Bilingualism, assessment language, and the Montreal Cognitive Assessment in Mexican Americans
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Assessment of cognition in linguistically diverse aging populations is a growing need. Bilingualism may complicate cognitive measurement precision, and bilingualism may vary across Hispanic/Latinx sub-populations. We examined the association among bilingualism, assessment language, and cognitive screening performance in a primarily non-immigrant Mexican American community. DESIGN: Prospective, community-based cohort study: The Brain Attack Surveillance in Corpus Christi (BASIC)-Cognitive study. SETTING: Nueces County, Texas. PARTICIPANTS: Community-dwelling Mexican Americans age 65+, recruited door-to-door using a two-stage area probability sampling procedure. MEASUREMENTS: Montreal Cognitive Assessment (MoCA); self-reported bilingualism scale. Participants were classified as monolingual, Spanish dominant bilingual, English dominant bilingual, or balanced bilingual based upon bilingualism scale responses. Linear regressions examined relationships among bilingualism, demographics, cognitive assessment language, and MoCA scores. RESULTS: The analytic sample included 547 Mexican American participants (60% female). Fifty-eight percent were classified as balanced bilingual, the majority (88.6%) of whom selected assessment in English. Balanced bilinguals that completed the MoCA in English performed better than balanced bilinguals that completed the MoCA in Spanish (b = -4.0, p < 0.05). Among balanced bilinguals that took the MoCA in Spanish, education outside of the United States was associated with better performance (b = 4.4, p < 0.001). Adjusting for demographics and education, we found no association between the degree of bilingualism and MoCA performance (p's > 0.10). CONCLUSION: Bilingualism is important to consider in cognitive aging studies in linguistically diverse communities. Future research should examine whether cognitive test language selection affects cognitive measurement precision in balanced bilinguals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".