Faculty Experiences with Integrating Mandated High-Fidelity Human Patient Simulation (HF-HPS) Into Clinical Practice: A Phenomenological Study
Bibliographic record
Abstract
Background High fidelity human patient simulation (HF-HPS) is a teaching innovation in nursing education which may not be used to its full potential. This study seeks to understand the lived experiences of nurse faculty who are required to integrate HF-HPS into their teaching practice. Method A phenomenological methodology was used. Seventeen female nurse faculty teaching in the second year of a new collaborative bachelor of science in nursing program were interviewed about their experiences integrating mandated HF-HPS into their teaching practices. Results Six themes describing the participants' experiences were identified: striving for self-efficacy, struggling to maintain autonomy, being part of a community of practice, adopting HF-HPS as a teaching innovation, being an advocate, and being proud. An emerging theme, being an outsider, was discussed. Conclusion This research has implications for nurse faculty and educational administrators integrating a new teaching innovation.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".