Food and Beverage Marketing in Schools: A Review of the Evidence
Bibliographic record
Abstract
Despite growing interest from government agencies, non-governmental organizations and school boards in restricting or regulating unhealthy food and beverage marketing to children, limited research has examined the emerging knowledge base regarding school-based food and beverage marketing in high-income countries. This review examined current approaches for measuring school food and beverage marketing practices, and evidence regarding the extent of exposure and hypothesized associations with children's diet-related outcomes. Five databases (MEDLINE, Web of Science, CINAHL, Embase, and PsycINFO) and six grey literature sources were searched for papers that explicitly examined school-based food and beverage marketing policies or practices. Twenty-seven papers, across four high-income countries including Canada (n = 2), Ireland (n = 1), Poland (n = 1) and United States (n = 23) were identified and reviewed. Results showed that three main methodological approaches have been used: direct observation, self-report surveys, and in-person/telephone interviews, but few studies reported on the validity or reliability of measures. Findings suggest that students in the U.S. are commonly exposed to a broad array of food and beverage marketing approaches including direct and indirect advertising, although the extent of exposure varies widely across studies. More pervasive marketing exposure was found among secondary or high schools compared with elementary/middle schools and among schools with lower compared with higher socio-economic status. Three of five studies examining diet-related outcomes found that exposure to school-based food and beverage marketing was associated with food purchasing or consumption, particularly for minimally nutritious items. There remains a need for a core set of standard and universal measures that are sufficiently rigorous and comprehensive to assess the totality of school food and beverage marketing practices that can be used to compare exposure between study contexts and over time. Future research should examine the validity of school food and beverage marketing assessments and the impacts of exposure (and emerging policies that reduce exposure) on children's purchasing and diet-related knowledge, attitudes and behaviors in school settings.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".