Why Equity in Financing First Nations On-Reserve Health Services Matters: Findings from the 2005 National Evaluation of the Health Transfer Policy
Bibliographic record
Abstract
Background: This paper reports on selected findings from the 2005 National Evaluation of the Health Transfer Policy.Three hypotheses were tested, namely: (1) that inequalities in per capita financing exist between First Nations organizations, (2) that variations in per capita funding among communities cannot be explained by variations in the program responsibilities each assumed and (3) that First Nations organizations that transferred in the early 1990s now have access to fewer resources on a per capita basis than those that transferred more recently.Methods: We compared (1) the per capita funding for 30 medium-sized communities (population = 401-3,000) that have Health Centres and the 13 similarly sized communities that have Health Stations, (2) program responsibilities and per capita funding for the same 30 communities and (3) the relationship between 2001-2002 per capita funding and the year of transfer for the same communities.We used data provided to us by the First Nations and Inuit Health Branch of Health Canada from 1989 to 2002. Results:The results show that differences in per capita funding exist among and within regions.These differences cannot be explained by the responsibilities each community chose to assume.Differences are also related to the year First Nations entered into a transfer agreement.Conclusions: We recommend that formula-based financing be adopted to reduce inequalities.Such a formula should reflect needs, population growth and changes in costs of service delivery.
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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 | 0.043 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".