Simulation is more than working with a mannequin: Student’s perceptions of their learning experience in a clinical simulation environment
Bibliographic record
Abstract
Purpose: This paper describes undergraduate nursing students’ assessment of learning in a clinical teaching model that replaces 50% of the traditional clinical hours with high-fidelity simulation. We assessed students’ perceptions of the use of best practices in simulation teaching, and the importance assigned to each teaching practice to support learning.Methods: Longitudinal program evaluation design. We surveyed undergraduate nursing students with the Educational Practices Questionnaire (EPQ) at the mid-point (semester 2) and end of the program (semester 4). We used paired t-tests to assess changes in student EPQ scores between mid- and end-program.Results: Results showed that students’ reported greater exposure over time to clinical simulation activities that fostered active learning and high expectations; the degree to which they rated collaborative learning as important also increased.Conclusions: Students’ perceptions of the use of educational best practices and the importance of simulation in nursing education from program mid-point to end-point lends support for a clinical teaching model that uses a simulation to substitute for traditional clinical hours.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".