Creating context with prebriefing: A case example using simulation
Bibliographic record
Abstract
Background: Educational advantages of simulation have been widely reported. Pre-briefing and debriefing support simulation methods. However, few detailed accounts of how the learning activities surrounding simulation are implemented exist. Objectives: This case example provides a detailed description of learning activities surrounding a simulation experience with a deteriorating cardiac patient. The educational sequence integrates Benner et al. ’s goals for transforming nursing education. The study objectives were to design and evaluate an educational sequence using narrative, games, and simulation to teach students how to manage and anticipate the care of a deteriorating patient. Design: A case example with descriptive quantitative and qualitative evaluation is presented. Setting: The study took place on multiple days in a university simulation laboratory. Participants: All study participants (n = 43) were senior students enrolled in a Bachelor of Science in Nursing program. Methods: Students experienced an educational sequence and then rated and ranked educational activities. Results are reported with descriptive statistics. Students and faculty responded to the question, “What will you take from this experience?” Their responses were evaluated using constant comparison and expert review for themes. Results: Students identified ‘knowing how’, ‘increasing confidence’ and ‘understanding roles’ as what they took from the experience. Students ranked the simulation itself as the most helpful. Conclusions: Incorporating Benner et al .’s transformational educational goals informed the educational sequence and engaged students in the learning experience. This paper adds uniquely to the nursing literature by providing detailed accounts of the activities surrounding simulation that support student learning in multiple domains.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| 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".