Improving Fruit and Vegetable Consumption in Elementary School Students: A Systematic Review of Interventions
Bibliographic record
Abstract
Introduction: Less than 15% of the 4 to 8-year old children consume the recommended servings of fruit and vegetables (FV). Early years of life play an important role in establishing healthy eating habits. School is an appropriate setting for healthy eating habits interventions. The purpose of this systematic review is evaluation of school-based interventions to improve FV consumption in elementary school students. Methods: In this systematic review, we performed a search in several databases such as PubMed, Web of Science, Education Resources Information Center (ERIC), Science Direct and Google Scholar. Studies published between January 2005 and December 2012 were included. In examining the studies, we focused on design, strategies and outcomes of the interventions. Results: Eleven studies met the inclusion criteria. Interventions in these three classifications (gardening and education, educational programs and providing FV) have positive effects on children’s FV intake. Multi-component education is more effective than other cases in children willingness for FV consumption. Gardening strategies: participating in hands-on gardening experiences, engaging in gardening challenges and preparing a party by student’s garden products. Educational strategies: nutrition education classes, electronic learning by using the popular cartoon characters and child actors as symbolic role models and strategies to improve family awareness like series of newsletters for parents and homework tasks for parents and children. Providing strategies: providing fee-based or free FV at school. Conclusion: Long-term multi-component (gardening, education and providing FV) interventions with the application of behavioral change theories and models are effective to reach the expected results.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".