Responding to health inequities: Indigenous health system innovations
Bibliographic record
Abstract
Over the past decades, Indigenous communities around the world have become more vocal and mobilized to address the health inequities they experience. Many Indigenous communities we work with in Canada, Australia, Latin America, the USA, New Zealand and to a lesser extent Scandinavia have developed their own culturally-informed services, focusing on the needs of their own community members. This paper discusses Indigenous healthcare innovations from an international perspective, and showcases Indigenous health system innovations that emerged in Canada (the First Nation Health Authority) and Colombia (Anas Wayúu). These case studies serve as examples of Indigenous-led innovations that might serve as models to other communities. The analysis we present suggests that when opportunities arise, Indigenous communities can and will mobilize to develop Indigenous-led primary healthcare services that are well managed and effective at addressing health inequities. Sustainable funding and supportive policy frameworks that are harmonized across international, national and local levels are required for these organizations to achieve their full potential. In conclusion, this paper demonstrates the value of supporting Indigenous health system innovations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".