"Assessment of the Health System Performance in Ontario Major Cities (Canada)"
Bibliographic record
Abstract
"This study aims to provide evidence to support policymaking by assessing health system perfor mance in the province of Ontario, Canada, and its major cities (Toronto, Mississauga, Oakville, and Brampton). It reports on the performance of health care organizations and local health systems over the years 2012-2018. The performance indicators ana lyzed are grouped into two categories: health status and quality of service (Ontario Ministry of Health). The analysis reports health care waiting times for the year 2018, focusing on the most frequently re ported disease groups and procedures such as: pediatric, cancer, cardiac, orthopedic, eye, diagnos tic imaging, and emergency room. It also looks at government spending on health, the benchmark for surgical procedures, the number of physicians, the health status of the population, and rates of hospi talization and hospital admissions. As recommen dations, among policies to improve the health sys tem, the government should pay attention to health spending, increasing the number of doctors rather than beds, and introducing the privately-owned hos pitals that could coexist with the public ones."
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".