The Montreal Cognitive Assessment (MoCA) in a population-based sample of Turkish migrants living in Germany
Bibliographic record
Abstract
OBJECTIVES: Data on cognitive testing in migrants in Germany are scarce. We aimed to evaluate the Montreal Cognitive Assessment (MoCA) in Turkish migrants in Berlin and its association with demographics and health-related variables. METHOD: For this cross-sectional study, a random sample of persons with Turkish names was drawn from the registration-office. Cognitive function was assessed using the MoCA; 0 = worst, 30 = best total score. Multivariable linear regression models were calculated to determine associated factors with the total MoCA-score. RESULTS: In our analyses we included 282 participants (50% female), mean age 42.3 ± 11.9 years (mean ± standard deviation (SD)). The mean ± SD MoCA score was 23.3 ± 4.3. In the multivariable analysis, higher education (ß = 2.68; p < 0.001), and chosing the German version of the MoCA (ß = -1.13; p = 0.026), were associated with higher MoCA-scores, whereas higher age (ß = -0.08; p = 0.002) was associated with lower MoCA scores. CONCLUSION: In our study, a higher educational level, lower age, and German as the preferred test language (as compared to Turkish) were positively associated with the cognitive performance of Berliners with Turkish roots. To examine neurocognitive health of migrants, longitudinal population-based and clinical cohort studies that specifically compare migrants and their descendants with the original population of their home countries are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".