The impact of nutrition information labels on alcohol containers in Canada: an online randomized trial
Bibliographic record
Abstract
OBJECTIVE: To determine if nutritional information on an alcohol container influenced consumer perceptions of product healthiness, and whether the effect of information differed by display format. METHODS: Online randomized controlled trial among adults in Canada sampled from a commercial panel (analytical sample n = 3880) in November/December 2024. Participants were randomized to view a wine product in one of four conditions: (1) control (no label), (2) Nutrition Facts table (NFT), (3) textual nutrition information, (4) Alcohol Facts table, and were asked "How healthy would it be to drink this wine regularly?" (7-point Likert-type item, very unhealthy to very healthy). Logistic regression compared the likelihood of rating the product as "a little healthy/healthy/very healthy" between conditions. RESULTS: Compared to the control (16.5 %), those in the NFT condition had higher odds of rating the product as "a little healthy/healthy/very healthy" (28.3 %, AOR = 1.97, 95 %CI,1.57,2.47), as did those in the textual nutrition information condition (23.8 %, AOR = 1.60, 95 %CI,1.27,2.02). There were no differences between the control condition and the Alcohol Facts table condition (18.8 %). CONCLUSIONS: Nutritional information on alcohol products may lead consumers to falsely believe products are 'healthier'. Label design and features that make alcohol products distinct from non-alcoholic food and beverages may reduce unintended impacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".