<i>Fruit and Vegetable Intake</i> In Canadian Ethnic Populations
Bibliographic record
Abstract
PURPOSE: We explored whether Canada's diverse ethnic population consumes an adequate daily amount of fruit and vegetables. We also examined the association between fruit and vegetable consumption and long-term diseases. METHODS: The Canadian Community Health Survey, Cycle 2.2 (CCHS 2.2), was used to determine the fruit and vegetable intake (FVI) of 13 racial groups, as well as of the entire population. Specifically, we determined median intake and proportions of the group consuming five or more daily servings. Multiple pairwise comparisons among the proportions were performed to detect ethnic groups with significantly low FVI. Logistic regression was also used to describe the risk of long-term diseases associated with FVI and ethnicity. RESULTS: The percentages of Southeast Asian, Aboriginal (off-reserve), and Chinese people who consumed five or more daily servings of fruit and vegetables were significantly lower than percentages in all other ethnic groups surveyed. Aboriginal people with the lowest FVI demonstrated the highest propensity for developing most of the long-term diseases. CONCLUSIONS: The majority of Canada's ethnic groups identified in the CCHS 2.2 fell short of the recommended FVI target. This low-intake status might be a risk factor for common long-term diseases.
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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".