The variability of reported salt levels in fast foods across six countries: opportunities for salt reduction
Bibliographic record
Abstract
BACKGROUND: Several fast food companies have made commitments to reduce the levels of salt in the foods they serve, but technical issues are often cited as a barrier to achieving substantial reductions. Our objective was to examine the reported salt levels for products offered by leading multinational fast food chains. METHODS: Data on salt content for products served by six fast food chains operating in Australia, Canada, France, New Zealand, the United Kingdom and the United States were collected by survey in April 2010. Mean salt contents (and their ranges) were calculated and compared within and between countries and companies. RESULTS: We saw substantial variation in the mean salt content for different categories of products. For example, the salads we included in our survey contained 0.5 g of salt per 100 g, whereas the chicken products we included contained 1.6 g. We also saw variability between countries: chicken products from the UK contained 1.1 g of salt per 100 g, whereas chicken products from the US contained 1.8 g. Furthermore, the mean salt content of food categories varied between companies and between the same products in different countries (e.g., McDonald's Chicken McNuggets contain 0.6 g of salt per 100 g in the UK, but 1.6 g of salt per 100 g in the US). INTERPRETATION: The salt content of fast foods varies substantially, not only by type of food, but by company and country in which the food is produced. Although the reasons for this variation are not clear, the marked differences in salt content of very similar products suggest that technical reasons are not a primary explanation. In the right regulatory environment, it is likely that fast food companies could substantially reduce the salt in their products, translating to large gains for population health.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".