The socio-ecological model as a framework for understanding junk food consumption among schoolchildren in Nepal
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 it