Influence of front-of-pack labelling and regulated nutrition claims on consumers’ perceptions of product healthfulness and purchase intentions: A randomized controlled trial
Bibliographic record
Abstract
Mandatory front-of-pack (FOP) labelling was proposed in Canada to highlight foods with high contents of sugars, sodium and/or saturated fats, which would be displayed on labels along with the mandatory Nutrition Facts table and voluntary nutrition claims. In an online survey, participants (n = 1997) were randomized to one of four FOP labelling conditions: 1) control, 2) warning label, 3) health star rating or 4) traffic light labelling. Participants were shown four drinks (a healthier drink with or without a disease risk reduction claim, a healthier drink with or without a nutrient content claim, a less healthy drink with or without a disease risk reduction claim and a less healthy drink with or without a nutrient content claim) in random order and one at a time. Participants rated perceived product healthfulness and purchase intentions using a 7-point Likert scale. Participants could access the Nutrition Facts table while viewing labels. Results showed less healthy drinks displaying any FOP labelling were perceived as less healthy compared to the control. In healthier drinks, health star rating and traffic light labelling created a 'halo' effect, which was not observed with warning labels. Similar results were observed with purchase intentions. Drinks displaying a disease risk reduction claim were perceived as healthier than those without (p < 0.001) regardless of product's healthfulness. The effect of a nutrient content claim was not significantly different. The effect of FOP labelling and claims was mitigated for those who used the Nutrition Facts table. FOP labelling was likely helpful for consumers with different levels of health literacy. Overall, FOP labelling had significantly stronger influence than nutrition claims on consumers' perceptions; however, the effect of each FOP label varied on healthier and less healthy drinks.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".