Journey to self‐determination in Indigenous cognitive health research in Canada
Bibliographic record
Abstract
BACKGROUND: Indigenous communities in Canada call for decolonized dementia research, given their higher dementia rates and risk. Over the past 10 years, CCNA has supported increasing levels of self-determination in Indigenous dementia research. METHOD: Initially, Indigenous research was prioritized as one half of a funded Team that supported First Nations-focused research. In CCNA's second phase, we strengthened self-determined Indigenous-led research through a dedicated Indigenous-led research Team and through the establishment of the Indigenous Cognitive Health Program, a cross-cutting initiative designed to promote learning across the whole CCNA network. CCNA made deliberate efforts to build strength and support Indigenous cognitive health researchers, trainees, community partnerships, and community-based research advisory structures. The Indigenous team worked together to envision the next stage of Indigenous self-determination and decolonization of Indigenous dementia research through two in-person and two online gatherings in 2023 and 2024. RESULT: Two influential research outputs were the Canadian Indigenous Cognitive Assessment and the assessment of the Brain Health Pro platform. Since 2019, our Indigenous scientific team has added 5 Indigenous researchers, submitted 8 new funding proposals, hosted 15 webinars on Indigenous cognitive health research, and launched a website to support pathways to culturally safe Indigenous health research approaches. CONCLUSION: These efforts have contributed to a substantial shift in the readiness of Indigenous communities in Canada to address rising numbers of people living with dementia. The future vision is for an Indigenous self-determined Community-Centred Indigenous Cognitive Health Network (CICHN) in Canada that will build on the past 10 years of increasing capacity.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.061 | 0.030 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".