Development and Implementation of a Mobile-Integrated Simulation for COVID-19 Nursing Practice: A Randomized Controlled Pretest–Posttest Experimental Design
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Participants and Recruitment
2.3. Randomization and Blinding
2.4. Mobile Education Program
2.5. Measurements
2.5.1. COVID-19 Knowledge
2.5.2. Infection Prevention Practice Confidence
2.5.3. Clinical Decision-Making Confidence and Anxiety
2.6. High-Fidelity Simulation Training
2.7. Statistical Approach
3. Results
3.1. General Characteristics and Baseline Homogeneity
3.2. Effectiveness of Mobile-Integrated COVID-19 Nursing Practice Simulation Program
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Health Organization. Coronavirus Disease (COVID-19). 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed on 3 April 2020).
- Lee, S.; Choi, J.S. Factors influencing COVID-19 AstraZeneca (ChAdOx1) vaccination and side effects among health care workers in an acute general hospital. J. Korean Biol. Nurs. Sci. 2021, 23, 318–329. [Google Scholar] [CrossRef]
- Central Accident Investigation Headquarters, Central Discharge Countermeasures Headquarters. Promote the Securing of COVID-19 Beds in the Metropolitan Area in Response to the Increase in Patients. (13 August 2021, Regular Briefing). Available online: https://www.mohw.go.kr/react/al/sal0301ls.jsp (accessed on 13 October 2021).
- Monesi, A.; Imbriaco, G.; Mazzoli, C.A.; Giugni, A.; Ferrari, P. In-Situ simulation for intensive care nurses during the COVID-19 pandemic in Italy: Advantages and challenges. Clin. Simul. Nurs. 2022, 62, 52–56. [Google Scholar] [CrossRef] [PubMed]
- Labrague, L.J.; De Los Santos, J.A.A. COVID-19 anxiety among front-line nurses: Predictive role of organisational support, personal resilience and social support. J. Nurs. Manag. 2020, 28, 1653–1661. [Google Scholar] [CrossRef] [PubMed]
- Schwerdtle, P.N.; Connell, C.J.; Lee, S.; Plummer, V.; Russo, P.L.; Endacott, R.; Kuhn, L. Nurse expertise: A critical resource in the COVID-19 pandemic response. Ann. Glob. Health 2020, 86, 49. [Google Scholar] [CrossRef] [PubMed]
- Kang, H.; Im, J.; Kang, H.Y. Priority analysis of needs for COVID-19 infection control education for nurses at a medium-sized hospital. J. Korean Acad. Fundam. Nurs. 2022, 29, 472–483. [Google Scholar] [CrossRef]
- Ni, J.; Wu, P.; Huang, X.; Zhang, F.; You, Z.; Chang, Q.; Liao, L. Effects of five teaching methods in clinical nursing teaching: A protocol for systematic review and network meta-analysis. PLoS ONE 2022, 17, e0273693. [Google Scholar] [CrossRef] [PubMed]
- Hwang, W.J.; Lee, J. Effectiveness of the infectious disease (COVID-19) simulation module program on nursing students: Disaster nursing scenarios. J. Korean Acad. Nurs. 2021, 51, 648–660. [Google Scholar] [CrossRef] [PubMed]
- Lee, J.B.; Choi, J.S. Effect of an isolation-coping programme on patients isolated for colonization or infection with multi-drug-resistant organisms: A quasi-experimental study. J. Hosp. Infect. 2022, 129, 31–37. [Google Scholar] [CrossRef] [PubMed]
- Kanaki, K.; Kalogiannakis, M. Sample design challenges: An educational research paradigm. Int. J. Technol. Enhanc. Learn. 2023, 15, 266–285. [Google Scholar] [CrossRef]
- KCDC. MERS Response Guidelines in 5-1 ed. 2018. Available online: http://www.icdc.incheon.kr/upload/20180702140219123.pdf (accessed on 2 November 2023).
- KCDC. Ebola Virus Response Guidelines in 7 ed. 2017. Available online: http://www.gbcidc.or.kr/file/download.do?file_id=246 (accessed on 2 November 2023).
