A Virtual Care Unit for the Development of Clinical Monitoring Competencies in a Critical Care Setting: A Qualitative Descriptive Study
Bibliographic record
Abstract
Introduction: Virtual reality (VR) is a teaching method increasingly used to promote the acquisition of certain competencies by nursing students. One competency that can be developed with these tools is clinical judgement in the monitoring of critical care patients. Before incorporating this type of pedagogy into training initiatives for nurses, its acceptability and feasibility in nursing education programs must be validated. Purpose: This study describes the experience of nursing students who relied on UVS to develop clinical monitoring competencies in a critical care setting. Method: A qualitative descriptive design was used, and semi-structured interviews were held with 13 participants from three Quebec universities who underwent a VR experience requiring them to care for a hemodynamically unstable patient. Results: Participants identified 10 facilitating factors and nine hindering factors with regard to the use of UVS as a method for teaching students how to develop knowledge of clinical monitoring in a critical care setting. Conclusion: Use of VR in nursing education must consider both facilitating and hindering factors in order to foster positive student experiences and promote the method’s acceptance.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".