Ethical Data Linkage with Indigenous Communities: The Manitoba Experience
Bibliographic record
Abstract
IntroductionIndigenous populations are known to have poor health and health outcomes in many countries. Indigenous peoples continue to be the subjects of unethical research. Research that is undertaken without their consent, involvement in the design, delivery and interpretation of results that perpetuates negative stereotypes ignoring the historical and ongoing impacts of colonialism. Objectives and ApproachIn order to understand the health status and health system use of First Nations people in Manitoba Canada we developed a partnership between the First Nations Social Secretariat of Manitoba and researchers to link First Nations identifiers with administrative data. This partnership was based on long-standing relationships with researchers who were affiliated with the Manitoba Centre for Health Policy. ResultsA tripartite data sharing agreement set out the parameters of sharing data that supported the linkage of the Federal Registered First Nations database to the Manitoba Population Research Data Repository. The DSA facilitated direct First Nations input into the indicators chosen, the reporting cohorts, the interpretation of results and the language of the report. Conclusion / ImplicationsDSAs can be used as a tool to facilitate partnerships with Non-indigenous researchers and Indigenous Nations that lead to meaningful partnerships and lay the foundation for respectful and ethical research. The research presents findings the health of First Nations and shines a light on the underlying colonialism and racism that contributes to the health inequities. These findings have the potential to influence health and well-being of First Nation peoples in Manitoba. This model of collaboration can be used a model in other jurisdictions.
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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 | 0.101 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.040 | 0.024 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".