The Modification of Vital Signs According to Nursing Students’ Experiences Undergoing Cardiopulmonary Resuscitation Training via High-Fidelity Simulation: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: High-fidelity simulation represents a primary tool in nursing education, especially when hands-on practical training is involved. OBJECTIVE: We sought to determine the influence of high-fidelity clinical simulation, applied during cardiopulmonary resuscitation (CPR) training, on blood pressure, heart rate, stress, and anxiety levels in 2 groups of nursing students. One group had experience in health contexts, whereas the other group had none. METHODS: We performed a quasi-experimental study. Data were collected between May and June 2015 and included measurements of all the resting values, before and after participation in CPR clinical simulations regarding the 2 groups of university students (ie, with and without experience). RESULTS: An increase in vital signs was observed in students after participating in a clinical simulation scenario, especially the heart rate. In all students, increased stress and anxiety levels were observed before the first simulation case scenario. Also, in all study groups, a decrease in vital signs, stress levels, and anxiety was observed throughout the study. CONCLUSIONS: Participation in high-fidelity simulation experiences has both physiological and psychological effects on students.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".