The Whats and Whys of School Lunches: A mixed-methods study of the nutritional value of lunches eaten by elementary students during schooldays and their caregivers’ attitudes and practices towards school lunches.
Bibliographic record
Abstract
Background: School-age children spend a significant portion of their day at their educational facilities, consuming meals such as lunch. Evidence suggests that most children in Canada have poor-quality diets, especially during the school day. The lack of universal national or provincial school food programs and policies in Canada limits the utility of dietary recommendations. Families most often provide lunch during the school day, with a minority of students accessing small-scale school lunch programs.\nPurpose: To study the nutritional value of school lunches of elementary students, to explore their caregivers’ attitudes and practices towards these lunches, and to relate these attitudes and practices with what children eat for lunch at school. \nMethods: This is a mixed-method research design study. It starts with a descriptive quantitative component using plate waste methodology to assess nutritional contribution and NRF 9.3 Index of school lunches, followed by a naturalistic phase, involving interviews which explore caregivers’ attitudes and practices regarding school lunches. Lastly, it comprises the integration of the results from both the quantitative and qualitative phases.\nResults: Lunches’ mean, and median energy contribution was 442.6 kcal (SD 209.6) and 413.5 kcal (IQR 287.6), 13.6% of which were proteins. There were statistically significant differences in calorie content observed across grades. The lunches’ nutritional contribution fell below Canadian references for the most critical nutrients for childhood, such as calcium and vitamin D. The mean NRF9.3 Index score for school lunches was 346.8, with statistically significant variations between grades. \nIn terms of the context surrounding these lunches, parents reported that preparing them was a stressful chore. Various factors affected them, such as nutrition, cost, portability, and time constraints. Additionally, parents aimed to include options that their children would like to eat, ensuring they were going to be fed during school hours.\nConclusion: Most school lunches are characterized by a low contribution of critical nutrients for childhood, which can be attributed to various circumstances within the families' and students' contexts. The implementation of public policies could play a crucial role in addressing this issue, such as a universal school lunch program to guarantee all students have access to nutritious meals during the school day.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".