Breaking geographic and language barriers in neuropsychology: online administration of MoCA in diverse populations
Bibliographic record
Abstract
Traditional in-person neuropsychological tests remain inaccessible and not adapted to individuals in remote geographical locations and linguistically diverse populations. We aimed to make neuropsychological tests more accessible and adapted to diverse populations by leveraging the internet. We examined the feasibility, discriminability, and generalizability of the Montreal Cognitive Assessment-video conferencing version (MoCA-VC) across geographically and linguistically diverse populations. We tested 250 participants from 120+ locations in the USA and Israel, using a standardized MoCA-VC protocol in English, Hebrew, or Arabic. Performance followed expected significant trends across language-speaking cohorts: young adults (YA)>older adults (OA)>people with Parkinson's Disease (PwP), confirming discriminative abilities. However, while the YA groups performed similarly across the three language-speaking cohorts, the OA and PwP Arabic-speaking cohorts demonstrated significantly lower scores, indicating limited generalizability. While these findings support MoCA-VC's feasibility and discriminability, they underscore the need to adapt online cognitive assessments across geographical locations and languages, ensuring greater accessibility worldwide.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".