- Taghrir, M.H.; Borazjani, R.; Shiraly, R. COVID-19 and Iranian medical students; a survey on their related-knowledge, preventive behaviors and risk perception. Arch. Iran. Med. 2020, 23, 249–254. [Google Scholar] [CrossRef]
- Lee, S.J.; Jin, X.L.; Lee, S. Factors influencing COVID-19 preventive behaviors in nursing students: Knowledge, risk perception, anxiety, and depression. J. Korean Biol. Nurs. Sci. 2021, 23, 110–118. [Google Scholar] [CrossRef]
- Central Accident Investigation Headquarters, Central Discharge Countermeasures Headquarters. Coronavirus Disease-19 Response Manual (for Municipal). In 10-2 ed. 2021. Available online: http://ncov.mohw.go.kr/upload/viewer/skin/doc.html?fn=1637821171779_20211125151934.pdf&rs=/upload/viewer/result/202112/ (accessed on 2 November 2023).
- Polit, D.F.; Beck, C.T. The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Res. Nurs. Health 2006, 29, 489–497. [Google Scholar] [CrossRef]
- White, K.A. Development and validation of a tool to measure self-confidence and anxiety in nursing students during clinical decision making. J. Nurs. Educ. 2014, 53, 14–22. [Google Scholar] [CrossRef]
- Yu, M.; Eun, Y.; White, K.A.; Kang, K.J. Reliability and validity of Korean version of Nursing Students’ Anxiety and Self-Confidence with Clinical Decision Making Scale. J. Korean Acad. Nurs. 2019, 49, 411–422. [Google Scholar] [CrossRef]
- Ingrassia, P.L.; Ferrari, M.; Paganini, M.; Mormando, G. Role of health simulation centres in the COVID-19 pandemic response in Italy: A national study. BMJ Simul. Technol. Enhanc. Learn. 2021, 7, 379–384. [Google Scholar] [CrossRef] [PubMed]
- Zehler, A.; Cole, B.; Arter, S. Hyflex simulation: A case study of a creative approach to unprecedented circumstances. Clin. Simul. Nurs. 2021, 60, 64–68. [Google Scholar] [CrossRef]
- Aldekhyl, S.S.; Arabi, Y.M. Simulation role in preparing for COVID-19. Ann. Thorac. Med. 2020, 15, 134–137. [Google Scholar] [CrossRef] [PubMed]
- Suppan, L.; Abbas, M.; Stuby, L.; Cottet, P.; Larribau, R.; Golay, E.; Iten, A.; Harbarth, S.; Gartner, B.; Suppan, M. Effect of an e-learning module on personal protective equipment proficiency among prehospital personnel: Web-based randomized controlled trial. J. Med. Internet Res. 2020, 22, e21265. [Google Scholar] [CrossRef]
- Kasai, H.; Saito, G.; Ito, S.; Kuriyama, A.; Kawame, C.; Shikino, K.; Takeda, K.; Yahaba, M.; Taniguchi, T.; Igari, H.; et al. COVID-19 infection control education for medical students undergoing clinical clerkship: A mixed-method approach. BMC Med. Educ. 2022, 22, 453. [Google Scholar] [CrossRef] [PubMed]
- Alzoubi, H.; Alnawaiseh, N.; Al-Mnayyis, A.; Abu-Lubad, M.; Aqel, A.; Al-Shagahin, H. COVID-19—Knowledge, attitude and practice among medical and non-medical University Students in Jordan. J. Pure Appl. Microbiol. 2020, 14, 17–24. [Google Scholar] [CrossRef]
- Puspitasari, I.M.; Yusuf, L.; Sinuraya, R.K.; Abdulah, R.; Koyama, H. Knowledge, attitude, and practice during the COVID-19 pandemic: A review. J. Multidiscip. Healthc. 2020, 13, 727–733. [Google Scholar] [CrossRef]
