The impact of health warnings for sugar-sweetened beverages on consumer perceptions of advertising
Bibliographic record
Abstract
OBJECTIVE: In February 2020, San Francisco proposed mandatory health warnings for sugar-sweetened beverage (SSB) advertisements. Industry legal challenges stated that the warning would detract from advertisers' ability to convey their intended message and mislead consumers into believing that SSB cause weight gain regardless of consumption amount, lifestyle or intake of other energy-dense foods. DESIGN: Online between-group experiments tested the impact of SSB warnings on advertising outcomes and consumer perceptions. Respondents were randomised to view six SSB print advertisements with or without a health warning ('Warning' and 'No Warning' condition, respectively). Linear and binary logistic regression models tested differences between groups, including ad recall, brand perceptions and beliefs about SSB health effects. SETTING: Panelists from the US Nielsen Global Panel. PARTICIPANTS: Sixteen to 65-year-old respondents (n 1064). RESULTS: Overall, 69·2 % of participants in the 'Warning' condition recalled seeing warnings on SSB ads. Compared with the 'No Warning' condition, participants in the 'Warning' condition who reported noticing the warnings were equally likely to recall the brands featured in the SSB ads and to recall specific attributes of the final ad they viewed. Similarly, no differences were observed between groups in perceptions of SSB, such as perceived taste, or in the prevalence of false beliefs regarding the health effects of SSB and intake of other sugary foods on weight gain. CONCLUSIONS: Overall, there was no evidence that SSB health warnings detracted from attention to promotional elements in advertisements or that the warnings misled consumers into false beliefs about SSB as the exclusive cause of weight gain.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".