Learning during simulation training is prone to retroactive interference
Bibliographic record
Abstract
CONTEXT: Retroactive interference occurs when newly acquired information inhibits recall of previously learned information. This has been shown to influence recall of sounds, tastes and word associations, and is typically seen when learners receive training on one area of content and are then exposed to new content before being evaluated on the original content. Thus far, retroactive interference has received little attention in medical education and has not been studied during simulation training. Our objective was to evaluate whether retroactive interference occurs during simulation training. METHODS: We randomised 167 Year 1 medical students to one of two training protocols. After training on a cardiac murmur, participants were tested either on the same cardiac murmur followed by a novel murmur (the non-interference protocol), or on the novel murmur followed by the training murmur (the interference protocol). We evaluated performance on both murmurs at 1 hour and 6 weeks post-training. RESULTS: We found a significant interaction between training protocol and diagnostic performance on training versus novel murmurs at both testing time-points. Students in the non-interference protocol had increased odds of achieving success on the training murmur relative to the novel murmur at 1 hour (odds ratio [OR] 4.96; p < 0.001) and at 6 weeks (OR 4.23; p = 0.001) after training. By comparison, students in the interference protocol did not demonstrate improved performance on the training murmur relative to the novel murmur at either evaluation (1 hour post-training: OR 0.56 [p = 0.08]; 6 weeks post-training: OR 0.66 [p = 0.23]). CONCLUSIONS: Consistent with the theory of retroactive interference, students who encountered a novel murmur between training and evaluation on the murmur on which they had been trained showed no improvement in diagnostic performance following simulation training. These findings should serve to warn educators to consider retroactive interference when designing simulation training sessions.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".