Emotion, cognitive load and learning outcomes during simulation training
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
CONTEXT: Simulation training has emerged as an effective way to complement clinical training of medical students. Yet outcomes from simulation training must be considered suboptimal when 25-30% of students fail to recognise a cardiac murmur on which they were trained 1 hour previously. There are several possible explanations for failure to improve following simulation training, which include the impact of heightened emotions on learning and cognitive overload caused by interactivity with high-fidelity simulators. This study was conducted to assess emotion during simulation training and to explore the relationships between emotion and cognitive load, and diagnostic performance. METHODS: We trained 84 Year 1 medical students on a scenario of chest pain caused by symptomatic aortic stenosis. After training, students were asked to rate their emotional state and cognitive load. We then provided training on a dyspnoea scenario before asking participants to diagnose the murmur in which they had been trained (aortic stenosis) and a novel murmur (mitral regurgitation). We used factor analysis to identify the principal components of emotion, and then studied the associations between these components of emotion and cognitive load and diagnostic performance. RESULTS: We identified two principal components of emotion, which we felt represented invigoration and tranquillity. Both of these were associated with cognitive load with adjusted regression coefficients of 0.63 (95% confidence interval [CI] 0.28-0.99; p = 0.001) and - 0.44 (95% CI - 0.77 to - 0.10; p = 0.009), respectively. We found a significant negative association between cognitive load and the odds of subsequently identifying the trained murmur (odds ratio 0.27, 95% CI 0.11-0.67; p = 0.004). CONCLUSIONS: We found that increased invigoration and reduced tranquillity during simulation training were associated with increased cognitive load, and that the likelihood of correctly identifying a trained murmur declined with increasing cognitive load. Further studies are needed to evaluate the impact on performance of strategies to alter emotion and cognitive load during simulation training.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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