The National Emergency Department Overcrowding Scale and Perceived Staff Workload
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to determine if there is a correlation between perceived staff workload, measured by the NASA Task Load Index (TLX), and the National Emergency Department Overcrowding Scale (NEDOCS) in a pediatric ED. METHODS: We collected staff questionnaires in a large, urban pediatric ED to assess perceived workload on each of six different TLX subscales, which we weighted evenly to create an overall estimate of workload. We evaluated the correlation between individual TLX responses and NEDOCS overall and by staff subgroup. Additionally, we analyzed: (1) the correlation between mean TLX responses and NEDOCS within a given hour and (2) the performance of a logistic regression model, using TLX as a predictor for "severely overcrowded," as measured by NEDOCS. RESULTS: Four hundred one questionnaires between 6/2018 and 1/2019 demonstrated significant variation between concurrently collected TLX responses and an overall poor correlation between perceived workload and NEDOCS ( R2 0.096 [95% confidence interval, 0.048-0.16]). TLX responses by subgroups of fellows (n = 4, R2 0.96) and patient financial advisors (n = 15, R2 0.58) demonstrated the highest correlation with NEDOCS. Taking mean TLX responses within a given hour, during periods with NEDOCS >60 (extremely busy or overcrowded), a polynomial trend line matched the data best ( R2 0.638). On logistic regression, the TLX predicts "severely overcrowded" with an area under the curve of the receiver operating characteristic of 0.731. CONCLUSIONS: NEDOCS does not have a strong correlation with individual responses on questionnaires of perceived workload for staff in a pediatric ED. NEDOCS, as a measure of overcrowding, may be better correlated with perceived workload during periods with elevated crowding or when interpreted categorically as yes/no "severely overcrowded".
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".