The socio-ecological model as a framework for understanding junk food consumption among schoolchildren in Nepal
Bibliographic record
Abstract
Background: The consumption of industrially processed food, popularly known as junk food, is a growing public health concern worldwide, including in Nepal. Schoolchildren are a vulnerable group and they consume junk food at school. Aim: The aim of this study was to examine multi-level determinants of junk food consumption among basic schoolchildren using the socio-ecological model as a framework. Methods: A cross-sectional study was conducted among students ( n = 404), and a self-reported questionnaire was used to collect the data. The chi-square test and logistic regression were applied to analyse the results using SPSS version 26. Results: Nearly half (47%) of the students reported that they consumed junk foods at snack time. Important variables for explaining junk food consumption were knowledge of food and nutrition—a micro-level determinant; sharing knowledge of food and nutrition with classmates at school—a meso-level determinant; grade of student—an exo-level determinant; and occupation of parents—a macro-level determinant. However, multivariate analysis found that knowledge of food and nutrition ( p < 0.05), and sharing knowledge of food and nutrition with classmates at school ( p < 0.05) were the significant predictors of junk food consumption. Conclusions: Junk food consumption is common among basic-level students in the study schools. Multi-level determinants explain the factors associated with this behaviour, extending from micro to macro as the socio-ecological model asserts. This study points to the need for comprehensive school-based nutrition education that targets multiple levels of influence, focusing on active learning approaches to promote healthy dietary behaviour in students.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".