Overcoming Cultural Barriers in Undergraduate Nursing Education Using Voice Enhanced High Fidelity Simulation: The Sultan Qaboos University Experience
Bibliographic record
Abstract
Cultural barriers can significantly diminish educator's chances of teaching clinical skills and competencies to students [1].We report about Voice Enhanced High Fidelity Simulation (VES) using Gaumard's NOELLE® Advanced Maternal Care simulator to teach undergraduate male nursing students maternity nursing skills.This innovation was essential because as a minimum entry-topractice competency, baccalaureate nursing graduates are required to competently care for mothers and their families during labor and childbirth, and to provide safe and supportive environment, while being able to identify complications and respond to psychological needs [2][3].In our study, we found that simulation helped students to fill the gap between theoretical knowledge and practical skills.Secondly, simulation helped the students to experience heightened awareness and deeper appreciation of the process of labour and childbirth.Thirdly, simulation enhanced student's communication ability with the mother.Furthermore, simulated experiences taught the students when to call for help.Finally, simulation enabled the students to better understand the role and tenets of interprofessional collaboration in the management of labour.We conclude that VES can be used to overcome barriers that hinder the teaching of male nursing student's attitudes, skills and competencies to provide safe care to childbearing mothers and their families, including the tenets of how to effectively collaborate with others during their care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".