Dietary Patterns in an Ethnoculturally Diverse Population: Of Young Canadian Adults
Bibliographic record
Abstract
PURPOSE: Dietary patterns of food consumption were investigated among young urban Toronto adults, including men and women from different ethnocultural groups. METHODS: We performed a cross-sectional analysis among 1153 adults aged 20 to 29 years, from the Toronto Nutrigenomics and Health Study. Principal components analysis of food intake scores was used to identify food consumption patterns. Logistic regression, analysis of variance, and t-tests were used to test for differences in dietary patterns between ethnocultural groups and between men and women. Partial correlations were used to investigate the relationship between patterns and nutrient intake. RESULTS: Three predominant patterns were identified and termed "prudent," "Western," and "Eastern" patterns. Caucasians had significantly higher prudent pattern scores than did Asians and South Asians, while Asians had significantly higher Eastern pattern scores than did other ethnocultural groups (p<0.01). Women had higher prudent pattern scores (odds ratio [OR]=4.31, 95% confidence interval [CI]=3.11-5.96) and lower Western pattern scores (OR=0.62, 95% CI=0.45-0.84) than did men. Dietary pattern scores were correlated with nutrient and energy intakes. CONCLUSIONS: We observed distinct dietary patterns in this population of young adults. These dietary patterns varied significantly between ethnocultural groups and between men and women. The patterns were associated with nutrient intake levels; this association may have important public health implications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".