Language Dominance and Education Considerations in the Neuropsychological Assessment of Southwestern American Indians Using the National Alzheimer Coordinating Center’s Uniform Data Set Version 3
Bibliographic record
Abstract
To address disparities in dementia diagnosis and care in American Indian and Alaska Native communities, it is crucial to understand how sociocultural factors, such as language dominance and education, impact performances on standardized neuropsychological assessments. We discuss sociocultural considerations that are important to consider when evaluating cognition in American Indians. We conducted t tests/Kruskal-Wallis tests and correlation analyses to evaluate the impact of language and education factors on performances on the National Alzheimer Coordinating Center's Uniform Data Set Version 3 Neuropsychological assessments in a community of Southwestern American Indians. There were no significant differences in cognitive performances between the Zuni (Shiwi)-dominant and English-dominant individuals. Number of years of education had a greater effect on cognitive performances relative to language dominance, particularly for the common cognitive screening measure, the Montreal Cognitive Assessment. Our results highlight that education factors have a greater effect on cognitive performances relative to language dominance in this unique cohort. The associations with the Montreal Cognitive Assessment raise concerns for the use of this tool in this population, highlighting a need to develop culturally appropriate cognitive testing tools as well as ensuring comprehensive, culturally competent neuropsychological assessments are accessible.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".