Using Case Study to Examine Simulation in a Problem-based Course
Bibliographic record
Abstract
Background: In the last decade, simulation-based learning has flourished with the context of professional practice and education. Within this development, the value of enhancing problem-based learning (PBL) with technology, specifically high-fidelity simulation has not been well-investigated. More specifically, baccalaureate nursing students’ perspectives in using a high-fidelity simulation (HFS) activity during a theoretical problem-based nursing course have not been examined. Purpose: This study explored the perceptions of second year nursing students when HFS and PBL were integrated in a theoretical nursing course. Method: In this study, a descriptive, qualitative research design, specifically case study methodology (Stake, 2005) was used to explore the research inquiry. A convenience sample of 19 nursing students were recruited to participate in one of three focus groups. Results: The findings of the study highlighted the educational value of integrating simulation-based learning in a problem-based theoretical nursing course. Students commented on the importance of understanding new knowledge in the classroom context with the following thematic perceptions: 1) bridging theory and practice, 2) integrating knowledge from other courses, 3) enhancing confidence for practice, 4) learning together, and 5) learning in a safe environment. Conclusion: As nursing students engage in problem-based learning, it is valuable to consider opportunities whereby professional practice concepts are better understood with the merging of two active forms of teaching and learning, PBL and simulation- based learning.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".