity simulation sessions in healthcare. BMC Medical Education, 23(1), 751. [Google Scholar] [CrossRef]
  19. Liaw, S. Y., Chan, S. W., Chen, F., Hooi, S. C., & Siau, C. (2014). Comparison of Virtual Patient Simulation With Mannequin-Based Simulation for Improving Clinical Performances in Assessing and Managing Clinical Deterioration: Randomized Controlled Trial. Journal of Medical Internet Research, 16(9), e214. [Google Scholar] [CrossRef]
  20. Liaw, S. Y., Rethans, J., Scherpbier, A., & Piyanee, K. (2011). Rescuing A Patient In Deteriorating Situations (RAPIDS): A simulation-based educational program on recognizing, responding and reporting of physiological signs of deterioration. Resuscitation, 82(9), 1224–1230. [Google Scholar] [CrossRef]
  21. Lim, W. Y., Ong, J., Vimal, V., Lim, H., Tan, H. C., Wong, P., Ho, V. K., & Ong, S. G. K. (2020). High-fidelity simulation training with PPE may optimise resuscitation outcomes in the COVID-19 era. Resuscitation, 159, 42–44. [Google Scholar] [CrossRef]
  22. Martín Rodríguez, F., Fernández Pérez, C., Castro Villamor, M., Martín Conty, J. L., Arnillas Gómez, P., & Casado Vicente, V. (2018). Does level D personal protective equipment guard against hazardous biologic agents during cardiopulmonary resuscitation? Emergencias, 30(2), 119–122. [Google Scholar] [PubMed]
  23. Martín-Rodríguez, F., Sanz-García, A., López-Izquierdo, R., Benito, J. F. D., Martín-Conty, J. L., Villamor, M. A. C., & Ortega, G. J. (2020). Predicting health care workers’ tolerance of personal protective equipment: An observational simulation study. Clinical Simulation in Nursing, 47, 65–72. [Google Scholar] [CrossRef] [PubMed]
  24. Martín-Rodríguez, F., Villamor, M. A. C., López-Izquierdo, R., Rubiales, R. M. P., Ortega, G. J., & Sanz-García, A. (2021). Can anxiety in undergraduate students in a high-fidelity clinical simulation be predicted? A randomized, sham-controlled, blinded trial. Nurse Education Today, 98, 104774. [Google Scholar] [CrossRef]
  25. McCoy, C. E., Rahman, A., Rendon, J. C., Anderson, C. L., Langdorf, M. I., Lotfipour, S., & Chakravarthy, B. (2018). Randomized controlled trial of simulation vs. standard training for teaching medical students high-quality cardiopulmonary resuscitation. Western Journal of Emergency Medicine, 20(1), 15–22. [Google Scholar] [CrossRef] [PubMed]
  26. Métais, A., Omarjee, M., Valero, B., Gleich, A., Mekki, A., Henry, A., Duclos, A., Lilot, M., Rode, G., & Schlatter, S. (2025). Determining the influence of an intervention of stress management on medical students’ levels of psychophysiological stress: The protocol of the PROMESS-Stress clinical trial. BMC Medical Education, 25(1), 225. [Google Scholar] [CrossRef]
  27. Nembhard, M. D., Burton, D. J., & Cohen, J. M. (2020). Ventilation use in nonmedical settings during COVID-19: Cleaning protocol, maintenance, and recommendations. Toxicology and Industrial Health, 36(9), 644–653. [Google Scholar] [CrossRef]
  28. Pandemic Fatigue. (2020). Pandemic fatigue—Reinvigorating the public to prevent COVID-19. Policy framework for supporting pandemic prevention and management (Licence: CC BY-NC-SA 3.0 IGO). WHO Regional Office for Europe. [Google Scholar]
  29. Rajab, M. H., Gazal, A. M., & Alkattan, K. (2020). Challenges to online medical education during the COVID-19 pandemic. Cureus, 12(7), e8966. [Google Scholar] [CrossRef]
  30. Rose, S. (2020). Medical student education in the time of COVID-19. JAMA, 323(21), 2131–2132. [Google Scholar] [CrossRef]
  31. Saddik, B., Hussein, A., Sharif-Askari, F. S., Kheder, W., Temsah, M., Koutaich, R. A., Haddad, E. S., Al-Roub, N. M., Marhoon, F. A., Hamid, Q., & Halwani, R. (2020). Increased levels of anxiety among medical and non-medical university students during the COVID-19 pandemic in the united Arab emirates. Risk Management and Healthcare Policy, 13, 2395–2406. [Google Scholar] [CrossRef]
