Policy silences: why Canada needs a National First Nations, Inuit and Métis health policy
Bibliographic record
Abstract
OBJECTIVES: Despite attempts, policy silences continue to create barriers to addressing the healthcare needs of First Nations, Inuit and Métis. The purpose of this article is to answer the question, if what we have in Canada is an Aboriginal health policy patchwork that fails to address inequities, then what would a Healthy Aboriginal Health Policy framework look like? METHODS: The data collected included federal, provincial and territorial health policies and legislation that contain Aboriginal, First Nation, Inuit and/or Métis-specific provisions available on the internet. Key websites included the Parliamentary Library, federal, provincial and territorial health and Aboriginal websites, as well as the Department of Justice Canada, Statistics Canada and the Aboriginal Canada Portal. RESULTS: The Indian Act gives the Governor in Council the authority to make health regulations. The First Nations and Inuit Health Branch (FNIHB) of Health Canada historically provided health services to First Nations and Inuit, as a matter of policy. FNIHB's policies are few, and apply only to Status Indians and Inuit. Health legislation in 2 territories and 4 provinces contain no provision to clarify their responsibilities. In provinces where provisions exist, they broadly focus on jurisdiction. Few Aboriginal-specific policies and policy frameworks exist. Generally, these apply to some Aboriginal peoples and exclude others. CONCLUSION: Although some Aboriginal-specific provisions exist in some legislation, and some policies are in place, significant gaps and jurisdictional ambiguities remain. This policy patchwork perpetuates confusion. A national First Nation, Inuit and Métis policy framework is needed to address this issue.
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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 | 0.031 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.040 | 0.022 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".