Weaving Promising Practices to Transform Indigenous Population Health and Wellness Reporting by Indigenizing Indicators in First Nations Health
Bibliographic record
Abstract
In Canada and across the globe, indicators play a fundamental role in measuring, tracking, and reporting on the overall health of the population. Mainstream population health indicators used to measure the health and well-being of First Nations peoples are constrained by the Western biomedical paradigm which focuses solely on illness and disease. These indicators are limited and fail to capture aspects of cultural, spiritual, and interconnected aspects of Indigenous health such as spirit, ceremony, and the connection to land. To advance First Nations self-determination in the healthcare system, it is essential for Indigenous narratives and knowledges to thrive in population health data and reporting. Five promising practices are shared to guide the development of First Nations health and wellness indicators and reporting: (1) be culturally relevant and centred on First Nations worldviews on health and wellness (2) must honour Indigenous knowledges and methods; (3) must involve respectful relationships & meaningful engagement with Indigenous peoples’; (4) “Nothing about us, without us”- Indigenous leadership and self-determination at all stages of indicator development; and (5) taking a strength-based approach & contextualizing indicators within historical, socio-political contexts. The co-development of indicators between the [First Nations Health Organization] and the [Office of the Executive Health Officer] in the Province of [Name of Canadian Province] are discussed as promising practices in action. Celebrating the strength and resilience of First Nations health which is required to pave a new way forward in Indigenous grounded population health.
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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 | 0.224 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.004 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".