The Influence of Sugar-Sweetened Beverage Health Warning Labels on Parents’ Choices
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: US states have introduced bills requiring sugar-sweetened beverages (SSBs) to display health warning labels. This study examined how such labels may influence parents and which labels are most impactful. METHODS: In this study, 2381 demographically and educationally diverse parents participated in an online survey. Parents were randomly assigned to 1 of 6 conditions: (1) no warning label (control); (2) calorie label; or (3-6) 1 of 4 text versions of a warning label (eg, Safety Warning: Drinking beverages with added sugar[s] contributes to obesity, diabetes, and tooth decay). Parents chose a beverage for their child in a vending machine choice task, rated perceptions of different beverages, and indicated interest in receiving beverage coupons. RESULTS: Regression analyses controlling for frequency of beverage purchases were used to compare the no warning label group, calorie label group, and all warning label groups combined. Significantly fewer parents chose an SSB for their child in the warning label condition (40%) versus the no label (60%) and calorie label conditions (53%). Parents in the warning label condition also chose significantly fewer SSB coupons, believed that SSBs were less healthy for their child, and were less likely to intend to purchase SSBs. All P values <.05 after correcting for multiple comparisons. There were no consistent differences among different versions of the warning labels. CONCLUSIONS: Health warning labels on SSBs improved parents' understanding of health harms associated with overconsumption of such beverages and may reduce parents' purchase of SSBs for their children.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".