Assessing cognitive decline in Vietnamese older adults using the Montreal Cognitive Assessment-Basic (MoCA-B) and Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) during the COVID-19 pandemic: A feasibility study
Bibliographic record
Abstract
Objectives: The lack of cognitive assessment tools suitable for people with minimal formal education is a barrier to identify cognitive impairment in Vietnam. Our aims were to (i) evaluate the feasibility of conducting the Montreal Cognitive Assessment-Basic (MoCA-B) and Informant Questionnaire On Cognitive Decline in the Elderly (IQCODE) remotely on the Vietnamese older adults, (ii) examine the association between the two tests, (iii) identify demographic factors correlated with these tools. Methods: The MoCA-B was adapted from the original English version, and a remote testing procedure was conducted. One hundred seventy-three participants aged 60 and above living in the Vietnamese southern provinces were recruited via an online platform during the COVID-19 pandemic. Results: IQCODE results showed that the proportions of rural participants classified as having mild cognitive impairment and dementia were substantially higher than those in urban areas. Levels of education and living areas were associated with IQCODE scores. Education attainment was also the main predictor of MoCA-B scores (30% of variance explained), with an average of 10.5 points difference between those with no formal education and those who attended university. Conclusions: It is feasible to administer the IQCODE and MoCA-B remotely in the Vietnamese older population. Education attainment played a stronger role in predicting MoCA-B scores than IQCODE, suggesting the influence of this factor on MoCA-B scores. Further study is needed to develop socio-culturally appropriate cognitive screening tests for the Vietnamese population.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".