Adolescent exposure to food and beverage marketing on social media by gender: a pilot study
Bibliographic record
Abstract
Abstract Objective: The objective of this research was to determine if, based on gender, adolescents were exposed to different marketing techniques that promoted food and beverages over social media. Design: A secondary analysis of adolescent boy (n 26) and girl (n 36) exposures (n 139) to food and beverage marketing was conducted. Mann–Whitney U and Fisher’s exact tests were conducted to compare the number, healthfulness and the marketing techniques of exposures viewed by boys and girls. Setting: Ottawa, Ontario, Canada. Participants: Sixty-two adolescents aged 12–16 years. Results: Boys and girls were exposed to similar volumes of food marketing instances (median = 2 for both boys and girls, Mann–Whitney U = 237, P = 0·51) per 10-min period of social media use. More girls viewed products that were excessive in total fat compared to boys (67 % v. 35 %, P = 0·02). Boys were more likely to view instances of food marketing featuring a male as the dominant user (50 % v. 22 %, P = 0·03), appeals to achievement (42 % v. 17 %, P = 0·04), an influencer (42 % v. 14 %, P = 0·02) and appeals to athleticism (35 % v. 11 %, P = 0·03), whereas girls were more likely to view instances of food marketing featuring quizzes, surveys or polls (25 % v. 0 %, P = 0·01). Conclusions: Food and beverage companies utilise marketing techniques that differ based on gender. More research examining the relationship between digital food and beverage marketing and gender is required to inform the development of gender-sensitive policies aimed at protecting adolescents from unhealthy 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".