  32. Segura Azuara, N. d. l. Á., Eraña Rojas, I. E., Valeria Luna-de-la-Garza, M., Castorena-Ibarr, J., & López Cabrera, M. V. (2020). Analysis of anxiety on early clinical encounters: Experiences using clinical simulation in undergraduate students. Educación Médica, 21(6), 377–382. [Google Scholar] [CrossRef]
  33. Shin, S., Park, J., & Kim, J. (2015). Effectiveness of patient simulation in nursing education: Meta-analysis. Nurse Education Today, 35(1), 176–182. [Google Scholar] [CrossRef] [PubMed]
  34. Sindiani, A. M., Obeidat, N., Alshdaifat, E., Elsalem, L., Alwani, M. M., Rawashdeh, H., Fares, A. S., Alalawne, T., & Tawalbeh, L. I. (2020). Distance education during the COVID-19 outbreak: A cross-sectional study among medical students in North of Jordan. Annals of Medicine & Surgery, 59, 186–194. [Google Scholar] [CrossRef]
  35. Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research, 22(9), e21279. [Google Scholar] [CrossRef] [PubMed]
  36. Tabah, A., Ramanan, M., Laupland, K. B., Buetti, N., Cortegiani, A., Mellinghoff, J., Morris, A. C., Camporota, L., Zappella, N., Elhadi, M., Povoa, P., Amrein, K., Vidal, G., Derde, L., Bassetti, M., Francois, G., Kai, N. S. Y., & De Waele, J. J. (2020). Personal protective equipment and intensive care unit healthcare worker safety in the COVID-19 era (PPE-SAFE): An international survey. Journal of Critical Care, 59, 70–75. [Google Scholar] [CrossRef]
  37. Wang, H., Zhou, Y., Dai, P., Guan, Y., Zhong, J., Li, N., & Yu, M. (2023). Anxiety symptoms and associated factors among school students after 2 years of the COVID-19 pandemic: A cross-sectional study in Zhejiang Province, China. BMJ Open, 13(12), e079084. [Google Scholar] [CrossRef]
  38. Weimer, J. M., Sprengart, F. M., Vieth, T., Göbel, S., Dionysopoulou, A., Krüger, R., Beer, J., Weimer, A. M., Buggenhagen, H., Kloeckner, R., Pillong, L., Helfrich, J., Waezsada, E., Wand, P., & Weinmann-Menke, J. (2025). Simulator training in focus assessed transthoracic echocardiography (FATE) for undergraduate medical students: Results from the FateSim randomized controlled trial. BMC Medical Education, 25(1), 21. [Google Scholar] [CrossRef]
Behavsci 15 00447 g001
Figure 1. Diagram of study participation.
Figure 1. Diagram of study participation.
Behavsci 15 00447 g001
Behavsci 15 00447 g002
Figure 2. Differences pre- and posttraining for the trait STAI (a) and state STAI (b). Group 1 = prepandemic, 2 = pandemic, 3 = postvaccination. For the trait STAI, pandemic vs prepandemic (p < 0.001), postvaccination (p = 0.001), and prepandemic vs postvaccination (p = 0.974) periods were compared. For the state, the STAI pandemic was compared with the prepandemic (p < 0.001), postvaccination (p < 0.001), and prepandemic vs. postvaccination (p = 0.512) groups.
Figure 2. Differences pre- and posttraining for the trait STAI (a) and state STAI (b). Group 1 = prepandemic, 2 = pandemic, 3 = postvaccination. For the trait STAI, pandemic vs prepandemic (p < 0.001), postvaccination (p = 0.001), and prepandemic vs postvaccination (p = 0.974) periods were compared. For the state, the STAI pandemic was compared with the prepandemic (p < 0.001), postvaccination (p < 0.001), and prepandemic vs. postvaccination (p = 0.512) groups.
Behavsci 15 00447 g002
Table 1. Descriptive data by study period.
Table 1. Descriptive data by study period.
Study Period
Variable aPrepandemicPandemicPostvaccinationp Value b
No. (%) with data150 (40.2)76 (20.4)147 (39.4)N.A.
Sex at birth, female99 (66)54 (71.1)103 (70.1)0.660
Age, year23 (23–24)24 (23–24)23 (23–24)0.338
Body mass index, kg/m221.6 (19.8–23.3)22.2 (20.1–24.7)21.1 (19.5–23.2)0.078
Lifestyle habits
 Smoking20 (13.3)15 (19.7)7 (4.76)0.002
 Coffee/tea130 (86.7)64 (84.2)110 (74.8)0.025
 Energy drinks9 (6)12 (15.8)8 (5.44)0.014
Prior simulation experience, yes0 (0)51 (67.1)102 (69.4)<0.001
