Prevalence and Correlates of Mild Cognitive Impairment Among Older Adults in Ho Chi Minh City: A Cross-Sectional Study
Bibliographic record
Abstract
ABSTRACT Introduction This study aims to estimate the prevalence of mild cognitive impairment (MCI) and identify associated sociodemographic, lifestyle, and functional factors among older adults attending outpatient care in a secondary-level clinical setting in Vietnam. Methods A cross-sectional study was conducted from May 2024 to February 2025. Participants were adults aged 60 years or older, literate in Vietnamese, and able to complete the cognitive screening protocol. Data were collected through structured interviews, medical record reviews, and assessments using the Mini-Mental State Examination (MMSE) and the Lawton Instrumental Activities of Daily Living (IADL) scale. Results Of the 631 participants, 30.1% screened positive for MCI. Factors significantly associated with MCI included older age, being unmarried, unstable income, lower educational attainment, reduced recreational activity, and lower IADL scores. Multivariable logistic regression analysis confirmed these associations, with older age and reduced recreational activity showing the strongest links to MCI. Conclusion The high prevalence of MCI among older outpatients highlights the need for integrating cognitive and social-functional screening into outpatient care. Addressing social vulnerabilities and promoting recreational activities may help mitigate cognitive decline in this population.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".