Equity in Health Care Funding: Comparison of Expenditures in Ontario to Allocations Based on Population Need
Bibliographic record
Abstract
Background - The geographic distribution of health care funding in Canada has traditionally been based on past allocations and the distribution of health care facilities and providers. Whether this approach has succeeded in distributing resources among populations in keeping with relative health care needs is unknown. Methods - Using data on self-assessed health status and utilization of health care services from the Ontario Health Survey, data on health care expenditures from the Ontario Ministry of Health and Long-Term Care, and population data from Statistics Canada, we compared actual health care expenditures for geographically-defined populations in Ontario to allocations based on relative population need as represented by age, sex and self-assessed health status. Comparisons were made at the regional (Health Region), district (District Health Council) and local (Public Health Unit) levels. Results - Expenditures and needs-based allocations were significantly different for 4 of 7 regions, 9 of 15 districts and 23 of 42 local areas. At the regional level, needs-based allocations ranged from 8.9% higher to 6.4% lower than actual expenditures. For districts, needs-based allocations ranged from 12.9% higher to 9.8% lower than expenditures. At the local level, needs-based allocations ranged from 23.8% higher to 18.8% lower than expenditures. Intraclass correlation coefficients measuring agreement between needs-based per capita expenditures and actual per capita expenditures were 0.86, 0.74 and 0.58 for regions, districts and local areas respectively. Interpretation - Although, on average, the differences between needs-based allocations and actual health care expenditures were not large, the discrepancies were substantial for many geographic areas. The adoption in Ontario of funding methods based on relative population needs would improve equity in the allocation of health care resources to populations and result in a considerable redistribution of resources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".