Consumer preferences for milk and yogurt attributes: How health beliefs and attitudes affect choices
Bibliographic record
Abstract
Using data from a Canadian national survey assessing dairy product preferences in 2011, individual preferences for milk and yogurts with specific attributes are examined in this study. Statements developed based on the Health Belief Model, food attitudes, beliefs about the role that nutrition plays in health, nutrition knowledge, and an individual’s propensity to make changes to improve their health are used to predict whether or not respondents consume milk/yogurt, the frequency with which they consume it, which type of product they typically consume, and how much they would be willing to pay for new milk or yogurt attributes. Results indicate that several aspects of the Health Belief Model as well as general nutrition knowledge can predict purchasing and consumption intentions for milk and yogurt products. All else being equal, the influences on an individual’s willingness to pay for unique milk or yogurt characteristics in stated choices are different then the influences on their self-identified willingness to seek out milk or yogurt to increase calcium in their diet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".