Cognitive assessment tools for Arabic‐speaking older adults: A systematic review
Bibliographic record
Abstract
This systematic review aims to identify available cognitive assessments for Arabic-speaking older adults and to assess their validity and performance. A comprehensive search was conducted using Medline, Embase, and APA PsycInfo up to November 2023, encompassing studies validating or using cognitive tools in Arabic for individuals aged ≥ 50. We identified 29 validation studies for 20 cognitive tools and 125 studies using cognitive tools. Three tools were validated in more than one study/setting. Cut-offs for dementia were validated for 16 tools (including two domain-specific tools) and for cognitive impairment for three tools. The Mini-Mental State Examination and Montreal Cognitive Assessment were the most frequently validated and used tools. The results highlight a large need for improved psychometric data for cognitive assessments for Arabic-speaking older adults and identify important gaps in knowledge regarding domain-specific tools, the detection of cognitive changes, and the suitability of assessments across different settings and subgroups. HIGHLIGHTS: We reviewed the availability and properties of cognitive assessments in Arabic. Psychometric data on cognitive tools for older Arabic-speaking adults are scarce. Only three tools are validated in more than one study/setting. Data are largely lacking for domain-specific tools and early cognitive changes. The review identifies important methodology, reporting, and reproducibility issues.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".