Behavioural Risk Factors of Noncommunicable Diseases among Nepalese Urban Poor: A Descriptive Study from a Slum Area of Kathmandu
Bibliographic record
Abstract
There has been a rapid rise in the burden of noncommunicable diseases in low-income countries like Nepal. Political and economical instability leading to internal migration give rise to haphazard urbanization in Nepal. This, coupled with negative effects of globalization, is largely responsible for changing lifestyle and developing risky behaviour among the urban poor that put them at high risk of developing noncommunicable diseases. A descriptive cross-sectional quantitative study was conducted from September to December 2012 in an urban slum of Kathmandu to explore the prevalence of four major behaviour risk factors namely physical inactivity, low fruit and vegetable consumption, and tobacco and alcohol use and to measure the burden of obesity and hypertension in the population. We used WHO NCDs Risk Factor steps 1 and 2 questionnaires in all the 689 households of the slum. The major behavioral risk factors for noncommunicable diseases were very common with at least a quarter of the population having the major risk factors. The results may serve to form a framework to future planning, policy-making, implementation, and evaluation of any measures undertaken to reduce these risk factors, especially as the government is planning to unveil the National Urban Health Policy soon.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".