A <scp>cross‐cultural</scp> study of the Montreal Cognitive Assessment for people with hearing impairment
Bibliographic record
Abstract
BACKGROUND: Cognitive screening tools enable the detection of cognitive impairment, facilitate timely intervention, inform clinical care, and allow long-term planning. The Montreal Cognitive Assessment for people with hearing impairment (MoCA-H) was developed as a reliable cognitive screening tool for people with hearing loss. Using the same methodology across four languages, this study examined whether cultural or linguistic factors affect the performance of the MoCA-H. METHODS: The current study investigated the performance of the MoCA-H across English, German, French, and Greek language groups (n = 385) controlling for demographic factors known to affect the performance of the MoCA-H. RESULTS: In a multiple regression model accounting for age, sex, and education, cultural-linguistic group accounted for 6.89% of variance in the total MoCA-H score. Differences between languages in mean score of up to 2.6 points were observed. CONCLUSIONS: Cultural or linguistic factors have a clinically significant impact on the performance of the MoCA-H such that optimal performance cut points for identification of cognitive impairment derived in English-speaking populations are likely inappropriate for use in non-English speaking populations. To ensure reliable identification of cognitive impairment, it is essential that locally appropriate performance cut points are established for each translation of the MoCA-H.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".