Behavioural risk factors for cardiovascular diseases among adolescents of secondary school in Tulsipur Sub-Metropolitan City, Nepal: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) are a leading cause of global death and disability, affecting one-third of adult population. Often overlooked in school-going adolescents, behavioural risk factors are crucial contributors to CVD risk which begin early and accelerate during adolescent period. This study aims to assess the behavioural risk factors and their associated determinants among adolescents of Tulsipur Sub-Metropolitan City, Nepal. METHODS: A school-based cross-sectional study was conducted among 361 adolescents aged 16-19 years studying in grade 11 and 12 from public and private schools. Schools were selected using a stratified proportionate sampling method. Data were collected through a self-administered, structured, and validated questionnaire covering socio-demographic characteristics, behavioural risk factors of CVDs, and parental information. Descriptive and analytical statistics were used to analyse the data. RESULTS: The most prevalent behavioural risk factor was the consumption of calorie drinks (99%), followed by sedentary behaviour (60%), insufficient fruit and vegetable intake (57%), physical inactivity (35%), and consumption of processed food high in salt (33%). The prevalence of current smoking, alcohol consumption, and smokeless tobacco use was 12%, 10%, and 9% respectively. Key factors associated with the behavioural risk include maternal education, ethnicity, and education system. Parental tobacco and alcohol use were also associated with adolescent smoking and alcohol consumption. CONCLUSIONS: The high prevalence of CVD risk factors among adolescents in Nepal highlights the urgent need for targeted interventions in both household and school settings. These interventions should aim to reduce behavioural risk factors to prevent the future burden of CVDs in resource-limited areas like 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 | 0.001 |
| 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".