Assessing MyPlate Familiarity and Typical Meal Composition using Food Models in Children Aged 7-13
Bibliographic record
Abstract
The Dietary Guidelines for Americans serve as a basis for developing federal nutrition education materials for the public, such as MyPlate. MyPlate is a visual cue that uses food groups as a guide to building healthy plates at mealtime. The objective of this study was to determine factors associated with child familiarity with MyPlate guidelines and to determine if typical meals met MyPlate guidelines using food models. A convenience sample of 250 children (aged 7-13 years) and their parent/guardian were recruited at a local science and history museum. Children viewed a picture of the MyPlate icon and were asked to identify the picture. Next, participants used a nine-inch plate to build a typical meal (meals that they would regularly consume) from a buffet of food and beverages models (>65 items to choose from). Research team members took photographs of the plates. A Registered Dietitian Nutritionist determined the percentage of plates that met MyPlate guidelines. Eighty-six percent of children recognized the MyPlate icon upon viewing the image; 7.6% could accurately identify the icon by name. When participants were asked to build a typical meal, however, only 3.43% met MyPlate guidelines. The results of this study suggest that despite being familiar with MyPlate, children built typical meals that did not meet MyPlate guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".