Indigenous Genomic Databases: Pragmatic Considerations and Cultural Contexts
Bibliographic record
Abstract
The potential to grow genomic knowledge and harness the subsequent clinical benefits has escalated the building of background variant databases (BVDs) for genetic diagnosis across the globe. Alongside the upsurge of this precision medicine, potential benefits have been highlighted for both rare genetic conditions and other diagnoses. However, with the ever-present "genomic divide," Indigenous peoples globally have valid concerns as they endure comparatively greater health disparities but stand to benefit the least from these novel scientific discoveries and progress in healthcare. The paucity of Indigenous healthcare providers and researchers in these fields contributes to this genomic divide both in access to, and availability of culturally safe, relevant and respectful healthcare using this genetic knowledge. The vital quest to provide equitable clinical research, and provision and use of genomic services and technologies provides a strong rationale for building BVDs for Indigenous peoples. Such tools would ground their representation and participation in accompanying genomic health research and benefit acquisition. We describe two, independent but highly similar initiatives-the "Silent Genomes" in Canada and the "Aotearoa Variome" in New Zealand-as exemplars that have had to address the aforementioned issues and work to create Indigenous BVDs with these populations. Taking into account the baseline inequities in genomic medicine for Indigenous populations and the ongoing challenges of implementing genomic research with Indigenous communities, we provide a rationale for multiple changes required that will assure communities represented in BVDs, as well as Indigenous researchers, that their participation will maximize benefits and minimize risk.
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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 | 0.148 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".