Sugar‐Sweetened Beverage Consumption Among a Subset of Canadian Youth
Bibliographic record
Abstract
BACKGROUND: Sugar-sweetened beverages (SSBs) may play a role in increased rates of obesity. This study examined patterns and frequencies of beverage consumption among youth in 3 distinct regions in Canada, and examined associations between beverage consumption and age, sex, body mass index (BMI), physical activity and dieting behavior, as well as beverage displacement. METHODS: The study included data from 10,188 youth (ages 13-18) from Hamilton and Thunder Bay, Ontario, and Prince Edward Island (PEI) in 2009 to 2010. The study used in-school self-reported surveys with 12 questions regarding beverage consumption during the previous day, along with self-reported height, weight, physical activity levels, and demographic information. Logistic regression analyses were conducted to examine variables associated with SSB intake. RESULTS: Overall, 80% of youth consumed at least 1 SSB in the previous day, with 44% consuming 3 or more SSBs. Youth in Thunder Bay consumed significantly more SSBs than Hamilton and PEI, and youth in Hamilton consumed more SSBs than PEI. Boys consumed significantly more SSBs than girls. Older and more physically active youth consumed significantly fewer SSBs. No significant association between BMI and SSB consumption was observed in any model. A modest positive correlation was identified between SSB consumption and milk (r = .06, p < .001) and 100% fruit juice (r = .10, p < .001). CONCLUSIONS: A large proportion of youth consumed SSBs, many at high levels. Research evaluating SSB policy and interventions should be considered a priority.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".