Enhancing nutritional environments through access to fruit and vegetables in schools and homes among children and youth: a systematic review
Bibliographic record
Abstract
BACKGROUND: Low fruit and vegetable (FV) consumption is one of the top 10 global risk factors for mortality, and is related to increased risk for cancer, cardiovascular disease and diabetes. Many environmental, sociodemographic and personal factors affect FV consumption. The purpose of this review is to examine the effects of interventions delivered in the home, school and other nutritional environments designed to increase FV availability for five to 18-year olds. METHODS: The search included: 19 electronic bibliographic databases; grey literature databases; reference lists of key articles; targeted Internet searching of key organization websites; hand searching of key journals and conference proceedings; and consultation with experts for additional references. Articles were included if: in English, French and Spanish; from high-, middle-, and low-income countries; delivered to anyone who could bring about change in FV environment for 5 to 18 year olds; with randomized and non-randomized study designs that provided before-after comparisons, with or without a control group. Primary outcomes of interest were measures of FV availability. RESULTS: The search strategy retrieved nearly 23,000 citations and resulted in 23 unique studies. Interventions were primarily policy interventions at the regional or state level, a number of curriculum type interventions in schools and community groups and a garden intervention. The majority of studies were done in high-income countries.The diversity of interventions, populations, outcomes and outcome measurements precluded meta-analysis. The most promising strategies for improving the FV environment for children are through local school food service policies. Access to FV was successfully improved in four of the six studies that evaluated school-based policies, with the other two studies finding no effect. Broader state or federally mandated policies or educational programs for food service providers and decision makers had mixed or small impact. Similarly family interventions had no or small impact on home accessibility, with smaller impact on consumption. CONCLUSIONS: The studies have high risk of bias but more rigorous studies are difficult to impossible to conduct in naturalistic settings and in policy implementation and evaluation. However, there are promising strategies to improve the FV environment, particularly through school food service policies.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".