Nursing Student Perceptions of Intraprofessional Team Education Using High-Fidelity Simulation
Bibliographic record
Abstract
High-fidelity simulation in health professional programs helps educators and students meet the challenges of increasingly complex clinical practice settings. Simulation has been used primarily to train nursing students either in interprofessional teams or within their respective nursing training levels. However, students' experiences of learning alongside others in different levels or years of the nursing program have not been explored. BSN students (N = 48) were placed in intraprofessional teams (i.e., one student from each nursing level) to manage acute pediatric and adult simulation scenarios. Students were instructed to manage the clinical scenario based on their level of clinical competence and education. Following debriefing, students responded to a satisfaction survey regarding their simulation experiences and their perceptions of learning within an intraprofessional nursing team. Project results suggest that intraprofessional educational experiences provide rich learning opportunities for both third-year and fourth-year nursing students. In addition, simulation provides a context within which to support intraprofessional nursing student education.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".