Dairy and Plant-Based Dairy Alternatives Purchasing Habits of Guelph-Based Families with Preschool-Aged Children
Bibliographic record
Abstract
Purpose: To investigate dairy and plant-based dairy alternatives (DPBDA) purchasing habits, including comparisons among locations of purchase and among subtypes of DPBDA, of families with preschool-aged children. Methods: Expenditures on food and DPBDA were calculated using grocery and food receipts collected for 3 weeks from 51 households in and around Guelph, Ontario, Canada. DPBDA were coded by subtypes (alternatives, cheese/yogurt, cow’s milk, cream, and ice cream/other) and by locations of purchase, which were coded as big-box, discount, high-end, local/other, and midrange stores. Logistic regression using generalized estimating equations was used to investigate odds of purchasing DPBDA by location of purchase. All models included family income and number of children as potential confounders. Results: Ninety-eight percent of families purchased cheese/yogurt, 92% purchased cow’s milk, and 35% of families purchased plant-based dairy alternatives. Families were more likely to purchase DPBDA from big-box stores than discount, midrange, or local/other stores (P < 0.01) and were more likely to purchase cheese/yogurt than dairy alternatives, cream, or ice cream/other subtypes (P < 0.01). Odds of purchasing were not different between cheese/yogurt and cow’s milk. Conclusion: Families’ DPBDA purchasing habits differ by purchase location and subtype. Further research is warranted to understand the factors affecting these purchasing 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".