Montreal Cognitive Assessment test: Psychometric analysis of a South African workplace sample
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) test is a widely used tool to screen for mild neurocognitive impairment. However, its structural validity has not been fully described in South Africa. The study aimed to replicate and extend earlier work with South African samples, to provide an expanded description of the psychometric properties of the MoCA. The study examined the MoCA in a sample of neurocognitively healthy working adults (N = 402) and individuals diagnosed with mild neurocognitive disorders (N = 42); both groups reported good English proficiency. Analysis included general scale descriptions, and structural and discriminant validity. Age and language, but not gender, influenced MoCA scores, with mean total scores of healthy individuals falling below the universal cut-off. Structural analysis showed that a multidimensional model with a higher-order general factor fit the data well, and measurement invariance for gender and language was confirmed. Discriminant validity was supported, and receiver operating characteristics curve analysis illustrated the potential for grey-zone lower and upper thresholds to identify risk. Contribution: This study replicated previous findings on the effects of age, language and gender, and challenged the universal application of ≤ 26 as cut-off for cognitive impairment indiscriminately across groups or contexts. It emphasised the need for context-specific adaptation in cognitive assessments, especially for non-English first language speakers, to enhance practical utility. Novel to this study, it extended knowledge on the structural validity of the test and introduced grey-zone scores as a potential guide to the identification of risk in resource-restricted settings.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".