Bibliographic record
Abstract
OBJECTIVES: This article describes the prevalence of overweight and obesity among Canadian children and youth aged 2 to 17, based on direct measurements of their height and weight. Data from 1978/79 and 2004 are compared, and trends by sex and age groups are presented. DATA SOURCES: Data based on direct measurements are from the 2004 Canadian Community Health Survey (CCHS): Nutrition. Other information is from the 1978/79 Canada Health Survey and the 1999-2002 National Health and Nutrition Examination Survey, conducted in the US. ANALYTICAL TECHNIQUES: The estimated prevalence of overweight and of obesity, including an overall rate reflecting both, was based on 2004 CCHS data for 8,661 children and youth whose height and weight were measured. MAIN RESULTS: In 2004, 26% of Canadian children and adolescents aged 2 to 17 were overweight or obese, and 8% were obese. Over the past 25 years, the prevalence of overweight and obesity combined has more than doubled among youth aged 12 to 17, while the prevalence of obesity alone has tripled. Children and youth who ate fruit and vegetables at least five times a day were substantially less likely to be overweight or obese than were those who ate these foods less often. The likelihood of being overweight/obese rose as "screen time" (watching TV, playing video games or using a computer) increased.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".