Nutrition cues on ready-to-drink alcoholic beverage containers sold in grocery stores in Québec City, Canada
Bibliographic record
Abstract
Nutrition cues on ready-to-drink alcoholic beverages (RTDs) may create an illusion of healthfulness; however, nutrition information on alcohol in Canada is seldom regulated. This research aimed to systematically record the use of nutrition cues on a subsample of RTDs sold in grocery stores. In July 2021, all available RTDs were purchased from three major grocery store banners in Québec City, Canada. Data regarding container size, purchase format, alcohol-by-volume (ABV), presence of nutrition cues (nutrient claims, other food-related claims and nutrition facts tables [NFTs]) and container surface occupied by nutrition cues were recorded. RTDs were classified as hard seltzers or pre-mixed cocktails and their ABV as "light-strength" (3.5%-4.0% ABV) and "regular-strength" (>4.0%-7.0% ABV). In total (n = 193), 23% were hard seltzers and 17% light-strength. Most RTDs (68%) had ≥1 type of nutrition cue, most often natural flavour claims (45%), an NFT (38%), and calorie claims (29%). Light-strength beverages were more likely than regular-strength to carry any nutrient claim (97% vs. 19%, p < 0.0001), an NFT (97% vs. 26%, p < 0.0001) and other food-related claims (e.g., natural flavour) (88% vs. 52%, p = 0.0002). In adjusted regression analyses, hard seltzers were more likely than pre-mixed cocktails to carry any nutrient claim (AOR = 19.1, 95% CI:7.5,48.7), any other food-related claim (AOR = 7.5, 95% CI:2.9,19.4), and an NFT (AOR = 45.5, 95% CI:12.6,163.9). The mean container surface occupied by nutrition cues was higher for hard seltzers compared to pre-mixed cocktails (13% vs 3%, p < 0.0001). The high proportion of RTDs carrying nutrition cues supports the need to further regulate labelling and marketing of RTDs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".