Overcrowding in medium‐volume emergency departments: Effects of aged patients in emergency departments on wait times for non‐emergent triage‐level patients
Bibliographic record
Abstract
This study aims to examine patient wait times from triaging to physician assessment in the emergency department (ED) for non-emergent patients, and to see whether patient flow and process (triage) are impacted by aged patients. A retrospective study method was used to analyse 185 patients in three age groups. Key data recorded were triage level, wait time to physician assessment and ED census. Multiple linear regression analysis was used to determine the strength of association with increased wait time. A longer average wait time for all patients occurred when there was an increase in the number of patients aged > or = 65 years in the ED. Further analysis showed 12.1% of the variation extending ED wait time associated with the triage process was explained by the number of patients aged > or = 65 years. In addition, extended wait time, overcrowding and numbers of those who left without being seen were strongly associated (P < 0.05) with the number of aged patients in the ED. The effects of aged patients on ED structure and process have significant implications for nursing. Nursing process and practice sets clear responsibilities for nursing to ensure patient safety. However, the impact of factors associated with aged patients in ED, nursing's role and ED process can negatively impact performance expectations and requires further investigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".