Applying theory to practice in undergraduate education using high fidelity simulation
Bibliographic record
Abstract
High-fidelity patient simulation allows students to apply their theoretical knowledge of pharmacology and physiology to practice. The purpose of this study was to determine if experiential education using high-fidelity simulation improves undergraduate performance scores on simulation-based and written examinations. After receiving research ethics board approval, students completed a consent form and then answered a ten question multiple-choice quiz to identify their knowledge regarding the management of cardiac arrhythmias. Four simulation scenarios were presented and students worked through each scenario as a team. Faculty facilitated the sessions and feedback was given using students' videotaped performances as a template for discussion. Performance evaluation scores using predetermined checklists and global rating scales were completed. Students then reviewed the American Heart Association guidelines for the management of unstable cardiac arrhythmias. The afternoon session involved repetition of the four case scenarios with the same teams involved but different team leaders. Students then repeated the quiz they received in the morning. Descriptive statistics, paired t-test and repeated measures analysis of variance (ANOVA) were used to analyse results. Two hundred and ninety-nine students completed the study. There was a statistically significant improvement in performance on the pharmacology written test. Simulation team performance also statistically improved and a good correlation between checklist and global rating scores were demonstrated in all but one scenario. Student evaluation of the experience was extremely positive. High-fidelity simulation can be used to allow students to apply theoretical knowledge to practice in a safe and realistic environment. Results of this study indicate that simulation is a valuable learning experience and bridges the gap between theory and practice. Simulation technology has the potential to provide an enriching venue to examine the role of communication and dynamics of novice learners in team environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".