Psychometric Reliability, Validity, and Generalizability of MoCA in American Indian Adults: The Strong Heart Study
Bibliographic record
Abstract
Standardized neuropsychological instruments are used to evaluate cognitive impairment, but few have been psychometrically evaluated in American Indians. We collected Montreal Cognitive Assessment (MoCA) in 403 American Indians 70 to 95 years, as well as age, sex, education, bilingual status, depression symptoms, and other neuropsychological instruments. We evaluated inferences of psychometric validity, including scoring inference using confirmatory factor analysis and structural equation modeling, generalizability inference using reliability coefficient, and extrapolation inference by examining performance across different contexts and substrata. The unidimensional (total score) model had good fit criteria. Internal consistency reliability was high. MoCA scores were positively associated with crystallized cognition (ρ = 0.48, p < .001) and inversely with depression symptoms (ρ = −0.27, p < .001). Significant differences were found by education ( d = 0.79, p < .05) depression ( d = 0.484, p < .05), and adjudicated cognitive status ( p = .0001) strata; however, MoCA was not sensitive or specific in discriminating cognitive impairment from normal cognition (area under the curve <0.5). MoCA scores had psychometric validity in older American Indians, but education and depression are important contextual features for score interpretability. Future research should evaluate cultural or community-specific adaptations, to improve test discriminability in this underserved population.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".