THE ASSOCIATIONS AMONG ETHNICITY, CONTEXTUAL FACTORS, AND DIETARY INTAKE IN THE CANADIAN ALLIANCE FOR HEALTHY HEARTS AND MINDS: A CROSS-SECTIONAL STUDY.
Bibliographic record
Abstract
Introduction: Unhealthy diets are significant contributors to chronic diseases. Variations in CVD rates across ethnicities in Canada could be attributable to diverse dietary habits and nutrition environmental influences. The extent to which individuals’ food environment perceptions influence dietary intake is also understudied. Methods: This cross-sectional study, utilizing data from 7,077 of the 10,100 adults in the Canadian Alliance for Healthy Hearts and Minds (CAHHM) cohort, assessed associations of elements of the nutrition environment (food prices, advertisements, and availability) and ethnicity with dietary intakes. Results: Self-reported intakes of carbohydrates, junk foods, meat, and cholesterol varied significantly across Asians and White Europeans (p<0.0001). Rural/urban differences were also observed in carbohydrate, fat, protein, cholesterol, vegetable, meat, and sweet drink intakes (p<0.0001), excluding junk foods, and fruits. Interestingly, while individuals' perceptions of their food environment did not correlate with objective measures of the same environment, a 1$ increase in vegetable prices was significantly associated with a decrease in vegetable consumption by 0.0078 In(servings/day) (p= 0.0233), after adjusting for rural/urban living, ethnicity and BMI. No associations were found between fruits, meat, bread, eggs, cola, chocolate, poultry, rice, and milk prices and respective intakes. No association was also found between fruit/vegetable availability and consumption; nor between junk foods, sweet drinks and fruit/vegetable ads and consumption. Notably, alcohol advertisement was associated with alcohol intake. Discussion/Conclusion: The price-inelastic nature of foods like milk and eggs due to their perceived essentiality, implies the superimposing effects of other factors on consumption aside, price. While food advertisements undoubtedly impact eating behaviours, their influence might be subtle considering factors like price which could pose barriers to healthy eating. These findings emphasize the intricate interplay between prices, availability, advertisement, and other factors and dietary choices. Policymakers, food industries, and health advocates can leverage these insights to create healthier food environments for improved health.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".