The Home Food Environment and a Young Child's Weight Status
Bibliographic record
Abstract
The majority of a young child's diet is from foods available in the home. We investigated how a home's nutritional profile associates with a young child's weight. Data were from a national household panel completed 2006‐2008. Participants scanned all foods and beverages purchased for the home with a handheld UPC‐barcode scanner and data were linked to a nutritional database. Households completed a survey including demographics, weight and height for all household members. Analyses were limited to families with young children 2‐12 years old with a year of purchase data and complete anthropometric data (N=4,276). A series of adjusted logistic regression models fit the odds of a young child in the home being overweight (蠅85th age and sex adjusted BMI percentile) on the nutritional profile of the home. Households purchased 985 calories in packaged foods per capita daily. Total calories purchased increased with head of household weight status. Thirty‐nine percent of homes had at least one young child who was overweight. In a logistic regression model adjusted for household size, dual vs. single head of household, head of household weight status and total protein and total calories in the home, total levels of fat in the home was statistically, positively associated with a child being overweight. Results were similar when further adjusting for total levels of sodium in the home and sodium was statistically, positively associated with a child being overweight: aOR 4th vs. 1st quartile 1.73 (p=0.001). In conclusion, a greater availability of foods high in fat and sodium in the home may be obesogenic for young children. Funded by Norris Cotton Cancer Center.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".