Bibliographic record
Abstract
Objective: Canadians consume approximately twice the daily Adequate Intake of sodium. The present study examined the efficacy of four types of front-of-package (FOP) sodium labels at influencing consumers ’ selection of products low v. high in sodium. Design: Participants were randomly assigned to one of five experimental conditions: (i) control condition with no FOP label; (ii) basic numeric FOP label; (iii) numeric FOP label with ‘high ’ and ‘low ’ sodium content descriptors; (iv) detailed Traffic Light (TL) label with colour coding, content descriptors and numeric information; and (v) simple TL label with no numeric information. Participants were shown pairs of grocery products that varied in sodium content and told they could choose a free sample. Selection of the low-sodium v. the high-sodium product was the primary behavioural outcome, in addition to ratings of effectiveness, understanding, liking and believability. Setting: Waterloo, Ontario, Canada. Subjects: Adults (n 430) aged $18 years, recruited from community settings. Results: Participants in the three FOP conditions with ‘high/low ’ sodium content descriptors were significantly more likely to choose the lower-sodium product compared with the control group. The detailed TL label was ranked most effective at helping participants select low-sodium products, and was rated significantly higher than other formats in liking, understanding and believability. Product selection did not differ significantly across sociodemographic groups. Conclusions: FOP labels that include content descriptors may be more effective in helping consumers to select lower-sodium products. TL labels, which incorporate content descriptors and colour coding, should be considered for future FOP labelling initiatives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 0.021 |
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".