Emergency department overcrowding and access block
Bibliographic record
Abstract
defined as a situation where the demand for emergency services exceeds the ability of an emergency department (ED) to provide quality care within appropriate time frames.1,2 ED overcrowding has been a key issue in Emergency Medicine in Canada for more than 20 years. Despite increased political, administrative, and public awareness, EDOC situations continue to rise in frequency and severity.3 Patient suffering, prolonged wait times, deteriorating levels of service, adverse patient outcomes and the ability to retain experienced staff in an ED are all ill effects of this ongoing problem. Contrary to popular perceptions, ED overcrowding is not caused by inappropriate use of ED’s, or by high numbers of lower acuity patients presenting to the ED; the inability of admitted patients to access in-patient beds from the ED is the most significant factor causing EDOC in Canadian hospitals. Despite its importance, there currently are no national benchmarks in place to determine severity (and thus identify the factors causing poor perfor-mance). Through this position statement, CAEP will put forth recommended national benchmarks (targets) for ED performance to help address the issue. The suggested targets are as follows: i. Time to physician initial assessment (PIA):
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".