Multidimensional dietary patterns and their joint associations with intersecting sociodemographic characteristics among adults in Canada: a cross-sectional study
Bibliographic record
Abstract
Background: Dietary patterns consist of multiple interrelated components, while individuals have numerous characteristics that may jointly influence dietary patterns. Studies to assess associations between sociodemographic characteristics and dietary patterns typically do not consider this complexity. Objective: The objective of this study was to examine joint relationships between dietary patterns and sociodemographic characteristics among adults in Canada. Methods: 24-hour dietary recall data for adults ≥18 years were drawn from the 2015 Canadian Community Health Survey Nutrition (n=14 097). Three mixed graphical models were developed to explore networks of sociodemographic characteristics, dietary components, and sociodemographic characteristics and dietary components together. Networks included 30 log-transformed food groups (grams), sex, age, household food security status, income, employment status, education, geographic region, and smoking status. Results are expressed as (edge weight; [95% CI]). Results: The strongest pairwise relationships were observed among dietary components and among sociodemographic characteristics. Positive linear relationships were observed among vegetable groupings; for example, between green and orange vegetables (0.12; [0,08, 0.16]). Negative relationships were observed among subgroups of each of animal foods, beverages, and grains; for example, between refined and whole grains (-0.30; [-0.33, -0.26]). In the model including dietary components and sociodemographic characteristics, age was associated with grains (other) (-0.12; [-0.16, -0.09]), coffee/tea (0.21; 95% CI [0.17, 0.24]), and whole grains (0.12; [0.08, 0.15]). Sex was associated with sweet beverages (0.11; [0.06, 0.17]), alcohol (0.18; [0.13, 0.24]), cured meat (0.20; [0.15, 0.26]), and red meat (0.16; [0.11, 0.21]). Conclusions: In some cases, pairwise relationships between dietary components suggest displacement, for example, of whole grains by refined grains. Age and sex were the characteristics most strongly connected to dietary components. Statement of significance: Exploring joint relationships between intersecting sociodemographic characteristics and multidimensional dietary patterns can assist with better understanding dietary heterogeneity to inform policies and programs that support healthy eating.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".