Examining Cognitive and Psychological Health in Malaysia: A Socio-demographic Analysis using MoCA and DASS
Bibliographic record
Abstract
This paper analysed data from the Ageless Cognitive Assessment, a tool used to evaluate cognitive function in older Malaysians. Its objective was to examine the relationship between cognitive function, psychological distress, and socio-demographic characteristics among the elderly in Malaysia, using the MoCA and DASS. The study included 919 participants aged 60 and above from three cohorts (MELoR, TUA, and New Recruit). Kruskal-Wallis and Mann-Whitney tests identified factors influencing the MoCA and DASS scores. Results showed Chinese ethnicity had a superior cognitive capacity (p < 0.05), with an average rank of 348.79. DASS ratings indicated significant differences (p < 0.05) in psychological distress, with the Chinese group exhibiting higher scores, reflecting increased depression, anxiety, and stress. A significant difference (p < 0.05) in DASS scores was observed between genders, with females experiencing higher psychological distress than males. Despite these differences, no significant variations were found in the total MoCA or DASS scores between single or married respondents, indicating marital status did not significantly affect cognitive function or psychological suffering. These findings underscore the influence of ethnicity and gender on psychological distress and cognitive function, highlighting the need for targeted interventions to address disparities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".