Daily Fruit-Vegetable Consumption, Morbidity Pattern and Healthcare Seeking Behaviour in General Adults Living on the Outskirts of a Sub-Metropolitan City
Bibliographic record
Abstract
Background & Objectives: Fruits and vegetables are essential for a healthy diet, yet global consumption remains below recommended levels, particularly in developing countries like Nepal, increasing the risk of non-communicable diseases. This study examines fruit and vegetable intake, morbidity patterns, and healthcare-seeking behavior among adults in the outskirts of Janakpur Sub-Metropolitan. Materials and Methods: A community-based cross-sectional study was conducted between September 2024 and December 2024 with a sample of 466 general adult population aged 18 years and above. Data were collected through face-to-face interviews with structured questionnaire selected through multistage random sampling. Binary logistic regression was employed to identify predictors of fruit and vegetable consumption. Results: Out of 466 participants, only 15.23% of participants consumed fruit on a daily basis. Majority (68.3%) sought care from qualified doctors followed by qualified paramedical (23.8%) and few (7.9%) sought care from non-health professionals. Further, people who sought health care from qualified doctor had a significantly higher likelihood of consuming leafy vegetable 2-3 times per week. Similarly, Participants with anaemia were significantly less likely to consume leafy vegetable frequently. Conclusion: This study highlights the low consumption of fruits and vegetables, with a higher likelihood of consuming leafy vegetables among those who seeks care from qualified medical doctors. However, frequent consumption of leafy greens has been observed to be greatly constrained among anaemic. Public health education should focus on the importance of fruit and vegetable intake and its impact on overall health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".