Prevention of overweight and obesity in children under the age of 6 yearsA report commissioned by the Canadian Council of Food and Nutrition.
Bibliographic record
Abstract
Although obesity was rarely observed among children 30 years ago, it is now evident among Canadian children of all ages. Currently, 15.2% of 2- to 5-year-old children are overweight, whereas 6.3% are obese. Limited data suggest that poor dietary and physical activity patterns are increasing obesity risk among these young children. Body weight and lifestyle behaviours are known to track from childhood to adulthood, thereby increasing the risk for obesity and other chronic diseases later in life. Intrauterine life, infancy, and the preschool years may all include critical periods that program the long-term regulation of energy balance, and therefore obesity-prevention strategies should be initiated in utero and continue throughout childhood and adolescence. Although single-strategy obesity-prevention initiatives have had limited success, programs that target multiple behaviours may help reduce body weight and body fat among young children. Parental involvement is key to the success of obesity-prevention programs at a young age, as parents have primary control over their children's food and activity environments. Accordingly, parental obesity is the best predictor of childhood obesity. Parents should be encouraged to teach and role model healthy lifestyle behaviours for their young children. Health professionals can also be involved in obesity prevention, as they are ideally placed to identify young children at risk for obesity. By calculating and plotting the body mass index for all children, and initiating obesity-prevention strategies in utero, health professionals can help curb the rise in overweight and obesity among young children.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".