The Relationship between Eating Behaviors of Children and Mothers, and Nutritional Status of Children
Bibliographic record
Abstract
Introduction: Parents have a significant impact on child nutrition and the child's eating habits in long-term behavior. This study aims to examine the effects of mothers' attitudes on the eating behaviors of children and the determination of their nutritional status. Method: Children's eating habits and mothers' eating attitudes were examined in 417 children with face-to-face interviews using the Child Feeding Questionnaire (CFQ). The participants' anthropometric characteristics were determined, and body mass indexes (kg/m2) were calculated. AnthroPlus software was used to determine the children’s BMI-for-age Z-scores. BeBiS nutrition information system was used to assess children’s food intake. Results: The mothers' median BMI was above the overweight threshold. The lowest CFQ score was for perceived child weight, and the maximum score was for food restriction. Significant differences were found in mothers ages, mothers' BMIs, children's WAZ and BAZ scores, and BMIs, weight (p<0.001), perceived parent weight scores (p=0.04), and pressure to eat (p=0.04). Families should be conscious about nutrition for the protection and promotion of 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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".