Marketing Cardiovascular Mortality? Healthy vs. Unhealthy Food in Television Advertising
Bibliographic record
Abstract
Background:Cardiovascular disease has been the leading killer of Americans since the Spanish flu pandemic of 1918. During the SARS-CoV-2 pandemic, social distancing and stay-at-home requests, there has been increased television (TV) engagement, and marketing has become more impactful in modifying consumer behaviors. Objective: We evaluated the healthfulness of food marketing, based on commercials most frequently aired on American primetime networks during the SARS-CoV-2 pandemic. Methods:We reviewed a total of 104 TV commercials, 89 chosen randomly during TV watching and 14 targeted to enrich the sample with the leading quick service restaurants (“fast-food chains”). The commercials fell 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, assigning either positive or negative values for each food item in the commercial. Results:We found that 58% of the commercials advertised fast-food chains (mean score = -3.1, i.e., 3.1 more unhealthy than healthy items per commercial), while 27% were brand-recognized individual items (-0.82), 9% were grocery chains (-0.4), and 6% were for home-delivery meals (0.83); each was less unhealthy than fast-food (p< 0.0001). Conclusions:Commercial TV in the US routinely promotes the consumption of foods that are known to be unhealthy, particularly those underpinning cardiovascular disease and its risk factors. 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.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".