- Rojo, E.; Oruña, C.; Sierra, D.; García, G.; Del Moral, I.; Maestre, J.M. Simulation as a tool to facilitate practice changes in teams taking care of patients under investigation for Ebola virus disease in Spain. Simul. Healthc. 2016, 11, 89–93. [Google Scholar] [CrossRef]
- Jang, I.S.; Park, M.H. Effect of infection control education based on isolation room-simulation for nursing students. J. Korean Acad. Nurs. Adm. 2021, 27, 379–389. [Google Scholar] [CrossRef]
- Kang, J. Simulation results for contamination comparisons by various use protocols of personal protective equipment. Korean J. Med. 2018, 93, 41–49. [Google Scholar] [CrossRef]
- Woda, A.; Hansen, J.; Paquette, M.; Topp, R. The impact of simulation sequencing on perceived clinical decision making. Nurse Educ. Pract. 2017, 26, 33–38. [Google Scholar] [CrossRef]
- Higgs, J.; Jensen, G.M.; Loftus, S.; Christensen, N. (Eds.) Clinical Reasoning in the Health Professions E-Book; Elsevier Health Sciences: Amsterdam, The Netherlands, 2018. [Google Scholar]
- Hoernke, K.; Djellouli, N.; Andrews, L.; Lewis-Jackson, S.; Manby, L.; Martin, S.; Vanderslott, S.; Vindrola-Padros, C. Frontline healthcare workers’ experiences with personal protective equipment during the COVID-19 pandemic in the UK: A rapid qualitative appraisal. BMJ Open 2021, 11, e046199. [Google Scholar] [CrossRef] [PubMed]
- Atthill, S.; Witmer, D.; Luctkar-Flude, M.; Tyerman, J. Exploring the impact of a virtual asynchronous debriefing method after a virtual simulation game to support clinical decision-making. Clin. Simul. Nurs. 2021, 50, 10–18. [Google Scholar] [CrossRef]
Topic | COVID-19 Care |
---|---|
Learning objective | Adhere to infection control guidelines when caring for COVID-19 patients admitted to the hospital. |
Outline | |
Supplies |
Characteristic | Category | Experimental Group (n = 55) | Control Group (n = 54) | x2/t/Z | p | |
---|---|---|---|---|---|---|
n (%) or M ± SD | n (%) or M ± SD | |||||
General characteristics | ||||||
Sex | Male | 3 (5.5) | 0 | 3.001 * | 0.243 | |
Female | 52 (94.5) | 54 (100) | ||||
Marital status | Single | 47 (85.5) | 42 (77.8) | 1.072 | 0.332 | |
Married | 8 (14.5) | 12 (22.2) | ||||
CLS | None | 41 (74.5) | 36 (66.7) | 1.546 | 0.460 | |
≥CN1 | 14 (25.5) | 18(33.3) | ||||
Age | M ± SD (years) | 27.74 ± 5.66 | 28.78 ± 5.5 | −0.856 | 0.395 | |
<30 years | 41 (74.5) | 35 (64.8) | 2.159 * | 0.369 | ||
31 to <40 years | 10 (18.2) | 15 (27.8) | ||||
≥40 years | 4 (7.3) | 4 (7.4) | ||||
Period of employment | M ± SD (months) | 57.63 ± 63.39 | 65.41 ± 62.47 | −1.044 | 0.301 | |
6 to <12 months | 10 (18.2) | 7 (13.0) | 2.252 | 0.532 | ||
1 to <2 years | 15 (27.3) | 10 (18.5) | ||||
2 to <5 years | 13 (23.6) | 15 (27.8) | ||||
≥5 years | 17 (30.9) | 22 (40.7) | ||||
Duration of current employment | M ± SD (months) | 13.76 ± 5.31 | 13.72 ± 4.95 | −0.384 | 0.702 | |
