Using Immersive Virtual Reality to Impact Clinical Reasoning of New Graduate Nurses
Bibliographic record
Abstract
New graduate nurses (NGNs) face challenging work environments due to experiential learning gaps, complex patients, and high rates of attrition (Tomblin Murphy et al., 2022). Clinical reasoning (CR) improves nursing quality by fostering confidence, autonomy, readiness for practice, and minimizing patient safety events such as failing to recognize a patient who is decompensating (Mohammadi‐Shahboulaghi et al., 2021; Powers et al., 2019). CR is a complex and iterative cognitive process whereby nurses apply knowledge and experience to a clinical situation (Benner, 1984; Kavanagh & Szweda, 2017; Levett-Jones et al., 2010). Virtual reality (VR) is a technology increasingly used to support CR in undergraduate nursing students (Sim et al., 2022). VR reduces the cost, space, and equipment required to develop CR while increasing access to diverse scenarios (R. P. Cant & Cooper, 2017). There is limited research examining the impact of VR on NGNs’ CR. Research questions: This work addresses the following: A. What are NGNs’ perspectives on integrating a VR experience into their transition to practice? B. How does an immersive VR experience impact NGNs’ CR skill development? Methods: A triangulation mixed methods with a single-group quasi-experimental design and an interpretive description approach was used to collect data from 12 NGNs in Halifax, Nova Scotia. Participants either had an active Registered Nurse or Licensed Practical Nurse license with the Nova Scotia College of Nurses, and started work within the last 12 months within an acute care nursing unit in the Central zone of Nova Scotia Health. The nurses’ CR cycle framework was used to design a VR experience using the Edify VR platform and the HP Reverb G2 head-mounted display (Levett-Jones et al., 2010). Data collection occurred pre-test, during the VR experience, post-test, and one-month distant post-test. The mixed methods analysis integrated qualitative interviews, surveys, and field notes, with quantitative measurements of cybersickness, using the Simulator Sickness Questionnaire (SSQ) and CR, using the Nurse’s Clinical Reasoning Scale (NCRS). Results: Participants shared five qualitative themes: VR as a contributor to a positive learning environment, VR hardware and navigation challenges, minimal cybersickness, improving CR with repeated practice, and participant VR recommendations. The themes of minimal cybersickness and improving CR with repeated practice were congruent with the quantitative findings. Participants’ median total SSQ scores were 3.78 (95% CI –34.26, -22.92, p < 0.05) and associated sub-scores were significantly lower than those who completed a similar gaming VR experience (M= 34.26) (Saredakis et al., 2020). Pre-test (M = 60.19, SD = 7.19) and post-test (M = 66.18, SD = 7.22) NCRS revealed a significant increase in CR (p = 0.0013). This increase was sustained with no significant difference in NCRS between the post-test and the distant post-test time points (p = 0.76). Conclusions: This is the first study to examine the impact of an immersive VR experience on NGNs’ CR skills. NGNs expressed enthusiasm for the VR experience noting its ability to promote a positive learning environment. While cybersickness was not a significant barrier to VR use, participants did note challenges with the VR equipment and the virtual environment. The findings support that VR is an impactful tool to promote NGNs’ CR. Additional research is needed to compare the efficacy of VR to other teaching modalities such as mannikin-based high-fidelity simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".