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Record W6962229085 · doi:10.17605/osf.io/gk5sc

Impact of virtual reality simulation on pre-registration nursing students’ preparation for clinical practice

2024· other· en· W6962229085 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityInstructional simulationHaptic technologyClinical PracticeMixed realityHealth careNurse educationClinical judgment

Abstract

fetched live from OpenAlex

Scoping Review Protocol: Impact of virtual reality simulation on pre-registration nursing students’ preparation for clinical practice Introduction: VR as a form of simulation is an innovative and emerging technology that is scalable and capable of offering large numbers of students with a fully immersive simulation experience. Immersive VR engages the user’s senses (vision, hearing, and motion) in a 3D virtual world. It requires a head mounted display e.g. VR headsets/googles that may be enhanced by haptic devices, visual tracking, motion sensors or speech (Cant et al., 2019; Moro et al., 2017). Less immersive VR learning (virtual simulation) offers repetition and accessibility of content through screen-based simulation (Phillips, Harper, & DeVon, 2023). The evidence for the effectiveness of VR in health professions education is mounting. A systematic review and meta-analysis of VR in nursing education found that VR offers superior potential in advancing nursing students’ theoretical knowledge, practice proficiencies and overall satisfaction (Liu et al., 2023). Other literature reviews emphasise the effectiveness of VR at improving cognitive outcomes such as theoretical knowledge (Chen et al., 2020; Shorey & Ng, 2021; Woon et al., 2021). Kiegaldie and Shaw (2023) found that VR fostered critical thinking and provided an efficient and sustainable platform for learning about complex clinical situations. Little is known about how well undergraduate nurses are prepared for clinical practice or placements when offered simulation in a virtual environment. The objective of this research is to evaluate the effectiveness of virtual reality-based simulation on pre-registration students’ preparation for clinical practice. An examination of the literature revealed that many studies examined perceptions of learning and learner satisfaction rather than the achievement of learning outcome. As academics we need to examine the effectiveness and retention of learning resulting from virtual reality simulation. If it is shown to be effective, we also need to assess whether virtual reality simulation could be a suitable alternative for a select amount of clinical practicum in the future. Additionally, virtual reality-based simulation may offer a solution to managing the difficulties of simulation physical space thus enabling education providers the ability to expand the number of clinical scenarios available for student education. Key definitions: Virtual reality – The use of computer technology to create an interactive three-dimensional world in which the objects have a sense of spatial presence: virtual environment and virtual world are synonyms for virtual reality (Healthcare Simulation Dictionary, 2020) Augmented reality - A technology that overlays digital computer-generated information on objects or places in the real world for the purpose of enhancing the user experience (Healthcare Simulation Dictionary, 2020) Virtual simulation – According to the Healthcare Simulation Dictionary (2020) and a study conducted by Cant et al. (2023) virtual simulation offers real people (learners) the opportunity to engage with interactive technology to immerse themselves in a virtual world—a world that does not exist in real time—using a computer screen, haptic devices and audio-visual aides so they may develop their psychomotor and psychosocial skills and learn to apply theoretical knowledge. Mixed reality - A simulator that combines virtual and physical components (Healthcare Simulation Dictionary, 2020) Virtual environment - A simulated environment rendered by a computer, mobile device, or virtual reality / augmented reality / mixed reality device (Healthcare Simulation Dictionary, 2020) Virtual world - Like Virtual Environment, though implies multiple characters, learners, or participants and potentially, a larger scale than a virtual environment. A virtual world or massively multiplayer online world (MMOW) in a computer-based simulated environment (Healthcare Simulation Dictionary, 2020) Virtual patient - A representation of an actual patient. Virtual patients can take many forms such as software-based physiological simulators, simulated patients, physical manikins, and simulators (Healthcare Simulation Dictionary, 2020) Aim: The aim of this scoping review is to synthesize the best available evidence regarding the use and effectiveness of virtual reality simulation for pre-registration nursing students’ preparation for clinical practice. Question: How is virtual reality simulation being used to prepare pre-registration nursing students for clinical