Developing the Standardized Workload Assessment Metric for Pediatric Emergency Departments
Bibliographic record
Abstract
OBJECTIVES: We aimed to develop a comprehensive list of patient care components performed by pediatric emergency department (PED) physicians that could be individually scored on their subjective workload using the National Aeronautics and Space Administration Task Load Index (NASA-TLX). These "care components," alongside patient and environmental factors that influence workload ("modifiers"), will form the basis of the Standardized Workload Assessment Metric for Pediatric Emergency Departments (SWAMPED). We sought to obtain preliminary workload scores for each care component and assess the face validity of the NASA-TLX-derived workload tool. METHODS: After establishing a working list of "care elements" and modifiers, we convened an expert panel during a 3-day workshop to curate a comprehensive list of PED patient care components and modifiers affecting physician workload using a modified Delphi process. Experts completed a pilot version of the NASA-TLX-derived workload survey for each care component. A virtual follow-up was held 5 months after the initial meeting to finalize the list of modifiers and care components. RESULTS: Of the 93 initial care elements and 75 modifiers, 46 care components were retained, alongside 6 final modifiers. Preliminary workload scores showed "high acuity, low occurrence procedures (cricothyroidotomy, thoracotomy, pericardiocentesis, burr hole, etc.)," with the highest median workload score of 106, while "immobilization device simple (prefabricated)" had the lowest median workload score of 22. CONCLUSIONS: The SWAMPED, derived through expert consensus, holds promise as a standardized assessment tool for PED physician workload. Validation studies involving larger cohorts are crucial for refining the SWAMPED and allowing widespread adoption of this novel quantitative workload metric.
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 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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".