Breakfast eating and overweight in a pre-school population: is there a link?
Bibliographic record
Abstract
OBJECTIVES: To analyse the socio-economic factors related to breakfast eating, the association between breakfast eating and overweight, and to gain a more thorough understanding of the relationship between these two elements in a population-based cohort of 4.5-year-old children. We hypothesised that a relationship could be observed between breakfast skipping and overweight independently of socio-economic factors such as ethnicity, maternal education, single parenting and family income. DESIGN: A population-based study whereby standardised nutritional interviews were conducted with each child's parent. The children's height and weight were taken by a trained nutritionist and parents were asked about their child's breakfast eating. SETTING: The analyses were performed using data from the Québec Longitudinal Study of Child Development (1998-2002), conducted by Santé Québec (Canada). SUBJECTS: Subjects were 1549 children between the ages of 44 and 56 months, with a mean age of 49 months. RESULTS: Almost a tenth (9.8%) of the children did not eat breakfast every day. A greater proportion of children with immigrant mothers (19.4% vs. 8.3% from non-immigrant mothers), with mothers with no high school diploma (17.5% vs. <10% for higher educated mothers) and from low-income families (15% for income of $39,999 or less vs. 5-10% for better income) did not eat breakfast every day. Not eating breakfast every day nearly doubled the odds (odds ratio=1.9, 95% confidence interval 1.2-3.2) of being overweight at 4.5 years when mother's immigrant status, household income and number of overweight/obese parents were part of the analysis. CONCLUSION: Although our results require replication before public policy changes can be advocated, encouraging breakfast consumption among pre-school children is probably warranted and targeting families of low socio-economic status could potentially help in the prevention of childhood obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".