Strengths and Limitations of the MoCA for Assessing Cognitive Functioning
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is a very widely used test for mild cognitive impairment. Differing recommendations have been made regarding its utility in providing a profile of performance across several cognitive domains. OBJECTIVES: To examine the factor structure of the MoCA in a nationally representative population study of older Irish adults and evaluate its utility in providing domain-specific information. METHODS: A cross-sectional analysis of wave 1 data from the Irish Longitudinal Study on Ageing was undertaken. Data from a subset of 2342 participants assessed using the MoCA were analyzed using both confirmatory factor analytic (CFA) and exploratory factor analytic (EFA) methods. RESULTS: Mean age was 72.64 (range 65 to 98), 53% female. The CFA provided evidence of adequate overall model fit for a previously proposed 6-factor model. In contrast, EFA yielded a 3-factor solution and test items cross-loaded onto a number of factors with no clear pattern of underlying cognitive domains. Using EFA to explore the 6-factor model yielded good fit, but again test items cross-loaded onto a number of factors with no clear pattern evident. CONCLUSION: Lack of concordance between the CFA and EFA findings demonstrates that the correspondence between individual tests and their assumed cognitive domains is not robust, reflecting at least in part a current lack of consensus on how core cognitive constructs are defined and on what subcomponents can be subsumed under different cognitive domains. The MoCA should not be viewed as a substitute for more in-depth neuropsychological assessment when domain-specific information is required.
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 | 0.097 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".