Dietary Patterns Among Canadian Caucasians and Their Association With Chronic Conditions
Bibliographic record
Abstract
BACKGROUND: Understanding the dietary patterns of populations is crucial in addressing chronic health conditions that are influenced by diet and lifestyle. We aimed to identify the dietary patterns among adult Caucasian Canadians and examine their associations with socioeconomic and sociodemographic factors and chronic health conditions. METHODOLOGY: We used two comprehensive national nutrition surveys: Canadian Community Health Survey (CCHS)2015 and CCHS Cycle 2.2 Nutrition 2004, which encompass sociodemographic and socioeconomic profiles, nutrient-rich food diet quality scores and prevalence of chronic conditions. Through cluster analysis, dietary patterns were identified among Caucasians and further analysed with stratification by age/sex groups. RESULTS: Our analysis of dietary patterns among Caucasian adults showed a transition from "High-Fibre" and "Mixed" patterns in 2004 to "Unhealthy," "Healthy-like" and "Potato, Beef and Vegetables" in 2015. In 2004, the "Mixed" pattern was prevalent, but by 2015, a shift towards the "Unhealthy" pattern was notable, with a significant portion of the population, 18.8%, reporting chronic diseases and 19.6% being classified as obese. The "Healthy-like" pattern in 2015 saw lower rates of chronic diseases (6.8%) and obesity (6.1%). Gender-specific patterns showed women favoring healthier options like "Healthy-like" in 2015. The prevalence of chronic diseases and obesity varied significantly with dietary patterns. The "High-Fibre" pattern in 2004 showed lower prevalence rates of chronic diseases (6.6%) and obesity (5.8%) compared to the "Unhealthy" pattern in 2015. CONCLUSIONS: The findings highlight the impact of dietary choices on health outcomes over time, underscoring the importance of promoting healthier eating habits to mitigate the risk of chronic diseases and obesity.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".