Size Matters: Package Size Influences Recognition of Serving Size Information
Bibliographic record
Abstract
PURPOSE: To identify the impact of package size on an individual's use of serving size information. The hypothesis was that participants would make more serving size assumption errors on a nutrition facts table (NFT) interpretation task when assessing packages that appear as a single serving but contain multiple servings, compared with products that appear as a multi-serving and contain multiple servings. METHODS: Sixty participants were randomized into 1 of 3 conditions (n = 20 each); products that appeared as a single serving and contain a single serving (SSSS), products that appeared as a single serving and contain multiple servings (SSMS), and products that both appear as a multi-serving and contain multiple servings (MSMS). All 3 conditions were tested on a NFT interpretation task while participants were being presented food items that were appropriate to their given condition. RESULTS: Participants in the SSMS (9.55 ± 7.78) condition made significantly more serving size assumption errors than the SSSS (0.00 ± 0.00; P < 0.001) and MSMS (0.40 ± 0.75; P < 0.001) conditions. CONCLUSIONS: Participants did not address serving size information when they perceived a product to be a single serving. This resulted in people misinterpreting nutritional and caloric content of foods that were single unit foods with multiple servings.
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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 | 0.002 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".