Can kids identify unprocessed fruit as healthier than an ultra-processed sugar-sweetened beverage? Functional versus self-reported nutrition knowledge and dietary intake among youth from six countries: findings from the International Food Policy Study
Bibliographic record
Abstract
BACKGROUND: Consumption of ultra-processed foods is associated with a range of poor dietary and health outcomes. Although lower nutrition knowledge is associated with higher consumption of ultra-processed foods, few studies have directly compared nutrition knowledge among youth from countries with different food environments and nutrition policies. This study examined whether youth could identify differences in nutritional quality between a commonly consumed ultra-processed and unprocessed food. METHODS: Cross-sectional surveys were conducted with youth aged 10-17 (n = 12,489) from Australia, Canada, Chile, Mexico, United Kingdom (UK), and United States (US) as part of the 2020 International Food Policy Study. Participants were shown images of two products in random order, corresponding to "unprocessed or minimally processed" (apple) and "ultra-processed" (apple fruit drink) foods under NOVA classification system, and asked to rate the healthiness of each. Respondents who rated the apple higher than the apple fruit drink were assigned a "correct" score. Regression models examined differences in "correct" responses by country, perceived nutrition knowledge, perceived diet healthiness, intake of fruits/vegetables, and intake of less healthy foods. RESULTS: Mexican (96.5%) and Chilean (94.3%) youth were most likely to correctly identify the unprocessed apple as "healthier" than the ultra-processed apple fruit drink, whereas US youth were the least likely (79.6%, p < 0.001 for all). Perceived nutrition knowledge was inversely associated with correct scores (p < 0.001). Youth who reported the highest (AOR: 0.43, p < 0.001) and lowest (AOR: 0.57, p < 0.05) categories of perceived diet healthiness had the lowest odds of correct responses. Higher intake of both less healthy foods (AOR: 0.70, p < 0.001) and fruits/vegetables (AOR: 0.87, p < 0.001) were associated with lower odds of correct responses. CONCLUSIONS: Across countries, 5-20% of youth were unable to correctly identify an unprocessed fruit as 'healthier' than an ultra-processed fruit drink, with notable country differences. Further research is needed to examine differences for a broader range of foods and levels of processing. Education campaigns should ensure that young people have basic knowledge about the relative dietary quality of commonly consumed foods, particularly in the US. Discrepancies between perceived and objective nutrition knowledge additionally highlight the need for objective measures of knowledge to be included in assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".