Examining Differences in Children’s Reported Exposure to Food Advertising in Amateur Sports Settings in Canada’s 2 Policy Environments: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Food marketing influences children's diet. Although food companies advertise to children via sports sponsorship, little research has examined this type of advertising. OBJECTIVE: The aims of the study were to describe and compare children's self-reported exposure to food advertising in amateur sports settings in 2 Canadian policy environments, including Quebec (QC; where sponsorship-related advertising directed to children younger than 13 years is regulated) and Ontario (ON; where there are no regulations). It was hypothesized a priori that reported exposure to food advertising would be lower among children younger than 13 years in QC. DESIGN: This was a cross-sectional online survey. PARTICIPANTS/SETTING: One thousand and twenty children aged 10 to 17 years living in ON and QC were recruited via a commercial panel from February to April 2023. Those who reported playing on a sports team outside of school (ON: n = 239; QC: n = 200) were included in the analysis. MAIN OUTCOME MEASURES: Children were asked to self-report exposure to food advertising when playing sports, including (1) signs, (2) branded sports equipment provided by their sports team, (3) branded awards, (4) free food, and (5) coupons or gift certificates. ANALYSIS: Logistic regression analysis was used to examine differences in reported exposure between provinces after adjusting for socioeconomic status and child age, sex, and race. Interaction terms were included to examine differences between age groups within and between provinces. RESULTS: The prevalence of reported exposure was highest for branded equipment (ON: 57%; QC: 44%) and signs (ON: 47%; QC: 43%), followed by branded prizes (ON: 30%; QC: 28%), free food (ON: 25%; QC: 27%), and coupons or gift certificates (ON: 26%; QC: 20%). The odds of reporting exposure to branded equipment were significantly lower among children in QC than those in ON (adjusted odds ratio 0.68; 95% CI, 0.46 to 0.99; P = .049). No other differences in reported exposure, including differences between children aged 10 to 12 years in QC and other children, were found to be statistically significant for the above outcomes. CONCLUSIONS: The study's findings suggest QC's advertising restrictions are not adequately protecting children from exposure to food advertising in amateur sports 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".