Bibliographic record
Abstract
Ontario is Canada's most populous province, with approximately 12 million people and 130 emergency departments (EDs). Canada has a national single-payer universal health care system, but provinces are responsible for administration. After years of problems and failed attempts to address chronic ED overcrowding, in April 2008 Ontario embarked on an ambitious program to improve system performance through targeted investments (initially CAN$500 million over 3 years) and realigned incentives. Supporting the program were requirements for hospitals to submit timely data and targets for length of stay (LOS) and annual improvements; results are publicly reported. The program has been continued this year. While not all our provincial level targets have been met as yet, major improvements have been made, especially in access to care and LOS in the ED for patients eventually discharged home. The greatest improvements were made among the cohort of mainly urban, high-volume EDs that had the worst performance at baseline. This presentation will highlight some of the controversies and challenges and key lessons learned. Overall, the Ontario experience suggests ED overcrowding is a soluble problem, but requires a system-level intervention.
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 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 | 0.000 |
| 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) | 0.010 | 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".