AMICA DEMENTIA SCREENING TOOLS ADAPTATION: AN INTERCULTURAL CONSENSUS APPROACH
Bibliographic record
Abstract
Abstract Existing clinical tools for assessing dementia demonstrate varying degrees of cultural, educational and language biases, and are not validated for use with Indigenous populations in the US. The American Indigenous Cognitive Assessment (AMICA) project aims to develop and validate a culturally appropriate dementia evaluation toolkit that consists of four assessments. Participating Indigenous populations include the Red Lake Nation in Minnesota; an urban Indigenous population receiving services at the First Nations Community HealthSource in Albuquerque, New Mexico; and the Oneida Nation in Wisconsin. Our goal is to create one set of assessments that is agreed upon through intercultural consensus. Assessment adaptation across the three sites will focus on the Canadian Indigenous Cognitive Assessment (CICA), Kimberley Indigenous Cognitive Assessment (KICA) Carer, KICA Depression Scale, and KICA Activities of Daily Living (ADLs). We use a community-based participatory research (CBPR) approach and the Two-Eyed Seeing integrated knowledge framework to engage Tribal and community collaborators. In this paper, we detail the process of convening Indigenous Knowledge Advisory Groups (IKAGs) at each site and an Assessment Expert Panel (AEP), building a team science approach, and facilitating tool adaptation with communities. We provide an update on the CICA adaptation and offer lessons learned that inform culturally relevant and safe adaptation of the remaining tools. Finally, we report on our process for achieving intercultural consensus in developing a single set of dementia assessment tools for Indigenous peoples across the US.
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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 | 0.389 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".