Impact of Simulation-Based Medical Education on Pre-clerkship Medical Students’ Confidence in Key Areas of Clinical Competence: An Exploratory Pre- and Post-survey Study
Bibliographic record
Abstract
Background Medical education is adapting to meet the growing demands of healthcare and patient care complexities. Traditional clinical training often relies on limited patient encounters, which may not fully develop clinical competence. Simulation-based medical education (SBME) offers controlled, immersive environments for practicing clinical skills and decision-making without risking patient safety. While SBME has been well-studied in advanced training, its effectiveness in first- and second-year medical students remains underexplored. This study aims to explore and quantify pre-clerkship medical students' perspectives on how SBME impacts confidence in clinical decision-making, communication, and clinical skills, compared to traditional learning methods alone. Methods This pre- and post-survey-based study assessed the impact of simulation (SIM) on students' self-reported confidence in clinical decision-making, communication, and clinical skills. Six simulation scenarios that aligned with the undergraduate medical curriculum of one Canadian institution were conducted from October 2023 to March 2024. Participants completed pre- and post-simulation surveys using 5-point Likert scales. A total of 67 surveys were analyzed. Results All 67 surveys were analyzed (35 pre-, 32 post-simulation) using one-sided Wilcoxon Signed Rank Tests. Pre-simulation responses indicated low baseline confidence, with only one item rated above neutrality. Post-simulation ratings showed statistically significant improvements across all domains (p < 0.01). Students also reported that they perceived simulation as more effective than traditional didactic learning in preparing them for clinical practice. Conclusions This exploratory study suggests that simulation-based education can enhance pre-clerkship students' confidence in clinical decision-making, communication, and procedural skills, domains often underdeveloped at this stage of training. These findings offer early evidence that high-fidelity simulation may accelerate perceived clinical readiness. However, due to the small, self-selected sample, non-parallel survey design, and reliance on subjective outcomes, results should be treated as exploratory. Further multi-site studies using objective measures are needed to assess long-term impact on knowledge and skill retention as well as clinical performance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".