The State of Knowledge Regarding the Use of Simulation in Pre-Licensure Nursing Education: A Mixed Methods Systematic Review
Bibliographic record
Abstract
This project is a mixed-methods systematic review on the use of simulation in pre-licensure nursing. This research question guided this review: What is the best evidence available upon which to base decisions regarding the use of simulation experiences with pre-licensure nursing students? Searches of CINAHL Plus with Full Text, MEDLINE, and ERIC were performed to identify relevant literature. These searches yielded 1220 articles. After duplicates were removed and titles and abstracts were reviewed for relevance to the inclusion criteria, the remaining 852 articles were independently assessed for quality by pairs of researchers. Forty-seven articles were retained. Findings were grouped into research using high-, medium-, and low-fidelity simulations and a group where researchers included several or all types of simulation. The conclusion is that insufficient quality research exists to guide educators in making evidence-based decisions regarding simulation. More rigorous and multi-site research is needed.
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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 | 0.045 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".