High fidelity simulator experience for enhancing communication effectiveness: Applications to quality and safety education for nurses
Bibliographic record
Abstract
The United States Joint Commission identified miscommunication as the main cause of unexpected injuries and mortality not related to the patient’s medical condition. One strategy for improving information transfer and inter-professional communication is the Situation-Background-Assessment-Recommendation (SBAR) communication model. The purpose of this study was to test the effects of exposure to a real time high fidelity simulation experience. A quasi-experimental design, consisting of one treatment group was conducted. A purposeful sample of N = 45 respondents was drawn from students registered in an undergraduate level clinical course. Pre-tests and post-tests based on the American Association of Colleges of Nursing's (AACN's) Quality and Safety in Nursing Education (QSEN) were administered to measure if exposure to the simulation training impacted knowledge and attitudes regarding communication in the delivery of patient care. Results based on a paired t-test revealed an 8.27 point increase in scores after exposure to the intervention. This outcome was highly significant ( df = 44, t = -4.86, p = .000). Although a few students did not appear to benefit, results suggest that exposure to the SBAR model during a high fidelity simulation experience is generally a successful method for enhancing student knowledge and attitudes regarding effective communication in health care.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".