Impact of providing case-specific knowledge in simulation: a theory based study of learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 it