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Record W3004418889 · doi:10.3390/nu12020428

Exposure to Food and Beverage Advertising on Television among Canadian Adolescents, 2011 to 2016

2020· article· en· W3004418889 on OpenAlexafffundabout
Christine D Czoli, Elise Pauzé, Monique Potvin Kent

Bibliographic record

VenueNutrients · 2020
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsHeart and Stroke FoundationUniversity of Ottawa
FundersCanadian Institutes of Health ResearchCenters for Disease Control and PreventionHeart and Stroke Foundation of Canada
KeywordsTelevision advertisingAdvertisingFood marketingConsumption (sociology)Unhealthy foodFood consumptionPsychologyEnvironmental healthMarketingBusinessMedicineAgricultural economicsEconomicsSociology

Abstract

fetched live from OpenAlex

Adolescents represent a key audience for food advertisers, however there is little evidence of adolescent exposure to food marketing in Canada. This study examined trends in Canadian adolescents' exposure to food advertising on television. To do so, data on 19 food categories were licensed from Nielsen Media Research for May 2011, 2013, and 2016 for the broadcasting market of Toronto, Canada. The average number of advertisements viewed by adolescents aged 12-17 years on 31 television stations during the month of May each year was estimated using television ratings data. Findings revealed that between May 2011 and May 2016, the total number of food advertisements aired on all television stations increased by 4%, while adolescents' average exposure to food advertising decreased by 31%, going from 221 ads in May 2011 to 154 in May 2016. In May 2016, the advertising of fast food and sugary drinks dominated, relative to other categories, accounting for 42% and 11% of all exposures, respectively. The findings demonstrate a declining trend in exposure to television food advertising among Canadian adolescents, which may be due to shifts in media consumption. These data may serve as a benchmark for monitoring and evaluating future food marketing policies in Canada.

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)0.000
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 score0.080
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations32
Published2020
Admission routes3
Has abstractyes

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