Do front‐of‐pack nutrition rating systems and symbols (FOPS) direct consumers to the healthiest products in an unregulated environment?
Bibliographic record
Abstract
Concern has been raised that unregulated FOPS may mislead consumers into believing that a single food is ‘healthier’ than foods not bearing the FOPS. The nutritional criteria of a non‐profit (Health Check™) and a manufacturer (Sensible Solutions™) FOPS were applied to a national database of packaged food products. The proportion of foods qualifying for a given FOPS was compared to the proportion carrying the FOPS using exact binomial test. 7503 and 3010 of the 10,487 foods in the database could be assigned a Health Check™ or Sensible Solutions™ food category, respectively. 3360 (44.8%) of the foods assigned a Health Check™ category qualified for a Health Check™ symbol and 560 (7.5%) foods carried the symbol. Up to 2380 (79.1%) of the foods assigned a Sensible Solutions™’ category qualified for a Sensible Solutions™ symbol and 122 (4.1%) foods carried the symbol. The discord between products qualifying for and carrying these FOPS persisted at the food category and subcategory level. More than 75% of the products in many of the subcategories of either FOPS qualified for their respective symbols. These results suggest that FOPS are not always a useful guide to identifying the healthiest food products as more products qualify for these systems than are identified by the systems’ symbols. Grant Funding Source : Earle W. Mc Henry Research Chair Award (M.L.), CIHR Frederick Banting and Charles Best Canada Graduate Scholarship, Cancer Care Ontario/CIHR Training Grant in Population Intervention for Chronic Disease Prevention: A Pan‐ Canadian Program (#53893), and CIHR Strategic Training Program in Public Health Policy (T.E.)
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".