6 to <12 months | 19 (34.5) | 19 (35.2) | 0.052 | 0.820 | ||
1 to <2 years | 36 (65.5) | 35 (64.8) | ||||
Education level | Associate degree | 9 (16.4) | 10 (18.5)) | 0.002 | 0.996 | |
Bachelor’s degree or higher | 46 (83.6) | 44 (81.5) | ||||
COVID-19-related characteristics | ||||||
PPE training | Yes | 42 (76.4) | 42 (77.8) | 0.175 | 0.861 | |
No | 13 (23.6) | 12 (22.2) | ||||
COVID-19 education | Yes | 41 (74.5) | 32 (59.3) | 2.878 | 0.106 | |
No | 14 (25.5) | 22 (40.7) | ||||
COVID-19 care experience | Yes | 44 (80) | 42 (77.8) | 0.081 | 0.776 | |
No | 11 (11) | 12 (22.2) | ||||
COVID-19 infection history | Yes | 30 (54.5) | 25 (46.3) | 0.742 | 0.446 | |
No | 25 (45.5) | 29 (53.7) | ||||
Cohabiting family COVID-19 infection history | Yes | 28 (50.9) | 30 (55.6) | 0.236 | 0.702 | |
No | 27 (49.1) | 24 (44.4) | ||||
Prior simulation other than COVID-19 simulation | Yes | 22 (40) | 28 (51.9) | 1.541 | 0.251 | |
No | 33 (60) | 26 (48.1) | ||||
Baseline homogeneity in dependent variables | ||||||
Knowledge | 19.98 ± 2.50 | 20.65 ± 2.59 | −1.471 † | 0.141 | ||
Practice confidence | 110.16 ± 19.63 | 117.06 ± 20.01 | −1.850 | 0.067 | ||
Clinical decision-making anxiety | 50.40 ± 20.18 | 49.27 ± 19.58 | 0.392 | 0.696 | ||
Clinical decision-making confidence | 74.89 ± 25.27 | 79.01 ± 27.20 | 0.957 † | 0.339 |
Variable | Experimental Group (n = 55) | Control Group (n = 54) | t/Z | p | ||||
---|---|---|---|---|---|---|---|---|
Pre | Post | Difference | Pre | Post | Difference | |||
M ± SD | M ± SD | M ± SD | M ± SD | M ± SD | M ± SD | |||
Knowledge | 19.98 ± 2.50 | 22.88 ± 2.31 | 3.31 ± 3.20 | 20.65 ± 2.59 | 21.63 ± 2.39 | 1.33 ± 3.39 | 3.13 | 0.002 |
Practice | 110.16 ± 19.63 | 130.91 ± 18.88 | 20.75 ± 18.36 | 117.05 ± 20.01 | 120.56 ± 21.64 | 3.5 ± 22.80 | −4.175 * | <0.001 |
Clinical | 50.40 ± 20.18 | 47.89 ± 23.56 | −2.50 ± 25.45 | 49.27 ± 19.58 | 47.78 ± 22.13 | −1.49 ± 25.79 | −0.268 * | 0.789 |
decision-making anxiety | ||||||||
Clinical | 74.89 ± 25.27 | 95.20 ± 24.89 | 20.31 ± 23.66 | 79.19 ± 27.43 | 90.46 ± 26.51 | 11.28 ± 34.41 | −1.347 * | 0.178 |
decision-making confidence |
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. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lee, S.-H.; Choi, J.-S. Development and Implementation of a Mobile-Integrated Simulation for COVID-19 Nursing Practice: A Randomized Controlled Pretest–Posttest Experimental Design. Healthcare 2024, 12, 419. https://doi.org/10.3390/healthcare12040419
Lee S-H, Choi J-S. Development and Implementation of a Mobile-Integrated Simulation for COVID-19 Nursing Practice: A Randomized Controlled Pretest–Posttest Experimental Design. Healthcare. 2024; 12(4):419. https://doi.org/10.3390/healthcare12040419
Chicago/Turabian StyleLee, Sun-Hwa, and Jeong-Sil Choi. 2024. "Development and Implementation of a Mobile-Integrated Simulation for COVID-19 Nursing Practice: A Randomized Controlled Pretest–Posttest Experimental Design" Healthcare 12, no. 4: 419. https://doi.org/10.3390/healthcare12040419