Fruit and Vegetable Intake and Preferences Associated with the Northern Fruit and Vegetable Program (2014–2016)
Bibliographic record
Abstract
Purpose: To examine overall usual fruit and vegetable (FV) intake and preferences among grade 5–8 students participating in the Northern Fruit and Vegetable Program (NFVP) over 3 years (2014–2016). Methods: In each year, a survey was administered 4 months into the NFVP in Northern Ontario, Canada. Results: A total of 4744 students participated (2014 = 1551; 2015 = 1617; 2016 = 1576). Overall usual FV intake did not change over the 3 years, yet preferences generally increased. FVs offered by the NFVP were rated higher on preference than those not offered (fruit P < 0.001; vegetables P < 0.005). In each year, participants were more likely to consume a higher overall usual fruit intake if they had higher preference for fruit as offered by the NFVP (all P < 0.05) as opposed to not offered by the NFVP (all P > 0.05). For vegetables, participants were more likely to consume higher overall usual vegetables if they had a higher preference for vegetables as offered (all P < 0.05) and not offered by the NFVP (all P < 0.05). Conclusions: This study documented that higher preferences for fruit (as offered) and vegetables (as offered and not offered) were associated with higher overall usual FV intakes within each of the 3 years.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".