Nutrition and body weights of Canadian children watching television and eating while watching television
Bibliographic record
Abstract
OBJECTIVE: To examine whether eating while watching television poses a risk for poor nutrition and excess body weight over and above that of time spent watching television. DESIGN: We analysed data of grade 5 students participating in a comprehensive population-based survey in the Canadian province of Nova Scotia. This survey included the Harvard's Youth Food Frequency Questionnaire, students' height and weight measurements, and a parent survey. We applied multivariable linear and logistic random effects models to quantify the associations of watching television and eating while watching television with diet quality and body weight. SETTING: The province of Nova Scotia, Canada. SUBJECTS: Grade 5 students (n 4966). RESULTS: Eating supper while watching television negatively affected the consumption of fruits and vegetables and overall diet quality. More frequent supper while watching television was associated with more soft drink consumption, a higher percentage energy intake from sugar out of total energy from carbohydrate, a higher percentage energy intake from fat, and a higher percentage energy intake from snack food. These associations appeared independent of time children spent watching television. Both watching television and eating while watching television were positively and independently associated with overweight. CONCLUSIONS: Our observations suggest that both sedentary behaviours from time spent watching television as well as poor nutrition as a result of eating while watching television contribute to overweight in children. They justify current health promotion targeting time spent watching television and call for promotion of family meals as a means to avoid eating in front of the television.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".