Patterns and correlates of nutrition knowledge across five countries in the 2018 international food policy study
Bibliographic record
Abstract
BACKGROUND: Nutrition knowledge is an important determinant of diet-related behaviour; however, the use of disparate assessment tools creates challenges for comparing nutrition knowledge levels and correlates across studies, geographic contexts, and populations. Using the Food Processing Knowledge (FoodProK) score - a measure of nutrition knowledge based on consumers' ability to understand and apply the concept of food processing in a functional task - nutrition knowledge levels and associated correlates were assessed in five countries. METHODS: Adults, aged ≥18 years, were recruited through the Nielsen Consumer Insights Global Panel in Australia (n = 3997), Canada (n = 4170), Mexico (n = 4044), the United Kingdom (UK) (n = 5363), and the United States (US) (n = 4527). Respondents completed web-based surveys in November-December 2018. Functional nutrition knowledge was measured using the FoodProK score. Linear regression models examined associations between FoodProK score and sociodemographic, dietary behaviours, and knowledge-related characteristics. RESULTS: FoodProK scores (maximum, 8 points) were highest in Canada (mean: 5.1) and Australia (5.0), followed by the UK (4.8), Mexico (4.7), and the US (4.6). Health literacy and self-rated nutrition knowledge were positively associated with FoodProK scores (p < .001). FoodProK scores were higher among those who reported vegetarian/other dietary practices (p < .001); made efforts to consume less sodium, trans fats, or sugars (p < .001); ≥60 years (p = 0.002), female (p < .001), and 'majority' ethnic group respondents in their respective countries (p < .001). CONCLUSIONS: This study found differences in consumers' ability to distinguish levels of food processing for common foods, with somewhat lower levels of nutrition knowledge in countries with the highest intake of highly processed foods. Nutrition knowledge differences based on consumer characteristics highlight the need for accessible policy interventions that support uptake of healthy eating efforts across populations to avoid exacerbating nutrition-related disparities. Tools such as the FoodProK can be used to evaluate the impact of policies targeting nutrition knowledge across contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".