Exploring the meaning of psychological harm experienced by undergraduate nursing students in simulation: A hermeneutic study
Bibliographic record
Abstract
BACKGROUND: Undergraduate nursing students who engage in simulation-based learning may experience psychological harm. Little is known about psychological harm in this context as opposed to psychological safety. METHOD: Gadamer's philosophical hermeneutics was used to explore the meaning of psychological harm experienced by undergraduate nursing students during simulation. A purposive sample was used to recruit nine students who were enrolled in a Canadian university baccalaureate nursing program. Data collection involved conducting semi-structured interviews over 12-months (January-December 2024). The interviews with participants were recorded and transcribed and the data was analyzed using the interpretive method of hermeneutics. RESULTS: Findings from this research revealed that students may experience psychological harm in simulation accompanied by a range of responses. Psychological harm has the potential to impact students' engagement and learning in the debrief and clinical environment. Participants' experiences emphasized increased hesitance to accept subsequent leadership roles in simulation and uncertainty relative to future career choices. CONCLUSIONS: This study highlighted that positive relationships with simulationists and student peers, and recognizing the impact of emotions requires consideration. Psychological harm has implications for nursing students and simulationists.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".