SCHOOL FOOD: AN ANALYSIS OF SCHOOL FOOD ENVIRONMENT, FOOD AND NUTRITION-RELATED KNOWLEDGE, ATTITUDES, AND PRACTICES AFTER A TWO-YEAR UNIVERSAL COMPREHENSIVE ELEMENTARY SCHOOL LUNCH PROGRAM
Bibliographic record
Abstract
Background: Child nutrition influences health, well-being, and learning across the lifespan. The quality of the diets of Canadian children during school hours is poor across the socio-economic spectrum. School lunch programs can help to maintain nutrition during the school years. Purpose: To assess the school food environment and food and nutrition-related knowledge, attitudes, and practices (KAP) among elementary school students enrolled in a two-year curriculum-integrated school lunch program compared to students without the intervention. Methods: Two surveys from the Good Food for Learning project were used for this research. Students’ food and nutrition-related KAP were assessed by a self-administered student survey conducted on 185 participants at baseline and 192 at the endpoint. The school food environment was assessed through a self-administered staff survey with 94 and 20 participants at baseline and endpoint, respectively. Software R was used to analyze the data. Multivariable logistic regression models assessed the factors associated with students’ KAP at the endpoint. The difference in differences (DID) approach was used to assess the effects of the intervention on students’ KAP. Due to some limitations, only frequency and percentages were used for the staff survey data. Results: Knowledge related to waste management and daily fruit and vegetable consumption improved for the student intervention participants. However, local food production knowledge, environmentally sustainable dietary attitudes, and practices showed no significant improvement. Overall household food insecurity also increased from 38.2% at baseline to 40.3% at endpoint, but it decreased for the intervention group. Multivariable analysis showed that the students who reported their ethnicity as White consumed sugar-sweetened beverages and fast food less frequently than their peers who reported their ethnicity as First Nations, Métis, or Inuit. Frequency percentages increased for food and nutrition-related initiatives in the school context at the endpoint, including school gardening, food-related activities, offering healthy food and food preparation activities. However, there was no increase in composting. Conclusion: The study results will help us better understand how a school food program can impact students’ food and nutrition-related KAP and student nutrition. The findings can inform the development and implementation of Canada's national school food policy and program
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".