Beverage consumption and BMI of British schoolchildren aged 9–13 years
Bibliographic record
Abstract
OBJECTIVE: Adequate fluid intake has been well documented as important for health but whether it has adverse effects on overall energy and sugar intakes remains under debate. Many dietary studies continue to refrain from reporting on beverage consumption, which the present study aimed to address. DESIGN: A cross-sectional survey investigated self-reported measures of dietary intake and anthropometric measurements. SETTING: Primary and secondary schools in south-west London, UK. SUBJECTS: Boys and girls (n 248) aged 9–13 years. RESULTS: Boys consumed 10 % and girls consumed 9 % of their daily energy intake from beverages and most children had total sugar intakes greater than recommended. Beverages contributed between a quarter and a third of all sugars consumed, with boys aged 11–13 years consuming 32 % of their total sugar from beverages. There was a strong relationship between consumption of beverages and energy intake; however, there was no relationship between beverage type and either BMI or BMI Z-score. Fruit juices and smoothies were consumed most frequently by all girls and 9–10-year-old boys; boys aged 11–13 years preferred soft drinks and consumed more of their daily energy from soft drinks. Milk and plain water as beverages were less popular. CONCLUSIONS: Although current health promotion campaigns in schools merit the attention being given to improving hydration and reducing soft drinks consumption, it may be also important to educate children on the energy and sugar contents of all beverages. These include soft drinks, as well as fruit juices and smoothies, which are both popular and consumed regularly.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".