Socioeconomic, Behavioural, and Health-related Characteristics of Older Adults
Bibliographic record
Abstract
BACKGROUND: The global aging population is growing rapidly, and Nepal is no exception. This increase is driven by changes in socioeconomic conditions, health behaviours, and advancements in the health system. In Nepal, almost a quarter of the national population are older adults (≥45 years), whose health status is rarely elaborated. This study was carried out to assess the socioeconomic, behavioural, and health-related characteristics of older adults in Nepal. METHODS: A community-based cross-sectional study was conducted among 4,179 randomly selected older adults residing in Bagmati Province from July 2022 to June 2023, via a multi-stage sampling technique. A semi-structured questionnaire including Geriatric Depression Scale, Activity of Daily Living, and Instrumental Activity of Daily Living along with sociodemographic and health profiles were used for the data collection through face-to-face interviews. The data were described in frequency and percentage across the local levels (urban/rural) and gender. Chi-square tests were done for bivariate analyses. RESULTS: The mean age of the population was 61.66±11.1 years. The prevalence of multimorbidity, disability, and depression was found to be 27.6%, 23.3%, and 35.1% respectively. There was no significant difference between multimorbidity and depression across local levels, while there was a significant difference across disability status. There was a significant difference between multimorbidity and depression across genders. CONCLUSIONS: This study provides comprehensive insights into the socioeconomic status, behavioural factors, and health status of older adults in Nepal. Study findings can inform interventions and policies at local levels to consider the unique needs of the older population 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".