Genomics and inclusion of Indigenous peoples in high income countries
Bibliographic record
Abstract
Genomics research related to Indigenous people has been at worst exploitative and at best, retrospectively on a journey to improve effective engagement of Indigenous individuals and communities. Genomics can positively impact all stages of clinical management, and to improve genomic effectiveness researchers aggregate genomic data from diverse global sub-populations, such as shared ancestry groupings, as people within these groupings will have a greater proportion of shared DNA traits. While genomics is already being used worldwide to improve lives, its utility and effectiveness has not been maximized for individuals with Indigenous ancestry. Several large datasets of human genetic variation have been made publicly available, of which the most widely used is the Genome Aggregation Database (gnomAD), but none of these databases currently contain any population-specific data for Indigenous populations. There are many reasons why Indigenous people have been largely left out of genomics research and, because of this, miss out on the benefits offered. It is also clear that if research is to be effective, it needs to be done 'with' and not 'on' Indigenous communities. This systematic review of the literature regarding Indigenous peoples (in high income countries) and genomics aims to review the existing literature and identify areas of strength and weakness in study design and conduct, focusing on the effectiveness of Indigenous community engagement.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".