Consumer perceptions of specific design characteristics for front-of-package nutrition labels
Bibliographic record
Abstract
An increasing number of countries are developing front-of-package (FOP) labels; however, there is limited evidence examining the impact of specific design characteristics for these labels. The current study investigated consumer perceptions of several FOP label design characteristics, including potential differences among sociodemographic sub-groups. Two hundred and thirty-four participants aged 16 years or older completed nine label rating tasks on a laptop at a local shopping mall in Canada. The rating tasks asked participants to rate five primary design characteristics (border, background presence, background colour, 'caution' symbol and government attribution) on their noticeability, readability, believability and likelihood of changing their beverage choice. FOP labels with a border, solid background and contrasting colours increased noticeability. A solid background increased readability, while a contrasting background colour reduced it. Both a 'caution' symbol and a government attribution increased the believability of the labels and the perceived likelihood of influencing beverage choice. The effect of the design characteristics was generally similar across sociodemographic groups, with modest differences in five of the nine outcomes. Label design characteristics, such as the use of a border, colour and symbols can enhance the salience of FOP nutrition labels and may increase the likelihood that FOP labels are used by consumers.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".