Healthcare Simulation Use to Support Guidelines Implementation: An Integrative Review
Bibliographic record
Abstract
INTRODUCTION: Simulation-based education is a useful teaching and learning strategy that can help to implement guidelines into healthcare settings. Therefore, the purpose of this paper is to collate, synthesize, and analyze the literature focusing on the use of simulation as an educational strategy to support guidelines implementation among healthcare providers (HCPs). MATERIALS AND METHODS: Integrative literature review using the methodology proposed by Ganong. RESULTS/DISCUSSION: Twenty-three articles were selected, the majority (n=19, 82%) used simulation in practice settings and pre- and post-test measurement (n=16, 69%). All studies that assessed simulation effects highlighted that the use of simulation improved the measured outcomes related to guideline implementation. Simulation-based education can be an effective strategy to support guidelines implementation among HCPs, but aspects such as cost involved, time constraints, training of educators, and the HCPs' learning needs can affect its applicability. Future research should focus on more transparent reports related to the guidelines for simulation content, virtual learning, costs of simulation, and measurement of the long-term effects of simulation-based education.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".