Validation of a modified MOCA translation for use among Arabic‐speaking immigrants in the U.S.
Bibliographic record
Abstract
Abstract Background The only validated, widely‐used dementia screen that has Arabic language norms/cutoffs is the Montreal Cognitive Assessment (MoCA). Yet, Arabic translations of the MoCA vary across countries. This study considers various Arabic translations of the MoCA, and presents a modified translation for use among Arabic‐speaking immigrants in the U.S. Method The modified translated version of the MoCA was administered to 32 Arabic‐speaking adults age 65+ living in metro‐Detroit. Eight (25%) had an ADRD diagnosis. To assess the reliability of the MoCA, each item was standardized and Cronbach’s alpha was calculated. To assess the similarity of ADRD and non‐ADRD respondents with regards to each MoCA item as well as with regards to demographics we used Fisher’s exact test for binary variables and t‐test for continuous variables. Ordinary least squares models were used to examine how an ADRD diagnosis predicts the MoCA score, adjusting for demographics. Result The mean age of the sample is 73 years old. Sixty‐two percent (62%) are female and 28% have a high school education or more. The alpha was acceptably high at .87. The MoCA item‐level scores for respondents with and without ADRD diagnoses showed that all respondents correctly identified the picture of a camel. There are also five items for which none of the ADRD respondents gave correct responses: trail‐making, verbal fluency F, both abstraction questions, and the cube‐copy test. Bivariate analyses further indicate that ADRD respondents are older than non‐ADRD respondents (p<.001). There is no significant difference in gender or education level. Those with ADRD diagnosis scored lower overall on the MoCA ( = ‐.35; se = .37), with 58% of the variation explained by the diagnosis and demographics. We interpret the result as a small effect size as indicated by the Cohen’s heuristic for the difference between means (0.2‐0.49). Conclusion The Arabic language MoCA is able to distinguish respondents with an ADRD diagnosis. Decision points about translations should consider national contexts to maximize equivalencies across samples.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".