practice? Search Criteria: Population Concept Context Nursing student Virtual reality Pre-registration Nursing Augmented reality Pre-licensure Nursing Virtual simulation Undergraduate Nursing VR Mixed reality Virtual environment Virtual world ** When screening look for ‘preparation for clinical practice” Inclusion & Exclusion Criteria: Inclusion Criteria Exclusion Criteria All studies published between 2013 to 2024 Any study outside the year range Peer-reviewed Non- peer reviewed Must be primary studies (quantitative, qualitative and/or mixed methods) English language Non-English Full-text available Full text not available Under-graduate / pre-licensure nursing education Include if mixed studies with undergraduate plus postgraduate student cohorts Post graduate studies Nursing only Any other health profession Among virtual simulation technologies, there are several technologies: virtual simulation (VS), virtual reality (VR), mixed reality (MR), augmented reality (AR) and 3D simulation, virtual worlds (VWs) delivered via any digital device (headset, phone, iPad, laptop, tablet, computer). In person simulation e.g. mannikin based, simulated participants, part-task skills education Must make reference to VR etc used to prepare students for clinical placement/practice No explicit reference to preparation for clinical placement/practice Search Strategy: From year 2013 to current • CINAHL • ProQuest • ERIC • Ovid-Medline (Pub Med and Medline is 98% same) Screening and extraction protocol: Covidence • 2 people screen title and abstracts • 3rd person to manage any disagreements • 2 people screen full text • 3rd person to manage any disagreements • 1 person to extract data using piloted form below (need to finalise) • Second reviewer to check for correctness and completeness of extracted data Data Extraction and Charting Results: Title Format Covidence # Numerical Title Text Authors Text Journal Text Year Text Country(s) of origin Code 1=AU, 2=USA, 3=UK, 4=Canada, 5=Europe, 6=Asia etc Study Aim Text Study Method for Primary Research 1=Quantitative, 2=Qualitative, 3=Mixed methods, 4=RCT, 5=Quasi experimental, 6=Case study etc Population/Year level/progress Code 1= First year, 2= Second year, 3= Third year + Population size Numerical Course Text Cultural Context Text Description of the type of VR modality e.g. VR, AR, MR, Virtual Sim and how it is used Text Clinical practice focus Text Length of intervention Numerical (days, hours, weeks, semester) Instances of intervention Numerical Type of outcome measure used Code Communication platform technologies Text Educational technologies Text General findings Text Educational impact Clinical placement/practice impact Attrition Text Limitations Text References: Cant, R., Cooper, S., Roland, S., & Bogossian, F. (2019). What’s in a Name? Clarifying the Nomenclature of Virtual Simulation. Clinical Simulation in Nursing, 27, 26-30. https://doi-org.ez.library.latrobe.edu.au/10.1016/j.ecns.2018.11.003 Chen, F. Q, Leng, Y. F., Jian, F. G., Dan, W. W., Cheng, L., Bin, C., Zhi, L. S. (2020). Effectiveness of virtual reality in nursing education: metanalysis. Journal of Medical Internet Research, 22(9). https://www.jmir.org/2020/9/e18290/ Kiegaldie, D., & Shaw, L. (2023). Virtual reality simulation for nursing education: effectiveness and feasibility. BMC Nursing, 22, 1-13. https://doi.org/10.1186/s12912-023-01639-5 Liu, K., Zhang, W., Li, W., Wang, T., & Zheng, Y. (2023). Effectiveness of virtual reality in nursing education: a systematic review and meta-analysis. BMC Medical Education, 23, 1-10. https://doi.org/10.1186/s12909-023-04662-x Moro, C., Stromberga, Z., Raikos, A., & Stirling, A. (2017). The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anatomical Sciences Education, 10(6), 549-559. https://anatomypubs-onlinelibrary-wiley-com.ez.library.latrobe.edu.au/doi/full/10.1002/ase.1696 Phillips. J.M., Harper. M.G., & DeVon. H.A. (2023). Virtual Reality and Screen-Based Simulation Learner Outcomes Using Kirkpatrick's Evaluation Levels: An Integrative Review. Clinical Simulation in Nursing, 79, 46-60. https://www-sciencedirect-com.ez.library.latrobe.edu.au/science/article/pii/S1876139923000154?via%3Dihub Shorey, S., & Ng, E.D. (2021). The use of virtual reality simulation among nursing students and registered nurses: A systematic review. Nursing Education Today, 98, 1-12. https://search.lib.latrobe.edu.au/permalink/f/rl56ei/TN_cdi_openaire_primary_doi_dedup_d94ceed6b04bb335028ca3cb7584bb98 Woon, A.P.N., Mok, W.Q., Chieng, Y.J.S., Zhang, H.M., Ramos, P., Mustadi, H.B., & Lau, Y. (2021). Effectiveness of virtual reality training in improving knowledge among nursing students: A systematic review, meta-analysis and meta-regression. Nursing Education Today, 98, 1-9. https://doi-org.ez.library.latrobe.edu.au/10.1016/j.nedt.2020.104655

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.189
GPT teacher head0.651
Teacher spread0.462 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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