Comprehension and Use of Nutrition Facts Tables among Adolescents and Young Adults in Canada
Bibliographic record
Abstract
PURPOSE: Limited evidence exists on the comprehension and use of Nutrition Facts tables (NFt) among adolescents and young adults. This study provides an account of how young people engage with, understand, and apply nutrition information on the current and modified versions of the NFt to compare and choose foods. METHODS: Participants aged 16-24 years (n = 26) were asked to "think aloud" while viewing either the current or 1 of 5 modified NFts and completing a behavioural task. The task included a questionnaire with 9 functional items requiring participants to define, compare, interpret, and manipulate serving size and percentage daily value (%DV) information on NFts. Semi-structured interviews were conducted to further probe thought processes and difficulties experienced in completing the task. RESULTS: Equal serving sizes on NFts improved ability to accurately compare nutrition information between products. Most participants could define %DV and believed it can be used to compare foods, yet some confusion persisted when interpreting %DVs and manipulating serving-size information on NFts. Where serving sizes were unequal, mathematical errors were often responsible for incorrect responses. CONCLUSIONS: Results reinforce the need for equal serving sizes on NFts of similar products and highlight young Canadians' confusion when using nutrition information on NFts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".