The Role of Provinces, States, and Territories in Shaping Federal Policy for Indigenous Peoples’ Health
Bibliographic record
Abstract
Under both Canadian and United States law, the availability and quality of healthcare and health services to Indigenous peoples are primarily a federal responsibility. Nevertheless, sub-national authorities—most importantly provinces, states, and territories—play a crucial role by virtue of covering (often through federal mandate) services, and regulating health facilities and health personnel off-reserv(ation). While both federal governments have undertaken efforts to transfer, within their fiduciary obligations, their responsibilities for Indigenous peoples’ health to the management of Indigenous peoples themselves, that transfer has considered or included provincial, state, and territorial authorities and resources unevenly, and, in some cases, in tension with the objectives of respecting standards for quality and access. This article applies the methodology used by Canadian researchers of the sub-national health authority issue to the health transfer experience in the United States. The article summarizes findings that demonstrate similar deficiencies as those present in the Canadian transfer process. The article further outlines the experiences of Hawai`i and Ontario as offering models through which to address some of these deficiencies. The article finally suggests that there is a positive relationship between greater participatory models adopted by provinces, states, and territories and better health outcomes among Indigenous groups so included.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".