Racial and Ethnic Disparities in Fathers' Food Parenting Practices and Children's Diets
Bibliographic record
Abstract
Abstract Racial and ethnic disparities in children's diets are prevalent. Little is known about how fathers' food parenting practices may contribute to these disparities. We examined racial and ethnic variations in food parenting practices and their associations with 2–6-year-old children's diets in a cross-sectional sample of U.S. fathers surveyed in 2021–2023 (N = 1015; 16% Asian, 9% Black, 6% Hispanic, 70% White; Mage = 37 years) using path analysis. Fathers' food parenting practices were significantly associated with children's diets, yet little evidence emerged that fathers' food parenting practices explained racial and ethnic disparities in children's diets. These findings suggest the potential importance of structural constraints on healthy eating (e.g., access to healthy food) among minoritized children beyond fathers' food parenting practices.
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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 | 0.002 |
| 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".