The Role of Virtual Simulation in De-Escalating a Patient Demonstrating Escalating Behavior
Bibliographic record
Abstract
Background Undergraduate nursing students are unprepared to manage patients demonstrating escalating aggressive behavior encountered during their clinical placements. Confidence and competence surrounding de-escalation skills can be achieved through virtual simulated learning opportunities. This study evaluated undergraduate nursing students' perceptions of confidence and success in their de-escalation skills following a virtual simulation intervention. Method A quantitative, one-group pretestposttest design was used to complete this study. Students ( n = 33) completed a 10-question demographic questionnaire with four additional questions on participants' psychosocial well-being considering the pandemic, and a nine-question pre- and postvirtual simulation de-escalation confidence and knowledge survey. Results Virtual simulation had positive effects on participants' feelings of confidence and success. Male students and students who reported Caucasian as their ethnicity were the most comfortable with de-escalating behaviors. Conclusion These findings emphasize the effectiveness of de-escalation education. [ J Nurs Educ . 2024;63(10):698–702.]
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".