Ethnic-Specific Sociodemographic Factors as Determinants of Cognitive Performance: Cross-Sectional Analysis of the Malaysian Elders Longitudinal Research Study
Bibliographic record
Abstract
Introduction: Despite cognitive impairment being a major health issue within the older population, limited information is available on factors associated with cognitive function among Asian ethnic groups. The objective of this study was to identify ethnic-specific sociodemographic risk factors which are associated with cognitive performance. Methods: Cross-sectional analysis of the Malaysian Elders Longitudinal Research (MELoR) study involving community-dwelling individuals aged >55 years was conducted. Information on sociodemographic factors, medical history, and lifestyle were obtained by computer-assisted interviews in participants' homes. Cognitive performance was assessed with the Montreal Cognitive Assessment (MoCA) tool during subsequent hospital-based health checks. Hierarchical multiple linear regression analyses were conducted with continuous MoCA scores as the dependent variable. Results: Data were available for 1,140 participants, mean (standard deviation [SD]) = 68.48 (7.23) years, comprising 377 (33.1%) ethnic Malays, 414 (36.3%) Chinese, and 349 (30.6%) Indians. Mean (SD) MoCA scores were 20.44 (4.92), 23.97 (4.03), and 22.04 (4.83) for Malays, Chinese, and Indians, respectively (p = 0.01). Age >75 years, <12 years of education, and low functional ability were common risk factors for low cognitive performance across all three ethnic groups. Cognitive performance was positively associated with social engagement among the ethnic Chinese (β [95% CI] = 0.06 [0.01, 0.11]) and Indians (β [95% CI] = 0.16 [0.09, 0.23]) and with lower depression scores (β [(95% CI] = -0.08 [-0.15, -0.01]) among the ethnic Indians. Conclusion: Common factors associated with cognitive performance include age, education, and functional ability, and ethnic-specific factors were social engagement and depression. Interethnic comparisons of risk factors may form the basis for identification of ethnic-specific modifiable risk factors for cognitive decline and provision of culturally acceptable prevention measures.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".