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Record W4413109405 · doi:10.1186/s40795-025-01148-5

Youth exposure to unhealthy digital food marketing in relation to race/ethnicity and income adequacy in Canada

2025· article· en· W4413109405 on OpenAlexafffundabout
Laura Vergeer, Carolina Soto, Mariangela Bagnato, Elise Pauzé, Ashley Amson, Tim Ramsay, Dana Lee Olstad, Vivian Welch, Monique Potvin Kent

Bibliographic record

VenueBMC Nutrition · 2025
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of CalgaryOttawa HospitalBruyèreUniversity of Ottawa
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéPublic Health AgencyPublic Health Agency of Canada
KeywordsEthnic groupOddsMedicineDemographyLogistic regressionClinical nutritionOdds ratioRace (biology)Household incomeEnvironmental healthFood marketingGerontologyAdvertisingGeographyBusinessPolitical scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Youth of racial/ethnic minority groups and lower-income households are disproportionately exposed to unhealthy food marketing on television; however, there is limited evidence concerning digital marketing. This study examined differences in Canadian youth's exposure to digital food marketing by race/ethnicity and household income adequacy. METHODS: Frequency of food marketing exposure via digital platforms and digital food marketing techniques were self-reported by 996 youth in Canada aged 10-17 years. Proportional odds and logistic regression models explored differences between racial/ethnic (White vs. racial/ethnic minority) and income adequacy groups (low vs. medium vs. high), adjusted for sociodemographic and digital device usage variables. RESULTS: White participants had lower odds of more frequent exposure to digital marketing of sugary drinks (OR: 0.70; 95% CI: 0.52-0.94), sugary cereals (OR: 0.56; 95% CI: 0.42-0.76), fruits/vegetables (OR: 0.63; 95% CI: 0.45-0.87), salty/savoury snacks (OR: 0.63; 95% CI: 0.47-0.85), fast food (OR: 0.74; 95% CI: 0.55-0.99), and desserts/sweets (OR: 0.68; 95% CI: 0.50-0.91) than racial/ethnic minority youth. Compared to youth from low income adequacy households, those with medium income adequacy were less likely to report more frequent exposure to marketing of sugary drinks (OR: 0.67; 95 CI: 0.51-0.89), fast food (OR: 0.66; 95% CI: 0.50-0.87), and desserts/sweets (OR: 0.65; 95% CI: 0.49-0.87). White youth were less likely than racial/ethnic minority youth to report exposure to unhealthy food marketing on ≥ 1 social media platform(s) (OR: 0.45; 95% CI: 0.30-0.68) and gaming/TV/music streaming platform/website(s) (OR: 0.71; 95% CI: 0.51-0.99); no differences were observed between income groups. White youth were less likely than racial/ethnic minority youth to report exposure to marketing featuring incentives/premiums (OR: 0.72; 95% CI: 0.52-0.99) and cross-promotions (OR: 0.71; 95% CI: 0.51-0.99). Participants of higher (OR: 0.68; 95% CI: 0.49-0.95) and medium (OR: 0.69; 95% CI: 0.50-0.93) income adequacy were less likely to report exposure to marketing featuring celebrities than those with low income adequacy. CONCLUSIONS: Youth of racial/ethnic minorities report more frequent exposure to digital food marketing, especially for unhealthy foods, than White youth in Canada. Differences were also observed between income groups. Comprehensive marketing regulations are needed to limit all youths' exposure to unhealthy digital food marketing.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.262
Teacher spread0.244 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2025
Admission routes3
Has abstractyes

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