Level of cognitive function and associated factors among community-dwelling older adults in a semi-urban area in Colombo district, Sri Lanka
Bibliographic record
Abstract
Older adults face various health challenges, including cognitive decline, which significantly affects memory, decision-making abilities, and overall quality of life. Despite the significance of research in this area, limited studies have been conducted in Sri Lanka to identify the level of cognitive function and associated factors. This study aimed to identify the level of cognitive function and the associated factors among community-dwelling older adults in the Homagama Divisional Secretariat area. A descriptive cross-sectional study was conducted among 420 community-dwelling older adults aged ≥60 years. The cluster sampling technique was used to achieve the required study sample. Data were collected by administering the Montreal Cognitive Assessment (MoCA) tool, which has been validated for the Sri Lankan context. The maximum score is 30 points and the cut-off value for mild cognitive impairment is <26 points. Data analysis was performed using Statistical Package of Social Sciences (version 27.0). Descriptive statistics, chi-Square test and Pearson’s correlation test were performed to assess significant associations. The level of significance was set as p<0.05. Mean age of the study sample was 69 + 6.83 years. The majority (282, 67.1%) of the participants were females. Mean score for MoCA was 19.30 + 4.16. The prevalence of cognitive impairment in the study sample was 93.6%. Cognitive function was significantly associated with hypertension (p=0.013), sleep problems (p<0.001), use of antihypertensive drugs (p=0.002), antihyperglycemic drugs (p=0.042), lipid lowering drugs (p=0.009), monthly income (p<0.001), employment status (p=0.002). There was a significant negative correlation between age and cognitive function (r = -0.285, p<0.001). The prevalence of cognitive impairment among older adults in this sample was high. Cognitive function was significantly associated with age, health conditions, medication use and socioeconomic factors. These findings highlight the need for regular cognitive assessments and targeted interventions to promote healthy aging among older Sri Lankan adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".