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Record W3048798283 · doi:10.2196/20249

Learning With Virtual Reality in Nursing Education: Qualitative Interview Study Among Nursing Students Using the Unified Theory of Acceptance and Use of Technology Model

2020· article· en· W3048798283 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Nursing · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityQualitative researchNurse educationPsychologyMedical educationNursingComputer scienceMedicineHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Digital games-based learning is a method of using digital games to impart knowledge. Virtual reality (VR) programs are a practical application of this method. Due to demographic changes, the nursing profession will become increasingly important. These VR applications can be of use in training nurses for future professional challenges they may encounter. The continuous development of VR applications enables trainees to encounter simulated real life effectively and to experience increasingly concrete situations. This can be of great importance in nursing education, since 3-dimensionality enables a better visualization of many fields of activity and can prevent potential future errors. In addition to this learning effect, VR applications also bring an element of fun to learning. OBJECTIVE: The aim of this qualitative research effort is to observe the degree of acceptance of VR applications by nursing students in Germany. Various factors, including social influences, performance expectations, and effort expectations, are taken into consideration. METHODS: With a qualitative cohort study, the acceptance of nursing students towards VR applications in anatomy teaching was determined. The 12 participants were first asked to fill out a quantitative questionnaire on their sociodemographic characteristics and the extent to which they valued and liked using technology. The participants were then allowed to test the VR application themselves and were finally asked about their experience in a qualitative interview. For the collection of data and the analysis of results, the unified theory of acceptance and use of technology was used in this study. RESULTS: Overall, the study shows that the interviewed persons rated the VR application quite positively. The greatest influence in this was the personal attitude towards technology; the higher this affinity is, the more useful the VR application appears. Social influences can also increase the participant's own acceptance if peers have a positive attitude towards such applications. The study shows that the trainees' motivation to learn was increased by using VR. We believe this is because each trainee could learn individually and the VR application was perceived as an enjoyable activity. Nevertheless, the cost factor of implementing VR applications in nursing training is currently still an obstacle, as not every institution has such financial capacities. CONCLUSIONS: The extent to which the use of VR applications in the training of nursing staff is justified depends on the degree of personal acceptance. The collected results give good practice-oriented insight into the attitude of trainees towards VR. Many of the interviewed persons saw benefits in the use of VR technologies. As VR applications are constantly developing, it is necessary to conduct further studies on VR applications in nursing education and to include other possible disciplines in which these applications can be helpful.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.108
GPT teacher head0.431
Teacher spread0.323 · 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