Shaken and stirred: emotional state, cognitive load, and performance of junior residents in simulated resuscitation
Bibliographic record
Abstract
Background: Patient resuscitation can be overwhelming for junior postgraduate medical residents due to its inherent complexity and high-stakes environment. Emotional states of unpleasant hyperarousal burden cognitive resources, contributing to cognitive overload and performance decline. Our objective is to characterize the associations between pre-scenario emotional state and junior residents’ cognitive load and performance in a simulated-resuscitation, to provide evidence for informed curricular development. Methods: PGY-1 residents self-rated their emotional state before four simulated-resuscitation scenarios, and their cognitive load after. Faculty assessed performance with entrustment scores. Factor analysis identified the principal components of emotional state data. Linear regression models examined the relationship between pre-scenario emotional components, cognitive load, and performance scores. Results: 47/47 medical and surgical residents (100%) participated and completed Emotional State (99.5%) and Cognitive Load (98.9%) surveys. Positive invigoration and negative tranquility were the principal components. Pre-scenario tranquility was negatively associated with cognitive load (b= -0.23, p < 0.0001), and cognitive load was negatively associated with performance scores (b= -0.27, p < 0.0001). Pre-scenario invigoration was negatively associated with cognitive load (b=-0.18,p = 0.0001), and positively associated with performance scores (b= 0.08, p = 0.0193). Conclusion: Amongst junior residents participating in simulated resuscitation scenarios, pre-scenario agitation (negative tranquility) is associated with increased cognitive load, which itself is associated with lower performance scores. These findings suggest residency programs should consider developing curriculum aimed at modulating residents’ emotional agitation and reducing residents’ cognitive burden to improve resuscitation performance.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".