Trends in food and nutrition behaviours, knowledge and attitudes among youth in six countries: findings from the 2019–2021 International Food Policy Study Youth Surveys
Bibliographic record
Abstract
OBJECTIVE: This commentary highlights the release of findings now available in the report DESIGN: The survey data described in this commentary consist of repeated cross-sectional surveys conducted annually beginning in 2019. SETTING: Online surveys were conducted in 2019 to 2021 among respondents living in Australia, Canada, Chile, Mexico, the United Kingdom and the USA. PARTICIPANTS: 10 459). RESULTS: The report described in this commentary summarises findings on food and nutrition behaviours, attitudes and knowledge among youth, including their diet sources and patterns, school nutrition environments, food security, diet intentions, weight perceptions and weight loss behaviours, sugary drink perceptions, awareness of public education and mass media campaigns, perceptions of food labels and exposure to food and beverage marketing. CONCLUSION: Results from the IFPS Youth surveys provide important insights into key policies of global interest, including front-of-package nutrition labelling, levies on sugary beverages and restrictions on marketing unhealthy food and beverages to children. As policymakers continue to seek effective strategies to improve adolescent health outcomes, ongoing cross-country monitoring of food and nutrition-related indicators, such as the data from the International Food Policy Study, will be critical in assessing dietary trends and evaluating upcoming policies.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".