Exploring the use of the RISK21 approach for Indigenous community-based human health risk assessments: two case studies
Bibliographic record
Abstract
Indigenous peoples in North America are disproportionately exposed to environmental contaminants and may face elevated health risks related to unique socio-cultural ties to the land. Conventional human health risk assessment (HHRA) methods do not account for these unique contexts. Regulators (i.e., Health Canada, US EPA) have called for the development of more ethical and efficient HHRA approaches, but to our knowledge no such approaches have been designed in consideration of Indigenous community contexts. RISK21 is a new HHRA approach gaining regulatory attention. We present two case studies piloting RISK21’s use in collaboration with three unique communities (Cold Lake First Nations, Chipewyan Prairie First Nation, Apsáalooke/Crow Nation). Our objectives are first, reflect upon the benefits and challenges of using RISK21 in these contexts; second, compare RISK21-based to conventional assessments; and third, ideate adaptations and improvements to the approach. The RISK21-based analyses had similar descriptive results to the original conventional assessments, including when using less information. We found RISK21 useful for rapid chemical assessment and visually representing data from multiple sources. We recommend areas where RISK21 (and other next-generation HHRA approaches) might be improved for Indigenous community contexts, including increasing the community relevance of communication tools and incorporating holistic and non-conventional information.
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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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".