Impact of providing case-specific knowledge in simulation: a theory based study of learning
Bibliographic record
Abstract
Background: Simulation-based education (SBE) has been lauded for its ability to help students recognise and react appropriately to common and rare circumstances. While healthcare professions have started to implement SBE into their curriculum, there is no evidence to suggest which educational theory is best for implementation. This study explores the usage of cognitive load theory (CLT) and the unified theory of emotional learning (UTEL). Study design: A mixed methods ordered-allocation cohort study. Methods: 23 patient management teams were allocated into 2 groups. The first group received prior information about the simulation scenario; the second group did not. Each team had 1 student assigned to the role of doctor. The scenarios were filmed at time 1 (T1), time 2 (T2) and follow-up (F/U). The 'doctor' role was then graded with a validated checklist by a three-judge panel. The scores were evaluated to determine if prior information enabled better performance. Secondary analysis evaluated the role of gender on performance and also evaluated anxiety at the onset of the simulation. Results: 23 doctors were evaluated. There was no difference between groups in performance (t=1.54, p=0.13). Secondary analysis indicated that gender did not play a role. There was no difference in anxiety between groups at baseline (t=0.67, p=0.51). Conclusions: Trends were observed, suggesting that when students enter a simulation environment with prior knowledge of the event they will encounter, their performance may be higher. No differences were observed in performance at T2 or F/U. Withholding information appeared to be an inappropriate proxy for emotional learning as no difference in anxiety was observed between groups at baseline. All trends require confirmation with a larger sample size.
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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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".