Developing a High-Fidelity Simulation Program in a Nursing Educational Setting
Bibliographic record
Abstract
This change project was developed in response to the lack of a high-fidelity simulation program at a midwestern university in the United States. The use of clinical simulation as a teaching-and-learning strategy has significantly increased within nursing education. Unlike some colleges, this university had a dedicated simulation laboratory with two high-fidelity simulators; however, there was no clinical simulation program to use this equipment. The expensive simulation equipment sat unused because of the lack of funding for dedicated faculty, lack of a champion to implement, shortage of faculty time, minimal knowledge of the use of high-fidelity simulators, and a lack of curriculum integration. The purpose of the project was to create a simulation program, including faculty development and curriculum integration of simulation-based experiences. The framework of the program was based on the International Nurses Association of Clinical Simulation and Learning "Standards of Best Practice: Simulation." The high-fidelity simulation program grew from 0 simulation encounter per year to greater than 250 per year from the onset of the project. Faculty accepted high-fidelity simulation as a new teaching strategy and incorporated a minimum of at least one simulation-based experience within their courses. Simulation has been integrated successfully into the current curriculum. Students and faculty have positively evaluated simulation as an effective teaching/learning strategy. Each semester has seen an increase in the number of simulations, types of simulations, and acuity of simulations offered in clinical courses for 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".