Speaking Up Against Hierarchy: A Simulation Geared Towards Nursing Students
Bibliographic record
Abstract
Background As simulation science continues to advance, the focus previously put on scenario creation and debriefing must now be applied to other components of the learning experience. There is a need to examine the effectiveness of pre-simulation activities and how they relate to the overall simulation experience and learning outcomes. However, few randomized controlled trials have been conducted comparing different approaches in the pre-simulation preparatory phase and the impact on learning outcomes. Methods A randomized controlled trial was conducted with undergraduate nursing students (n=83) who were randomized to a traditional paper case study (control group) or an interactive pre-simulation activity (intervention group). The use of the two-challenge rule and Satisfaction and Self Confidence in Learning (SSL) was evaluated. Results The proportion of students who utilized the two-challenge rule in the intervention group was significantly higher than the control group. Results from the two independent-samples Wilcoxon-Mann-Whitney test showed a significant difference in the median of the total score of the SSL W=2.5, p <0.001, satisfaction W=6.0, p <0.001, and self-confidence W=68.0, p <0.001 in learning between third-year nursing students in the control and intervention groups. Conclusion Our results showed significant differences in the use of the two-challenge rule by students who completed an interactive pre-simulation activity (intervention group) compared to those who completed the paper case study (control group). Additionally, students in the intervention group were more self-confident and satisfied with the entire simulation intervention than the control group. From a pedagogical perspective, this study also emphasizes the need to ground simulations in theory. Moreover, there is value in using progressive frameworks, i.e., revised Medical Research Council (2014) in simulation design and research to ensure high quality. More studies are required to examine the right dosage and type of pre-simulation activity and impact on learning outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".