Alice’s Delirium: A Theatre-based Simulation Scenario for Nursing
Bibliographic record
Abstract
As an educational methodology, simulation has been used by nursing education at the academic level for numerous years and has started to gain traction in the onboarding education and professional development of practicing nurses. Simulation allows the learner to apply knowledge and skills in a safe environment where mistakes and learning can happen without an impact on patient safety. The development of a simulation scenario to demonstrate the benefits of simulation education methodologies to a large group of nurse educators was requested by nursing education leadership at The Ottawa Hospital (TOH). Since the demonstration of this scenario in the fall of 2016, there has been significant uptake and adaptation of this particular scenario within the nursing education departments of TOH. Originally written to be used with a simulated patient (SP), "Alice" has since been adapted to be used with a hi-fidelity manikin within an inpatient surgery department continuing professional development (CPD) program for practicing nurses, orientation for nurses to a level 2 trauma unit and at the corporate level of nursing orientation using an SP. Therefore, this scenario is applicable to nurses practicing in an area of inpatient surgery at varying levels, from novice to expert. It could easily be adapted for use with medicine nursing education programs. The case presented in this technical report is of the simulation scenario used for the inpatient surgery CPD program. Varying adaptations of the case are included in the appendices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".