A pre-clerkship simulation-based procedural skills curriculum: decreasing anxiety and improving confidence of procedural skill performance
Bibliographic record
Abstract
INTRODUCTION: Pre-clerkship procedural skills training is not yet a standard across Canadian medical school curricula, resulting in limited exposure to procedures upon entering clerkship. While simulated skills training has been documented in the literature to improve performance in technical ability, anxiety and confidence have yet to be investigated despite their documented impact on performance and learning. This study therefore aims to evaluate the effect of pre-clerkship procedural skills training on medical student anxiety and confidence. METHODS: A procedural skills training program was designed based on an evidence-based near-peer, flipped classroom model of education. Ninety-two second-year medical students volunteered for the study. Fifty-six were randomized to the training group, and 36 were randomized to the control group. Students in the training group attended seven procedural skills tutorials over seven months. The control group represented the average medical school student without standardized procedural training. Student anxiety and confidence were assessed at the beginning and end of the program using the State Trait Anxiety Inventory and Confidence Questionnaires. RESULTS: Students who participated in the procedural skills program demonstrated greater reductions in their state anxiety and greater improvements in confidence compared to the control group. CONCLUSION: Longitudinal procedural skills training in the simulation setting has demonstrated improvements in anxiety and confidence among pre-clerkship medical students. These added benefits to training have the potential to ease medical students' transition into clerkship, while also contributing to a safer and more effective clinical experience. Therefore, future integration of standardized pre-clerkship procedural skills training within medical school curricula should be considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".