Facilitating Reflection-in-Action During High-Fidelity Simulation
Bibliographic record
Abstract
Background: High-fidelity simulation has become common practice in undergraduate nursing education and highly skilled educators are needed to facilitate these complex learning opportunities. Reflective practice is considered an essential step to learning in simulation, starting with reflection-before-action through prebriefing, and ending with reflection-on-action, through debriefing. However, reflection-in-action may be the hallmark of artistry or mastery of a subject. Therefore, undergraduate nursing simulation facilitators need to develop skills to identify and support learners to reflect-in-action. Methods: I conducted a concept analysis to develop an understanding of the phenomena of reflection-in-action during high-fidelity simulation. I then conducted a descriptive phenomenology study with 11 undergraduate nursing simulation facilitators from eight colleges and universities across Alberta. Participants underwent a semi-structured interview, and Colazzi’s seven step process for analysis was utilized to understand the phenomenon of reflection-in-action as experienced by undergraduate nursing simulation facilitators during high-fidelity simulation. Results: Through the concept analysis, I identified four defining attributes of reflection-in-action: (a) reflection-in-action occurs during simulation scenarios; (b) a critical learning juncture occurs and is identified by the learners; (c) a pause in student action occurs; and (d) knowledge sharing through discussion. The experiences of the participants aligned with the findings from the concept analysis. Participants in the study were experienced simulation facilitators. Despite this, they had little formal training regarding reflection-in-action. Participants 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 inadequate preparation. Participants supported reflection-in-action through prebriefing, remaining curious, and providing cues, prompts, or facilitated paused. The benefits of reflection-in-action include collaborative learning, building confidence, critical thinking, and embedding reflection into practice. Conclusions: Phenomenological exploration of experiences of participants was able to add insights to enhance understanding of a poorly defined subject. The insights from this work may enhance simulation facilitator’s ability to effectively support reflection-in-action within high-fidelity simulation. These findings may contribute to theory development, checklists, and decision trees to support the facilitation of reflection-in-action during high-fidelity simulation. Keywords: nursing, education, simulation, reflection, reflection-in-action
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".