Implications of Simulation and Real-Life Learning for Novice Emergency Nurses in COVID-19
Bibliographic record
Abstract
Abstract In recent decades, technological influences have propelled the advancement of nursing education, both in practical and theoretical andragogy. Simulation technology has become an integral component of many nursing programs and clinical practice settings. The introduction of simulation challenges current mentorship and practice-based real-life learning, alluding to the question: Is the use of simulation to educate nurses within the clinical environment a sufficient replacement for real-life learning? The recent severe acute respiratory syndrome coronavirus 2 disease (SARS-CoV-2 or COVID-19) pandemic has caused emergency departments (EDs) to re-examine educational practices, potentially replacing real-life learning with simulation technology to support novice nurses as they care for acutely ill COVID-19 patients. Many experienced ED nurses have left the profession during the COVID-19 pandemic, and novice ED nurses with minimal ED experience have been hired in their places. While their enthusiasm, skill, and knowledge are highly valued, novice ED nurses face many challenges in the complex ED environment, particularly in the rapidly changing COVID-19 pandemic. This article provides an overview of simulation learning and real-life learning and how both of these models, along with their educational strategies, may be implemented by ED nurse educators in assisting novice ED nurses transitioning to independent practice. Keywords: simulation, real-life learning, novice nurse education, emergency department, COVID-19
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.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".