Breakfast skipping is associated with differences in meal patterns, macronutrient intakes and overweight among pre-school children
Bibliographic record
Abstract
OBJECTIVES: To examine the association between skipping breakfast, daily energy, macronutrients and food intakes, and BMI in pre-school children. DESIGN: A cross-sectional study using information on children's food consumption and measured height and weight. Energy and macronutrient intakes of the children were derived from parent/day-care attendant's responses to 24 h recall interviews and eating behaviour questionnaires. SETTING: Data obtained from a representative sample (n 2,103) of children born in Quebec (Canada) in 1998. SUBJECTS: One thousand five hundred and forty-nine children, with a mean age of 49 (sd 3.12) months. RESULTS: Ten per cent of children ate breakfast on fewer than 7 days per week. This behaviour was associated with a lower diet quality and concentrated energy intakes through higher protein intakes at lunch and the consumption of snacks higher in energy and carbohydrate in the afternoon and evening; yet total daily energy intakes were not significantly different from those of pre-school children who ate breakfast every day. Breakfast skippers' mean BMI increased as intake of energy, carbohydrates or servings of grain products increased; however, this was not the case for breakfast eaters. When Cole's cut-off for overweight/obesity was used, overweight/obesity in breakfast skippers was related to the dinner-time consumption of approximately 3,000 kJ (700 kcal) or more for energy intake, approximately 100 g or more of carbohydrates, or approximately 3 servings or more of grain products. CONCLUSIONS: Eating breakfast every day is associated with having a healthy body weight, likely due to a more even distribution of energy intake across meals throughout the day.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".