Perceptions of Food Marketing and Media Use among Canadian Teenagers: A Cross-Sectional Survey
Bibliographic record
Abstract
Despite the prevalence of digital food marketing to teenagers and its potential impact on food preferences and consumption, little is known about the specific food advertisements teenagers see in Canada and how they perceive them. Further, few studies consult teenagers directly about their perceptions of teen-specific food marketing content. To shed light on such issues, this study examines perceptions of food marketing and self-reported media use of Canadian teenagers via an online survey. Four hundred and sixty-four teenagers (ages 13-17) participated. Overall, teenagers identified Instagram and TikTok as the most popular social media platforms. The top food or beverage brands that teens felt specifically targeted them were McDonald's, Starbucks, Coca-Cola and Tim Hortons, while Instagram was deemed the most important media platform when it comes to teen-targeted food marketing. Teens deemed "celebrity" and "visual style" as the most important (food and beverage) advertising techniques when it comes to persuading teenagers to buy. Overall, the study provides insights into teen media use and brand preference, including the brands teens feel target them most directly and what they consider to be salient in terms of the food advertising they see. It also provides valuable details for researchers seeking to further identify and measure elements of teen-targeted food marketing.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".