Utilization of simulation-based learning in undergraduate nursing education in Universities, Kenya: Scoping review protocol
Bibliographic record
Abstract
Simulation is an active teaching and learning method with proven effectiveness in the cognitive and affective domain (Kim et al., 2016). Nursing education requires adequate preparation in the cognitive, affective and psychomotor domains. A nurse professional is required to demonstrate highest levels of critical thinking and decisional making in the clinical field (Koukourikos et al., 2021) The scoping review will explore utilization of simulation in undergraduate nursing education in Kenyan universities. According to Nursing council of Kenya, the regulator for nursing education, Kenya has 37 universities both private and public institutions (NCK, 2025). Nursing education is offered to preservice and in-service students. Several advancements have been made in creating an enabling simulation environment. Artificial intelligence has been integrated in the teaching methodology and influences the outcome in the learners. For example, in a study done in Canada among undergraduate nursing students, some students prefer artificial intelligence enhanced simulation over patient simulation. Further, the study concludes, the two modalities should be integrated to supplement skills acquisition in the training of nursing students (Harder et al., 2025). There is lack of context specific frameworks in designing of simulation implementation in low resourced countries e.g. Kenya (Abdulai et al., 2022) This review aims to explore the utilization of simulation in Kenyan Universities offering nursing education at undergraduate level. RATIONALE To the best of knowledge of the authors, this is the first scoping review aimed to explore the utilization of the simulation across the 37 universities offering nursing education at undergraduate level. The results of the review will supplement the ongoing discussion on changing and adoption of simulation in nursing education. The review will highlight the level of utilization, challenges experienced, opportunities available and future recommendations. REVIEW QUESTIONS The research question was developed based on guidance of Joanna Briggs Institute scoping review methodology namely; Population, Concept and Context (PCC) (M. D. Peters et al., 2024) What is the level of utilization of simulation in Kenyan universities training Undergraduate nursing students.
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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 | 0.052 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".