Fruit and Vegetable Preferences and Intake: Among Children in Alberta
Bibliographic record
Abstract
PURPOSE: The association between preference for and intake of fruits and vegetables was examined among Albertan children. METHODS: Data used were collected as part of a provincial population-based survey among grade 5 children in Alberta. Intake of two fruits and five vegetables was assessed using the Harvard food frequency questionnaire, and preference for individual fruit and vegetable items was rated using a three-point Likert-type scale. Random effects models with children nested within schools were used to test for associations between fruit and vegetable preference and intake. RESULTS: A total of 3398 children aged 10 to 11 years returned completed surveys. Children who reported a greater liking for fruits and vegetables also reported significantly (p<0.001) higher intake. On average, children who liked a food a lot ate 0.5 to 2.7 more weekly servings of the food than did children who did not like the food. CONCLUSIONS: These findings suggest that focusing on interventions designed to increase taste preference may lead to increased fruit and vegetable intake among children. Introducing children to unfamiliar fruits and vegetables through taste testing may be an effective and practical health promotion approach for improving dietary habits.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".