210 Preliminary Validation of the Arabic Global Neuropsychological Assessment
Bibliographic record
Abstract
OBJECTIVES/GOALS: Language is the main barrier to equitable access of neuropsychological resources. In our preliminary study, using an Arabic translation of the Global Neuropsychological Assessment (GNA), we assessed 27 Arabic-speaking participants and compared them to English-speaking controls. Our goal was to assess the Arabic GNA’s validity and feasibility. METHODS/STUDY POPULATION: The Global Neuropsychological Assessment (GNA) is a brief 15-minute assessment of cognition. 27 Arabic-speaking participants were recruited and assessed with the GNA and an Arabic translation of the Montreal Cognitive Assessment (MoCA) by community health workers (CHWs). 17 English-speaking participants GNA data were gleaned from a previous validation study and compared to the Arabic sample via independent samples t-tests. Correlations between the GNA sub-tests and Arabic-translated MoCA are reported in the Arabic-speaking sample. RESULTS/ANTICIPATED RESULTS: ): Independent samples t-tests revealed that Arabic and English-speaking groups significantly differed on education (Arabic: M = 10.3, SD = 3.4, English: M = 15.4, SD = 2.43 t(41) = 6.2, p < .05) but not age (p > .05). A one-way ANCOVA model controlling for education revealed that Arabic and English-speaking groups were not significantly different in any GNA subtest (all p’s > .05) except for the perceptual comparison task (Arabic: M = 22.4, SD = 6.9, English: M = 38.4, SD = 9.9, p < .05). Arabic GNA subtests correlated with each other as expected. Logical memory delayed recall was modestly correlated with the MoCA total score (r = .386, p < .05). DISCUSSION/SIGNIFICANCE: Our preliminary results suggest that the Arabic translation of the GNA is suitable for assessment of Arabic-speaking individuals. Brief educable assessments like the Arabic GNA are essential to meet the needs of these English new language populations and reduce the need for live translations that reduce the reliability of assessment.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".