Undergraduate nursing simulation facilitators lived experience of facilitating reflection-in-action during high-fidelity simulation: A phenomenological study
Bibliographic record
Abstract
BACKGROUND: Reflective practice is an essential step to learning in high-fidelity simulation, yet, reflection-in-action is an often overlooked yet important opportunity to support student learning. OBJECTIVES: To explore and describe the lived experience of undergraduate nursing simulation facilitators use of reflection-in-action during high-fidelity simulation. DESIGN: A descriptive phenomenological study. SETTING: A western Canadian province. PARTICIPANTS: Undergraduate nursing simulation facilitators with experience in nursing education and simulation facilitation. METHODS: We conducted 11 semi-structured interviews and utilized Colaizzi's seven step process of analysis to discover the essence of undergraduate nursing simulation facilitators use of reflection-in-action during high-fidelity simulation. RESULTS: Simulation facilitators were able to identify reflection-in-action during high-fidelity simulation when students paused, collaborated, shared their thinking aloud, and changed their course of action. Barriers to reflection-in-action included learner fear and anxiety, poor simulation design, and inadequately prepared students and facilitators. Simulation facilitators supported reflection-in-action through prebriefing, facilitator curiosity, and providing cue, prompts, and facilitated paused. Some of the noted benefits to reflection-in-action include promoting collaborative learning, building confidence and critical thinking, and embedding reflection into practice. CONCLUSIONS: The insights from this research can be used to guide reflection-in-action strategy development and future research in high-fidelity simulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".