A Virtual, Simulated Code White for Undergraduate Nursing Students
Bibliographic record
Abstract
BACKGROUND: Nurses and nursing students are increasingly vulnerable to workplace violence, both verbal and physical, as health care settings and clients cope with unprecedented challenges including the COVID-19 pandemic. Concurrently, clinical learning opportunities for nursing students have been curtailed by public health restrictions and limited capacity. While virtual simulations have been promoted as an alternative to clinical hours, their effectiveness as an educational intervention on workplace violence has yet to be assessed. PURPOSE: The authors sought to evaluate a virtual, simulated code white-a set of organized responses to a client, visitor, or staff member exhibiting the potential for violence-involving 4th year undergraduate nursing students, randomly sorted into an intervention group and a control group. METHODS: Pre and post test measures of knowledge and attitudes about mental health, workplace violence and virtual simulation were collected, as well as qualitative data from focus groups. FINDINGS: While the sample size (n = 24) was insufficient to detect meaningful differences between the intervention and control groups, descriptive statistics and focus group data revealed significant gaps in participants' knowledge around managing workplace violence. Participants rated the virtual simulation highly for its realism and the opportunity to experience working in a virtual environment, while they felt the preamble and debrief were too short. CONCLUSIONS: The findings illustrate a virtual code white simulation has clear educational benefits, and that multiple iterations, both virtual and in person, would most likely increase the benefits of the intervention.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".