Efficacy of “High in” Nutrient Specific Front of Package Labels—A Retail Experiment with Canadians of Varying Health Literacy Levels
Bibliographic record
Abstract
BACKGROUND: In 2018, Health Canada, the Federal department responsible for public health, put forward a regulatory proposal to introduce regulations requiring a "High in" front-of-package label (FOPL) on foods that exceed predetermined thresholds for sodium, sugars, or saturated fat. This study evaluated the efficacy of the proposed FOPL as a quick and easy tool for making food choices that support reduction in the intakes of these nutrients. METHODS: = 625) of varying health literacy levels (HL) were assigned to control (current labeling with no FOPL) or one of four FOPL designs. They completed six shopping tasks, designed to control for internal motivations. Efficacy was measured with correct product selection and response time (seconds) to make food choices using repeated measures statistical modeling, adjusting for HL, task type, and task order. Eye-tracking and structured interviews were used to gather additional insights about participants' choices. RESULTS: Overall, FOPL was significantly more effective than current labeling at helping consumers of varying HL levels to identify foods high in nutrients of concern and make healthier food choices. All FOPL were equally effective. CONCLUSIONS: "High in" FOPL can be effective at helping Canadians of varying HL levels make more informed food choices in relation to sugars, sodium, and saturated fat.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".