Testing the influence of nutrient content claims and a health warning label on consumer perceptions of ready-to-drink alcoholic beverages: an online randomized experiment
Bibliographic record
Abstract
AIMS: To investigate the influence of health-oriented nutrient content claims (NCC; e.g. 140 calories) and a health warning label (HWL) depicting cancer risks on consumer product perceptions of ready-to-drink (RTD) alcoholic beverages, including potential differences in subgroups. METHODS: An online within-participants experiment was conducted among alcohol consumers ages 18-64 in Canada (n = 4689). Participants viewed the front panel of RTD alcoholic beverage containers according to four label conditions: (1) NCC vs. no label, (2) NCC + HWL vs. no label, (3) HWL vs. no label, (4) no label vs. no label. When viewing each pair, participants were asked to select which product they: (a) are interested in trying, and (b) buy to reduce health risks. Responses were assessed using generalized estimating equation and logistic regression models within the overall sample and subgroups, including gender, age group, education, alcohol use, and body weight intentions. RESULTS: When asked to select the product to try and to reduce health risks, participants were more likely to select the product with NCC, compared to NCC + HWL or HWL. When NCC were present, women were more interested in trying the product and those trying to lose weight had higher odds of perceiving reduced health risks. No other characteristics were associated with label effects. CONCLUSIONS: NCC on RTD alcohol products influenced product appeal and risk perceptions among consumers, particularly women and those trying to lose weight. Prohibiting NCC on alcohol packaging could be an effective policy strategy to prevent increased appeal and minimize consumer confusion about alcohol health risks.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".