Factors Associated with Dietary Patterns of Schoolchildren: A Systematic Review
Bibliographic record
Abstract
The evaluation of food consumption in childhood is essential to help understand the effect of food choices on health. The objective of this study was to conduct a systematic review of studies that identified the dietary patterns in schoolchildren (7-10 years old) and their associated factors. Observational studies published in the last ten years were searched in the databases BVS (Virtual Health Library), Embase, PubMed, Scopus, and Web of Science. The Newcastle Ottawa Scale was adopted to evaluate the articles' quality. The studies covered schoolchildren, children, and adolescents as part of the sample. We selected 16 studies, 75% of which were considered good/very good and seven mentioned three food patterns. A dietary pattern considered unhealthy was identified in 93.75% of the studies, having as associated factors to its consumption: higher screen time, low bone mass, gain of weight and fat in children, and meal skipping. The children who usually had breakfast showed greater adherence to the dietary pattern consisting of healthier foods. The children's dietary patterns were related to their behavior, nutritional status, and family environment habits. Food and nutrition education's effective actions, as well as the regularization of the marketing of ultra-processed foods, must be stimulated and inserted in public policies as a way to promote and protect children's 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".