Optimizing the Timing of Expert Feedback During Simulation-Based Spaced Practice of Endourologic Skills
Bibliographic record
Abstract
INTRODUCTION: Provision of expert feedback is widely acknowledged to be an essential component of simulation-based training. However, little is known about the most effective and efficient ways to provide feedback to novices. Optimizing the timing of expert feedback may improve outcomes while reducing resource requirements. The main objective of this study was to determine the impact of providing early versus late expert feedback to novice learners engaged in a flexible ureteroscopy (fURS) training curriculum. METHODS: Senior medical students were recruited to participate in this study. Each student participated in a comprehensive fURS training curriculum that included 3 deliberate, independent practice sessions. Baseline and postcourse fURS skill was assessed for each student using a standardized fURS test task. Each student was randomized to either an early feedback group (EFG) or late feedback group (LFG). The EFG participants were provided expert feedback immediately after the baseline skill test, whereas LFG participants were given feedback before their final deliberate, independent practice session. RESULTS: Eighteen senior medical students completed the study (9 EFG and 9 LFG participants). There were no discernible demographic differences between the groups at baseline. When controlling for pretest performance, early rather than late feedback was associated with both shorter postcourse time to completion of the task (19.2 vs. 21.5 minutes, P < 0.01) and higher performance scores (13.1 vs. 10.5, P < 0.01). CONCLUSIONS: This study offers preliminary evidence that novice learners may benefit more from early feedback when learning a novel skill. Further study is required.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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".