Addressing Cognitive Assessment Disparities Among Hispanic Adults: Adapting the MoCA-SA for Improved Accuracy and Accessibility Among Spanish Speakers
Bibliographic record
Abstract
OBJECTIVES: Hispanic adults display a higher likelihood of early-stage cognitive decline than their White counterparts yet receive fewer clinical diagnoses. This troubling trend highlights the significance of longitudinal surveys like the National Social Life, Health, and Aging Project (NSHAP) in monitoring cognitive changes in aging Hispanics. Using NSHAP's Rounds 2 and 3, we observed notable cognitive score differences between English and Spanish speakers, as assessed by the survey-adapted version of the Montreal Cognitive Assessment (MoCA-SA). Our study aims to discern if statistical adjustments can reduce measurement variance in global cognition scores between these language groups. METHODS: We applied modification indexes, 2-parameter item response theory models, and split-sample testing to pinpoint items that exhibit resilience to language-related bias among our Hispanic sample. From this analysis, an abbreviated version of the MoCA-SA, termed MoCA-SAA, was introduced. Subsequently, we juxtaposed the performance and predictive validity of both MoCA versions against four consequential outcomes indicative of cognitive decline. RESULTS: Our refined methodologies enabled the identification of consistent items across both language cohorts. The MoCA-SAA demonstrated performance and predictive validity in line with the original MoCA-SA concerning outcomes linked to cognitive deterioration. DISCUSSION: The translated measures ensure the inclusion of Hispanic Spanish speakers in NSHAP, who might otherwise be overlooked. The statistical adjustment outlined in this study offers a means to mitigate potential measurement disparities when assessing overall cognition. Despite these advancements, we acknowledge persistent issues related to the translation of the MoCA-SA into Spanish that warrant further attention.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".