Investigation on the Status and Influential Factors of Cognitive Function of Elderly People in Lalitpur
Bibliographic record
Abstract
BackgroundCognitive function comprises essential mental processes that enable individuals to perceive, learn, remember, and adapt to their environment. With the rapid growth of the global elderly population (aged ≥65 years), maintaining cognitive health has become an important public health concern. This study aimed to assess the cognitive status of elderly individuals in the Lalitpur district of Nepal and to identify factors influencing cognitive function. MethodA descriptive cross-sectional study was conducted among 307 randomly selected elderly respondents (both males and females) from Ward No. 8 of Lalitpur district. Socio-demographic characteristics and cognitive status were assessed using a structured questionnaire and the Montreal Cognitive Assessment (MoCA) scale. Data was analyzed using SPSS version 23.0. Descriptive statistics, independent t-tests, one-way ANOVA, and logistic regression analysis were performed. ResultThe mean MoCA score of respondents was 20.87 ± 5.22. Overall, 78.17% of the elderly population demonstrated cognitive impairment (MoCA score < 26). Cognitive function was found to be significantly associated with age, sex, level of education, and engagement in physical exercise. ConclusionA high prevalence of cognitive impairment was observed among elderly individuals in Lalitpur district. Interventions targeting modifiable factors particularly education and physical activity along with gender-sensitive strategies may help reduce cognitive decline among aging populations in Nepal.
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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".