Cognitive differences between healthy monolingual and bilingual anglophones on the English version of the <i>Dépistage Cognitif de Québec</i> : A new screening tool for atypical dementia
Bibliographic record
Abstract
Aims and Objectives: Cognitive abilities between monolingual and bilingual individuals may differ, making it an important factor to consider during the administration of cognitive screening tools. Otherwise, assessments could be subject to misinterpretation, leading to possible inaccurate diagnoses. The current project aimed to compare cognitive performance of healthy older monolingual and bilingual anglophones on the English version of a new cognitive screening test designed for better recognition of atypical dementia: the Dépistage Cognitif de Québec (DCQ; www.dcqtest.org ). Design: The DCQ was administered by qualified psychometricians to 85 native English-speaking participants aged 50 years and over, in various sites across Canada. Language proficiency was established using the Language Experience and Proficiency Questionnaire (LEAP-Q). The Montreal Cognitive Assessment (MoCA) was used to exclude individuals with cognitive impairments. Data and Analysis: Amid the anglophone participants recruited, 30 monolingual anglophones and 29 bilingual anglophones (English and French) met inclusion criteria. Groups had similar age, education, and MoCA scores. Monolinguals and bilinguals were compared on their total DCQ scores and each of the five DCQ indexes: Memory, Visuospatial, Executive, Language, and Behavioural. Findings: The bilingual participants performed better on the Language Index, which contributed to the significant bilingual advantage for the overall DCQ scores. When applying a Bonferroni correction, the differences between groups were, however, not maintained. No differences were found on any of the other indexes. Originality: This study is the first to explore psychometric properties of the DCQ in older monolingual and bilingual participants tested in their native language. Implications: Results highlight the importance of identifying and characterizing linguistic diversity before using new screening tools in clinical settings. The potential cognitive advantages of bilingualism should be considered when interpreting test data and explicitly discussed in neuropsychological reports.
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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 | 0.001 |
| 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".