Prevalence and Associated Factors of Cognitive Impairment, Depression, and Disability among Older Adults with Chronic Illnesses in Batticaloa, Sri Lanka: A Cross-sectional Study
Bibliographic record
Abstract
Older adults with chronic illnesses are at high risk of developing depression, cognitive impairment, and disability. However, limited research has examined the inter relationships between these conditions in post conflict regions such as Batticaloa, Sri Lanka. This study aimed to assess prevalence and associated factors of depression, cognitive impairment, and disability among older adults with chronic illnesses in Batticaloa, Sri Lanka. A descriptive cross-sectional study was conducted among 427 older adults (≥60 years) with chronic illnesses attending follow up clinics at Batticaloa Teaching Hospital. Validated assessment tools were used, including the Geriatric Depression Scale, the World Health Organisation Disability Assessment Schedule 2.0, and the Montreal Cognitive Assessment. Descriptive statistics were used to summarise the data. Pearson's correlation was applied to assess the associations between depression, cognitive impairment, and disability. Depression was prevalent in 69% of participants, with 22% experiencing severe symptoms. Cognitive impairment and disability were observed in 68% and 86% of participants, respectively. Depression was significantly correlated with disability (r=0.418, p<0.001), while cognitive impairment was negatively correlated with both depression (r=-0.216, p<0.001) and disability (r=-0.305, p<0.001). This study found that depression, cognitive impairment and disability are highly prevalent among older adults with chronic illnesses in Batticaloa, Sri Lanka. Depression was closely linked to higher levels of disability, while cognitive impairment showed weaker associations with both conditions. The findings point to an urgent need for comprehensive geriatric assessments, community-based rehabilitation programs, and caregiver support mechanisms in Sri Lanka and similar settings.
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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 | 0.000 | 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".