Promotion of Fruits and Vegetables Consumption: Results of a School-Based Intervention in a Sample of 13-15 Years Old Italian Students
Bibliographic record
Abstract
Background: The …e vai con la frutta (let’s go with fruit) project was designed to respond to the worrying low consumption of fruits and vegetable in the large portion of population, especially in youth. The main objective was to increase fruit and vegetable consumptions at school level. Methods: In 2010/11, we randomly selected subjects from middle and high schools in five Italian regions. The subjects were randomly divided into two groups: intervention (I) and control (C). A questionnaire on fruits and vegetables consumption was administered, at the baseline as well as at end of the period. The sample size was evaluated on the expected frequency of 0.5 for a binomial random variable, of ± 1.54% with IC of 95%. Results: These results confirmed that adolescent students at baseline survey eat less than the amount of fruits and vegetables consumption recommended by the International guidelines. After the intervention time, we observed an increment in fruits and vegetables consumptions both in middle and high school students. Regarding the impact of the intervention on the factors like knowledge, attitudes and behaviour, there was a positive change in the behaviour only in the intervention group, in coherence with the consumption variations examined. Conclusions: This study is the first time that fruits and vegetables were offered at such a large scale through vending machines at schools in five regions. Several vending companies under this project agreed to revise the list of healthy snacks offered to eliminate those clearly classified as junk foods.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".