Article Component of Statistics Canada Catalogue no. 82-003-X Health Reports Beverage consumption of children and teens
Bibliographic record
Abstract
Beverage consumption of children and teens • Health Matters Beverage consumption of children and teens by Didier Garriguet Liquids, notably water, are essential to good nutrition. While some water comes from foods, most is derived from beverages. As well, beverages provide vitamins and minerals. However, beverages can also be a major source of sugar, and may contribute to excess calories. Sugar-sweetened drinks have been linked to weight gain and higher body mass index in children and teenagers.1 Sweetened drinks, and even fruit juice, have been associated with an increased risk of tooth decay.2 This article is an overview of beverage consumption by Canadian children and teens aged 1 to 18. It examines the quantity and type of beverages consumed, differences by age and gender, and beverages ’ contribution to calorie and nutrient intake. Water More than 85 % of all the beverages consumed by children and teens fall into five categories: water, milk, fruit juice, fruit drinks, and regular soft drinks. Asubstantial proportion of Canadians ’ daily calories come not from what they eat, but from what they drink. This is particularly true for children. According to results from the 2004 Canadian Community Health Survey (CCHS) – Nutrition (see The data), beverages make up almost 20 % of the calories consumed by children and teens aged 4 to 18 (Table 1). At ages 1 to 3, beverages account for an even higher 30%. According to results from the 2004 Canadian Community Health Survey – Nutrition, children and teens get about one-fifth of their daily calories from beverages. As they get older, boys and girls drink less milk and fruit juice, and more soft drinks and fruit drinks. By ages 14 to 18, boys ' average daily consumption of soft drinks matches their consumption of milk, and exceeds their consumption of fruit juice and fruit drinks. Beverage consumption by children and teens varies little by province, except in Newfoundland and Labrador where it tends to be comparatively high, and in British Columbia where it tends to be low.
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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".