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Record W4416716804 · doi:10.1186/s13643-025-02978-x

What is the effect of commercial food and non-alcoholic beverage marketing on the dietary intake of children and adolescents? Protocol for systematic review and meta-analysis with an equity lens

2025· article· en· W4416716804 on OpenAlexaffabout
Qiuyu Chen, Hadis Mozaffari, Jaithri Ananthapavan, Brendan T. Smith, Gavin Wong, Mahsa Jessri

Bibliographic record

VenueSystematic Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsCanadian Association for Health Services and Policy ResearchVancouver Coastal Health Research InstituteVancouver Coastal HealthUniversity of British ColumbiaPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsProtocol (science)Equity (law)Lens (geology)MEDLINEFood marketing

Abstract

fetched live from OpenAlex

Abstract Background Previous reviews have shown that food and beverage marketing increases dietary intake among children. However, no updated review has been conducted since the COVID-19 pandemic. Furthermore, limited evidence exists on how this effect varies by sociodemographic factors and marketing media. The proposed study aims to assess and quantify the effect of food and beverage marketing exposure on children’s dietary intake and to examine variations based on age, sex, ethnicity, socioeconomic position, and marketing medium. Methods A systematic review of peer-reviewed primary studies will be conducted by including studies on dietary intake from two previous reviews conducted for the World Health Organization (WHO) from 1970 to March 2020, and by searching 19 databases from April 2020 to October 2024. The literature search will be supplemented with backward citation searching of retrieved reviews and included studies. The search terms will encompass three key concepts: food- and beverage-related marketing, dietary intake, and population. Eligible studies must assess the impact of advertising on dietary intake. Two independent reviewers will conduct literature review, data extraction, and quality assessment, with discrepancies resolved by consensus with a third reviewer. Data will be extracted into a standardized evidence table and, where appropriate, included in a meta-analysis to synthesize quantitative findings. Risk of bias will be assessed using the Risk of Bias 2 tool for randomized controlled trials and the Newcastle–Ottawa Scale for non-randomized studies. Publication bias will be examined using funnel plots and trim-and-fill methods, while heterogeneity will be assessed with the I 2 statistic. The overall quality of evidence will be evaluated using the GRADE approach. Discussion This review will be the first comprehensive assessment of the effect of food and beverage marketing exposure on children’s dietary intake, encompassing studies from 1970 to 2024. It will also assess the potential heterogeneity of effects by sociodemographic groups and marketing media. The findings will inform evidence-based policies aimed at reducing marketing-driven dietary risks among children. Limitations include reliance on previous WHO-commissioned reviews for studies published before April 2020, which may introduce selection bias, and a focus on acute exposure, potentially limiting applicability to real-world, long-term effects. Systematic review registration PROSPERO CRD42025641870.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.102
metaresearch head score (Gemma)0.148
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.102
Threshold uncertainty score0.538

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1020.148
Meta-epidemiology (narrow)0.0090.007
Meta-epidemiology (broad)0.0330.046
Bibliometrics0.0190.016
Science and technology studies0.0040.006
Scholarly communication0.0100.010
Open science0.0070.007
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0700.008

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.

Opus teacher head0.065
GPT teacher head0.373
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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