Leader, yes78 (52)41 (53.9)78 (53.1)0.960
Randomization 0.998
 Hip fracture20 (13.3)11 (14.5)20 (13.6)
 Asthmatic exacerbation20 (13.3)10 (13.2)20 (13.6)
 Sepsis20 (13.3)9 (11.8)20 (13.6)
 Acute heart failure18 (12.0)7 (9.21)17 (11.6)
 Polytraumatized20 (13.3)11 (14.5)18 (12.2)
 Anaphylactic shock20 (13.3)11 (14.5)18 (12.2)
 Acute myocardial infarction19 (12.7)10 (13.2)19 (12.9)
 Thromboembolism in PW13 (8.67)7 (9.21)15 (10.2)
Baseline evaluation
Vital signs
 Oxygen saturation, %98 (97–98)98 (97–98)98 (97–98)0.743
 Perfusion index, %2.1 (1.1–4.4)2.1 (1–4.3)1.9 (0.9–4.4)0.989
 SBP, mmHg130 (122–146)125 (115–136)134 (124–154)<0.001
 DBP, mmHg84 (75–91)76 (71–86)84 (77–90)0.006
 MBP, mmHg100 (92–110)92 (87–102)102 (93–112)<0.001
 Heart rate, beats/min84 (74–96)91 (74–104)93 (80–111)0.002
 Temperature, °C37 (36.8–37.3)37.1 (36.6–37.3)36.9 (36.6–37.2)0.076
Postsimulation evaluation
Vital signs
 Oxygen saturation, %98 (97–99)98 (97–99)98 (97–99)0.905
 Perfusion index, %1.4 (0.9–2.4)1.3 (0.7–2.3)1.4 (0.8–2.4)0.787
 SBP, mmHg130 (121–149)132 (122–137)133 (123–160)0.005
 DBP, mmHg83 (76–91)84 (79–91)83 (77–92)0.932
 MBP, mmHg101 (91–113)101 (95–105)104 (90–114)0.206
 Heart rate, beats/min85 (74–98)88 (75–100)91 (80–114)0.001
 Temperature, °C36.9 (36.7–37.2)36.9 (36.6–37.2)36.8 (36.6–37.1)0.044
Abbreviations: NA: not applicable; SBP: systolic blood pressure; DBP: diastolic blood pressure; MBP: mean blood pressure; PW: pregnant woman. a Values are expressed as the total number (percentage) and median (25th–75th percentile), as appropriate. b The Mann–Whitney U test or chi-square test was used as appropriate.
Table 2. Anxiety level by the group during the study period.
Table 2. Anxiety level by the group during the study period.
Study Period
Variable aPrepandemicPandemicPostvaccinationp Value b
No. (%) with data150 (40.2)76 (20.4)147 (39.4)N.A.
Baseline evaluation
State-Trait Anxiety Inventory
 State, points41 (37–47)57 (50–63)41 (37–45)<0.001
 Trait, points31 (27–37)41 (33–48)32 (29–37)<0.001
Postsimulation evaluation
State-Trait Anxiety Inventory
 State, points45 (40–50)66 (62–71)46 (41–50)<0.001
 Trait, points32 (27–39)47 (40–51)34 (29–39)<0.001
State-Trait Anxiety Inventory difference
State, points2.5 (−1 to 6)9 (4–17)3 (0–7)<0.001
Trait, points1 (−1 to 3)3 (0.2–8)1 (−1 to 5)<0.001
Abbreviations: NA: not applicable. a Values are expressed as the total number (percentage) and median (25th–75th percentile), as appropriate. b The Mann–Whitney U test or chi-square test was used as appropriate.
Table 3. Multinomial logistic regression.
Table 3. Multinomial logistic regression.
Variable aEstimateStandard ErrorZ Valuep Value
 Previous experience: prepandemic20.5831.8NANA
 Previous experience: postvaccination−0.230.36−0.660.508
 SBP: prepandemic0.040.022.040.040
 SBP: postvaccination0.050.01NANA
 MBP: prepandemic−0.020.02−0.810.415
 MBP: postvaccination−0.010.02−0.720.466
 STAI-State: prepandemic−0.170.03−5.43<0.001
 STAI-State: postvaccination−0.120.02−4.63<0.001
 STAI-Trait: prepandemic−0.0010.04−0.030.974
 STAI-Trait: postvaccination−0.030.03−1.030.302
Abbreviations: SBP: systolic blood pressure; MBP: mean blood pressure; STAI-State: differences pre/post for State-STAI; STAI-Trait: differences pre/post for trait-STAI. a The group name following the variable name refers to the time period for which the comparison was performed. The reference group is the pandemic time period.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Martín-Sánchez, R.; Sanz-García, A.; Diaz-Gonzalez, S.; Castro Villamor, M.Á.; Sáez-Belloso, S.; Rabanales Sotos, J.; Pinilla-Arribas, L.T.; González-Izquierdo, P.; de Santos Sánchez, S.; Martín-Rodríguez, F. A Comparison of Pre- and Post-Clinical Simulation Anxiety Levels of Undergraduate Medical Students Before and During the COVID-19 Pandemic: A Prospective Cohort Study. Behav. Sci. 2025, 15, 447. https://doi.org/10.3390/bs15040447

