Impact of the removal of chocolate milk from school milk programs for children in Saskatoon, Canada
Bibliographic record
Abstract
Studies in the United States report inclusion of flavoured milk in the diets of children and youth improves nutrient intakes. No research has investigated the contribution of flavoured milk to overall milk intake or the milk preferences of Canadian children. The objective of the study was to measure milk consumption (plain milk and flavoured milk) by children in an elementary school environment and investigate factors contributing to milk choice. A mixed-method research design was applied across 6 schools for 12 weeks. Milk waste was measured in grades 1-8 for 12 weeks. Weeks 1-4 (phase 1) and 9-12 (phase 3) provided both plain milk and flavoured milk as chocolate milk while weeks 5-8 (phase 2) provided plain milk only. Beverage Frequency Questionnaires were used in each phase (in grades 5-8 only) to assess usual beverage consumption. Statistical nutrient modelling was conducted to determine the effects of removing chocolate milk during phase 2 as a milk choice. Later, focus groups were conducted with students in grades 5-8 to determine what influences them to choose/not choose to drink milk. Total milk intake decreased by 12.3% when chocolate milk was removed from the schools (26.6% ± 5.2% to 14.31% ± 1.6%, p < 0.001). Milk choice was influenced by environmental factors as well as taste, cost, convenience, and variety. Total milk intake was associated with location (p = 0.035) and cost (p < 0.001), with rural students and/or those students receiving free milk drinking the greatest amount of milk. Nutrient modelling revealed chocolate milk is more cost-efficient and convenient at providing nutrients than alternative food/drink combinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".