Immediate repeat of a septic shock simulation: Nursing students’ lived experience
Bibliographic record
Abstract
Background and objective: The COVID-19 pandemic has placed a significant increase for the need of high quality, high fidelity simulation practices to replace limited clinical experiences. Repetitive experiential practice is a training strategy used among professionals to bridge theoretical concepts to action. Furthermore, immediate repetitive experiential practice in a simulation environment is a novel approach that holds promise for learners to improve their response to critical conditions through increased faculty guided reflection. This study aimed to explore student attitudes regarding an immediate repeat of a simulation as a first step to explore training effectiveness.Methods: Students enrolled in a complex health baccalaureate nursing course participated in an immediate repeat of a septic shock simulation. An interpretive phenomenological approach was utilized to better understand undergraduate nursing students' lived experience of learning through a repeat septic shock simulation.Results: Three themes emerged: Appreciation of Knowledge, Awareness of Skill, and Awareness of Attitudes.Conclusions: Learners found an immediate repeat of the simulation a valuable teaching strategy. Participants described a growing sense of differentiating priorities when managing a patient in septic shock. The immediate repeat simulation was deemed impactful to the learners’ knowledge, skills, and attitudes. This is a viable option for educators to incorporate at a time when forced to utilize simulation experiences to replace limited clinical opportunities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".