Concurrent <i>versus</i> terminal feedback: The effect of feedback delivery on lumbar puncture skills in simulation training
Bibliographic record
Abstract
INTRODUCTION: Simulation-based medical education (SBME) is widely used to teach bedside procedural skills. Feedback is crucial to SBME but research on optimal timing to support novice learners' skill development has produced conflicting results. METHODS: We randomly assigned 32 novice medical students to receive feedback either during (concurrent) or after (terminal) trialing lumbar puncture (LP). Participants completed pre- and post-acquisition tests, as well as retention and transfer tests, graded on a LP checklist by two blinded expert raters. Cognitive load and anxiety were also assessed, as well as learners' perceptions of feedback. RESULTS: = 1.90), collapsed across post, retention, and transfer tests. There was no difference in cognitive load and anxiety between groups. In open-ended responses, participants who received concurrent feedback more often expressed satisfaction with their learning experience compared to those who received terminal feedback. DISCUSSION AND CONCLUSIONS: Concurrent may be superior to terminal feedback when teaching novice learners complex procedures and has the potential to improve learning if incorporated into SBME and clinical teaching. Further research is needed to elucidate underlying cognitive processes to explain this finding.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".