Bilingual Spanish-English speakers’ performance on the Montreal Cognitive Assessment (MoCA) in English: implications for test bias and score adjustments
Bibliographic record
Abstract
BackgroundThe Montreal Cognitive Assessment (MoCA) is a commonly used cognitive screener for detecting cognitive impairment. However, the originally suggested cutoff score of 26/30 has not held up in other diagnostic accuracy studies, and it is unclear if bilingualism impacts this score. The test items have not been sufficiently examined for test bias, particularly in bilingual speakers. Aims The aims of this study were to investigate the performance of Spanish-English bilingual speakers on MoCA total score and item scores, factors influencing total and item scores, and the need for score adjustments for bilingual speakers. Methods & Procedures One hundred and one bilingual neurotypical Spanish-English speaking individuals (age range = 25-76 years, 27M / 74F) were virtually administered the Montreal Cognitive Assessment in English. Language proficiency was measured using objective vocabulary measures as well as self-ratings using a validated tool. Subgroups of participants with high proficiency in both languages, and mid-proficiency in English were identified and separately analyzed. A four-predictor model (age, English education, socioeconomic status, and English proficiency) was used to examine factors that influence the MoCA total score and individual item accuracies. Outcomes & ResultsMoCA total scores were significantly lower than the normative scores of the Anglophone/Francophone population originally published by the developers. Seven and fifteen items had low passing rates (below 87%) for the entire group and the mid-proficiency English groups respectively. English language proficiency emerged as the single significant predictor of total scores and of over one-fourth (8 out of 28) of the item accuracies. ConclusionsThe normative data obtained from the present study shows that Spanish-English bilingual speakers’ total scores on the English MoCA need to be adjusted and raw scores might not be sensitive to identify cognitive impairment. Measuring language proficiency is critical for determining the need for and type of score adjustments for bilingual speakers.
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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 | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".