Improving the Comparability of National Estimates of Fruit and Vegetable Consumption for Cross-National Studies of Dietary Patterns
Bibliographic record
Abstract
BACKGROUND: Developing global approaches to the problem of low fruit and vegetable consumption requires cross-nationally comparable estimates of fruit and vegetable consumption. National differences in the definitions of fruits and vegetables and serving size amounts limit the comparability of estimates. OBJECTIVES: To describe national differences in fruit and vegetable definitions, serving size amounts, and how these factors can influence the comparability of fruit and vegetable consumption estimates; and to provide a series of reporting recommendations that could facilitate cross-national studies of fruit and vegetable consumption. METHODS: A comprehensive review of national dietary guidelines, fruit and vegetable definitions, and fruit and vegetable consumption recommendations was undertaken for Canada, the United States, and the United Kingdom. RESULTS: To improve cross-national comparability, the findings suggest that researchers could report fruit and vegetable consumption separately, provide separate average fruit and vegetable intake amounts, report potato and legume or pulse consumption separately from vegetable consumption, and report consumption of 100% fruit juice separately from fruit consumption. CONCLUSIONS: These four low-cost, high-value additions to conventional research reporting standards will aid in the development of cross-national research on global fruit and vegetable consumption and the design of global policies that can target low fruit and vegetable consumption in populations.
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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 | 0.309 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".