Marketing mortality? Healthy vs. unhealthy food in television advertising
Bibliographic record
Abstract
Abstract Background Cardiovascular disease has been the leading killer of Americans since the Spanish flu pandemic of 1918, despite recent COVID-19 mortality. During this global pandemic, the social distancing and stay-at-home requests, there was increased television (TV) engagement, and media marketing has become more impactful in modifying consumer behaviors. Purpose We evaluated the healthfulness of food marketing in the United States (US), based on TV commercials most frequently aired on American primetime networks during the COVID-19 pandemic. Methods We reviewed a total of 104 TV commercials between 2020–2021 on network and cable programs dividing them into 4 categories: 1) fast-food chains, 2) brand-recognized individual items, 3) grocery chains, and 4) home-delivery meals. The food items displayed in each commercial were recorded and scored based on the previously validated healthful versus unhealthful nutrition scoring system (Sajita, et al., JACC 2017), assigning either positive or negative values for each food item in the commercial. Results We found that 58% of the commercials advertised food from fast-food chains (mean score = −2.82, indicating an average of nearly 3 more unhealthy items than healthy items per commercial), 27% were brand-recognized individual items (−0.86), 9% were grocery chains (−0.90), and 6% were for home-delivery meals (−0.33), with significant differences noted between fast-food and individual items, home deliveries and grocery chains (each p<0.0001). Conclusions This study demonstrated that commercial TV in the US routinely promotes the consumption of foods that are known in published medical literature to be unhealthy, particularly those underpinning cardiovascular disease and its risk factors. In order to prevent an increase in cardiovascular mortality during and after this global pandemic, we suggest regulation and or legislation to curtail the frequency and/or content of these commercials, and consider a ban on such advertising to children, similar to that previously employed in Canada and the European Union. Funding Acknowledgement Type of funding sources: None.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".