Using verbal protocol to examine the comprehension and use of nutrition facts tables among young Canadians (390.8)
Bibliographic record
Abstract
OBJECTIVES: To explore how young Canadians engage with, understand, and use serving size and %DV information on current and modified Nutrition Facts Tables (NFts). METHODS: 26 participants aged 16‐24 years were recruited. Participants were randomly assigned to two NFts, one with high (蠅15%DV) and one with low (≤5%DV) sodium amount, altered to one of the 6 conditions: 1) Current NFts; 2) Standardized serving sizes (SSS); 3) High/Low (H/L) descriptors beside % Daily Value (%DV) for negative nutrients; 4) SSS + H/L descriptors; 5) H/L and color beside %DV; and 6) SSS + H/L and color. Participants completed a skill‐based questionnaire using the “think aloud” technique. Content analysis was used to examine how participants interpret, define, compare, and manipulate information in NFts. RESULTS: Exploration of participants’ thought processes identified barriers to understanding and using the current NFt. The barriers include failure to understand %DV, difficulty in manipulating %DV to compare nutrients across various serving sizes, and confusion about how to use NFt to choose foods. Analysis also revealed that the barriers mentioned above can be addressed by standardizing serving sizes on NFts and adding simple descriptors and/or colors to %DV information. CONCLUSIONS: SSS and H/L descriptors or color coded %DV information assists young people in understanding and using NFts when comparing and choosing foods.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".