Sodium Claims or Health Focused Messages Help Consumers to Identify Sodium Levels in Foods Better than the Nutrition Facts Table
Bibliographic record
Abstract
The aim of this study was to determine whether consumers can properly classify sodium levels (low, moderate, or high) based on the Nutrition Facts table (NFt) alone and whether sodium focused messages influence these judgements. Participants from a national online consumer panel (n=3,437) were randomly assigned to 1 of 5 treatment conditions on mock cheese products. Treatment group 1 was shown a Nutrition Facts table (NFt) with typical levels of sodium and groups 2 to 5 were shown a NFt with a 25% reduction. Groups 3 to 5 were shown various additional sodium focused messages. Groups 1 and 2 made similar classification judgements, regardless of whether or not the product was higher or reduced in sodium. By comparison, groups 3 and 4 who were shown a “reduced sodium” claim in addition to the NFt classified products as lower in sodium (P<0.05). This effect was even greater when consumers were shown a one‐page sodium advisory message (P<0.05). These results indicate that Canadian consumers generally have a poor understanding of what constitutes low, moderate, or high amounts of sodium based on information from the NFt only. Sodium focused messages significantly improve consumer judgements. Funding Source: Dairy Farmers of Canada
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".