The Good Food for Learning Universal Curriculum-Integrated Healthy School Lunch Intervention: Protocol for a Two-Year Matched Control Pre-Post and Case Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Good nutrition affects children’s health, well-being, and learning, and schools offer an important setting to promote healthy behaviors that can last a lifetime. Once children reach school age, they spend more of their waking hours in school than in any other environment. Children’s eating habits may be easier to influence than those of adults. In Canada, households with children are more likely to experience food insecurity, and school food programs that are universally available to all children can support the development of healthy eating patterns across groups of varying socioeconomic status. There is a significant gap in the rigorous community-engaged academic research on the impact of school meal programs, especially universal ones. OBJECTIVE The aim of this population health intervention research is to study the impact of a 2-year universal, curriculum-integrated healthy school lunch program in elementary schools in Saskatoon, Saskatchewan, Canada, on food consumption, dietary quality and food and nutrition-related knowledge, attitudes, and practices. METHODS This population health intervention study will be conducted in 2 intervention elementary schools matched with 2 control schools. We will collect preintervention data, including objective measurements of food eaten at school and food-related knowledge, attitudes, and behaviors. This will be followed by the intervention itself, along with qualitative case studies of the intervention process in the 2 intervention schools. Then, we will collect postintervention data similar to the preintervention data. Finally, we will finish the data analysis and complete the ongoing sharing of learning from the project. RESULTS This study was funded in April 2020 but because of the COVID-19 pandemic, data collection did not begin until May 2021. The intervention will begin in September 2021 and end in June 2023, with end point data collection occurring in May and June 2023. The case study research will begin in September 2021 and will be ongoing for the duration of the intervention. CONCLUSIONS The opportunity we have to systematically and comprehensively study a curriculum-integrated school lunch program, as well as the promising practices for school food programs across Canada, is without precedent. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/30899
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | high |
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 | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.081 | 0.016 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".