Psychological safety in simulation: Perspectives of nursing students and faculty
Bibliographic record
Abstract
BACKGROUND: As simulation education continues to grow, more consideration has been given to creating and maintaining a psychologically safe simulation learning environment. It is known that failing to provide psychological safety can lead to feelings of incompetence and a lack of confidence with students. However, it is essential to understand what makes and maintains psychological safety in simulation from both student and facilitator's perspectives. In further understanding psychological safety, nursing educators can challenge students to think beyond that of task attainment and into the deeper realm of critical thinking and critical reflection. OBJECTIVES: The aim of this study was to understand students' and facilitators perspectives of psychological safety in simulation. METHODS: Participants in this qualitative interpretive description study were seven students and four faculty that were chosen using convenience sampling. The data was collected over a 2-week period where semi-structured interviews were used to collect the participants perspectives. Data analysis was continuous and iterative and used inductive analysis. RESULTS: There were two student themes which focused on the student-facilitator interaction: 1) dynamic interaction, 2) student self-efficacy. The facilitators results showed two themes which focused on 1) simulation design and 2) trust. CONCLUSION: Diverging thoughts are present between faculty and students in what constitutes psychological safety. In describing both the similarities and differences, we have a better understanding on how to create and maintain psychological safety thereby, providing students with the best learning experience possible.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".