MétaCan
Menu
Back to cohort
Record W2806934047 · doi:10.1186/s12966-018-0673-5

Food marketing in recreational sport settings in Canada: a cross-sectional audit in different policy environments using the Food and beverage Marketing Assessment Tool for Settings (FoodMATS)

2018· article· en· W2806934047 on OpenAlexafffundabout
Rachel Prowse, Patti‐Jean Naylor, Dana Lee Olstad, Valerie Carson, Kate Storey, Louise C. Mâsse, Sara Kirk, Kim D. Raine

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2018
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of British ColumbiaDalhousie UniversityBC Children's HospitalUniversity of CalgaryUniversity of VictoriaUniversity of Alberta
FundersCanadian Institutes of Health ResearchBC Children’s Hospital FoundationWomen and Children's Health Research InstituteHeart and Stroke Foundation of Canada
KeywordsGuidelineMarketingRecreationAuditBusinessEnvironmental healthFood marketingPromotion (chess)MedicinePolitical scienceAccounting

Abstract

fetched live from OpenAlex

BACKGROUND: Children's recreational sport settings typically sell energy dense, low nutrient products; however, it is unknown whether the same types of food and beverages are also marketed in these settings. Understanding food marketing in sports settings is important because the food industry often uses the promotion of physical activity to justify their products. This study aimed to document the 'exposure' and 'power' of food marketing present in public recreation facilities in Canada and assess differences between provinces with and without voluntary provincial nutrition guidelines for recreation facilities. METHODS: Food marketing was measured in 51 sites using the Food and beverage Marketing Assessment Tool for Settings (FoodMATS). The frequency and repetition ('exposure') of food marketing and the presence of select marketing techniques, including child-targeted, sports-related, size, and healthfulness ('power'), were assessed. Differences in 'exposure' and 'power' characteristics between sites in three guideline provinces (n = 34) and a non-guideline province (n = 17) were assessed using Pearson's Chi squared tests of homogeneity and Mann-Whitney U tests. RESULTS: Ninety-eight percent of sites had food marketing present. The frequency of food marketing per site did not differ between guideline and non-guideline provinces (median = 29; p = 0.576). Sites from guideline provinces had a significantly lower proportion of food marketing occasions that were "Least Healthy" (47.9%) than sites from the non-guideline province (73.5%; p < 0.001). Use of child-targeted and sports-related food marketing techniques was significantly higher in sites from guideline provinces (9.5% and 10.9%, respectively), than in the non-guideline province (1.9% and 4.5% respectively; p values < 0.001). It was more common in the non-guideline province to use child-targeted and sports-related techniques to promote "Least Healthy" items (100.0% and 68.4%, respectively), compared to the guideline provinces (59.3% and 52.0%, respectively). CONCLUSIONS: Recreation facilities are a source of children's exposure to unhealthy food marketing. Having voluntary provincial nutrition guidelines that recommend provision of healthier foods was not related to the frequency of food marketing in recreation facilities but was associated with less frequent marketing of unhealthy foods. Policy makers should provide explicit food marketing regulations that complement provincial nutrition guidelines to fulfill their ethical responsibility to protect children and the settings where children spend time.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.340
Teacher spread0.315 · 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 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

Citations27
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Behavioral Nutrition and Physical ActivitySame topicObesity, Physical Activity, DietFrench-language works237,207