Effectiveness of simulation-based training for obstetric internal medicine: Impact of cognitive load and emotions on knowledge acquisition and retention
Bibliographic record
Abstract
BACKGROUND: Simulation-based training's impact on learning outcomes may be related to cognitive load or emotions during training. We evaluated the association of validated measures of cognitive load and emotion with learning outcomes in simulation-based obstetric internal medicine cases. METHODS: All internal medicine learners (n = 15) who completed the knowledge test pre-training, post-training (knowledge acquisition), and at 3-6 months (knowledge retention) for all three simulation cases were included. RESULTS: Mean knowledge scores differed over time in all three cases (p < 0.0001 for all). Knowledge retention scores were significantly higher only for cases 1 and 3. Cognitive load associated with frustration was positively associated with knowledge acquisition for case 2 (beta = 5.18, P = 0.007), while excitement was negatively associated with knowledge retention in case 1 (beta = -33.07, p = 0.04). CONCLUSION: Simulation-based education for obstetric internal medicine can be effective in select cases. Attention to cognitive load and emotion may optimize learning outcomes.
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.
How this classification was reachedexpand
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".