Screening for Cognitive Impairment in Bilinguals: What Is the Influence of the Language of Assessment?
Bibliographic record
Abstract
BACKGROUND: Bilingualism's impact on cognitive assessment remains underexplored. This study analyzes the efficacy of the Mini-Mental State Examination (MMSE) as a screening tool for bilinguals, specifically examining the influence of language choice on balanced and unbalanced Lebanese bilinguals (Arabic-French) and its implications for diagnosing cognitive impairment. METHODS: Ninety-three bilingual healthy controls (mean age = 67.99 ± 9.3) and 29 Alzheimer's disease patients (mean age = 77.2 ± 5.9), including 26 with mild and 3 with moderate dementia, underwent MMSE assessments in both Arabic and French. The study aimed to assess language impact on cognitive screening outcomes in different bilingual subtypes. RESULTS: Sensitivity in screening for cognitive impairment using the MMSE varied based on language and bilingualism subtype. For unbalanced bilinguals, using the prominent language increased sensitivity. Conversely, in balanced bilinguals, employing the societal majority language enhanced sensitivity. This suggests that the conventional use of the non-prominent language in cognitive screening for foreigners/immigrants may result in a subtle loss of MMSE sensitivity. CONCLUSION: This study emphasizes the critical role of language choice in cognitive assessment for bilinguals. The MMSE's sensitivity is influenced by language selection, with clinical implications for screening procedures. Recommendations include using the prominent language for cognitive screening in dominant bilinguals and the societal majority language for balanced bilinguals. This nuanced approach aims to improve the accuracy and cultural sensitivity of cognitive screening in bilingual populations, addressing the gap in current assessment practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".