Perceived healthiness of sugary drinks and related social norms among adults in five countries: evidence from the International Food Policy Study
Bibliographic record
Abstract
BACKGROUND: A better understanding of correlates of sugary drink consumption is essential to inform public health interventions. This study examined differences in perceived healthiness of sugary drinks and related social norms between countries, over time, and sociodemographic groups and associations with sugary drink intake. METHODS: This study used annual cross-sectional data from the International Food Policy Study from 2018 to 2021 in Australia, Canada, the United Kingdom, the United States, and Mexico. Analyses examined perceived healthiness of eight beverage types and two types of perceived social norms (descriptive, injunctive) that discourage sugary drink consumption. The 24-item Beverage Frequency Questionnaire was used to estimate beverage intake in the past 7 days. Logistic regression models examined trends over time in odds of perceiving each beverage type as unhealthy and agreeing with social norms discouraging sugary drink consumption, across countries and sociodemographic characteristics. Negative binomial regressions examined associations between perceived healthiness, social norms and consumption. RESULTS: Energy drinks, regular soft drinks, and diet soft drinks were most frequently perceived as unhealthy in all countries, while water and 100% juice were least frequently perceived as unhealthy. Participants in Mexico had higher odds of perceiving 100% juice, chocolate milk, and iced tea as unhealthy in 2021 compared to 2018 (AOR = 1.71 99%CI 1.10-2.64; AOR = 2.69, 99%CI 1.70-4.26; AOR = 1.79, 99%CI 1.15-2.76; respectively), with little change in other countries. Agreement with social norms discouraging consumption of sugary drinks was higher in Mexico than in other countries. Trends in social norms over time were mostly stable, except in Mexico where participants had higher odds of agreeing with both norms in 2020 compared to 2018 (AOR = 1.27, 99%CI 1.09-1.48 for a descriptive norm and AOR = 1.27 99%CI 1.09-1.49 for an injunctive norm). In most countries, perceiving a beverage as unhealthy and agreeing with social norms discouraging consumption of sugary drink were associated with lower sugary drink consumption, with varying strength of associations across countries and beverage types. CONCLUSIONS: Shifts over time in social norms and perceived healthiness observed in Mexico and associations with intake of sugary drinks in most countries suggest that targeted interventions to change norms and perceptions could help reduce sugary drink consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".