Frequency, Determinants and Impact of Overcrowding in Emergency Departments in Canada: A National Survey
Bibliographic record
Abstract
Several reports have documented the prevalence and severity of emergency department (ED) overcrowding at specific hospitals or cities in Canada; however, no study has examined the issue at a national level. A 54-item, self-administered, postal and web-based questionnaire was distributed to 243 ED directors in Canada to collect data on the frequency, impact and factors associated with ED overcrowding. The survey was completed by 158 (65% response rate) ED directors, 62% of whom reported overcrowding as a major or severe problem during the past year. Directors attributed overcrowding to a variety of issues including a lack of admitting beds (85%), lack of acute care beds (74%) and the increased length of stay of admitted patients in the ED (63%). They perceived ED overcrowding to have a major impact on increasing stress among nurses (82%), ED wait times (79%) and the boarding of admitted patients in the ED while waiting for beds (67%). Overcrowding is not limited to large urban centres; nor is it limited to academic and teaching hospitals. The perspective of ED directors reinforces the need for further examination of effective policies and interventions to reduce ED overcrowding.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".