The impact of a population-level school food and nutrition policy on dietary intake and body weights of Canadian children
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to assess population-level trends in children's dietary intake and weight status before and after the implementation of a provincial school nutrition policy in the province of Nova Scotia, Canada. METHOD: Self-reported dietary behavior and nutrient intake and measured body mass index were collected as part of a population-level study with grade 5 students in 2003 (n=5215) and 2011 (5508), prior to and following implementation of the policy. We applied random effects regression methods to assess the effect of the policy on dietary and health outcomes. RESULTS: In 2011, students reported consuming more milk products, while there was no difference in mean consumption of vegetables and fruits in adjusted models. Adjusted regression analysis revealed a statistically significant decrease in sugar-sweetened beverage consumption. Despite significant temporal decreases in dietary energy intake and increases in diet quality, prevalence rates of overweight and obesity continued to increase. CONCLUSION: This population-level intervention research suggests a positive influence of school nutrition policies on diet quality, energy intake and healthy beverage consumption, and that more action beyond schools is needed to curb the increases in the prevalence of childhood obesity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".