<i>Decision-making in the Dairy Aisle</i>: Maximizing Taste, Health, Cost and Family Considerations
Bibliographic record
Abstract
PURPOSE: To gain insight into the decision-making processes used by women when selecting dairy and dairy alternative foods, including examination of the role of bone health concerns. METHODS: Semi-structured, point-of-purchase interviews were conducted with a convenience sample of 30 female grocery shoppers. Constant comparative data analysis was used to generate themes on shoppers' decision-making processes. RESULTS: Women considered multiple issues in their dairy and dairy alternative food choice strategies: taste was most often associated with the fat and sugar content of foods; health concerns were centred on achieving an acceptable body weight and preventing osteoporosis and cardiovascular disease, and women chose foods to satisfy other family members' needs and preferences and to obtain "good food value." Women prioritized their food choices by weighing the value of each issue, which led to a strategic process for "maximizing the value" of their food choices. The availability of a wide range of dairy and dairy alternative foods meant that in most instances, it was unnecessary for women to "trade off" one area of concern for another. CONCLUSIONS: Dietitians and nutrition educators can help women make dietary changes by helping them identify foods that they perceive as meeting a variety of needs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".