Developing a High-Fidelity Simulation Program in a Nursing Educational Setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it