Consumer understanding of sugar types predicts food label use
Bibliographic record
Abstract
Purpose There is growing awareness internationally of the need to reduce intake of added sugars. The purpose of this study was to examine consumer sugar knowledge and food label use. Design/methodology/approach This cross-sectional online survey included 229 adult participants (85% female and 15% male). Participants completed measures of demographics, sugar knowledge, interest in food and nutrition, food choice motivations and beliefs and food label use. The sample of convenience showed that participants were from Australasia (n = 90), the USA (n = 90) and other Western (Europe and Canada, n = 49) countries. Findings Overall, participant sugar knowledge predicted nutrition label use over and above individual demographic and psychological characteristics (interest in food and nutrition, health beliefs and food choice motivations) (p < 0.001). Country comparisons revealed that those in Australasia reported lower sugar knowledge compared to the USA (p =< 0.001) and other Western countries (p = 0.028). Research limitations/implications Overall, participant sugar knowledge predicted nutrition label use over and above individual demographic and psychological characteristics (interest in food and nutrition, health beliefs and food choice motivations) (p < 001). Country comparisons revealed that those in Australasia reported lower sugar knowledge compared to the USA (p =< 0.001) and other Western countries (p = 0.028). Originality/value This study explored sugar knowledge as a unique predictor of food label use, taking into account individual characteristics in demographics, food choice motivations and health beliefs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".