The Frequency and Healthfulness of Food and Beverage Advertising in Movie Theatres: A Pilot Study Conducted in the United States and Canada
Bibliographic record
Abstract
The marketing of unhealthy foods and beverages contributes to childhood obesity. In Canada and the United States, these promotions are self-regulated by industry. However, these regulations do not apply to movie theatres, which are frequently visited by children. This pilot study examined the frequency and healthfulness of food advertising in movie theatres in the United States and Canada. A convenience sample of seven movie theatres in both Virginia (US) and Ontario (Canada) were visited once per month for a four-month period. Each month, ads in the movie theatre environment and before the screening of children's movies were assessed. Food ads were categorized as permissible or not permissible for marketing to children using the World Health Organization's European Nutrient Profile Model. There were 1999 food ads in the movie theatre environment in Ontario and 43 food ads identified in the movie theatre environment in Virginia. On average, 8.6 (SD = 3.3) and 2.2 (SD = 0.9) food ads were displayed before children's movies in Ontario and Virginia, respectively. Most or all (97%-100%) food ads identified in Virginia and Ontario were considered not permissible for marketing to children. The results suggest that movie theatre environments should be considered for inclusion in statutory food marketing restrictions in order to protect children's health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".