Identifying indigenous peoples for health research in a global context: a review of perspectives and challenges
Bibliographic record
Abstract
OBJECTIVES: Identifying Indigenous Peoples globally is complex and contested despite there being an estimated 370 million living in 70 countries. The specific context and use of locally relevant and clear definitions or characterizations of Indigenous Peoples is important for recognizing unique health risks Indigenous Peoples face, for understanding local Indigenous health aspirations and for reflecting on the need for culturally disaggregated data to plan meaningful research and health improvement programs. This paper explores perspectives on defining Indigenous Peoples and reflects on challenges in identifying Indigenous Peoples. METHODS: Literature reviews and Internet searches were conducted, and some key experts were consulted. RESULTS: Pragmatic and political definitions by international institutions, including the United Nations, are presented as well as characterizations of Indigenous Peoples by governments and academic researchers. Assertions that Indigenous Peoples have about definitions of indigeneity are often related to maintenance of cultural integrity and sustainability of lifestyles. Described here are existing definitions and interests served by defining (or leaving undefined) such definitions, why there is no unified definition and implications of "too restrictive" a definition. Selected indigenous identities and dynamics are presented for North America, the Arctic, Australia and New Zealand, Latin America and the Caribbean, Asia and Africa. CONCLUSIONS: While health researchers need to understand the Indigenous Peoples with whom they work, ultimately, indigenous groups themselves best define how they wish to be viewed and identified for research purposes.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".