‘How many calories did I just eat?’ An experimental study examining the effect of changes to serving size information on nutrition labels
Bibliographic record
Abstract
OBJECTIVE: To test modifications to nutrition label serving size information on understanding of energy (calorie) content among youth and young adults. DESIGN: Participants completed two online experiments. First, participants were randomly assigned to view a beverage nutrition label with a reference amount of per serving (250 ml), per container (473 ml) or a dual-column format with both reference amounts. Participants were then randomized to view a cracker nutrition label which specified a single serving in small font, a single serving in large font, or the number of servings per bag with single serving information below. In both experiments, participants estimated energy content. Logistic regression analysis modelled correct energy estimation. Finally, participants reported their preference for serving size display format. SETTING: Canada. SUBJECTS: Canadian youth and young adults (n 2008; aged 16-24 years). RESULTS: In experiment 1, participants randomized to view the nutrition label with per container or dual column were more likely to correctly identify energy content than those using per serving information (P<0·01). For experiment 2, the serving size display format had no association with correct energy estimation. The majority of participants (61·9 %) preferred the serving size format that included servings per package. CONCLUSIONS: Labelling foods with nutrition information using a serving size reference amount for the entire container increased understanding of energy content. Consumers prefer nutrition labels that include more prominently featured serving size information. Additional modifications that further improve consumers' accuracy should be examined. These results have direct implications for nutrition labelling policy.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".