Ethics of Biological Sampling Research with Aboriginal Communities in Canada
Bibliographic record
Abstract
BACKGROUND: The objective of this paper is to identify key ethical issues associated with biological sampling in Aboriginal populations in Canada and to recommend approaches that can be taken to address these issues. METHODS: Our work included the review of notable biological sampling cases and issues. We examined several significant cases (Nuu-chah-nult people of British Columbia, Hagahai peoples of Papua New Guinea and the Havasupai tribe of Arizona) on the inappropriate use of biological samples and secondary research in Aboriginal populations by researchers. RESULTS: Considerations for biological sampling in Aboriginal communities with a focus on community-based participatory research involving Aboriginal communities and partners are discussed. Recommendations are provided on issues of researcher reflexivity, ethical considerations, establishing authentic research relationships, ownership of biological material and the use of community-based participatory research involving Aboriginal communities. CONCLUSIONS: Despite specific guidelines for Aboriginal research, there remains a need for biological sampling protocols in Aboriginal communities. This will help protect Aboriginal communities from unethical use of their biological materials while advancing biomedical research that could improve health outcomes.
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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 | 0.096 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.024 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".