Full capacity protocol: an end to double standards in acute hospital care provision
Bibliographic record
Abstract
Hospitals with overcrowded Emergency Departments (EDs) are overcrowded hospitals that have chosen to manifest the overcrowding in a single location. This choice is unfair on the acutely sick requiring hospital admission. The National Emergency Nurses Affiliation of Canada have referred to the practice of only allowing the ED to house additional patients as a ‘double standard’ and they have advocated that one possible solution to ED overcrowding is to distribute patients equally between all wards including the ED.1 Emergency Department overcrowding has been defined as an ED operating beyond its available capacity, which results in a situation where the demand for emergency services exceeds the ability of a department to provide quality care within acceptable time frames.2 There are a number of suggested means of measuring ED crowding including the Emergency Department Work Index, the National Emergency Department Overcrowding Scale, the Demand Value of the Real-time Emergency Analysis of Demand Indicators and the Work Score.2 3 The most simple to use crowding metric is the occupancy rate, which is defined as the total number of patients in the ED divided by the number of licensed beds/ED cubicles.4 There are multiple factors that contribute to ED crowding, and the relative contribution of each of these varies between EDs.5 Patient flow analysis can detect ED overcrowding and may help find appropriate solutions to reduce it.4 Access block, the inability to transfer emergency admitted patients to inpatient beds, is the single most important factor contributing to ED overcrowding.6 Hospital overcrowding is primarily due to a shortage of inpatient beds and many factors contribute to the shortage. These factors include:
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.026 | 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".