Construct validity of the Visual Cognitive Assessment Test (VCAT)—a cross-cultural language-neutral cognitive screening tool
Bibliographic record
Abstract
BACKGROUND: The Visual Cognitive Assessment Test (VCAT) is a language-neutral cognitive screening tool designed for use in culturally diverse populations without the need for translations or adaptations. While it has been established to be language-neutral, the VCAT's construct validity has not been investigated. METHODS: 471 participants were recruited, comprising 233 healthy comparisons, 117 mild cognitive impairment (MCI), and 121 mild Alzheimer's disease (AD) patients. VCAT and domain-specific neuropsychological tests were administered in the same sitting. Construct validity was assessed by analyzing domain-specific associations between the VCAT and well-established cognitive assessments. Reliability (internal consistency) was measured by Cronbach's alpha. Diagnostic ability (area under the curve) and recommended cutoffs were determined by receiver operating characteristic (ROC) analysis. RESULTS: The VCAT and its subdomains demonstrated good construct validity in terms of both convergent and divergent validity and good internal consistency (α = .74). ROC analysis found that the VCAT was on par with the Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at distinguishing between healthy comparisons, MCI, and mild AD. Consistent with previous studies, VCAT scores were not affected by language of administration or ethnicity in our cohort. Findings suggest the following cutoffs: Dementia 0-19, MCI 20-24, Normal 25-30. CONCLUSION: This study established the construct validity of the VCAT, which is vital to ensure its subdomains effectively measure the cognitive processes they were designed to. The VCAT is capable of detecting early cognitive impairments and allows for meaningful cross-cultural comparisons, especially useful for international collaborations and clinical trials, and for clinical use in diverse multiethnic populations.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".