Simulation training for hyperacute stroke unit nurses
Bibliographic record
Abstract
National clinical guidelines have emphasized the need to identify acute stroke as a clinical priority for early assessment and treatment of patients on hyperacute stroke units. Nurses working on hyperacute stroke units require stroke specialist training and development of competencies in dealing with neurological emergencies and working in multidisciplinary teams. Educational theory suggests that experiential learning with colleagues in real-life settings may provide transferable results to the workplace with improved performance. Simulation training has been shown to deliver situational training without compromising patient safety and has been shown to improve both technical and non-technical skills (McGaghie et al, 2010). This article describes the role that simulation training may play for nurses working on hyperacute stroke units explaining the modalities available and the educational potential. The article also outlines the development of a pilot course involving directly relevant clinical scenarios for hyperacute stroke unit patient care and assesses the benefits of simulation training for hyperacute stroke unit nurses, in terms of clinical performance and non-clinical abilities including leadership and communication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".