Impact of Educational Level on Versions (Basic and Complete) of the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: One of the most marked problems in the use of screening instruments for the diagnosis of dementia or cognitive impairment in the elderly is the influence of educational level on the results of psychometric tests. The Montreal Cognitive Assessment (MoCA) questionnaire is one of the most widely used dementia screening instruments internationally and with greater proven validity. There is a version of this instrument called MoCA "Basic" which was developed to reduce education bias. The aim of the study was to compare the psychometric characteristics of the MoCA, full versus basic, versions in older adults. METHOD: Participants (N = 214) completed both versions of the MoCA, and screening measures to corroborate their health status. RESULTS: Internal consistency was satisfactory in both versions: MoCA full (0.79) and MoCA basic (0.76). The overall correlation between both tests was high (0.73). There was no relationship between the dimensions included in each version. Educational level and age explained 33.8% of the total variance in MoCA Full and 31.8% in MoCA Basic. Among educational levels, there are statistically significant differences in participants with <6 years of education. CONCLUSIONS: The results confirm that both versions are reliable instruments and also show that in both versions the educational level of <6 years of education continues to have an impact on performance. Therefore, it can be considered that the MoCA Basic version for the Ecuadorian population with <6 years of education continues to imply literacy competencies.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".