An exploratory investigation of the measurement of cognitive load on shift: Application of cognitive load theory in emergency medicine
Bibliographic record
Abstract
BACKGROUND: Emergency physicians often experience a high cognitive load (CL) due to the inherent nature of working in acute care settings. CL has traditionally been measured in educational studies but has not been well studied in the clinical environment. METHODS: Emergency medicine attending physicians and residents working in an academic urgent care center completed psychometric questionnaires while on shift to measure overall CL, intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and acute stress. Data regarding the patient load, patient acuity, and the number of patients in the waiting room were also collected. Correlational analysis and simple linear regression were used to evaluate predictors of CL on shift. RESULTS: < 0.001). No differences in mean overall CL, ICL, ECL, and acute stress were observed between attending physicians and residents. Bivariate analysis demonstrated associations between ICL, ECL, acute stress, and overall CL in attending physicians. In residents, acute stress was the only variable associated with overall CL and the number of high-acuity patients was associated with ICL. CONCLUSIONS: Factors influencing reported CL during clinical work are different between attending emergency physicians and residents. Further study to appreciate the impact of these differences is required and may help educators elucidate strategies to better manage CL, thereby improving clinical performance and potentially improving patient care.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".