A STRATEGIC GOVERNANCE MODEL TO IMPROVE THE PERFORMANCE OF EMERGENCY DEPARTMENTS IN PUBLIC HOSPITALS IN THE PROVINCE OF Ontario, Canada
Bibliographic record
Abstract
The rapidly increasing demand for health care in the province of Ontario has led to greater numbers of patients turning to public hospitals for the care they need. The primary entrance for them into the public hospital system is through Emergency Departments. The poor performance of public hospital Emergency Departments in handling the demands put on them calls into question the quality of the Emergency Departments. Assuming that the management of hospitals focuses their attention and resources on problem areas, the quality of management in the Emergency Departments are likely symptomatic of the quality of the management throughout the hospital. Ultimately, responsibility for the quality of management in the hospital rests with the board of directors and is a matter of governance. While prior studies have examined the quality of health care as affected by governance, none appear to have considered the quality of management. This study is a first to our knowledge in addressing whether the quality of management is a reason for differences in performance across hospitals. This study connects the performance of the Emergency Departments with the ultimate
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".