Cognitive screening and education: assessing the Montreal Cognitive Assessment’s validity in older Ugandan populations
Bibliographic record
Abstract
Background: Cognitive screening tools such as the Montreal Cognitive Assessment (MoCA) are widely used to detect cognitive impairments. However, their accuracy in low- and middle-income countries (LMICs) may be affected by variations in educational levels. This study examined the impact of educational attainment on MoCA performance in older Ugandan adults, considering sex- and age-related differences. Methods: A cross-sectional study was conducted in Wakiso District, Uganda, involving adults aged ≥ 65 years. Their MoCA scores were analyzed in relation to their educational attainment, sex, and age. Multiple linear regression models were used to determine the independent effect of education on cognitive performance after adjusting for age and sex. Sensitivity analyses were conducted using multiple imputations for missing data. Results: Higher educational attainment was significantly associated with a better MoCA performance (β = 1.73, 95% CI: 1.22–2.24, p < 0.001). Age was negatively associated with MoCA scores (β = −0.13, 95% CI: −0.19 to −0.07, p < 0.001), whereas male sex was positively associated (β = 1.89, 95% CI: 0.56–3.22, p = 0.005). The interaction terms (education × sex and education × age) were not significant, indicating that the effect of education was consistent across demographic subgroups. The final regression model explained 42.7% of the variance in the MoCA scores (adjusted R2 = 0.43, p < 0.001). The sensitivity analysis confirmed the robustness of the findings. Conclusions: Educational attainment impacts MoCA performance in older Ugandans, highlighting the need for region-specific norms and culturally adapted cognitive screening in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".