Overcoming Reluctance towards High Fidelity Simulation - A Mutual Challenge for Nursing Students’ and Faculty Teachers
Bibliographic record
Abstract
BACKGROUND: One strategy to develop nursing students’ clinical judgment are the use of high-fidelity patient simulation (HFS). The aim of the study was twofold. Firstly, the aim of this study was to describe the nursing students’ experiences while participating in HFS, and secondly to describe faculty teachers’ reflections about nursing students’ need in HFS and the related teaching challenges.METHOD: Data was collected in focus group discussions and individual interviews, analyzed using thematic qualitative content analysis.FINDINGS: The nursing students’ experienced HFS as being thrown into an uncertain, exposure situation. This were for some, reason for reluctance. The teachers challenge was motivating and coaching the students throughout a demanding teaching situation. DISCUSSION: Students’ ability to perform in HFS is influenced by self-perceived efficacy, own attitudes and responsibility for one’s learning, which are a challenge for the teachers.CONCLUSION: HFS methodology can be useful to identify gaps and strengths in students’ professional transition towards becoming registered nurses. Overcoming reluctance towards HFS is a mutual challenge for faculty teachers and nursing students. By entering the scenario with a positive mindset, nursing students can improve their ability to perform clinical judgments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".