Nutrient Contribution of the Dinner Meal Consumed by Low-Income Minority Preschool Children
Bibliographic record
Abstract
Objective: To examine the energy and nutrient intake of dinner of low income preschool minority groups, African-Americans and Mexican-Americans, attending Head Start (HS). Design: Cross-sectional study of intake at dinner using digital photography was undertaken. Pictorial records were converted to energy and nutrient intakes using NDS-R Nutritional software. Means±SE for total grams of food and beverages, energy, and macro- and micro-nutrients were determined and compared with recommendations. Setting: Home assessment dinner of children enrolled in HS in Houston, TX. Subjects: Low-income children (n=214), 3 to 5 years (mean age 4.4 ± 0.7 years; 48% boys; 53% Mexican-American). Results: Energy from food and beverages and food was 350.29±10.36 kcals and 302.35±10.46 kcals, respectively. Mean protein, carbohydrate, and total fat intakes were 14.22±0.52 g (16.32% of total energy), 44.33±1.25 g (52.24%), and 13.18±0.56 g (32.29%), respectively, which provided 95.4±0.52%, 44.5±1.25%, and 10.4±0.11% of the requirements for protein, carbohydrate, and dietary fiber, respectively; these were consistent with recommendations. Intakes of vitamin D, calcium, and potassium were 0.66±0.08 mcg, 102.61±5.03 mg, and 404.42±13.63 mg, respectively; which constituted only 6.6±0.08%, 12.7±5.03%, and 10.7±13.63% of these nutrients of public health concern. Conclusions: Children participating in HS had low intakes of nutrients of public health concern at the dinner meal. The dinner meal is an integral part of the daily intake of preschool children and this study suggests that micronutrient intakes could be improved. It is important to educate mothers and children as to what constitutes a nutrient-dense meal and to confront barriers to consumption of these meals.
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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 | 0.000 | 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) | 0.002 | 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".