Trends and correlates of frequency of fruit and vegetable consumption, 2007 to 2014.
Bibliographic record
Abstract
BACKGROUND: Eating fruit and vegetables is recommended as part of a healthy diet. This study describes trends in the frequency of fruit and vegetable consumption in Canada, the contribution of fruit juice to these trends, and correlates of the frequency of fruit and vegetable consumption. DATA AND METHODS: The data are from the annual Canadian Community Health Survey for the 2007-to-2014 period and pertain to the household population aged 12 or older. Weighted frequencies and cross-tabulations were used to estimate the average frequency of fruit and vegetable consumption by socio-demographic characteristics and body mass index, age-standardized to the 2014 Canadian population. Multivariate logistic regressions were used to examine correlates of frequency of fruit and vegetable intake in 2014. RESULTS: In 2014, Canadians reported consuming fruit and vegetables an average of 4.7 times a day, a slight, but significant, decrease from 5.0 times a day in 2007. The decrease over time was no longer significant when fruit juice was excluded (dropping to an average of 4.1 times a day in both years). Canadians drank less juice in 2014 than in 2007, a decline that was apparent across all age, sex and household income quintiles, all regions, and all weight categories. In 2014, Canadians who reported consuming fruit and vegetables 5 or more times a day tended to be female, in younger age groups, in the highest household income quintile, and neither overweight nor obese. DISCUSSION: Between 2007 and 2014, Canadians' reported frequency of fruit and vegetable consumption was consistently low. Correlates of fruit and vegetable consumption can be used to target nutrition policy and education efforts to improve intake.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".