Outcome evaluation of fruits and vegetables distribution interventions in schools: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Fruits and vegetables (FV) distribution interventions have been implemented as a public health strategy to increase children's intake of FV at school settings. The purpose of this review was to examine whether snack-based FV distribution interventions can improve school-aged children's consumption of FV. DESIGN: Systematic review and meta-analysis of articles published in English, in a peer-reviewed journals, were identified by searching six databases up to August 2020. Standardised mean differences (SMD) and 95 % CI were calculated using a random effects model. Heterogeneity was quantified using I2 statistics. SETTING: Population-based studies of interventions where the main focus was the effectiveness of distributed FV as snacks to schoolchildren in North America, Europe and Pacific were included. RESULTS: Forty-seven studies, reporting on fifteen different interventions, were identified; ten studies were included in the meta-analysis. All interventions were effective in increasing children's consumption of FV, with only one intervention demonstrating a null effect. Pooled results under all classifications showed effectiveness in improving children's consumption of FV, particularly for multi-component interventions at post-intervention (SMD 0·20, 95 % CI 0·13, 0·27) and free distribution interventions at follow-up (SMD 0·19, 95 % CI 0·12, 0·27). CONCLUSIONS: Findings suggest that FV distribution interventions provide a promising avenue by which children's consumption can be improved. Nonetheless, our results are based on a limited number of studies, and further studies should be performed to confirm these results. More consistent measurement protocols in terms of rigorous study methodologies, intervention duration and follow-up evaluation are needed to improve comparability across studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".