An Exploration of Milk Product Health Beliefs and Dietary Calcium Intake in Young Adults
Bibliographic record
Abstract
Purpose: Milk products (fluid milk, cheese, yogurt) typically provide a rich source of calcium and other nutrients, yet consumption is declining in Canada. This study examined milk product health beliefs among young adults and the association between these beliefs and dietary calcium intake. Methods: Seventy-nine participants (25 ± 4 y; 40 males) completed a milk product health belief questionnaire to determine a milk product health belief score (MPHBS) and a 3-day food record to assess dietary intake. Results: Despite generally positive views, young adults were uncertain about milk products in relation to health, weight management, and ethical concerns. Females would be more likely than males to increase milk product intake if they were confident that milk products are ethically produced. There was no significant association between MPHBS and dietary calcium intake. Energy-adjusted dietary calcium intake was positively associated with intakes of vitamin A (r = 0.3, P < 0.05), riboflavin (r = 0.5, P < 0.01), vitamin B12 (r = 0.5, P = < 0.01), vitamin D (r = 0.4, P < 0.01), phosphorus (r = 0.4, P < 0.01), zinc (r = 0.3, P < 0.01), and with milk and alternatives servings (r = 0.8, P < 0.01). Conclusion: Nutrition education efforts focused on increasing calcium-rich food consumption will help consumers to be better informed when making dietary choices.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".