Environmental Factors of Youth Milk and Milk Alternative Consumption
Bibliographic record
Abstract
Objective: The objective of this research was to determine the school and community characteristics associated with milk and milk alternative (MMA) consumption by Canadian youth. Methods: We analyzed self-reported data from 50,058 Canadian students participating in the 2017-2018 wave of the COMPASS survey. We used logistic and linear regression analyses to identify school- and community-level factors associated with students meeting the MMA guidelines, and factors associated with daily number of MMA servings consumed, respectively. Results: Student-level factors were more strongly associated with MMA consumption than school- and community-level factors. Students who attended schools that provided staff with nutrition training consumed fewer daily servings of MMAs and were less likely to meet MMA guidelines. Students attending schools that received healthy eating grants were more likely to meet MMA guidelines, whereas students attending schools that sold flavored milk in their vending machines were less likely to meet MMA guidelines. Conclusion: Our findings suggest that student-level factors have a stronger association with MMA consumption than school or community factors. Additional research is needed to understand how factors associated with MMA consumption may influence behaviours over time, and how changes to Canada's food guide may impact youth eating habits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".