DIETARY PATTERNS OF CANADIANS ACROSS DIFFERENT ETHNIC GROUPS AND THE ASSOCIATION WITH CHRONIC DISEASES IN 2004 AND 2015
Bibliographic record
Abstract
Canada’s growing multi-cultural society justifies the need for an in-depth understanding of dietary patterns and associated factors. The aims of this research were 1) to identify the knowledge gaps in the literature on the gender differences in dietary acculturation of adult immigrants in Canada and the US. 2) to determine the dietary patterns of Canadians across different ethnic groups including white, Chinese, Filipino, Asian, Latin American, Arab, South Asian and Black in 2004 and 2015 and their relationship with socioeconomic, sociodemographic factors, and chronic conditions. This research included a scoping review, which synthesized literature and identified the gaps, to address the first objective. Cluster analysis was selected to address the second objective using the Canadian Community Health Survey (CCHS) Cycle 2.2, Nutrition (2004) and CCHS 2015 data. The six main findings of the research include; first, the diet quality of both adults (519 ± 3 vs. 492 ± 7.5) and children (511 ± 2.6 vs. 470 ± 5.7) in 2015 was higher than 2004 in Canada. Second, the majority of the Canadian population had the “Unhealthy” dietary pattern with poor diet quality. Third, the highest prevalence of chronic diseases and obesity was among the White adults in both years 18% and 19.2% respectively. The higher prevalence of chronic diseases and obesity were associated with the consumption of “Unhealthy” and “Mixed” dietary patterns across most of ethnicities. Fourth, the gender-specific results indicated that women had healthier dietary patterns as well as higher diet quality than men across ethnicities in 2004 (502 ± 11 vs. 482 ± 4.1) and in 2015 (536 ± 3 vs. 501 ± 3). Fifth, the unhealthy dietary pattern was most common among White and Arab adults, while Chinese, Asians, and Filipino adults had healthier dietary pattern and most of the children had the “Unhealthy” dietary pattern across all ethnicities. Sixth, consumption of the healthy dietary pattern was associated with being active, being an immigrant, and having higher income and educational attainment across all ethnic groups. To conclude, the research suggests ethnic-specific dietary recommendations need to consider socioeconomic and sociodemographic factors to support improved health benefits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".