Low Frequency of Fruit and Vegetable Consumption Among Canadian Youth: Findings From the 2012/2013 Youth Smoking Survey
Bibliographic record
Abstract
BACKGROUND: Frequent fruit and vegetable (FV) consumption is protective against some cancers, cardiovascular disease, and other chronic diseases. This study explores self-reported frequency of FV consumption in a nationally generalizable sample of Canadian youth in grades 6-12. METHODS: Data from grades 6-12 students who participated in the 2012-2013 Youth Smoking Survey (N = 47,203) were used to examine frequency of FV consumption. Logistic regression models were fitted to examine differences in meeting national FV intake recommendations by sociodemographic, student, and regional characteristics. RESULTS: Approximately 10% of Canadian grade 6-12 students met FV recommendations. Students in grades 6 and 7 had significantly higher odds of meeting recommendations relative to students in grades 8-12. Students who reported achieving "mostly As" on their report cards had significantly higher odds of meeting FV recommendations relative to those receiving As and Bs, Bs and Cs, or Cs (OR = 0.71, OR = 0.53, and OR = 0.46, respectively, p < .0001 for each). Students in British Columbia and Ontario had higher odds of meeting recommendations relative to students in Newfoundland, Prince Edward Island, and Nova Scotia. CONCLUSIONS: Only 1 in 10 Canadian youth are meeting FV recommendations. Programs and policies to encourage FV consumption are required to help mitigate future health issues associated with inadequate FV consumption.
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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 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".