Contextualizing Learning in a Resuscitation Simulation Experience: A Supportive Approach to Simulation for Novice Learners
Bibliographic record
Abstract
Introduction: Simulation-based experiences (SBEs) continue to be utilized in undergraduate nursing programs as a teaching strategy to contextualize learning. This is especially important for novice students in a resuscitation SBE, where it is important to learn not only psychomotor skills but also the skills required for teamwork, understanding roles, and communication in teams, inherent to nursing practice. Background: Undergraduate nursing students desire teaching strategies to support their learning during high impact SBEs, such as cardiopulmonary resuscitation. These strategies need to support novice learners by providing timely feedback on performance, uncover immediate knowledge gaps, and offer opportunities for deliberate practice. An alternate facilitation approach including deliberate practice with multiple in-event short debriefing sessions was compared to the traditional post-event debriefing approach to facilitation in a resuscitation scenario. Method: Using a mixed-methods descriptive study, a traditional facilitated guided post-event debrief approach to SBE was compared to a facilitated guided in-event approach using rapid cycle deliberate practice and debriefing using the promoting excellence and reflective learning in simulation (PEARLS). Data collection included Likert scale and open-ended survey questions completed post simulation (n = 161). Results: Quantitative results indicated a higher level of support, improved areas of hand-over communication and early recognition of a deteriorating patient with the facilitator guided in-event group. Qualitative results identified three themes in the facilitator guided in-event group: supportive and deeper level learning, operationalized reflection, and debriefing-in-the-moment. Conclusion: The facilitator guided in-event group using rapid cycle deliberate practice with PEARLS framework lends to a more supportive approach and a deeper level of learning in simulation with undergraduate student nurses while contextualizing their learning.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".