AMA Style

Martín-Sánchez R, Sanz-García A, Diaz-Gonzalez S, Castro Villamor MÁ, Sáez-Belloso S, Rabanales Sotos J, Pinilla-Arribas LT, González-Izquierdo P, de Santos Sánchez S, Martín-Rodríguez F. A Comparison of Pre- and Post-Clinical Simulation Anxiety Levels of Undergraduate Medical Students Before and During the COVID-19 Pandemic: A Prospective Cohort Study. Behavioral Sciences. 2025; 15(4):447. https://doi.org/10.3390/bs15040447

Chicago/Turabian Style

Martín-Sánchez, Rafael, Ancor Sanz-García, Samantha Diaz-Gonzalez, Miguel Ángel Castro Villamor, Silvia Sáez-Belloso, Joseba Rabanales Sotos, Leyre T. Pinilla-Arribas, Pablo González-Izquierdo, Sara de Santos Sánchez, and Francisco Martín-Rodríguez. 2025. "A Comparison of Pre- and Post-Clinical Simulation Anxiety Levels of Undergraduate Medical Students Before and During the COVID-19 Pandemic: A Prospective Cohort Study" Behavioral Sciences 15, no. 4: 447. https://doi.org/10.3390/bs15040447

APA Style

Martín-Sánchez, R., Sanz-García, A., Diaz-Gonzalez, S., Castro Villamor, M. Á., Sáez-Belloso, S., Rabanales Sotos, J., Pinilla-Arribas, L. T., González-Izquierdo, P., de Santos Sánchez, S., & Martín-Rodríguez, F. (2025). A Comparison of Pre- and Post-Clinical Simulation Anxiety Levels of Undergraduate Medical Students Before and During the COVID-19 Pandemic: A Prospective Cohort Study. Behavioral Sciences, 15(4), 447. https://doi.org/10.3390/bs15040447

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop