Going global: Indian adolescents’ eating patterns
Bibliographic record
Abstract
OBJECTIVE: To describe adolescents' eating patterns of traditional, global/non-local and mixed foods, and the factors that may influence food consumption, access and preferences, in a globalizing city. DESIGN: A representative sample of school-going adolescents completed a cross-sectional survey including an FFQ designed to identify traditional and global foods. Student's t test and ordinal logistic regression were used to examine weekly food intake, including differences between boys and girls and between adolescents attending private and public schools. SETTING: Vijayapura city, Karnataka State, India. SUBJECTS: Adolescents (n 399) aged 13-16 years. RESULTS: Compared with dietary guidelines, adolescents consumed fruit, green leafy vegetables, non-green leafy vegetables and dairy less frequently than recommended and consumed energy-dense foods more frequently than recommended. Traditional but expensive foods (fruits, dairy, homemade sweets and added fat) were more frequently consumed by private-school students, generally from wealthier, more connected families, than by public-school students; the latter more frequently consumed both traditional (tea, coffee, eggs) and mixed foods (snack and street foods; P≤0·05). Girls reported more frequent consumption of global/non-local packaged and ready-to-eat foods, non-green leafy vegetables and added fat than boys (P≤0·05). Boys reported more frequent consumption of eggs and street foods than girls (P≤0·05). CONCLUSIONS: Adolescents' eating patterns in a globalizing city reflect a combination of global/non-local and traditional foods, access and preferences. As global foods continue to appear in low- and middle-income countries, understanding dietary patterns and preferences can inform efforts to promote diversity and healthfulness of foods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".