CLOSING THE EQUITY GAP: DEMENTIA RESEARCH WITH INDIGENOUS POPULATIONS
Bibliographic record
Abstract
Abstract This symposium highlights community-based work focusing on Alzheimer’s disease and related dementias (ADRD) conducted with Indigenous populations across the United States and Canada. We describe not only outcomes and findings of these various projects but also the essential research processes necessary for creating relationships and working with Indigenous partners and communities. We begin with Whetung, who takes a quantitative approach to describe the landscape of cognitive disparities among Indigenous older adults due to structural inequities, in particular, everyday and lifetime discrimination experiences. Next, Blind et al. describe the in-depth and iterative process of cultural adaptation of cognitive assessments. They are currently adapting validated cognitive assessments for Indigenous populations in Canada, this time for urban and reservation-residing American Indian populations. The final presentations by Jacklin et al. and Lewis will focus on qualitative research of cultural explanatory models and lived experience of dementia from the viewpoints of two different participant groups: healthy older adults (Indigenous communities in Minnesota, Wisconsin, and Ontario) and caregivers of people living with dementia (Alaska Native). ADRD research with Indigenous Peoples is scarce but growing, and this symposium highlights the innovate, various, and exceptional approaches to this research. This symposium highlights research projects which are rooted in the values of Indigenous knowledges and research methods, community-based participatory research, qualitative research, and the Six Rs of research (respect, relationship, representation, relevance, responsibility, reciprocity; Tsosie et al. 2022). Using these approaches, our research seeks to understand and close the equity gap for Indigenous older adults dealing with ADRD. Indigenous Peoples Interest Group Sponsored Symposium
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.143 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.028 | 0.027 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.005 | 0.009 |
| 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".