Markers of Overcrowding in a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to identify markers of overcrowding in pediatric emergency departments (PEDs) according to expert opinion and then to use statistical methods to further explore the underlying construct of overcrowding. METHODS: A cross-sectional survey of all PED directors (n = 12) and pediatric emergency medicine fellowship program directors (n = 10) across Canada was conducted to elicit expert opinion on relevant markers of emergency department (ED) crowding. The list of markers was reduced to those specific to the ED for which data could be extracted from one tertiary care PED from an existing computerized patient tracking system. Data representing 2,190 consecutive shifts and 138,361 patient visits were collected between April 2005 and March 2007. Common factor analysis (CFA) was used to determine the underlying factors that best represented overcrowding as determined by markers identified by experts in pediatric emergency medicine RESULTS: The main markers of overcrowding identified by the survey included measures of patient volume (25%), ED operational processes (55%), and delays in transferring patients to inpatient beds (13%). Data collected on 41 markers were retained for the CFA. The results of the CFA indicated that the largest portion of variation in the data (48%) was accounted for by markers describing patient volumes and flow through the ED. Measures of admission delays accounted for a smaller proportion of variability (9%). CONCLUSIONS: The results suggest that for this tertiary PED, markers of ED operational processes and patient volume may be more relevant for determination of overcrowding than markers reflecting delays in transferring patients to inpatient beds. This study provides a foundation for further research on markers of overcrowding specific to the pediatric setting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".