Students’ Satisfaction with Simulation Learning Environment in Relation to Self-confidence and Learning Achievement
Bibliographic record
Abstract
The evolution in patient simulation as educational tool is being driven by a number of factors. Priority of patient safety, patient availability, and the ever increasing body of medical knowledge presents new challenges to curriculum planners. Student's satisfaction is an important element of the investigation of simulation learning environment efficacy. There are suggestions that student satisfaction may have some correlation with self-confidence and learning achievement. This is a prospective exploratory study that evaluates learners' satisfaction with simulation learning environment and self-conference utilized satisfaction with SSE (simulation experience scale) and pre and post simulation test for learning achievement. Participants was third-year medical students (n = 45) participating in their regular simulation-based session at Center of Excellence for Simulation Education and Innovation (CESEI), University of British Columbia. A comparison between pre-and post-test results was conducted on the basis of t-test for related samples. Correlation was used to explore the relationships among students satisfaction with the simulation environment and students' self-confidence and achievement. The simulation exercise, completed by 45 students, increased correct test answers on average from 72% to 89% (P-Value < 0.0001 by paired t-test). Increases in test results were between pre-and post-simulation identical multiple choice questions. The mean score for satisfaction with simulation environment items was 4.47, SD (0.45), using a 5-point Likert scale with 5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, and 1 = strongly disagree. The mean score for self-confidence in performing BLS, evaluating and managing acute cardiac patient was 3.83, SD (1.02). For evaluating the relationship between students satisfaction with simulation learning environment and learning achievement, bivariate analysis revealed a significant positive relationship between satisfaction with simulation learning environment and learning achievement (Pearson r = 0.80, P-Value < 0.01). The magnitude or strength of the correlation coefficient (r = 0.80) indicated satisfaction with simulation environment and learning achievement have a strong effect and positive correlation. Simulation learning environment for medical students is effective in improving students' overall comprehension and better learning achievements. Furthermore, students' basic clinical skills are improved associated with higher self-confidence.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".