Community-based interventions for enhancing access to or consumption of fruit and vegetables among five to 18-year olds: a scoping review
Bibliographic record
Abstract
BACKGROUND: Low fruit and vegetable ( FV) consumption is a key risk factor for morbidity and mortality. Consumption of FV is limited by a lack of access to FV. Enhanced understanding of interventions and their impact on both access to and consumption of FV can provide guidance to public health decision-makers. The purpose of this scoping review is to identify and map literature that has evaluated effects of community-based interventions designed to increase FV access or consumption among five to 18-year olds. METHODS: The search included 21 electronic bibliographic databases, grey literature, targeted organization websites, and 15 key journals for relevant studies published up to May 2011. Retrieved citations were screened in duplicate for relevance. Data extracted from included studies covered: year, country, study design, target audience, intervention setting, intervention strategies, interventionists, and reported outcomes. RESULTS: The search located 19,607 unique citations. Full text relevance screening was conducted on 1,908 studies. The final 289 unique studies included 30 knowledge syntheses, 27 randomized controlled trials, 55 quasi-experimental studies, 113 cluster controlled studies, 60 before-after studies, one mixed method study, and three controlled time series studies. Of these studies, 46 included access outcomes and 278 included consumption outcomes. In terms of target population, 110 studies focused on five to seven year olds, 175 targeted eight to 10 year olds, 192 targeted 11 to 14 year olds, 73 targeted 15 to 18 year olds, 55 targeted parents, and 30 targeted teachers, other service providers, or the general public. The most common intervention locations included schools, communities or community centres, and homes. Most studies implemented multi-faceted intervention strategies to increase FV access or consumption. CONCLUSIONS: While consumption measures were commonly reported, this review identified a small yet important subset of literature examining access to FV. This is a critically important issue since consumption is contingent upon access. Future research should examine the impact of interventions on direct outcome measures of FV access and a focused systematic review that examines these interventions is also needed. In addition, research on interventions in low- and middle-income countries is warranted based on a limited existing knowledge base.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".