Bibliographic record
Abstract
What Is the Issue? Emergency departments (EDs) across Canada are under strain and experiencing overcrowding, a situation that arises when the demand for health services in the ED exceeds the capacity of the health system — which includes the ED, hospital, and community — to provide quality care in a reasonable amount of time. ED overcrowding is contributing to a deteriorating standard of care as health care providers and staff become overworked and burned out, is putting health and lives at risk, and is placing additional strain on an already overwhelmed health care system. What Did We Do? The CADTH Health Technology Expert Review Panel (HTERP) convened to develop objective, impartial, trusted pan-Canadian guidance to inform decisions about which evidence-informed solutions should be considered to help alleviate ED overcrowding in Canada. What Is HTERP’s Position on ED Overcrowding? ED overcrowding is a complex health system issue. EDs operate within hospitals and broader health and social systems, which means that accountability for causes, impacts, and solutions do not lie solely within the ED and its operations. Output factors (e.g., misalignment between acute care bed capacity within the hospital and population needs) and input factors (e.g., misalignment between care available in the community and population needs, including care outside of regular business hours) respectively, are the main contributors to ED overcrowding in Canada. Health system capacity is not aligned with, nor has kept pace with, the growing and changing health care needs of the population, which results in overcrowded EDs. Health systems will observe better results by implementing strategies that improve patient flow and focus on output and input factors relative to throughput factors. What Is HTERP’s Guidance to Help Alleviate ED Overcrowding? Interventions to alleviate overcrowding need to align with the main contributing factors to ED overcrowding in the particular context in which they will be implemented. Understanding the context in which ED overcrowding is occurring, with attention to bottlenecks to patient flow, should be the first step to identify evidence-informed solutions. Transparency and accountability should be key principles in ED, hospital, and health system operations. HTERP recommends identifying and ensuring clear roles, responsibilities, and reporting relationships embedded within an accountability framework for ED overcrowding across health system partners, including a commitment to act on data. Each province and territory should mandate consistent and comprehensive reporting by all hospitals to the Canadian Institute of Health Information’s National Ambulatory Care Reporting System (NACRS) database. HTERP’s guidance includes an Evidence Navigation Guide to support identification of evidence-informed interventions to help alleviate 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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".