SEEDS of Indigenous Population Health Data Linkage
Bibliographic record
Abstract
INTRODUCTION: Globally, the ways that Indigenous data are collected, used, stored, shared, and analyzed are advancing through Indigenous data governance movements. However, these discussions do not always include the increasingly sensitive nature of linking Indigenous population health (IPH) data. During the International Population Data Linkage Network Conference in September of 2018, Indigenous people from three countries (Canada, New Zealand, and the United States) gathered and set the tone for discussions around Indigenous-driven IPH data linkage. OBJECTIVES: Centering IPH data linkage and research priorities at the conference led to budding discussions from diverse Indigenous populations to share and build on current IPH data linkage themes. This paper provides a braided summary of those discussions which resulted in the SEEDS principles for use when linking IPH data. METHODS: During the Conference, two sessions and a keynote were Indigenous-led and hosted by international collaborators that focused on regional perspectives on IPH data linkage. A retrospective document analysis of notes, discussions, and artistic contributions gathered from the conference resulted in a summary of shared common approaches to the linkage of IPH data. RESULTS: The SEEDS Principles emerge as collective report that outlines a living and expanding set of guiding principles that: 1) prioritizes Indigenous Peoples' right to Self-determination; 2) makes space for Indigenous Peoples to Exercise sovereignty; 3) adheres to Ethical protocols; 4) acknowledges and respects Data stewardship and governance, and; 5) works to Support reconciliation between Indigenous nations and settler states. CONCLUSION: Each of the elements of SEEDS need to be enacted together to create a positive data linkage environment. When implemented together, the SEEDS Principles can lead to more meaningful research and improved Indigenous data governance. The mindful implementation of SEEDS could lead to better measurements of health progress through linkages that are critical to enhancing health care policy and improving health and wellness outcomes for Indigenous nations.
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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 | 0.181 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.004 | 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".