Social epidemiology of early adolescent nutrition
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate associations between sociodemographic factors and dietary intake among a diverse population of early adolescents ages 10-13 years in the United States. METHODS: We examined data from the Adolescent Brain Cognitive Development (ABCD) Study in Year 2 (2018-2020, ages 10-13 years, N = 10,280). Multivariable linear regression models were conducted to estimate the adjusted associations between sociodemographic factors (age, sex, race and ethnicity, household income, parental education) and dietary intake of various food groups, measured by the Block Kids Food Screener. RESULTS: Older age among early adolescents was associated with slightly less fruit, whole grain, and dairy and more monounsaturated fat consumption. Male sex was associated with a lower intake of fruit, fruit juice, vegetables, whole grains, and fiber and a higher intake of meat/poultry/fish, added sugars, fat, as well as higher glycemic index and glycemic load compared to female sex. Racial and ethnic minority status, lower household income, and lower parental education were generally associated with less fruit and vegetable consumption and more added sugars. CONCLUSION: These findings can guide public health interventions to reduce diet quality disparities by targeting key populations and addressing differences according to socioeconomic status, sex, and race. IMPACT: Sociodemographic disparities in diet quality have been studied, but none have explored sociodemographic associations with specific food groups and components (e.g., different types of fat) in early adolescence. In this demographically diverse sample of 10-13-year-old early adolescents in the US, we found sociodemographic disparities in dietary intake across various food groups. Most notably, male sex, racial and ethnic minority status, lower household income, and lower parental education were associated with less fruit and vegetable consumption and more added sugars.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".