Commentary: Ontario's Efforts to Reduce Time Spent in Hospital Emergency Departments
Bibliographic record
Abstract
The authors of this article are to be congratulated for analyzing key data, reiterating the major reasons for emergency department (ED) overcrowding and presenting their results in such a way that rational management decisions can be made that focus on solutions.The work of Dr. Michael Schull -as referenced by the authors -and others has been instrumental in guiding efforts to reduce overcrowding and the time spent in EDs in Ontario.Over the past four years, the province completed a number of major reviews related to EDs, including the following: • Improving Access to Emergency Services: A System Commitment.The Report of the Hospital Emergency Department and Ambulance Effectiveness Working Group (Schwartz 2005) -this 2005 review identified ways to address ambulance off-load delays in EDs • Improving Access to Emergency Care: Addressing System Issues (Bell et al. 2006) -this 2006 review made evidencebased, practical recommendations to improve patient access to emergency care
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.049 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".