Precision health equity for racialized communities
Bibliographic record
Abstract
In the last three decades, a cohort of genomicists have intentionally sought to include more racially diverse people in their research in human genomics and precision medicine. How such efforts to be inclusive in human genomic research and precision medicine are modeled and enacted, specifically if the terms of inclusion are equitable for these communities remains to be explored. In this commentary, we review the historical context in which issues of racial inclusion arose with early genome and genetics projects. We then discuss attempts to include racialized peoples in more recent human genomics research. In conclusion, we raise critical issues to consider in the future of equitable human genomics and precision medicine research involving racialized communities, particularly as it concerns working towards what we call Precision Health Equity (PHE). Specifically, we examine issues of genetic data governance and the terms of participation in inclusive human genomics and precision health research. We do so by drawing on insights and protocols developed by researchers investigating Indigenous Data Sovereignty and propose exploring their application and adaptation to precision health research involving racialized communities.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.050 | 0.044 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".